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Effect of staining solutions on the color and translucency change of various resin based definitive CAD/CAM materials
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Effect of staining solutions on the color and translucency change of various resin based definitive CAD/CAM materials
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Content
Effect of Staining Solutions on the Color and Translucency Change of Various
Resin Based Definitive CAD/CAM Materials
by
Xin En Andrew Lim, BDS
A Thesis Presented to the
FACULTY OF THE HERMAN OSTROW SCHOOL OF DENTISTRY OF THE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
MASTER OF SCIENCE
(BIOMATERIALS AND DIGITAL DENTISTRY)
December 2023
Copyright 2023 Lim Xin En Andrew
ii
Acknowledgements
I would like to acknowledge, first, my Lord and Savior Jesus Christ for being my one
constant throughout life. The guidance, promptings, open doors, and provision has led me
down a path in life that I am forever grateful for. It is by His grace that I am where I am.
To my family, dad, mum, my two sisters Sophie and Christine, my brother-in-law
Benjamin, and two nieces Anna and Mikayla, thank you for the constant prayers and
unwavering support in my endeavors far away from home. I love you and look forward to
life ahead with all of you.
I would like to acknowledge the faculty in Advanced Operative, Dr. Duarte and Dr.
Phark. First, thank you for the opportunity to pursue this master’s program and second, for
the guidance provided throughout the process, without which this would not be possible.
I would like to acknowledge my faculty in Advanced Prosthodontics for their
understanding in my pursuing of this endeavor. Dr. Chee and Dr. Park, thank you for
teaching me how to think critically, to critique and evaluate myself. Thank you for the
encouragement and provisions made for me to pursue this master’s program in addition to
doing Advanced Prosthodontics at USC.
To my co-residents in Advanced Prosthodontics, you have made life so much more
vibrant and fun. The memories made in our short time here will last a lifetime. I am sure all
of you will be wildly successful in your own sphere of influence in the world, and I look
forward to sharing the joy of your continued success in this life.
iii
Table of Contents
Acknowledgements................................................................................................................................ii
List of Tables.......................................................................................................................................... vi
List of Figures ........................................................................................................................................ ix
List of Equations.................................................................................................................................. xiv
List of Abbreviations............................................................................................................................. xv
Abstract............................................................................................................................................... xvi
1. Chapter 1: Introduction .................................................................................................................1
1.1 CAD/CAM, Milling to Printing ...............................................................................................1
1.2 Composite Materials, Classification......................................................................................1
1.3 Color Science and Spectrophotometers ...............................................................................8
1.4 Color Stability and Staining Mediums.................................................................................12
1.5 Intro to Problem – No studies on color stability of printed definitive restorations............15
1.6 Purpose and Null Hypothesis..............................................................................................15
1.6.1 Null Hypotheses..............................................................................................................15
2. Chapter 2: Materials and Methods..............................................................................................17
2.1 Materials.............................................................................................................................17
2.2 Specimen Preparation.........................................................................................................19
2.2.1 Lava Ultimate..................................................................................................................20
2.2.2 VarseoSmile ....................................................................................................................22
2.2.3 Ceramic Crown ...............................................................................................................24
2.3 Polishing, Numbering and Grouping of Specimens.............................................................26
2.4 First Color Measurement (Baseline) ...................................................................................29
2.5 Immersion/Staining of Specimens......................................................................................32
2.6 pH Value Measurements.....................................................................................................37
2.7 Second Color Measurement (After Staining) ......................................................................37
2.8 Polishing..............................................................................................................................37
2.9 Third Color Measurement (After Polishing)........................................................................38
2.10 Color and Translucency Analysis.........................................................................................38
2.11 Color Analysis......................................................................................................................39
2.12 L*a*b* Analysis...................................................................................................................42
2.13 Translucency Analysis .........................................................................................................43
2.14 Statistical Analysis...............................................................................................................43
3. Chapter 3: Results........................................................................................................................45
3.1 Color Change Analysis.........................................................................................................45
3.1.1 ΔE0 Analysis....................................................................................................................46
3.1.1.1 Overall Comparisons of ΔE0 .......................................................................................47
3.1.1.1.1 Between Materials.....................................................................................................47
iv
3.1.1.1.2 Between Solutions......................................................................................................50
3.1.1.2 LU Comparisons Between Solutions...........................................................................53
3.1.1.3 VS Comparisons Between Solutions...........................................................................55
3.1.1.4 CC Comparisons Between Solutions...........................................................................57
3.1.1.5 Coffee Comparisons Between Materials....................................................................59
3.1.1.6 Wine Comparisons Between Materials......................................................................61
3.1.1.7 Coca-Cola Comparisons Between Materials...............................................................63
3.1.1.8 Water Comparisons Between Materials ....................................................................65
3.1.2 ΔE1 Analysis....................................................................................................................67
3.1.2.1 Overall Comparisons of ΔE1 .......................................................................................68
3.1.2.1.1 Between Materials.....................................................................................................68
3.1.2.1.2 Between Solutions......................................................................................................71
3.1.2.2 LU Comparisons Between Solutions...........................................................................74
3.1.2.3 VS Comparisons Between Solutions...........................................................................75
3.1.2.4 CC Comparisons Between Solutions...........................................................................77
3.1.2.5 Coffee Comparisons Between Materials....................................................................79
3.1.2.6 Wine Comparisons Between Materials......................................................................81
3.1.2.7 Coca-Cola Comparisons Between Materials...............................................................83
3.1.2.8 Water Comparisons Between Materials ....................................................................85
3.1.3 Color Change After Polishing ..........................................................................................86
3.2 CIE L*a*b* and Chroma Descriptive Analysis .....................................................................91
3.2.1 ΔL0 Results......................................................................................................................91
3.2.1.1 Overall Comparisons of ΔL0 .......................................................................................92
3.2.1.1.1 Between Materials.....................................................................................................92
3.2.1.1.2 Between Solutions......................................................................................................93
3.2.2 ΔL1 Results......................................................................................................................93
3.2.2.1 Overall Comparisons of ΔL1 .......................................................................................94
3.2.2.1.1 Between Materials.....................................................................................................94
3.2.2.1.2 Between Solutions......................................................................................................95
3.2.3 Δa0 Results .....................................................................................................................95
3.2.3.1 Overall Comparisons of Δa0 .......................................................................................96
3.2.3.1.1 Between Materials.....................................................................................................96
3.2.3.1.2 Between Solutions......................................................................................................97
3.2.4 Δa1 Results .....................................................................................................................97
3.2.4.1 Overall Comparisons of Δa1 .......................................................................................98
3.2.4.1.1 Between Materials.....................................................................................................98
3.2.4.1.2 Between Solutions......................................................................................................99
3.2.5 Δb0 Results.....................................................................................................................99
3.2.5.1 Overall Comparisons of Δb0.....................................................................................100
3.2.5.1.1 Between Materials...................................................................................................100
3.2.5.1.2 Between Solutions....................................................................................................101
3.2.6 Δb1 Results...................................................................................................................101
3.2.6.1 Overall Comparisons of Δb1.....................................................................................102
3.2.6.1.1 Between Materials...................................................................................................102
3.2.6.1.2 Between Solutions....................................................................................................103
3.2.7 ΔC0 Results...................................................................................................................103
3.2.7.1 Overall Comparisons of ΔC0.....................................................................................104
3.2.7.1.1 Between Materials...................................................................................................104
3.2.7.1.2 Between Solutions....................................................................................................105
3.2.8 ΔC1 Results...................................................................................................................105
3.2.8.1 Overall Comparisons of ΔC1.....................................................................................106
v
3.2.8.1.1 Between Materials...................................................................................................106
3.2.8.1.2 Between Solutions....................................................................................................107
3.3 Translucency Analysis .......................................................................................................107
3.3.1 ΔTP1 Analysis................................................................................................................110
3.3.1.1 Overall Comparisons of ΔTP1 ...................................................................................110
3.3.1.1.1 Between Materials...................................................................................................110
3.3.1.1.2 Between Solutions....................................................................................................114
3.3.1.2 LU Comparisons Between Solutions.........................................................................117
3.3.1.3 VS Comparisons Between Solutions.........................................................................119
3.3.1.4 CC Comparisons Between Solutions.........................................................................121
3.3.1.5 Coffee Comparisons Between Materials..................................................................123
3.3.1.6 Wine Comparisons Between Materials....................................................................125
3.3.1.7 Coca-Cola Comparisons Between Materials.............................................................127
3.3.1.8 Water Comparisons Between Materials ..................................................................129
3.3.2 ΔTP2 Analysis................................................................................................................131
3.3.2.1 Overall Comparisons of ΔTP2 ...................................................................................132
3.3.2.1.1 Between Materials...................................................................................................132
3.3.2.1.2 Between Solutions....................................................................................................135
3.3.2.2 LU Comparisons Between Solutions.........................................................................138
3.3.2.3 VS Comparisons Between Solutions.........................................................................139
3.3.2.4 CC Comparisons Between Solutions.........................................................................141
3.3.2.5 Coffee Comparisons Between Materials..................................................................143
3.3.2.6 Wine Comparisons Between Materials....................................................................145
3.3.2.7 Coca-Cola Comparisons Between Materials.............................................................147
3.3.2.8 Water Comparisons Between Materials ..................................................................149
3.3.3 Translucency Change After Polishing............................................................................151
4. Chapter 4: Discussion ................................................................................................................157
4.1 Null Hypotheses................................................................................................................157
4.2 Color Stability of Materials................................................................................................157
4.3 Staining Ability of Different Solutions...............................................................................159
4.4 Surface Roughness of Materials........................................................................................161
4.5 Thickness...........................................................................................................................163
4.6 Devices..............................................................................................................................163
4.7 CIELAB Formulas ...............................................................................................................164
4.8 Limitations and Future Considerations.............................................................................165
5. Chapter 5: Conclusions..............................................................................................................167
Disclaimer...........................................................................................................................................168
Funding...............................................................................................................................................169
References..........................................................................................................................................170
vi
List of Tables
Table 1: Materials and properties.........................................................................................................19
Table 2: Group division.........................................................................................................................20
Table 3: pH of solutions........................................................................................................................37
Table 4: Test for normality ΔE0 for groups of material .........................................................................45
Table 5: Test for normality ΔE0 for groups of solution .........................................................................45
Table 6: Test for normality ΔE1 for groups of material .........................................................................46
Table 7: Test for normality ΔE1 for groups of solution .........................................................................46
Table 8: ΔE0 overall mean, standard deviation, minimum and maximum values................................47
Table 9: Kruskall-Wallis test summary ΔE0 across material..................................................................49
Table 10: Pairwise comparison of ΔE0 of different materials...............................................................49
Table 11: Kruskall-Wallis test summary ΔE0 across solutions...............................................................51
Table 12: Pairwise comparison of ΔE0 of different solutions...............................................................52
Table 13: Kruskall-Wallis test summary ΔE0 in LU across solutions......................................................53
Table 14: Pairwise comparison of ΔE0 in LU for different solutions.....................................................54
Table 15: Kruskall-Wallis test summary ΔE0 in VS across solutions......................................................55
Table 16: Pairwise comparison of ΔE0 in VS for different solutions.....................................................56
Table 17: Kruskall-Wallis test summary ΔE0 in CC across solutions......................................................57
Table 18: Pairwise comparison of ΔE0 in CC for different solutions.....................................................58
Table 19: Kruskall-Wallis test summary ΔE0 in coffee across materials ...............................................59
Table 20: Pairwise comparison of ΔE0 of coffee for different materials...............................................60
Table 21: Kruskall-Wallis test summary ΔE0 in wine across materials..................................................61
Table 22: Pairwise comparison of ΔE0 of wine for different materials.................................................62
Table 23: Kruskall-Wallis test summary ΔE0 in Coca-Cola across materials .........................................63
Table 24: Pairwise comparison of ΔE0 of Coca-Cola for different materials.........................................64
Table 25: Kruskall-Wallis test summary ΔE0 in water across materials................................................65
Table 26: Pairwise comparison of ΔE0 of water for different materials...............................................66
Table 27: ΔE1 overall mean, standard deviation, minimum and maximum values..............................67
Table 28: Kruskall-Wallis test summary ΔE1 across material................................................................69
Table 29: Pairwise comparison of ΔE1 of different materials...............................................................70
Table 30: Kruskall-Wallis test summary ΔE1 across solutions...............................................................72
Table 31: Pairwise comparison of ΔE1 of different solutions...............................................................73
Table 32: Kruskall-Wallis test summary ΔE1 in LU across solutions......................................................74
Table 33: Kruskall-Wallis test summary ΔE1 in VS across solutions......................................................75
vii
Table 34: Pairwise comparison of ΔE1 in VS for different solutions.....................................................76
Table 35: Kruskall-Wallis test summary ΔE1 in CC across solutions......................................................77
Table 36: Pairwise comparison of ΔE1 in CC for different solutions.....................................................78
Table 37: Kruskall-Wallis test summary ΔE1 in coffee across materials ...............................................79
Table 38: Pairwise comparison of ΔE1 of coffee for different materials...............................................80
Table 39: Kruskall-Wallis test summary ΔE1 in wine across materials..................................................81
Table 40: Pairwise comparison of ΔE1 of wine for different materials.................................................82
Table 41: Kruskall-Wallis test summary ΔE1 in Coca-Cola across materials .........................................83
Table 42: Pairwise comparison of ΔE1 of Coca-Cola for different materials.........................................84
Table 43: Kruskall-Wallis test summary ΔE1 in water across materials................................................85
Table 44: Pairwise comparison of ΔE1 of water for different materials...............................................86
Table 45: ΔL0 overall mean, standard deviation, minimum and maximum values ..............................92
Table 46: ΔL1 overall mean, standard deviation, minimum and maximum values ..............................94
Table 47: Δa0 overall mean, standard deviation, minimum and maximum values..............................96
Table 48: Δa1 overall mean, standard deviation, minimum and maximum values..............................98
Table 49: Δb0 overall mean, standard deviation, minimum and maximum values............................100
Table 50: Δb1 overall mean, standard deviation, minimum and maximum values............................102
Table 51: ΔC0 overall mean, standard deviation, minimum and maximum values............................104
Table 52: ΔC1 overall mean, standard deviation, minimum and maximum values............................106
Table 53: Test for normality ΔTP1 for groups of material...................................................................108
Table 54: Test for normality ΔTP1 for groups of solution ...................................................................108
Table 55: Test for normality ΔTP2 for groups of material...................................................................109
Table 56: Test for normality ΔTP2 for groups of solution ...................................................................109
Table 57: Test for normality ΔTP2 across all groups ...........................................................................109
Table 58: ΔTP1 overall mean, standard deviation, minimum and maximum values..........................110
Table 59: Kruskall-Wallis test summary ΔTP1 across materials..........................................................112
Table 60: Pairwise comparison of ΔTP1 of different materials...........................................................113
Table 61: Kruskall-Wallis test summary ΔTP1 across solutions ..........................................................115
Table 62: Pairwise comparison of ΔTP1 of different solutions...........................................................116
Table 63: Kruskall-Wallis test summary ΔTP1 in LU across solutions..................................................117
Table 64: Pairwise comparison of ΔTP1 in LU for different solutions.................................................118
Table 65: Kruskall-Wallis test summary ΔTP1 in VS across solutions..................................................119
Table 66: Pairwise comparison of ΔTP1 in VS for different solutions.................................................120
Table 67: Kruskall-Wallis test summary ΔTP1 in CC across solutions .................................................121
viii
Table 68: Pairwise comparison of ΔTP1 in CC for different solutions.................................................122
Table 69: Kruskall-Wallis test summary ΔTP1 in coffee across materials ...........................................123
Table 70: Pairwise comparison of ΔTP1 of coffee for different materials...........................................124
Table 71: Kruskall-Wallis test summary ΔTP1 in wine across materials..............................................125
Table 72: Pairwise comparison of ΔTP1 of wine for different materials.............................................126
Table 73: Kruskall-Wallis test summary ΔTP1 in Coca-Cola across materials .....................................127
Table 74: Pairwise comparison of ΔTP1 of Coca-Cola for different materials.....................................128
Table 75: Kruskall-Wallis test summary ΔTP1 in water across materials............................................129
Table 76: Pairwise comparison of ΔTP1 of water for different materials...........................................130
Table 77: ΔTP2 overall mean, standard deviation, minimum and maximum values..........................131
Table 78: Kruskall-Wallis test summary ΔTP2 across materials..........................................................133
Table 79: Pairwise comparison of ΔTP2 of different materials...........................................................134
Table 80: Kruskall-Wallis test summary ΔTP2 across solutions ..........................................................136
Table 81: Pairwise comparison of ΔTP2 of different solutions...........................................................137
Table 82: Kruskall-Wallis test summary ΔTP2 in LU across solutions..................................................139
Table 83: Kruskall-Wallis test summary ΔTP2 in VS across solutions..................................................140
Table 84: Pairwise comparison of ΔTP2 in VS for different solutions.................................................140
Table 85: Kruskall-Wallis test summary ΔTP2 in CC across solutions .................................................142
Table 86: Pairwise comparison of ΔTP2 in CC for different solutions.................................................142
Table 87: Kruskall-Wallis test summary ΔTP2 in coffee across materials ...........................................144
Table 88: Pairwise comparison of ΔTP2 of coffee for different materials...........................................144
Table 89: Kruskall-Wallis test summary ΔTP2 in wine across materials..............................................145
Table 90: Pairwise comparison of ΔTP2 of wine for different materials.............................................146
Table 91: Kruskall-Wallis test summary ΔTP2 in Coca-Cola across materials .....................................147
Table 92: Pairwise comparison of ΔTP2 of Coca-Cola for different materials.....................................148
Table 93: Kruskall-Wallis test summary ΔTP2 in water across materials............................................149
Table 94: Pairwise comparison of ΔTP2 of water for different materials...........................................150
ix
List of Figures
Figure 1: Lava Ultimate (LU) .................................................................................................................17
Figure 2: VarseoSmile (VS)....................................................................................................................18
Figure 3: Ceramic Crown (CC)...............................................................................................................18
Figure 4: Sectioning machine ...............................................................................................................21
Figure 5: Sectioning blade ....................................................................................................................21
Figure 6: Sectioned LU specimen .........................................................................................................21
Figure 7: Digital design of supports and angulation of VS specimens..................................................22
Figure 8: Sprintray Pro 55.....................................................................................................................23
Figure 9: Sprintray Pro Wash/Dry.........................................................................................................23
Figure 10: Sprintray ProCure 2..............................................................................................................24
Figure 11: VS post-cured specimens.....................................................................................................24
Figure 12: Digital design of supports and angulation of specimen.......................................................25
Figure 13: CC post-cured specimens.....................................................................................................26
Figure 14: Sandpaper 600, 1000 and 1200 grit ....................................................................................27
Figure 15: Digital caliper.......................................................................................................................27
Figure 16: Prepared LU samples...........................................................................................................28
Figure 17: Prepared VS samples...........................................................................................................28
Figure 18: Prepared CC samples...........................................................................................................29
Figure 19: CrystalEye spectrophotometer............................................................................................30
Figure 20: Custom measurement chamber..........................................................................................31
Figure 21: White background ...............................................................................................................31
Figure 22: Black background ................................................................................................................32
Figure 23: Colored bags........................................................................................................................33
Figure 24: Coffee ..................................................................................................................................33
Figure 25: Wine ....................................................................................................................................34
Figure 26: Coca-Cola.............................................................................................................................34
Figure 27: Distilled water......................................................................................................................35
Figure 28: Incubator.............................................................................................................................35
Figure 29: Thermometer measuring coffee temperature ....................................................................36
Figure 30: Thermometer measuring Coca-Cola temperature ..............................................................36
Figure 31: Pumice and water mix .........................................................................................................38
Figure 32: Outline of specimen for color analysis ................................................................................39
Figure 33: L*a*b* values in CrystalEye software..................................................................................39
x
Figure 34: Graph of mean ΔE0 by material for different solutions.......................................................47
Figure 35: Boxplot of ΔE0 of materials.................................................................................................48
Figure 36: Pairwise comparison of ΔE0 of different materials .............................................................49
Figure 37: Graph of mean ΔE0 by solution for different materials.......................................................50
Figure 38: Boxplot of ΔE0 of solutions..................................................................................................51
Figure 39: Pairwise comparison of ΔE0 of different solutions..............................................................52
Figure 40: Boxplot of ΔE0 in LU for different solutions ........................................................................53
Figure 41: Pairwise comparison of ΔE0 in LU for different solutions....................................................54
Figure 42: Boxplot of ΔE0 in VS for different solutions.........................................................................55
Figure 43: Pairwise comparison of ΔE0 in VS for different solutions....................................................56
Figure 44: Boxplot of ΔE0 in CC for different solutions ........................................................................57
Figure 45: Pairwise comparison of ΔE0 in CC for different solutions ...................................................58
Figure 46: Boxplot of ΔE0 of coffee for different materials..................................................................59
Figure 47: Pairwise comparison of ΔE0 of coffee for different materials.............................................60
Figure 48: Boxplot of ΔE0 of wine for different materials....................................................................61
Figure 49: Pairwise comparison of ΔE0 of wine for different materials...............................................62
Figure 50: Boxplot of ΔE0 of Coca-Cola for different materials............................................................63
Figure 51: Pairwise comparison of ΔE0 of Coca-Cola for different materials.......................................64
Figure 52: Boxplot of ΔE0 of water for different materials...................................................................65
Figure 53: Pairwise comparison of ΔE0 of water for different materials..............................................66
Figure 54: Graph of mean ΔE1 by material for different solutions.......................................................68
Figure 55: Boxplot of ΔE1 of materials.................................................................................................69
Figure 56: Pairwise comparison of ΔE1 of different materials .............................................................70
Figure 57: Graph of mean ΔE1 by solution for different materials.......................................................71
Figure 58: Boxplot of ΔE1 of solutions..................................................................................................72
Figure 59: Pairwise comparison of ΔE1 of different solutions..............................................................73
Figure 60: Boxplot of ΔE1 in LU for different solutions ........................................................................74
Figure 61: Boxplot of ΔE1 in VS for different solutions.........................................................................75
Figure 62: Pairwise comparison of ΔE1 in VS for different solutions....................................................76
Figure 63: Boxplot of ΔE1 in CC for different solutions ........................................................................77
Figure 64: Pairwise comparison of ΔE1 in CC for different solutions ...................................................78
Figure 65: Boxplot of ΔE1 of coffee for different materials..................................................................79
Figure 66: Pairwise comparison of ΔE1 of coffee for different materials.............................................80
Figure 67: Boxplot of ΔE1 of wine for different materials....................................................................81
xi
Figure 68: Pairwise comparison of ΔE1 of wine for different materials...............................................82
Figure 69: Boxplot of ΔE1 of Coca-Cola for different materials............................................................83
Figure 70: Pairwise comparison of ΔE1 of Coca-Cola for different materials.......................................84
Figure 71: Boxplot of ΔE1 of water for different materials...................................................................85
Figure 72: Pairwise comparison of ΔE1 of water for different materials..............................................86
Figure 73: Graph of change in ΔE for LU after polishing.......................................................................87
Figure 74: Graph of change in ΔE for VS after polishing.......................................................................87
Figure 75: Graph of change in ΔE for CC after polishing.......................................................................88
Figure 76: Graph of change in ΔE for coffee after polishing.................................................................89
Figure 77: Graph of change of ΔE for wine after polishing...................................................................89
Figure 78: Graph of change of ΔE for Coca-Cola after polishing...........................................................90
Figure 79: Graph of change of ΔE for water after polishing .................................................................90
Figure 80: Graph of mean ΔL0 by material for different solutions.......................................................93
Figure 81: Graph of mean ΔL0 by solution for different materials.......................................................93
Figure 82: Graph of mean ΔL1 by material for different solutions.......................................................94
Figure 83: Graph of mean ΔL1 by solution for different materials.......................................................95
Figure 84: Graph of mean Δa0 by material for different solutions.......................................................97
Figure 85: Graph of mean Δa0 by solution for different materials.......................................................97
Figure 86: Graph of mean Δa1 by material for different solutions.......................................................98
Figure 87: Graph of mean Δa1 by solution for different materials.......................................................99
Figure 88: Graph of mean Δb0 by material for different solutions.....................................................101
Figure 89: Graph of mean Δb0 by solution for different materials.....................................................101
Figure 90: Graph of mean Δb1 by material for different solutions.....................................................102
Figure 91: Graph of mean Δb1 by solution for different materials.....................................................103
Figure 92: Graph of mean ΔC0 by material for different solutions.....................................................105
Figure 93: Graph of mean ΔC0 by solution for different materials.....................................................105
Figure 94: Graph of mean ΔC1 by material for different solutions.....................................................106
Figure 95: Graph of mean ΔC1 by solution for different materials.....................................................107
Figure 96: Graph of mean ΔTP1 by material for different solutions...................................................111
Figure 97: Boxplot of ΔTP1 of materials.............................................................................................112
Figure 98: Pairwise comparison of ΔTP1 of different materials .........................................................113
Figure 99: Graph of mean ΔTP1 by solution for different materials...................................................114
Figure 100: Boxplot of ΔTP1 of solutions ...........................................................................................115
Figure 101: Pairwise comparison of ΔTP1 of different solutions........................................................116
xii
Figure 102: Boxplot of ΔTP1 in LU for different solutions ..................................................................117
Figure 103: Pairwise comparison of ΔTP1 in LU for different solutions..............................................118
Figure 104: Boxplot of ΔTP1 in VS for different solutions...................................................................119
Figure 105: Pairwise comparison of ΔTP1 in VS for different solutions..............................................120
Figure 106: Boxplot of ΔTP1 in CC for different solutions ..................................................................121
Figure 107: Pairwise comparison of ΔTP1 in CC for different solutions .............................................122
Figure 108: Boxplot of ΔTP1 of coffee for different materials............................................................123
Figure 109: Pairwise comparison of ΔTP1 of coffee for different materials.......................................124
Figure 110: Boxplot of ΔTP1 of wine for different materials..............................................................125
Figure 111: Pairwise comparison of ΔTP1 of wine for different materials.........................................126
Figure 112: Boxplot of ΔTP1 of Coca-Cola for different materials......................................................127
Figure 113: Pairwise comparison of ΔTP1 of Coca-Cola for different materials.................................128
Figure 114: Boxplot of ΔTP1 of water for different materials.............................................................129
Figure 115: Pairwise comparison of ΔTP1 of water for different materials........................................130
Figure 116: Graph of mean ΔTP2 by material for different solutions.................................................132
Figure 117: Boxplot of ΔTP2 of materials...........................................................................................133
Figure 118: Pairwise comparison of ΔTP2 of different materials .......................................................134
Figure 119: Graph of mean ΔTP2 by solution for different materials.................................................135
Figure 120: Boxplot of ΔTP2 of solutions ...........................................................................................136
Figure 121: Pairwise comparison of ΔTP2 of different solutions........................................................137
Figure 122: Boxplot of ΔTP2 in LU for different solutions ..................................................................138
Figure 123: Boxplot of ΔTP2 in VS for different solutions...................................................................139
Figure 124: Pairwise comparison of ΔTP2 in VS for different solutions..............................................141
Figure 125: Boxplot of ΔTP2 in CC for different solutions ..................................................................141
Figure 126: Pairwise comparison of ΔTP2 in CC for different solutions .............................................143
Figure 127: Boxplot of ΔTP2 of coffee for different materials............................................................143
Figure 128: Pairwise comparison of ΔTP2 of coffee for different materials.......................................144
Figure 129: Boxplot of ΔTP2 of wine for different materials..............................................................145
Figure 130: Pairwise comparison of ΔTP2 of wine for different materials.........................................146
Figure 131: Boxplot of ΔTP2 of Coca-Cola for different materials......................................................147
Figure 132: Pairwise comparison of ΔTP2 of Coca-Cola for different materials.................................148
Figure 133: Boxplot of ΔTP2 of water for different materials.............................................................149
Figure 134: Pairwise comparison of ΔTP2 of water for different materials........................................150
Figure 135: Graph of change in ΔTP for LU after polishing.................................................................151
xiii
Figure 136: Graph of change in ΔTP for VS after polishing.................................................................152
Figure 137: Graph of change in ΔTP for CC after polishing.................................................................153
Figure 138: Graph of change of in ΔTP for coffee after polishing.......................................................153
Figure 139: Graph of change in ΔTP for wine after polishing .............................................................154
Figure 140: Graph of change in ΔTP for Coca-Cola after polishing.....................................................155
Figure 141: Graph of change in ΔTP for water after polishing............................................................155
xiv
List of Equations
Equation 1: CIEDE2000 formula ...........................................................................................................40
Equation 2: ΔL’ difference in value .......................................................................................................40
Equation 3: L' value ..............................................................................................................................40
Equation 4: ΔC’ chroma difference.......................................................................................................40
Equation 5: C’ chroma ..........................................................................................................................40
Equation 6: a’ coordinates....................................................................................................................41
Equation 7: b’ coordinates....................................................................................................................41
Equation 8: G switching function used in the modification of a*.........................................................41
Equation 9: ΔH’ hue difference.............................................................................................................41
Equation 10: Hue angle difference .......................................................................................................41
Equation 11: SL lightness weighting function .......................................................................................41
Equation 12: SC chroma weighting function .........................................................................................41
Equation 13: SH hue weighting function...............................................................................................41
Equation 14: T function for hue weighting...........................................................................................41
Equation 15: RT rotation function.........................................................................................................42
Equation 16: Δq hue dependence of rotation function........................................................................42
Equation 17: RC chroma dependence of rotation function...................................................................42
Equation 18: Translucency parameter..................................................................................................43
xv
List of Abbreviations
CAD/CAM: Computer assisted design/Computer assisted manufacturing
DC: Degree of conversion
CIE: Commission Internationale de l’Eclairage
SLA: Stereolithography
DLP: Digital light processing
LU: Lava Ultimate
VS: VarseoSmile Crown Plus
CC: Ceramic crown
ISO: International Organization for Standardization
ΔE: Color change
ΔTP: Translucency change
ΔL*: Change in value/lightness
Δa*: Change in color along the a* axis
Δb*: Change in color along the b* axis
xvi
Abstract
Title: Effect of Staining Solutions on the Color and Translucency Change of Various Resin
Based Definitive CAD/CAM Materials.
Aim: To assess the effect of different commonly consumed beverages (coffee, red wine,
Coca-Cola, and distilled water) on the color and translucency change in resin based
definitive CAD/CAM materials.
Materials and Methods: A total of 180 specimens (length 14.50 mm x width 7.10 mm x
thickness 2.00 mm) of 3 different resin based definitive CAD/CAM materials of shade A1,
Lava Ultimate (LU; 3M, St. Paul, MN, USA), VarseoSmile Crown plus (VS; Bego, Bremen,
Germany) and Ceramic Crown (CC; SprintRay, Los Angeles, CA, USA) were immersed in 3
different staining solutions (coffee, wine and Coca-Cola) twice a day, for 15 minutes each
time, for 2 weeks. Specimens were immersed in distilled water throughout the testing
period as a control. CIE L*a*b* values were measured against both white and black
backgrounds. Measurements were done at baseline, after staining and after polishing. The
color and translucency changes ΔE and ΔTP, respectively, were calculated. Data was
statistically analyzed by employing the Kolmogorov-Smirnov and Shapiro-Wilk tests to test
for normality, and the Kruskall-Wallis test with Bonferroni correction at α = 0.05.
Results: For ΔE0, overall comparisons showed significant differences between materials,
except for LU and VS (p=0.155). Overall comparisons also showed significant differences
between solutions, except for distilled water and Coca-Cola (p=1.000). For ΔE1, overall
comparisons showed significant differences between all materials (p<0.05). Overall
comparisons also showed significant differences between coffee and wine (p=0.038) and
coffee and water (p=0.040). Polishing showed a general decrease in ΔE for all materials,
except for when Coca-Cola and water where an increase in ΔE was seen after polishing.
For ΔTP1, overall comparisons showed a significant difference in materials only between LU
and VS (p=0.004). Overall comparisons also showed significant differences between
solutions except for coffee and wine (p=0.080) and Coca-Cola and water (p=1.000). For
ΔTP2, overall comparisons showed significant differences between materials, except for CC
xvii
and LU (p=1.000). Overall comparisons also showed significant differences between all
solutions, except for water and Coca-Cola (p=1.000).
Conclusions: Different staining solutions influenced the color and translucency of resin
based CAD/CAM materials. Coffee produced the most significant color and translucency
changes. Polishing improved the color change of coffee and wine-stained resin based
CAD/CAM materials. Printable resin based definitive CAD/CAM materials had more
significant color and translucency changes compared to the milled resin based definitive
CAD/CAM material.
Clinical significance: Differences in color and translucency changes in the new printable
resin based definitive CAD/CAM materials compared to the milled resin based definitive
CAD/CAM materials should be considered prior to their clinical use, as optical properties are
important in definitive materials that are expected to remain serviceable intra-orally for an
extended period. Caution should be employed when deciding on the new printable resin
based definitive CAD/CAM materials for definitive restorations due to the non-acceptable
color changes noted in this study.
Keywords:
CAD/CAM, Additive Manufacturing, Composite Resin, Color Stability, Translucency Change,
Staining, Digital Dentistry, 3D Printing, Permanent Crown
1
1. Chapter 1: Introduction
1.1CAD/CAM, Milling to Printing
CAD/CAM technology and its application in Dentistry was first explored by Duret and
Preston1 in the 1970s. It was later refined by Mormann2 who demonstrated the first
chairside fabrication of ceramic restorations, known as the CEREC (Chair-side Economical
Restoration of Esthetic Ceramic) system. Since then, CAD/CAM technology has made much
advancement in data acquisition ability, computer design potential and manufacturing
capabilities.
CAD/CAM manufacturing is broadly categorized into subtractive and additive
manufacturing.3 Subtractive manufacturing dominated the CAM industry in its earlier years,
but problems such as its inability to fabricate intricate shapes and complex geometries,
material wastage and slow manufacturing process (only one part/piece is fabricated at any
point in time), led to development in a different type of fabrication process, additive
manufacturing.4 Additive manufacturing had its origin in the process known as rapid
prototyping but in recent years has been utilized to fabricate final working parts.4
Not only has the technology improved dramatically, but the materials that are
compatible with CAD/CAM technologies have evolved as well. Most dental materials for
clinical use can be milled with subtractive manufacturing.3 Regarding additive
manufacturing, fabrication of polycrystalline structures and glass-ceramics are still being
developed for commercial use.3, 5 However, the use of resin based and metal materials with
additive manufacturing for the fabrication of provisional type restorations have been
demonstrated.5 The application of resin based materials fabricated with additive
manufacturing ranges from provisional restorations, surgical guides, occlusal guards,
teaching aids and orthodontic aligners.6
1.2Composite Materials, Classification
With dental polymers, dental clinicians have been able to provide restorations that
provide function and aesthetics predictably in the oral environment for patients. An
understanding of how polymers are formed and the polymerization reaction is important to
understand the limitations of this material.
2
The typical building blocks of resin polymer matrices are dimethacrylates such as bisGMA, TEGDMA or UDMA.7
There are four stages in initiation of a polymerization reaction: induction,
propagation, chain transfer and termination.8
Induction consists of 2 processes; activation and initiation. To start the reaction, a
molecule must be activated to become a free radical. This process can be started by the
introduction of energy in the form of either light in both visible and the ultraviolet
spectrum, heat, or chemical means. A free radical is defined as an atom or group of atoms
that possess an electron that is unpaired. This allows the free radical to possess an ability to
take away an electron from another group such as a double bond in a monomer. By doing
so, it forms a bond between the free radical and the monomer molecule and opens up
another electron of the double bond for further reaction. This reaction is now said to be
initiated. An example of an initiator is benzoyl peroxide (BPO) which is activated to produce
two free radicals per BPO molecule. Common methods to activate dental polymers are light,
heat and chemicals. An example of a common system of chemically activated dental
polymerization is a system using a tertiary amine, which acts as the activator, and BPO,
which acts as the initiator. For light activated dental polymers, photons from a light source
act as the activator of the initiator which was commonly camphorquinone (CQ) but has also
expanded to include other initiators such as Lucirin TPO (monoacylphosphine oxide) and
PPD (1-phenyl-1,2-propanedione).
There are typically two types of photoinitiators, type 1 and type 2 photoinitiators.9, 10
The difference between the two types lie in the differences in the production of the free
radical. Type 1 photoinitiators undergo alpha-cleavage to produce a free radical, whereas
type 2 photoinitiators undergo a H-abstraction type process to produce a free radical. The
type 1 photoinitiator have low energy bonds, that cleave when shorter wavelengths of light
(ultra-violet light) is absorbed and this produces two free radicals. Type 1 photoinitiators do
not require a co-initiator and are typically not as yellow as the type 2 photoinitiators. The
type 2 photoinitiator typically relies on a co-initiator and activation by longer wavelengths of
light (visible light), and their initiation is usually slower than the type 1 photoinitiators. One
potential benefit of the type 2 photoinitiator above the type 1 photoinitiator is better
absorption properties in the near visible wavelength spectrum of light.
3
Examples of type 1 photoinitiators and their absorption ranges are: 2,4,6-
trimethylbenzoyl-diphenylphosphine oxide (TPO) (380-425 nm), bisacrylphosphine oxide
(BAPO) (365-416nm), and 1-phenyl-1,2 propanedione (PPD) (300-400nm).9, 11, 12
Perhaps the most common example of a type 2 photoinitiator is camphorquinone
(CQ), with a absorption range of 360-510nm.10
The next step in polymerization is propagation. The initiated molecule then acts on
another double bond in another monomer to form a dimer, which also results in a free
radical formation due to the breaking of the double bond. This process continues until
theoretically all the monomer is converted to polymer, or when this transfer of the free
radical “reactive center” is terminated, as will be discussed in the later stages. This
propagation stage usually results in heat formation as the molecules link up between each
other.
The chain transfer step refers to the ability of the free radical transfer step to
happen between active and passive segments of the polymer. This allows for new nucleus
for growth as the polymer forms. Although this chain transfer step can result in the
termination of the reactive site on one segment, it should not be confused with the
termination stage of the reaction which will be discussed below. The chain transfer step
does not cause the reaction to cease but transfers the reactive site to another segment in
the polymer reaction.
The last step is the termination step. This happens usually in two main ways: direct
termination due to coupling of two radical chain ends, or by hydrogen atom transfer to the
radical chain end. In the former, the coupling of the two radical chain ends results in
deactivation of the free radical site by formation of a covalent bond. In the latter, the
transfer of the hydrogen atom results in a double bond formation again, thus deactivating
the free radical site and terminating the reaction.
An important factor to consider in the polymerization process is how complete it is
after the reaction is complete. The measure of this is known as the degree of conversion
(DC). DC refers to the percentage of double bonds that have been converted to single bonds
as the polymer chain is formed.13
One way to quantify the DC is via the depth of cure (DOC). An ISO standard (ISO
4049) has been proposed that uses a micrometer to measure the thickness remaining of a
cured composite resin material in a metal cylinder after removal of uncured material with a
4
plastic instrument.14 A minimum ratio of hardness of the top:bottom of the cylinder is
assigned to determine adequate DOC and values of 0.80-0.85 have often been used.15
It is well established, that insufficient DC, can lead to increased susceptibility of a dental
polymer to undergo staining.16, 17 This perhaps is due to the leeching of unreacted
monomers out of the polymerized material,18 leaving voids that not only affect mechanical
properties, but also potentially affecting optical properties as the voids could be filled in by
chromogens from extrinsic sources.
Another important factor to consider in the polymerization of dental resins is related
to the oxygen inhibited layer. Because polymerization is induced via free radicals, the
presence of free radical scavengers such as oxygen from the atmosphere oxidizes the free
radicals into stable peroxide species which have low reactivity to monomers. This causes a
resultant layer of resin on the surface that has a low degree of conversion due to the
inhibition of polymerization where the oxygen is in contact with the resin.19 This low degree
of conversion results in a polymer chain on the surface that can be more susceptible to stain
uptake.20 The surface roughness of a resin material with and without control of the oxygen
inhibited layer appears to also differ, which may also affect its eventual susceptibility to
stain uptake.21
As mentioned previously, CAD/CAM production of dental resins can be either
additive or subtractive. Alghazzawi3 demonstrated the differences between subtractive and
additive manufacturing. Subtractive manufacturing involves milling and grinding down a
block or puck of the chosen material to its eventual intended shape and size. This is
applicable to virtually all dental materials for clinical use. One disadvantage with this type of
manufacturing is that the available geometries allowed by this technique are limited by the
milling bur size. Additive manufacturing (3D printing), on the other hand, allows for more
complex geometries to be fabricated because the final product is built up layer by layer with
a 3D printer. However, some disadvantages with this technique is the limited application to
different materials (printing ceramics and titanium has not been made commercially
available yet), and typically weaker physical properties when compared to the subtractive
manufacturing materials.
There are different types of methods to fabricate an additively manufactured final
product.22 Stereolithography (SLA) is one of the most common and oldest methods of 3D
printing. The subcategorization of this technique is usually based on the different platform
5
and/or laser movements. The general concept of SLA is the use of a UV-sensitive liquid
monomer, which will be polymerized and solidified with a UV laser that acts as the energy
source for activation of the polymerization reaction. This is done layer by layer by varying
the height of the platform on which the object is fabricated. There are two main
approaches, the more popular top-down approach has the platform lowered into the
reservoir of resin and the laser coming from bottom up. The platform is then lifted as each
layer is cured. The bottom down approach has the laser coming from the top down to the
surface of the monomer and the platform moving down into the reservoir. The top-down
approach is favored because it is able to avoid oxygen inhibition of the polymerization
because it is done at the bottom of the reservoir. Also, there is constant infilling of
unpolymerized resin for the polymerization of the next layer, compared to the bottom down
approach where a roller has to ensure even spread of unpolymerized resin for the next
layer. A subcategory of the SLA technique is the digital light processing (DLP) technique.
Conceptually, DLP is similar to SLA in that it also utilizes photosensitive resins that are cured
in a layer on a build platform. The difference is in the energy source and how it is applied to
the resin. DLP utilizes mirrors that project the light source into a plane which allows curing
of the whole layer of the object instead of having pinpoint curing within the layer. This
significantly speeds up the printing time. Lately, a new technology called continuous liquid
interface projection (CLIP) was introduced that solved the issue of resin infilling between
layers by making use of the oxygen inhibited layer in the bottom-up approach. This oxygen
inhibited layer produces a dead zone that allows for fresh resin to infiltrate continuously to
the printed part.
Another method of 3D printing is photopolymer jetting and material jetting. The
basic premise of this method is the extrusion of photosensitive monomers (photopolymer
jetting) or waxes (material jetting) in layers and curing of each incremental layer by light or
heat.
Binder jetting is a similar but distinct method of 3D printing, in which a powdery
substrate mixed with adhesive is jetted similar to photopolymer and material jetting but is
able to hold its own shape without supports due to the adhesive that is mixed into the
substrate. This adhesive is then burnt off during post-sintering.
Metals can be printed by selective laser sintering (SLS) or selective laser melting
(SLM). This involves a laser application to a preheated powder tank of metallic powder,
6
which sinters the metal in layers by raising the temperature at point of application to above
the melting temperature of the metal.
Fused filament fabrication, also known as fused deposition modeling (FDM), utilizes
thermoplastic materials such as waxes, polylactides and acrylonitrile-butadiene-styrene that
are melted and extruded via a nozzle.
SLA and DLP methods are the most used in the dental field for fabrication of
restorations for clinical use. There are different resins available for use with this system, and
they bear some similarities with the conventionally fabricated light cured resins that we in
the dental field are familiar with.
Regarding conventional composite resins, Ferracane in 2011 published a summary of
composite resin materials and the categorization based on their compositions.23 The
categorization of composite resin materials is in accordance with their filler particle size.
They can be macrofilled, microfilled, microhybrid or nanohybrid. A composite resin material
is typically made of a resin matrix (usually a dimethacrylate such as bis-GMA, TEGDMA or
UDMA),7 filler particles (usually radiopaque glass), a silane coupling agent for binding of the
resin matrix and filler particles, and chemicals to start or control the polymerization reaction
(activators and initiators).23
Polymers used for fabrication of conventional provisional restorations have been
categorized by Burns.24 There are the methyl methacrylates, the ethyl methacrylates, the
Bis-acryl based composite resins and the light polymerized composite resins. Of these, the
bis-acryl based and light polymerized resins undergo a similar reaction during
polymerization to the common light polymerization based materials used in 3D printing. The
bis-acryl resin is similar to a bis-GMA resin and is mixed with a filler and allows for crosslinking during polymerization. These materials can be light cured, auto polymerizing or dual
polymerizing. Light polymerized composite resins can also be urethane dimethacrylate
(UDMA) containing. In general, these differ from the meth methacrylate materials in that
there is no free monomer remaining after polymerization.
A categorization of the different types of dental polymers compatible with 3D
printing technology was done by Jockusch.25
Vinyl polymers such as polyvinyl alcohol (PVAL) have poor mechanical properties, are
biodegradable and also soluble in water. They are also expensive to manufacture.
7
Styrene polymers such as acrylonitrile-butadiene-styrene (ABS) and polystyrene (PS)
are utilized widely in 3D printing. PS has low water solubility, good dimensional stability,
generally rigid but is brittle. ABS differs is that it has more impact strength and resists crack
propagation to a greater degree than PS, is biodegradable, has good overall strength,
hardness, dimensional stability, resistance to scratching and low water sorption.
Acrylates are commonplace in dentistry (e.g. polymethyl methacrylate PMMA) which
can be modified with copolymers to impart better chemical resistance and impact strength.
It is a highly repairable material, has good mechanical properties with low water absorption
and is dimensionally stable.
There are also polymers that are thermoplastic in nature. Polyether-ketones such as
polyether-etherketone (PEEK) is a material that has high impact, bending and tensile
strength. It is biocompatible and is resistant to chemical degradation. It can be reinforced
with carbon or glass-fiber to improve mechanical properties.
Polyamids (PA) is another thermoplastic polymer that is strong, rigid and hard with
good wear resistance and dimensional stability along with low chemical solubility.
Polyesters such as polycarbonate (PC) have high strength, low water solubility, high
toughness, resistance to heat, and good dimensional stability.
Epoxy resin has low shrinkage and minimal stress cracking tendency with good aging
properties. It has good strength and dimensional stability under high heat.
To improve the physical properties of CAD/CAM fabricated dental polymers, ceramic
filler particles were incorporated into the resin matrix. This combination of resin and
ceramic hybrid type materials was categorized as resin nanoceramics (e.g. Lava Ultimate,
3M ESPE), glass ceramic in a resin interpenetrating matrix (Vita Enamic, Vita Zahnfabrik, Bad
Säckingen, Germany), or zirconia-silica ceramic in a resin interpenetrating matrix.26 Perhaps
a more accurate distinction of these materials was presented by Duarte 2014.27 The term
ceramic-reinforced polymer was used to accurately define CAD/CAM hybrid type restorative
materials as being inherently resin-based, with the use of ceramic particles or network to
reinforce these polymers. These materials are used with the subtractive manufacturing
method.
With the increasing popularity of 3D printing in dentistry (commonly using a
photopolymerizable resin in a vat polymerization method) attempts have been made to
improve the properties of the materials compatible with this technology. Ceramic filler
8
particles in liquid photopolymerizable resins have been introduced to improve their
mechanical and optical properties. Recently, manufacturers have produced printable resin
based definitive CAD/CAM materials for permanent restorations. The demands of a
permanent restoration for clinical use certainly are higher, and more research is required to
validate the use of these new materials for fabrication of permanent restorations.
Resin based definitive CAD/CAM materials used in additive manufacturing differ
from conventional resin materials as they are processed differently. This was shown in a
study where significant differences in shade match were found between additively
manufactured provisional restorations and conventionally fabricated provisional
restorations.28 There is currently a lack of literature on the physical properties of printed
resin based definitive CAD/CAM materials for use as permanent restorations, especially on
color stability. The ability to match the existing dentition, and to maintain color stability, is
especially important in esthetic areas of the mouth.
1.3Color Science and Spectrophotometers
Color is perceived over a range and there are systems developed to describe and
quantify them. Albert Henry Munsell developed the first color system (Munsell Color
System) which is currently widely used.29 In this system, color is described in terms of 3
variables; value, hue, and chroma. Value describes the brightness of the color, while hue
describes the unique color (red, green, blue), and chroma describes the intensity of the
color.
The need for objectivity and quantifiability of color led to the development in the
1930s by the Commission Internationale de l’Eclairage (CIE - International Commission on
Illumination) of a standard color table.30 This table was based on the work of physicist James
C. Maxwell. Maxwell reported how any particular color can be obtained by mixing different
variations of spectral colors red, green and blue (RGB). CIE used x, y and z coordinates to
represent these 3 base spectral colors in their system.
The CIE proposed a new system to quantify color in 1976.30 Instead of x, y and z
coordinates, the L*a*b* coordinates was introduced, where L* indicates the brightness of
an object (a higher number indicates a brighter color), a* indicates the red-green coordinate
(positive value is more red, negative value is more green), and b* indicates the yellow-blue
9
coordinate (positive value is more yellow, negative value is more blue). Another way to
illustrate the CIE L*a*b* system is to use the L*C*h parameters to describe the location of
the color in the L*a*b* color space with the coordinate L, the degree C and the angle h.
With an objective way to quantify color in a defined color space, then comes an
objective way to compare the differences in these colors. The CIELAB ΔE formula was
introduced30 in 1976 and is shown below. A low ΔE indicates that the change in color is less
perceptible.
�!"# = [(�∗
% − �∗
&)% + (�∗
% − �∗
&)% + (�∗
% − �∗
&)%]
&
%
This formula has been updated over time. CIE developed an updated CIE94 formula
for ΔE in 1995,31 and then another update with CIEDE2000 in 2001.32 Main differences
between the CIE94 and CIE2000 are the number of corrections to CIELAB. CIE94 provided
only 2 corrections to CIELAB: the hue and chroma weighting functions. CIEDE2000 provided
5 corrections to CIELAB: lightness, chroma, hue weighting functions; the rotation term; and
correction for neutral colors.33 The CIE94 and CIEDE2000 formulas are represented below,
and the CIEDE2000 formula will be described later in the text.
�'( = -. �∗
�)�)
1
%
+ .
�∗
"#
�*�*
1
%
+ .
�∗
"#
�+�+
1
%
4
&
%
�,, = 56 �′
�)�)
8
%
+ 6 �′
�*�*
8
%
+ 6 �′
�+�+
8
%
+ �- 6 �′
�*�*
8 6 �′
�+�+
8:
&/%
It was found that there was significant improvements with the CIE94 over the CIELAB
formula, and significant difference between the CIE94 and the CIEDE2000 formulas.33 When
comparing the CIELAB with CIEDE2000 formulas, it has been shown that the CIEDE2000
formula performs better when reflecting the color difference that is perceived by the
human eye.34-36
Color and shade matching of dental restorations has been challenging for the dentist
as described by Clark.37 There has been much effort to identify factors that would affect
perception of tooth color and to quantify tooth colour.38-40 Different methods range from
recognizing the important role that light plays when shade matching teeth,39 to
development of shade guides and instruments that can measure shades. The shade guides
10
that we have in dentistry are mostly derived from the Vitapan Classical (Vita Zahnfabrik, Bad
Sackingen, Germany) shade guide. However, there are reports of a lack of consistency
between fabricated shade guides, even those made by the same manufacturer.41, 42 There
was therefore a need to adopt a standard to quantify shades and differences in shade
numerically for more objective differentiation.
Therefore, based on the developments of CIELAB and CIEDE2000 as described
earlier, we are able to utilize L, a* and b* values as a means of communication of shade and
differences of shade in the dental field with the use of the ΔE value.
Previous studies investigated ΔE values and its relation to clinical perceptibility and
tolerance. An average value of ≤ 3.7 ΔE for resin veneers were deemed a perfect match in
one study.43 Another study showed that a 2.6 ΔE color difference of denture teeth in a
patient’s mouth will be perceptible by half of dentist observers, and concluded that the
threshold for perceptibility is 2.6 ΔE, and the threshold for clinical tolerability was 5.6 ΔE.44
These values of ΔE, however, were based on the older CIELAB formula. A more recent article
reported the 50:50% perceptibility and acceptability thresholds using the CIEDE2000
formula derived ΔE values to be 0.8 and 1.8 respectively.45
When light interacts with tooth structure, color is not the only optical property that
can be visualized, other properties such as opalescence, and translucency can also be
described.
Opalescence is a unique property of an object which causes scattering of the shorter
wavelengths of visible light (blue range) which results in the appearance of a bluish color
when the object is subject to reflected light, while transmitted light through the object
would result in the appearance of an orange/brown color due to the shorter blue
wavelengths being scattered away from the incident beam of transmitted light.46
Of particular importance when restoring and considering teeth and restorations in
the esthetic zone is the translucency of teeth. Translucency is a property a substance to
disperse light as it passes through, which results in partial visibility through the object
ranging from between completely opaque to completely transparent.47 Translucency of
teeth have been shown to change by aging, which could be a result of the natural changes
within the tooth in size of the pulp, increasing dimensions of dentine, and reduction of
enamel from external wear factors.48, 49 It has been shown that aging of teeth causes a
corresponding increase in translucency.50
11
Quantification of how translucent an object is can be derived from the previously
mentioned CIE L*a*b* values, using what is called the translucency parameter (TP).51 TP is a
measure of the color difference of an object when measured against black and white
backgrounds, which gives an indication of the translucency of the object.52 Typically, a TP
value of zero indicates that an object is completely opaque, while an increasing value of TP
would indicate higher translucency of an object.51
Previous studies have shown the use of a spectrophotometer to also measure
translucency of restorative materials.53, 54 Using the CIEL*a*b* values obtained from the
spectrophotometer, the translucency parameter can be represented by the formula TP =
[(Lblack − Lwhite)
2 + (ablack − awhite)
2 + (bblack − bwhite)
2
]
1/2.
55
To quantify color and translucency according to the above coordinates and
equations, we need a way to acquire the coordinates of color in the CIE L*a*b* color space.
We are able to do so via devices such as colorimeters or spectrophotometers.
A colorimeter works by a filtering light in the red, green and blue parts of the visible
light spectrum, and measures tristimulus values.56 This device is subject to inaccuracies from
the aging of filters and are typically less accurate than the spectrophotometers.57
A spectrophotometer is one of the most accurate devices that acquires color data for
shade matching in dentistry.56 How it works is through the measurement of the energy of
reflected light at 1-25nm intervals along the wavelength of visible light.58 The use of a
spectrophotometer reduces inaccuracies in color measurement and determination and data
collected is expressed in the 3 coordinates (L*, a*, b*).56 There are two different types of
spectrophotometers, a transmittance type and a reflectance type. As their name suggests,
the transmittance type measures transmitted light through an object, while the reflectance
type measures light that is reflected off an object. The major difference lies in where the
sensor is located in the device, opposite to the source of light in the former, and on the
same side as the source of light in the latter. Lamda 35 Perkin Elmer (Perkin Elmer,
Waltham, MA, USA)59 and Evolution 300 UV-Vis (ThermoFisher)60 are examples of the
transmittance type spectrophotometers.
Vita Easyshade Compact (Vita Zahnfabrik, Bad Säckingen, Germany), Crystaleye
(Olympus, Tokyo, Japan), Shade-X (X-Rite, Grandville, MI), SpectroShade Micro (MHT Optic
Research, Niederhasli, Switzerland) are examples of reflectance type spectrophotometers
12
typically used in dentistry. Crystaleye (Olympus, Tokyo, Japan) was previously investigated
and has shown good reliability and accuracy in shade matching of dental restorations.61, 62
1.4Color Stability and Staining Mediums
In addition to providing a good color match to natural dentition, dental materials
should be color stable in the oral environment which is subject to harsh conditions that
encourages its deterioration.
Once placed intra-orally, aging is an inevitable phenomenon that all dental materials
are subject to. This has been shown to affect the optical properties of resin-based materials,
regardless of the method of processing.63, 64 The reasons for staining of these materials can
be categorized into endogenous and exogenous reasons.65, 66
Endogenous reasons are related to the material composition, degradation of the
matrix with time, degree of conversion (a measure of the amount of reacted vs unreacted
monomers after polymerization) and water sorption.67, 68
The type of monomer used in the resin composition has been shown to affect the
degree of conversion (DC) and water sorption and hence the color stability of the material in
the long term.69 It was mentioned above that a high degree of conversion would mean that
more double bonds have been converted to single bonds in the polymerization reaction.
There are many factors that can affect the resultant DC of a resin-based material. These
range from distance from the light curing source,70, 71 to irradiance at the light source,72 to
time for polymerization,70, 73 to uniformity of the light,74 to method of polymerization.75, 76
While these factors are difficult to control with traditional polymerization of resin based
materials that utilizes hand held curing lights and are subject to different applications intraorally, CAD/CAM technology has allowed for greater consistency and uniformity of
polymerization of resin based dental materials and a higher degree of conversion.77
It has also been shown that the type of photoinitiator used can affect the color
stability of the material in the long term.78, 79 The type of filler and accelerator used in
conjunction with the photoinitiator will also affect the color change of the restoration.80
Factors that are postulated to result in low stainability of a resin based material are
low water sorption, reduced filler particle size, high filler loading, and an optimal coupling
13
agent between the organic matrix and inorganic filler particles.81 A high degree of
conversion is also advocated to achieve a low stainability of a resin based material.16, 17
Exogenous reasons are related to staining derived from staining solutions that are
commonly consumed such as coffee, tea and wine.65, 82-84 These solutions have been shown
to affect the color stability of printed resins used to fabricate provisional restorations.85, 86
Furthermore, related factors such as contact time, composition of different materials and
surface treatment all affect the stainability of resin-based materials.87, 88
The type of staining can be further divided into extrinsic vs intrinsic staining. A
classification for the different types of staining was proposed by Nathoo in 1997.89 The
classification described N1-N3 types of extrinsic staining, and the associated chemistry and
mechanisms behind these different extrinsic stains. N1 type stain is classified when the
chromogen is the same color as the eventual discoloration, for example, by bacteria, food or
metals from restorations. N2 type stain is classified when the chromogens change in color
after adherence to the substrate, for example, in long term food stains. N3 type stain is
classified when the colorless materials bind to the substrate and then undergoes chemical
transformation to produce chromogens, for example, in antimicrobial stains. The main
mechanism in extrinsic staining is via the formation of a salivary pellicle via electrostatic
forces and calcium bridges. Chromogens from foods can also be directly deposited on to the
tooth/restorative material via an ion exchange mechanism with the salivary pellicle.89
Lee 200690 demonstrated the importance of saliva, its enzymes and proteins in the
interaction of salivary pellicle and restorative material. Adsorption of salivary proteins was
rapid on resin based materials, and the profile of the pellicle seemed to differ based on the
material itself.91 The surface roughness and surface free energy of the material also affects
the pellicle formation and plaque growth on a restorative material,92, 93 which can affect the
extrinsic staining effects on these materials. Aside from the formation of the pellicle,
interaction between salivary esterases,94 and the organic acids from the plaque formed on
the restoration95 was found to cause degradation of the restorative material which affects
its stain resistance ability.
14
Intrinsic staining of teeth can be classified into pre-eruptive or post-eruptive.89 Preeruptive intrinsic staining can be from fluorosis, tetracycline staining, and conditions such as
dentinogenesis imperfecta and amelogenesis imperfecta. Post-eruptive intrinsic staining can
be due to secondary/tertiary dentine formation, trauma to teeth and the resultant pulpal
changes, or from restorations with amalgam.89 Intrinsic staining of dental materials, on the
other hand, is related to absorption of chromogens into the material due to degradation of
the material over time.96, 97
Intrinsic staining in resin-based materials occurs because these materials constantly
interact with the oral environment after restorations are placed. The resin based material
absorbs water and other chemical components that are consumed, and in turn also releases
components into the oral cavity.98 This process happens via diffusion and causes the
material to degrade by separation of the filler particle/matrix bond and release of
monomers.96, 99 The degree of conversion also was related to the susceptibility of a resin
based material to undergo staining16, 17 because of leeching of monomers that leaves voids
which may then be filled in by chromogens. This phenomenon with resin based dental
materials could lead to deleterious effects on the physical and optical properties of the
material in the long term.96 Chromogens from extrinsic sources could permeate and perfuse
into the resin based material and lead to intrinsic staining of the material with time.97
In terms of management of stained restorations, Some methods for prevention of
staining, are adequate polishing100 and/or resin coating85 to reduce surface roughness and
surface free energy, and to manage the oxygen inhibited layer that has a low degree of
conversion which is linked to susceptibility to staining. Additional light-curing101 to ensure a
higher degree of conversion has also been advocated for resin based materials in order to
increase their color stability and stain resistance.
After restorations are stained, some other methods have been proposed to manage
these stains. One such method is bleaching of the restoration with hydrogen peroxide102 to
remove intrinsic stains within the material. The decomposition of hydrogen peroxide to free
radicals that permeates the restoration and undergo reduction-oxidation reaction with
chromogens to break them up to smaller non chromogenic molecules is the mechanism by
which bleaching is thought to help with stain removal.103 Another method is the use of
dentifrices that promote whitening by disruption of the biofilm and removal of extrinsic
staining.104, 105
15
With the introduction of resin based definitive CAD/CAM materials as mentioned
previously, the concerns related to color stability of these materials remain, regardless of
processing type (subtractive or additive manufacturing). This is because, from the material
science standpoint, these materials are still inherently resin-based and are subject to the
same aging effects encountered by conventional composite resin materials.
1.5Intro to Problem – No studies on color stability of printed definitive
restorations
The functional and esthetic demand placed on the newly introduced printed resin
based definitive CAD/CAM materials is increased as it is expected to be serviceable in the
oral environment for a longer period of time than a provisional restoration. These new
resins should be tested for their physical properties prior to their recommendation for
clinical use. However, little is currently known about the long-term behavior of these
restorations intra-orally, especially regarding color stability.
1.6Purpose and Null Hypothesis
The purpose of this study was to investigate, in an in-vitro setting, the color stability
of two 3D printed resin based definitive CAD/CAM materials (VarseoSmile Crown plus; Bego,
Bremen, Germany, Ceramic Crown; SprintRay, Los Angeles, CA, USA), which are both
marketed for the fabrication of definitive restorations, with one milled resin based definitive
CAD/CAM material (Lava Ultimate; 3M, St. Paul, Minnesota, USA) acting as a control. The
color stability (color and translucency change) was challenged with immersion into various
liquids (coffee, red wine, Coca-Cola, and distilled water), and quantified using a
spectrophotometer (Crystaleye; Olympus, Tokyo, Japan).
1.6.1 Null Hypotheses
Color change:
1. Different resin-based definitive CAD/CAM materials (Lava Ultimate, VarseoSmile,
Ceramic Crown) are not affected in color by staining with different staining solutions.
16
2. Different staining solutions (coffee, Coca-Cola, red wine, and distilled water) do not
affect the color of different resin-based definitive CAD/CAM materials.
Translucency change:
3. Different resin-based definitive CAD/CAM materials (Lava Ultimate, VarseoSmile,
Ceramic Crown) are not affected in translucency by staining with different staining
solutions.
4. Different staining solutions (coffee, Coca-Cola, red wine, and distilled water) do not
affect the translucency of different resin-based definitive CAD/CAM materials.
17
2. Chapter 2: Materials and Methods
2.1Materials
The studied materials were (Table 1):
1. Lava Ultimate, LU (3M, St. Paul, MN, USA) (Control Group) (Figure 1).
2. VarseoSmile, VS (Bego, Bremen, Germany) (Test Group 1) (Figure 2).
3. Ceramic Crown, CC (SprintRay, Los Angeles, CA, USA) (Test Group 2) (Figure 3).
Figure 1: Lava Ultimate (LU)
18
Figure 2: VarseoSmile (VS)
Figure 3: Ceramic Crown (CC)
19
MATERIALS
MILLED PRINTED
Name Lava Ultimate VarseoSmile Crown Plus Ceramic Crown
Brand 3M BEGO SprintRay
Content Matrix Highly cross-linked
polymeric matrix
20
wt%
Esterification products of
4,4‘-isopropylidiphenol,
ethoxylated and 2-
methyl- prop-2enoic acid.
50-75
wt%
Methacrylate oligomers 20-60%
Diphenyl(2,4,6-trimethylbenzoyl) phosphine
oxide.
< 2.5 wt% Methacrylate and acrylic
monomers
20-50%
Photoinitiators 0.1-10%
Filler Nano-ceramic
fillers (zirconium
dioxide and
silicone oxide
nano-particles)
80
wt%
Inorganic fillers (silanized
dental glass, particle size
0.7 μm)
30 - 50
wt%
Additives (inorganic
fillers)
>50 wt%
Propertie
s
Appearance/State Solid Liquid Liquid
Viscosity Not applicable 2,500 – 6,000 mPa.s 2,500 – 6,000 mPa.s
Density 2.1 g/cm3 1.4-1.5 g/cm3 1.6-1.7 g/cm3
Flash Point Not applicable 110ºC 93ºC
Flexural Strength 204 MPa 116 – 150 MPa 150 ±25 mPa
Flexural Modulus 12.80 MPa 4,090 MPa 7,800 ± 500 MPa
Water Solubility Negligible < 1 µg/mm3 2.16 ± 1.30 μg/mm3
Water Sorption Not applicable < 12 µg/mm3 17.35 ± 2.56 μg/mm3
Shade Used A1 A1 A1
Batch Number NA21524 600577 S23C14CA11
Table 1: Materials and properties
2.2Specimen Preparation
A total of 180 specimens (length 14.50 mm x width 7.10 mm x thickness
2.00 mm +/- 0.1 mm) were fabricated. According to the three different
materials, the specimens were divided into 4 groups (Table 2).
20
Number of
specimens
Lava Ultimate
(LU)
VarseoSmile
Crown Plus (VS)
Ceramic Crown
(CC)
Coffee 15 15 15
Wine 15 15 15
Coca-Cola 15 15 15
Distilled water 15 15 15
Total 60 60 60
Table 2: Group division
2.2.1 Lava Ultimate
Five Lava Ultimate blocks were sectioned into 60 cuboid specimens (14.5 mm x 7.1 mm x
2.3mm) using a precision saw (Isomet 1000, Buehler Ltd., Lake Bluff, IL, USA) (Figure 4) with
a diamond blade (102 mm diameter, 0.3 mm thickness; Isomet diamond blade, 15LC, 3in,
Buehler Ltd, Lake Bluff, IL, USA) (Figure 5), at a speed of 975 rpm under continuous cooling
with distilled water. The extra 0.3 mm thickness of the specimens was provided to account
for material loss after polishing of the specimens prior to testing (Figure 6).
21
Figure 4: Sectioning machine
Figure 5: Sectioning blade
Figure 6: Sectioned LU specimen
22
2.2.2 VarseoSmile
Cuboid specimens of 7.1 x 14.5 x 2.3 mm were designed digitally with Meshmixer 3.0
(Autodesk Research, CA, USA). The STL file of the design was saved, and a slicing software
Rayware (SprintRay, Los Angeles, CA, USA) was used to design supports and printing
orientation. The printing orientation used was 45 degrees. The extra 0.3 mm thickness of
the specimens was provided to account for material loss after polishing of the specimens
prior to testing (Figure 7).
To print the specimens, Varseosmile resin (Bego, Bremen, Germany) shade A1 was
vigorously shaken in the bottle for 2 minutes before loading of the printing tray. The printer
used to print the specimens was SprintRay Pro 55 (SprintRay, Los Angeles, CA, USA) (Figure
8).
The printed specimens were released from the build platform using a spatula (Stanley
28-139, Stanley Black & Decker, New Britain, CT, USA) and then cleaned in a fully
automated, two-stage wash and dry system Pro Wash/Dry (Sprintray, Los Angeles, CA, USA)
(Figure 9) with ethanol solution (96%) (Isopropyl rubbing alcohol, Breen Laboratories,
Carson, CA, USA). Following cleaning of the specimens, they were placed in a post curing
oven ProCure 2 (SprintRay, Los Angeles, CA, USA) (Figure 10) for post curing.
The supports of the specimens were then removed with a carbide bur (H79EC.11.040 HP
EC Cutter Carbide, Brasseler USA, Savannah, GA, USA) at 20,000 rpm using an electric motor
handpiece (Forza L50K, Brasseler USA, Savannah, GA, USA).
Figure 7: Digital design of supports and angulation of VS specimens
23
Figure 8: Sprintray Pro 55
Figure 9: Sprintray Pro Wash/Dry
24
Figure 10: Sprintray ProCure 2
Figure 11: VS post-cured specimens
2.2.3 Ceramic Crown
Cuboid specimens of 7.1 x 14.5 x 2.3 mm were designed digitally with Meshmixer 3.0
(Autodesk Research, CA, USA). The STL file of the design was saved, and a slicing software
Rayware (SprintRay, Los Angeles, CA, USA) was used to design supports and printing
orientation. The printing orientation used was 45 degrees. The extra 0.3 mm thickness of
25
the specimens was provided to account for material loss after polishing of the specimens
prior to testing (Figure 12).
To print the specimens, Ceramic crown resin (SprintRay, Los Angeles, CA, USA) shade A1
was vigorously shaken in the bottle for 2 minutes before loading of the printing tray. The
printer used to print the specimens was SprintRay Pro 55 (SprintRay, Los Angeles, CA, USA)
(Figure 8).
The printed specimens were released from the build platform using a spatula (Stanley
28-139, Stanley Black & Decker, New Britain, CT, USA) and then cleaned in a fully
automated, two-stage wash and dry system Pro Wash/Dry (Sprintray, Los Angeles, CA, USA)
(Figure 9) with ethanol solution (96%) (Isopropyl rubbing alcohol, Breen Laboratories,
Carson, CA, USA). Following cleaning of the specimens, they were placed in a post curing
oven ProCure 2 (SprintRay, Los Angeles, CA, USA) (Figure 10) for post curing.
The supports of the specimens were then removed with a carbide bur (H79EC.11.040 HP
EC Cutter Carbide, Brasseler USA, Savannah, GA, USA) at 20,000 rpm using an electric motor
handpiece (Forza L50K, Brasseler USA, Savannah, GA, USA).
Figure 12: Digital design of supports and angulation of specimen
26
Figure 13: CC post-cured specimens
2.3Polishing, Numbering and Grouping of Specimens
The final thickness and surface polish was achieved by polishing with 600-grit
(CarbiMet® 2 Abrasive Papers 600-grit, Buehler Ltd., Lake Bluff, IL, USA) 1000-grit and 1200-
grit (CarbiMet® 2, Buehler Ltd., Lake Bluff, IL, USA) sandpaper by hand (Figure 14). Constant
finger pressure was applied in the middle of the specimen during polishing to avoid sloped
surfaces. Constant irrigation with distilled water was provided.
After polishing, all specimens were measured for thickness using a digital caliper
(Mitutoyo digital caliper; Mitutoyo Corp, Kawasaki, Japan) (Figure 15). This was to ensure
thickness of correct dimensions (2.00 mm± 0.1 mm).
Following the polishing, all the specimens were numbered by inscribing the specimens at
the corner using a small carbide bur (1/2 HP round carbide H1.11.006 bur, Brasseler USA,
Savannah, GA, USA) at 20000 rpm using an electric motor handpiece (Forza L50K, Brasseler
USA, Savannah, GA, USA). The specimens were numbered from 1 to 180, and then placed in
clear, colored, sealable polyethylene bags (Colorful Snack Bags, Kroger, Cincinnati, OH, USA).
The specimens were finally grouped into groups of 15 (Figure 16, Figure 17, Figure 18),
according to material and staining solution.
27
Figure 14: Sandpaper 600, 1000 and 1200 grit
Figure 15: Digital caliper
28
Figure 16: Prepared LU samples
Figure 17: Prepared VS samples
29
Figure 18: Prepared CC samples
2.4First Color Measurement (Baseline)
The specimens were cleaned in an ultrasonic bath (Quantrex, L & R manufacturing
company, Kerney, New Jersey, USA) with distilled water for 10 minutes prior to baseline
measurements. The specimens were dried with filter paper (Hario V60 Paper Coffee Filters,
Size 01, White, Untabbed, HARIO Co., Ltd, Tokyo, Japan) by dabbing gently without rubbing
the surface.
Color measurements were taken of each specimen using a spectrophotometer
(Crystaleye Spectrophotometer, Olympus, Tokyo, Japan) (Figure 19). CIE L*a*b* values for
white background were recorded by taking three measurements of each specimen on a
white background (JJC 10" x 8" PVC White Balance Card Set, Amazon, Bellevue, WA, USA).
The same process was repeated for CIE L*a*b* values for black background using a black
background (JJC 10" x 8" PVC White Balance Card Set, Amazon, Bellevue, WA, USA).
Calibration of the spectrophotometer was done after measurement of every specimen. A
custom-made measurement chamber (Figure 20) was made with a cardboard box with its
top and front sides cut out to allow access for measurements and to block surrounding light
at the same time. The chamber was lined with black paper to control lighting conditions
30
within the measurement chamber. The white (Figure 21) and black (Figure 22) backgrounds
were taped to the bottom of the chamber such as to allow measurements against both
backgrounds by flipping them back and forth. The measurements were done in the same
location, by the same person and under the same ambient lighting conditions to ensure
consistency of measurement conditions.
Figure 19: CrystalEye spectrophotometer
31
Figure 20: Custom measurement chamber
Figure 21: White background
32
Figure 22: Black background
2.5Immersion/Staining of Specimens
The protocol for staining of specimens was set to be 15 minutes of immersion, twice a
day for a total of 14 days. Specimens were grouped in clear, colored, sealable polyethylene
bags (Colorful Snack Bags, Kroger, Cincinnati, OH, USA) (Figure 23), in which staining
solutions were poured into for staining purposes. After each staining cycle, specimens were
washed with distilled water (Arrowhead distilled water, Blue Triton, Stamford, CT, USA) and
placed in a separate colored, sealable polyethylene bag filled with distilled water and placed
in a laboratory incubator (Model 5510, National Appliance Company, Portland, OR, USA)
kept at 37°C.
The staining solutions were made fresh and/or replenished for each cycle. Coffee
(Nescafe Clásico, Nestle, Vevey, Switzerland) (Figure 24) was prepared by mixing 8 g of
instant coffee with 200 ml of hot distilled water and using 50ml for each group with a final
temperature of 60°C (Figure 29). Red Wine (Shiraz, Charles Shaw, California, USA) (Figure
25) was prepared by using 50 ml of wine at room temperature (23°C) for each group. CocaCola (The Coca-Cola Company, Atlanta, Georgia, USA) (Figure 26) was prepared by using 50
ml of Cola that was freshly retrieved from a refrigerator at 6.5°C (Figure 30) for each group.
Distilled water (Arrowhead distilled water, Blue Triton, Stamford, CT, USA) (Figure 27) was
used as a control and kept at 37°C in the laboratory incubator (Figure 28). For the control,
distilled water was replenished daily.
33
Figure 23: Colored bags
Figure 24: Coffee
34
Figure 25: Wine
Figure 26: Coca-Cola
35
Figure 27: Distilled water
Figure 28: Incubator
36
Figure 29: Thermometer measuring coffee temperature
Figure 30: Thermometer measuring Coca-Cola temperature
37
2.6pH Value Measurements
The pH of each solution was measured thrice using a pH meter (Oakton pH5 Acorn
Series, Vernon Hills, IL, USA) at the start of the staining procedure and the mean of the three
measurements is shown below (Table 3).
pH of Solutions Coffee Wine Coca-Cola
pH 4.89 3.62 2.63
Table 3: pH of solutions
2.7Second Color Measurement (After Staining)
The specimens were removed from the polyethylene bags, rinsed with distilled water
and dried with filter paper by dabbing gently without rubbing the surface.
Color measurements were taken of each specimen using a spectrophotometer
(Crystaleye Spectrophotometer, Olympus, Tokyo, Japan). CIE L*a*b* values for white
background were recorded by taking three measurements of each specimen on a white
background (JJC 10" x 8" PVC White Balance Card Set, Amazon, Bellevue, WA, USA). The
same process was repeated for CIE L*a*b* values for black background using a black
background (JJC 10" x 8" PVC White Balance Card Set, Amazon, Bellevue, WA, USA).
Calibration of the spectrophotometer was done after measurement of every specimen. A
custom-made measurement chamber was used, and measurements were done in the same
location, by the same person and under the same ambient lighting conditions to ensure
consistency of measurement conditions.
2.8Polishing
To remove extrinsic staining, all specimens were polished by light brushing of both
surfaces with soft prophy angles (DPAS Soft Disposable Prophy Angles, Brasseler USA,
Savannah, GA, USA) mounted on a slow speed handpiece at 1,000 rpm with flour of pumice
powder for 10 seconds. The pumice and water ratio was 12.5 g of pumice with 10 ml of
water (Figure 31).
Following the polishing, specimens were thoroughly rinsed with distilled water, dried
with filter papers, and replaced into polyethylene bags.
38
Figure 31: Pumice and water mix
2.9Third Color Measurement (After Polishing)
The specimens were removed from the polyethylene bags, rinsed with distilled water
and dried with filter paper by dabbing gently without rubbing the surface.
Color measurements were taken of each specimen using a spectrophotometer
(Crystaleye Spectrophotometer, Olympus, Tokyo, Japan). CIE L*a*b* values for white
background were recorded by taking three measurements of each specimen on a white
background (JJC 10" x 8" PVC White Balance Card Set, Amazon, Bellevue, WA, USA). The
same process was repeated for CIE L*a*b* values for black background using a black
background (JJC 10" x 8" PVC White Balance Card Set, Amazon, Bellevue, WA, USA).
Calibration of the spectrophotometer was done after measurement of every specimen. A
custom-made measurement chamber was used, and measurements were done in the same
location, by the same person and under the same ambient lighting conditions to ensure
consistency of measurement conditions.
2.10 Color and Translucency Analysis
The images were transferred into the corresponding computer software (Crystaleye®
Application Software, Olympus, Tokyo, Japan) on a laptop (VAIO Computer, Sony
39
Electronics, Minato City, Tokyo, Japan). The outline of each specimen was defined (Figure
32), and the color analysis for each specimen was done to obtain the L*a*b* values (Figure
33), which were then entered into an excel spreadsheet (Excel; Microsoft, Redmond, WA,
USA).
Figure 32: Outline of specimen for color analysis
Figure 33: L*a*b* values in CrystalEye software
2.11 Color Analysis
The CIEDE2000 color difference (ΔE) was calculated by applying the CIEDE2000 formula
for color difference:106
40
ΔE0 was calculated by comparing the color difference between baseline and after
staining using the L*a*b* values measured from the black background.
ΔE1 was calculated by comparing the color difference between baseline and after
polishing using the L*a*b* values measured from the black background.
Δ�,, = 56 Δ�′
�)�)
8
%
+ 6 Δ�′
�*�*
8
%
+ 6 Δ�′
�+�+
8
%
+ �- 6 Δ�′
�*�*
8 6 Δ�′
�+�+
8:
&/%
Equation 1: CIEDE2000 formula
The L* coordinate measures the value of the specimen, with a higher number
indicating a lighter shade. This coordinate is found on the Z axis of the CIE L*a*b* color
space. The values of L range from 0 (black) to 100 (white).30
The subscripts “0” and “1” refer to baseline measurement and the tested
measurement (after staining and after polishing), respectively.
ΔL’, ΔC’ and ΔH’ represent differences in the lightness, chroma and hue,
respectively.106
To calculate ΔL’ values, the formulas below were applied:
Δ�/ = �′& − �′,
Equation 2: ΔL’ difference in value
�/ = �∗
Equation 3: L' value
To calculate ΔC’ values, the formulas below were applied:
Δ�/ = �′& − �′,
Equation 4: ΔC’ chroma difference
�′ = (�′
% + �′
%)&/%
Equation 5: C’ chroma
The a* coordinate measures the chroma along the X axis of the CIE L*a*b* color
space. Positive values of a* indicates reddish colors and negative values of a* indicates
greenish colors.30, 107
The b* coordinate measures the chroma along the Y axis of the CIE L*a*b* color
space. Positive values of b* indicates yellowish colors and negative values of b* indicates
bluish colors.30, 107
41
�′ = (1 + �)�∗
Equation 6: a’ coordinates
�/ = �∗
Equation 7: b’ coordinates
� = 0.5 A1 − B C�∗ DDDD
��DDDE
0
C�∗ DDDD
��DDDE
0
+ 250
G
Equation 8: G switching function used in the modification of a*
�∗ DDDD
��DDD represents the arithmetic mean of the C*ab values for the two samples of the color
difference pair.106
To calculate ΔH’ values, the formulas below were applied:
Δ�/ = 2(�′,�′&)&/% ��� (Δℎ/
/2)
Equation 9: ΔH’ hue difference
Δℎ = 0°
Equation 10: Hue angle difference
The weighting functions (SL, SC, and SH) adjust the total color difference for
differences in the position of the color difference pair in the L*a*b* coordinates and are
calculated with the following formulas:106
�) = 1 +
⎝
⎛ 0.015 O�
P′ − 50Q
%
R20 + O�
P′ − 50Q
%
⎠
⎞
Equation 11: SL lightness weighting function
�* = 1 + 0.045C′ P
Equation 12: SC chroma weighting function
�+ = 1 + 0.015C′ P�
Equation 13: SH hue weighting function
� = 1 − 0.17 ��� ,ℎ
." − 30°0 + 0.24 ��� ,2ℎ
."
0 + 0.32 ��� ,3ℎ
." + 6°0 − 0.20 ��� ,4ℎ
." − 63°0
Equation 14: T function for hue weighting
The parametric factors (KL, KC and KH) are correction terms used in experimental
conditions. They were set to 1 for the calculation (KL = KC = KH = 1).106
42
The modified hue (h’) is a geometric interpretation in the a’–b* plane, it can be
visualized as an angular position of the point (a’, b*) measured from the positive a’ axis.108
Lastly, a rotation correction (RT) to take into account the interaction of chroma and
hue differences in the blue region is applied using the following formulas:
�- = ��� (2∆q) �*
Equation 15: RT rotation function
∆q = 30° ��� ]−^Ch′ P − 275°E/25° a
%
b
Equation 16: Δq hue dependence of rotation function
�* = 2B CC′ PE
0
CC′ PE
%
+ 250
Equation 17: RC chroma dependence of rotation function
The calculated color differences were then compared against a recent publication on the
CIEDE2000 50:50% perceptibility and acceptance thresholds of ΔE = 0.8 and ΔE = 1.8
respectively.45
2.12 L*a*b* Analysis
Descriptive analysis of L*a*b* to show direction of change of the color according to
value in the L axis and shade in the a* and b* axes was conducted.
The L* coordinates were compared from baseline to after staining and after polishing for
descriptive analysis. ΔL0 was calculated by subtracting the baseline L* value against black
background from the after staining L* value against black background. ΔL1 was calculated
by subtracting the baseline L* value against black background from the after polishing L*
value against black background. Positive values of ΔL indicate an increase in value/lightness.
Negative values of ΔL indicate a decrease in value/lightness.
The a* coordinates were compared from baseline to after staining and after polishing
for descriptive analysis. Δa0 was calculated by subtracting the baseline a* value against
black background from the after staining a* value against black background. Δa1 was
calculated by subtracting the baseline a* value against black background from the after
polishing a* value against black background. Positive values of Δa* indicate a shift to a
redder color. Negative values of Δa* indicate a shift to a greener color.
43
The b* coordinates were compared from baseline to after staining and after polishing
for descriptive analysis. Δb0 was calculated by subtracting the baseline b* value against
black background from the after staining b* value against black background. Δb1 was
calculated by subtracting the baseline b* value against black background from the after
polishing b* value against black background. Positive values of Δb* indicate a shift to a
yellower color. Negative values of Δb* indicate a shift to a bluer color.
2.13 Translucency Analysis
The translucency of each specimen was calculated by using the translucency parameter
(TP) in the equation shown below:
�� = d(�∗
1 − �∗
2)% + (�∗
1 − �∗
2)% + (�∗
1 − �∗
2)%
Equation 18: Translucency parameter
The subscripts “W” and “B” refer to the measurements of the specimens against white
and black backgrounds, respectively.
TP for baseline (TP0), TP for after staining (TP1) and TP for after polishing (TP2) were
calculated for each specimen.
The difference in translucency between baseline and after staining (ΔTP1), and the
difference in translucency between baseline and after polishing (ΔTP2) was calculated by
subtracting the TP0 (baseline) from TP1 (after staining) and TP2 (after polishing) to get ΔTP1
and ΔTP2 respectively.
The calculated translucency differences were the compared against a recent publication
on the ΔTP 50:50% perceptibility and acceptance thresholds of ΔTP = 1.33 and ΔTP = 4.43
respectively.109
2.14 Statistical Analysis
All data was entered into a digital sheet (Excel; Microsoft Corp, Redmond, WA, USA) and
calculations for ΔE0, ΔE1, ΔTP1 and ΔTP2 were done as previously mentioned.
Statistical analysis was performed using Statistical Package for Social Sciences (SPSS)
version 28 for Mac (IBM SPSS Statistics, IBM Corp., USA).
For color analysis, the analytical test applied to the values obtained depended on the
data distribution. The Kolmogorov-Smirnov and Shapiro-Wilk tests were used to test for
44
normality of the data. A two-way repeated measures ANOVA would be used if the data was
normal, using the material and staining solution as the two independent variables and color
as the dependent variable. Post-hoc multiple comparisons would then be performed using
the Tukey test at (α = 0.05).
Should the data be not normal, the Kruskall-Wallis test (to compare more than two
groups) with a Bonferroni correction for multiple tests would be applied. In this correction,
the p-value was adjusted by multiplying it by the number of comparisons and the ⍺ was kept
constant (⍺ = 0.05).
For translucency analysis, the analytical test applied to the values obtained depended on
the data distribution. The Kolmogorov-Smirnov and Shapiro-Wilk tests were used to test for
normality of the data. A two-way repeated measures ANOVA would be used if the data was
normal, using the material and staining solution as the two independent variables and color
as the dependent variable. Post-hoc multiple comparisons would then be performed using
the Tukey test at (α = 0.05).
Should the data be not normal, the Kruskall-Wallis test (to compare more than two
groups) with a Bonferroni correction for multiple tests would be applied. In this correction,
the p-value was adjusted by multiplying it by the number of comparisons and the ⍺ was kept
constant (⍺ = 0.05).
45
3. Chapter 3: Results
3.1Color Change Analysis
The Kolmogorov-Smirnov and Shapiro-Wilk tests demonstrated that the data for ΔE0
and ΔE1 was not normally distributed (p<0.05) within the groups of material and solution
(Table 4, Table 5, Table 6, Table 7). Parametric analysis of the ΔE data was not conducted.
The Kruskall-Wallis test was employed to compare between more than two groups (⍺=0.05).
The results show that when ΔE was higher, the more noticeable the color change
was.
Tests of Normality
Material Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔE0 LU .200 60 <.001 .826 60 <.001
VS .194 60 <.001 .805 60 <.001
CC .298 60 <.001 .677 60 <.001
a. Lilliefors Significance Correction
Table 4: Test for normality ΔE0 for groups of material
Tests of Normality
Staining
Solution
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔE0 Coffee .211 45 <.001 .766 45 <.001
Wine .098 45 .200* .932 45 .011
Coca-Cola .282 45 <.001 .696 45 <.001
Water .251 45 <.001 .713 45 <.001
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 5: Test for normality ΔE0 for groups of solution
46
Tests of Normality
Material Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔE1 LU .077 60 .200* .978 60 .354
VS .182 60 <.001 .775 60 <.001
CC .218 60 <.001 .748 60 <.001
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 6: Test for normality ΔE1 for groups of material
Tests of Normality
Staining
Solution
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔE1 Coffee .222 45 <.001 .739 45 <.001
Wine .215 45 <.001 .759 45 <.001
Coca-Cola .273 45 <.001 .756 45 <.001
Water .159 45 .006 .850 45 <.001
a. Lilliefors Significance Correction
Table 7: Test for normality ΔE1 for groups of solution
Based on the work of Paravina and colleagues,45 the 50:50% perceptibility and
acceptability thresholds using the CIEDE2000 formula derived ΔE values used in our present
study were 0.8 and 1.8, respectively.
3.1.1 ΔE0 Analysis
For ΔE0, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 8).
47
Material Solution Mean S.D. Min Max
LU
Coffee 1.34 0.16 1.02 1.53
Wine 0.54 0.15 0.38 0.89
Coca-Cola 0.25 0.11 0.10 0.58
Water 0.21 0.12 0.07 0.58
VS
Coffee 2.75 0.89 1.90 4.31
Wine 1.05 0.19 0.81 1.39
Coca-Cola 0.21 0.09 0.03 0.38
Water 0.30 0.20 0.10 0.92
CC
Coffee 4.34 2.56 1.41 7.91
Wine 1.19 0.41 0.69 1.78
Coca-Cola 0.95 0.55 0.25 1.67
Water 0.60 0.42 0.20 1.36
Table 8: ΔE0 overall mean, standard deviation, minimum and maximum values
3.1.1.1 Overall Comparisons of ΔE0
3.1.1.1.1 Between Materials
Figure 34: Graph of mean ΔE0 by material for different solutions
1.34
2.75
4.34
0.54
1.05
1.19
0.25
0.21
0.95
0.21
0.30
0.60
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
LU VS CC
ΔE0
Coffee
Wine
CocaCola
Water
48
Figure 34 shows that LU had mean values within the acceptability threshold of ΔE0 =
1.8 for all solutions tested. Coffee caused a perceptible change in LU of ΔE0 = 1.34±0.16.
For VS, staining with coffee resulted in non-acceptable color change of ΔE0 =
2.75±0.89. Wine caused a perceptible change in VS of ΔE0 = 1.05±0.19
For CC, staining with coffee resulted in non-acceptable color change of ΔE0 =
4.34±2.56. Wine and Coca-Cola caused a perceptible change in CC of ΔE0 = 1.19±0.41 and
ΔE0 = 0.90±0.42, respectively.
Figure 35: Boxplot of ΔE0 of materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 25.652a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
49
Table 9: Kruskall-Wallis test summary ΔE0 across material
The Kruskall-Wallis test showed that the differences in ΔE0 between the materials
were significant (p<0.001) (Table 9).
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -18.500 9.512 -1.945 .052 .155
LU-CC -47.775 9.512 -5.022 <.001 .000
VS-CC -29.275 9.512 -3.078 .002 .006
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 10: Pairwise comparison of ΔE0 of different materials
Figure 36: Pairwise comparison of ΔE0 of different materials
Pairwise comparisons of ΔE0 of the materials showed significant differences
between LU-CC (p=0.000) and VS-CC (p=0.006), but no significant difference between LU-VS
(p=0.155) (Table 10, Figure 36).
50
3.1.1.1.2 Between Solutions
Figure 37: Graph of mean ΔE0 by solution for different materials
Figure 37 shows that coffee resulted in non-acceptable color change of ΔE0 =
2.75±0.89 and ΔE0 = 4.34±2.56 in VS and CC respectively. It resulted in a perceptible change
in color of ΔE0 = 1.34±0.16 in LU.
Wine resulted in perceptible color change of ΔE0 = 1.05±0.19 and ΔE0 = 1.19±0.41 in
VS and CC respectively. It resulted in a non-perceptible change in color in LU.
Coca-Cola resulted in perceptible color change of ΔE0 = 0.95±0.55 in CC. It did not
result in any perceptible change in LU and VS
Water did not result in a perceptible color change in all materials. 1.34 0.54 0.25 0.21 2.75 1.05 0.21 0.30 4.34 1.19 0.95
0.60
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
Coffee Wine CocaCola Water
ΔE0
LU
VS
CC
51
Figure 38: Boxplot of ΔE0 of solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 114.303a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
.000
a. The test statistic is adjusted for ties.
Table 11: Kruskall-Wallis test summary ΔE0 across solutions
The Kruskall-Wallis test showed that the differences in ΔE0 between the solutions
were significant (p=0.000) (Table 11).
52
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Water-Coca-Cola 8.456 10.984 .770 .441 1.000
Water-Wine 54.578 10.984 4.969 <.001 .000
Water-Coffee 104.033 10.984 9.471 .000 .000
Coca-Cola-Wine 46.122 10.984 4.199 <.001 .000
Coca-Cola-Coffee 95.578 10.984 8.701 .000 .000
Wine-Coffee 49.456 10.984 4.502 <.001 .000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 12: Pairwise comparison of ΔE0 of different solutions
Figure 39: Pairwise comparison of ΔE0 of different solutions
Pairwise comparisons of ΔE0 of the solutions showed significant differences between
water-wine (p=0.000), water-coffee (p=0.000), Coca-Cola-wine (p=0.000), Coca-Cola-coffee
53
(p=0.000) and wine-coffee (p=0.000) but no significant difference between water-Coca-Cola
(p=1.000) (Table 12, Figure 39).
3.1.1.2 LU Comparisons Between Solutions
Figure 40: Boxplot of ΔE0 in LU for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 46.937a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 13: Kruskall-Wallis test summary ΔE0 in LU across solutions
The Kruskall-Wallis test showed that the differences in ΔE0 in LU between the
solutions were significant (p<0.001) (Table 13).
54
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Water-Coca-Cola 4.333 6.375 .680 .497 1.000
Water-Wine 22.267 6.375 3.493 <.001 .003
Water-Coffee 38.867 6.375 6.097 <.001 .000
Coca-Cola-Wine 17.933 6.375 2.813 .005 .029
Coca-Cola-Coffee 34.533 6.375 5.417 <.001 .000
Wine-Coffee 16.600 6.375 2.604 .009 .055
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 14: Pairwise comparison of ΔE0 in LU for different solutions
Figure 41: Pairwise comparison of ΔE0 in LU for different solutions
When ΔE0 was compared for LU between different solutions, pairwise comparisons
of solutions showed significant differences between water-wine (p=0.003), water-coffee
(p=0.000), Coca-Cola-wine (p=0.029) and Coca-Cola-coffee (p=0.000), but no significant
55
differences between water-Coca-Cola (p=1.000) and wine-coffee (p=0.055) (Table 14, Figure
41).
3.1.1.3 VS Comparisons Between Solutions
Figure 42: Boxplot of ΔE0 in VS for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 49.262a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 15: Kruskall-Wallis test summary ΔE0 in VS across solutions
The Kruskall-Wallis test showed that the differences in ΔE0 in VS between the
solutions were significant (p<0.001) (Table 15).
56
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coca-Cola-Water -3.600 6.376 -.565 .572 1.000
Coca-Cola-Wine 23.700 6.376 3.717 <.001 .001
Coca-Cola-Coffee 39.100 6.376 6.133 <.001 .000
Water-Wine 20.100 6.376 3.153 .002 .010
Water-Coffee 35.500 6.376 5.568 <.001 .000
Wine-Coffee 15.400 6.376 2.415 .016 .094
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 16: Pairwise comparison of ΔE0 in VS for different solutions
Figure 43: Pairwise comparison of ΔE0 in VS for different solutions
When ΔE0 was compared for VS between different solutions, pairwise comparisons
of solutions showed significant differences between Coca-Cola-wine (p=0.001), Coca-Colacoffee (p=0.000), water-wine (p=0.010) and water-coffee (p=0.000), but no significant
57
differences between Coca-Cola-water (p=1.000) and wine-coffee (p=0.094) (Table 16, Figure
43).
3.1.1.4 CC Comparisons Between Solutions
Figure 44: Boxplot of ΔE0 in CC for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 35.721a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 17: Kruskall-Wallis test summary ΔE0 in CC across solutions
The Kruskall-Wallis test showed that the differences in ΔE0 in CC between the
solutions were significant (p<0.001) (Table 17).
58
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Water-Coca-Cola 10.733 6.377 1.683 .092 .554
Water-Wine 17.333 6.377 2.718 .007 .039
Water-Coffee 37.000 6.377 5.802 <.001 .000
Coca-Cola-Wine 6.600 6.377 1.035 .301 1.000
Coca-Cola-Coffee 26.267 6.377 4.119 <.001 .000
Wine-Coffee 19.667 6.377 3.084 .002 .012
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 18: Pairwise comparison of ΔE0 in CC for different solutions
Figure 45: Pairwise comparison of ΔE0 in CC for different solutions
When ΔE0 was compared for CC between different solutions, pairwise comparisons
of solutions showed significant differences between water-wine (p=0.039), water-coffee
(p=0.000), Coca-Cola-coffee (p=0.000) and wine-coffee (p=0.012), but no significant
59
differences between water-Coca-Cola (p=0.554) and Coca-Cola-wine (p=1.000) (Table 18,
Figure 45).
3.1.1.5 Coffee Comparisons Between Materials
Figure 46: Boxplot of ΔE0 of coffee for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 26.662a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 19: Kruskall-Wallis test summary ΔE0 in coffee across materials
The Kruskall-Wallis test showed that the differences in ΔE0 in coffee between the
materials were significant (p<0.001) (Table 19).
60
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -19.333 4.796 -4.031 <.001 .000
LU-CC -23.067 4.796 -4.810 <.001 .000
VS-CC -3.733 4.796 -.778 .436 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 20: Pairwise comparison of ΔE0 of coffee for different materials
Figure 47: Pairwise comparison of ΔE0 of coffee for different materials
When ΔE0 was compared for coffee between different materials, pairwise
comparisons of materials showed significant differences between LU-VS (p=0.000) and LUCC (P=0.000), but no significant difference between VS-CC (p=1.000) (Table 20, Figure 47).
61
3.1.1.6 Wine Comparisons Between Materials
Figure 48: Boxplot of ΔE0 of wine for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 26.298a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 21: Kruskall-Wallis test summary ΔE0 in wine across materials
The Kruskall-Wallis test showed that the differences in ΔE0 in wine between the
materials were significant (p<0.001) (Table 21).
62
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -20.467 4.795 -4.269 <.001 .000
LU-CC -22.033 4.795 -4.595 <.001 .000
VS-CC -1.567 4.795 -.327 .744 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 22: Pairwise comparison of ΔE0 of wine for different materials
Figure 49: Pairwise comparison of ΔE0 of wine for different materials
When ΔE0 was compared for wine between different materials, pairwise
comparisons of materials showed significant differences between LU-VS (p=0.000) and LUCC (P=0.000), but no significant difference between VS-CC (p=1.000) (Table 22, Figure 49).
63
3.1.1.7 Coca-Cola Comparisons Between Materials
Figure 50: Boxplot of ΔE0 of Coca-Cola for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 23.472a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 23: Kruskall-Wallis test summary ΔE0 in Coca-Cola across materials
The Kruskall-Wallis test showed that the differences in ΔE0 in Coca-Cola between the
materials were significant (p<0.001) (Table 23).
64
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-LU 2.333 4.791 .487 .626 1.000
VS-CC -21.167 4.791 -4.418 <.001 .000
LU-CC -18.833 4.791 -3.931 <.001 .000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 24: Pairwise comparison of ΔE0 of Coca-Cola for different materials
Figure 51: Pairwise comparison of ΔE0 of Coca-Cola for different materials
When ΔE0 was compared for Coca-Cola between different materials, pairwise
comparisons of materials showed significant differences between VS-CC (p=0.000) and LUCC (P=0.000), but no significant difference between VS-LU (p=1.000) (Table 24, Figure 51).
65
3.1.1.8 Water Comparisons Between Materials
Figure 52: Boxplot of ΔE0 of water for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 16.615a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 25: Kruskall-Wallis test summary ΔE0 in water across materials
The Kruskall-Wallis test showed that the differences in ΔE0 in water between the
materials were significant (p<0.001) (Table 25).
66
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -6.667 4.792 -1.391 .164 .492
LU-CC -19.233 4.792 -4.014 <.001 .000
VS-CC -12.567 4.792 -2.622 .009 .026
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 26: Pairwise comparison of ΔE0 of water for different materials
Figure 53: Pairwise comparison of ΔE0 of water for different materials
When ΔE0 was compared for water between different materials, pairwise
comparisons of materials showed significant differences between LU-CC (P=0.000) and VSCC (p=0.026), but no significant difference between LU-VS (p=0.492) (Table 26, Figure 53).
67
3.1.2 ΔE1 Analysis
For ΔE1, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 27).
Material Solution Mean S.D. Min Max
LU
Coffee 0.49 0.15 0.30 0.72
Wine 0.53 0.20 0.33 1.02
Coca-Cola 0.59 0.14 0.38 0.81
Water 0.53 0.24 0.08 0.89
VS
Coffee 1.61 0.63 1.02 2.92
Wine 0.72 0.17 0.37 1.00
Coca-Cola 0.60 0.21 0.32 1.14
Water 0.69 0.23 0.47 1.28
CC
Coffee 2.85 2.19 0.35 6.33
Wine 1.19 0.74 0.25 2.23
Coca-Cola 1.52 0.66 0.59 2.34
Water 1.16 0.60 0.42 2.25
Table 27: ΔE1 overall mean, standard deviation, minimum and maximum values
68
3.1.2.1 Overall Comparisons of ΔE1
3.1.2.1.1 Between Materials
Figure 54: Graph of mean ΔE1 by material for different solutions
Figure 54 shows that only LU had mean values within the perceptibility threshold of
ΔE = 0.8 for all solutions tested.
For VS, staining with coffee resulted in perceptible color change of ΔE1 = 1.61±0.63.
For CC, staining with coffee resulted in non-acceptable color change of ΔE1 =
2.85±2.19, and staining with wine, Coca-Cola and water resulted in perceptible color change
of ΔE1 = 1.19±0.74, ΔE1 = 1.52±0.66, ΔE1 = 1.16±0.60, respectively. 0.49 1.61 2.85 0.53 0.721.19 0.59 0.60 1.52 0.53 0.691.16
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
LU VS CC
ΔE1
Coffee
Wine
CocaCola
Water
69
Figure 55: Boxplot of ΔE1 of materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 59.525a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 28: Kruskall-Wallis test summary ΔE1 across material
The Kruskall-Wallis test showed that the differences in ΔE1 between the materials
were significant (p<0.001) (Table 28).
70
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -42.200 9.512 -4.436 <.001 .000
LU-CC -73.100 9.512 -7.685 <.001 .000
VS-CC -30.900 9.512 -3.248 .001 .003
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 29: Pairwise comparison of ΔE1 of different materials
Figure 56: Pairwise comparison of ΔE1 of different materials
Pairwise comparisons of ΔE1 of the materials showed a significant difference
between LU-VS (p=0.000), LU-CC (0.000) and VS-CC (p=0.003) (Table 29, Figure 56).
71
3.1.2.1.2 Between Solutions
Figure 57: Graph of mean ΔE1 by solution for different materials
Figure 57 shows that coffee resulted in non-acceptable color change in CC of ΔE1 =
2.85±2.19, and perceptible color change in VS of ΔE1 = 1.61±0.63.
Wine caused perceptible color change in CC of ΔE1 = 1.19±0.74.
Coca-Cola caused perceptible color change in CC of ΔE1 = 1.52±0.66.
Water caused perceptible color change in CC of ΔE1 = 1.16±0.60. 0.49 0.53 0.59 0.53 1.61 0.72 0.60 0.69 2.85 1.191.52
1.16
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
Coffee Wine CocaCola Water
ΔE1
LU
VS
CC
72
Figure 58: Boxplot of ΔE1 of solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 10.022a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
.018
a. The test statistic is adjusted for ties.
Table 30: Kruskall-Wallis test summary ΔE1 across solutions
The Kruskall-Wallis test showed that the differences in ΔE1 between the solutions
were significant (p=0.018) (Table 30).
73
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Wine-Water -.211 10.984 -.019 .985 1.000
Wine-Coca-Cola -6.478 10.984 -.590 .555 1.000
Wine-Coffee 29.978 10.984 2.729 .006 .038
Water-Coca-Cola 6.267 10.984 .571 .568 1.000
Water-Coffee 29.767 10.984 2.710 .007 .040
Coca-Cola-Coffee 23.500 10.984 2.139 .032 .194
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 31: Pairwise comparison of ΔE1 of different solutions
Figure 59: Pairwise comparison of ΔE1 of different solutions
Pairwise comparisons of ΔE1 of the solutions showed significant differences between
wine-coffee (p=0.038) and water-coffee (p=0.040) but no significant differences between
74
wine-water (p=1.000), wine-Coca-Cola (p=1.000), water-Coca-Cola (p=1.000) and Coca-Colacoffee (p=0.194) (Table 31, Figure 59).
3.1.2.2 LU Comparisons Between Solutions
Figure 60: Boxplot of ΔE1 in LU for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 2.751a,b
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
.432
a. The test statistic is adjusted for ties.
b. Multiple comparisons are not performed because the
overall test does not show significant differences across
samples.
Table 32: Kruskall-Wallis test summary ΔE1 in LU across solutions
75
When ΔE1 was compared for LU between different solutions, the Kruskal-Wallis test
showed no significant differences between solutions, therefore, a pairwise comparison was
not conducted (Table 32).
3.1.2.3 VS Comparisons Between Solutions
Figure 61: Boxplot of ΔE1 in VS for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 33.641a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 33: Kruskall-Wallis test summary ΔE1 in VS across solutions
The Kruskall-Wallis test showed that the differences in ΔE1 in VS between the
solutions were significant (p<0.001) (Table 33).
76
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coca-Cola-Water -5.067 6.375 -.795 .427 1.000
Coca-Cola-Wine 9.800 6.375 1.537 .124 .745
Coca-Cola-Coffee 34.067 6.375 5.344 <.001 .000
Water-Wine 4.733 6.375 .742 .458 1.000
Water-Coffee 29.000 6.375 4.549 <.001 .000
Wine-Coffee 24.267 6.375 3.806 <.001 .001
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 34: Pairwise comparison of ΔE1 in VS for different solutions
Figure 62: Pairwise comparison of ΔE1 in VS for different solutions
When ΔE1 was compared for VS between different solutions, pairwise comparisons
of solutions showed significant differences between Coca-Cola-coffee (P=0.000), watercoffee (p=0.000) and wine-coffee (p=0.001), but no significant differences between Coca-
77
Cola-water (p=1.000), Coca-Cola-wine (p=0.745) and water-wine (p=1.000) (Table 34, Figure
62).
3.1.2.4 CC Comparisons Between Solutions
Figure 63: Boxplot of ΔE1 in CC for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 7.836a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
.050
a. The test statistic is adjusted for ties.
Table 35: Kruskall-Wallis test summary ΔE1 in CC across solutions
The Kruskall-Wallis test showed that the differences in ΔE1 in CC between the
solutions were significant (p=0.050) (Table 35).
78
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Wine-Water -.933 6.377 -.146 .884 1.000
Wine-Coca-Cola -9.133 6.377 -1.432 .152 .912
Wine-Coffee 15.400 6.377 2.415 .016 .094
Water-Coca-Cola 8.200 6.377 1.286 .198 1.000
Water-Coffee 14.467 6.377 2.269 .023 .140
Coca-Cola-Coffee 6.267 6.377 .983 .326 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 36: Pairwise comparison of ΔE1 in CC for different solutions
Figure 64: Pairwise comparison of ΔE1 in CC for different solutions
When ΔE1 was compared for CC between different solutions, pairwise comparisons
of solutions showed no significant differences between wine-water (p=1.000), wine-Coca-
79
Cola (p=0.912), wine-coffee (p=0.094), water-Coca-Cola (p=1.000), water-coffee (p=0.140)
and Coca-Cola-coffee (p=1.000) (Table 36, Figure 64).
3.1.2.5 Coffee Comparisons Between Materials
Figure 65: Boxplot of ΔE1 of coffee for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 25.243a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 37: Kruskall-Wallis test summary ΔE1 in coffee across materials
The Kruskall-Wallis test showed that the differences in ΔE1 in coffee between the
materials were significant (p<0.001) (Table 37).
80
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -19.200 4.795 -4.004 <.001 .000
LU-CC -22.200 4.795 -4.630 <.001 .000
VS-CC -3.000 4.795 -.626 .532 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 38: Pairwise comparison of ΔE1 of coffee for different materials
Figure 66: Pairwise comparison of ΔE1 of coffee for different materials
When ΔE1 was compared for coffee between different materials, pairwise
comparisons of materials showed significant differences between LU-VS (p=0.000) and LUCC (p=0.000), but no significant difference between VS-CC (p=1.000) (Table 38, Figure 66).
81
3.1.2.6 Wine Comparisons Between Materials
Figure 67: Boxplot of ΔE1 of wine for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 10.827a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
.004
a. The test statistic is adjusted for ties.
Table 39: Kruskall-Wallis test summary ΔE1 in wine across materials
The Kruskall-Wallis test showed that the differences in ΔE1 in wine between the
materials were significant (p=0.004) (Table 39).
82
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -10.433 4.795 -2.176 .030 .089
LU-CC -15.467 4.795 -3.226 .001 .004
VS-CC -5.033 4.795 -1.050 .294 .882
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 40: Pairwise comparison of ΔE1 of wine for different materials
Figure 68: Pairwise comparison of ΔE1 of wine for different materials
When ΔE1 was compared for wine between different materials, pairwise
comparisons of materials showed a significant difference between LU-CC (p=0.004), but no
significant differences between LU-VS (p=0.089) and VS-CC (p=0.882) (Table 40, Figure 68).
83
3.1.2.7 Coca-Cola Comparisons Between Materials
Figure 69: Boxplot of ΔE1 of Coca-Cola for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 22.732a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 41: Kruskall-Wallis test summary ΔE1 in Coca-Cola across materials
The Kruskall-Wallis test showed that the differences in ΔE1 in Coca-Cola between the
materials were significant (p<0.001) (Table 41).
84
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -.200 4.796 -.042 .967 1.000
LU-CC -19.900 4.796 -4.150 <.001 .000
VS-CC -19.700 4.796 -4.108 <.001 .000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 42: Pairwise comparison of ΔE1 of Coca-Cola for different materials
Figure 70: Pairwise comparison of ΔE1 of Coca-Cola for different materials
When ΔE1 was compared for Coca-Cola between different materials, pairwise
comparisons of materials showed significant differences between LU-CC (p=0.000) and VSCC (p=0.000), but no significant difference between LU-VS (p=1.000) (Table 42, Figure 70).
85
3.1.2.8 Water Comparisons Between Materials
Figure 71: Boxplot of ΔE1 of water for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 11.426a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
.003
a. The test statistic is adjusted for ties.
Table 43: Kruskall-Wallis test summary ΔE1 in water across materials
The Kruskall-Wallis test showed that the differences in ΔE1 in water between the
materials were significant (p=0.003) (Table 43).
86
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
LU-VS -7.133 4.794 -1.488 .137 .410
LU-CC -16.167 4.794 -3.373 <.001 .002
VS-CC -9.033 4.794 -1.884 .060 .179
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 44: Pairwise comparison of ΔE1 of water for different materials
Figure 72: Pairwise comparison of ΔE1 of water for different materials
When ΔE1 was compared for water between different materials, pairwise
comparisons of materials showed a significant difference between LU-CC (p=0.002), but no
significant differences between LU-VS (p=0.410) and VS-CC (p=0.179) (Table 44, Figure 72).
3.1.3 Color Change After Polishing
87
Figure 73: Graph of change in ΔE for LU after polishing
Figure 73 shows that for LU, polishing after staining with coffee and wine caused ΔE
to decrease from ΔE0 = 1.34±0.16 to ΔE1 = 0.49±0.15 and ΔE0 = 0.54±0.15 to ΔE1 =
0.53±0.20 respectively. Polishing after staining with Coca-Cola and water caused ΔE to
increase from ΔE0 = 0.25±0.11 to ΔE1 = 0.59±0.14 and ΔE0 = 0.21±0.12 to ΔE1 = 0.53±0.24
respectively.
Figure 74: Graph of change in ΔE for VS after polishing
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Material: LU
Coffee
Wine
CocaCola
Water
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Material: VS
Coffee
Wine
CocaCola
Water
88
Figure 74 shows that for VS, polishing after staining with coffee and wine caused ΔE
to decrease from ΔE0 = 2.75±0.89 to ΔE1 = 1.61±0.63 and ΔE0 = 1.05±0.19 to ΔE1 =
0.72±0.17 respectively. Polishing after staining with Coca-Cola and water caused ΔE to
increase from ΔE0 = 0.21±0.09 to ΔE1 = 0.60±0.21 and ΔE0 = 0.30±0.20 to ΔE1 = 0.69±0.23
respectively.
Figure 75: Graph of change in ΔE for CC after polishing
Figure 75 shows that for CC, polishing after staining with coffee caused ΔE to
decrease from ΔE0 = 4.34±2.56 to ΔE1 = 2.85±2.19. Polishing after staining with wine caused
no change in ΔE = 1.19. Polishing after staining with Coca-Cola and water caused ΔE to
increase from ΔE0 = 0.95±0.55 to ΔE1 = 1.52±0.66 and ΔE0 = 0.60±0.42 to ΔE1 = 1.16±0.60
respectively.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Material: CC
Coffee
Wine
CocaCola
Water
89
Figure 76: Graph of change in ΔE for coffee after polishing
Figure 76 shows that for coffee, polishing after staining caused ΔE in LU, VS and CC
to decrease from ΔE0 = 1.34±0.16 to ΔE1 = 0.49±0.15, ΔE0 = 2.75±0.89 to ΔE1 = 1.61±0.63,
and from ΔE0 = 4.34±2.56 to ΔE1 = 2.85±2.19, respectively.
Figure 77: Graph of change of ΔE for wine after polishing
Figure 77 shows that for wine, polishing after staining caused ΔE in LU and VS to
decrease from ΔE0 = 0.54±0.15 to ΔE1 = 0.53±0.20, and from ΔE0 = 1.05±0.19 to ΔE1 =
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Solution: Coffee
LU
VS
CC
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Solution: Wine
LU
VS
CC
90
0.72±0.17, respectively. Polishing after staining caused ΔE with wine caused no change in ΔE
= 1.19.
Figure 78: Graph of change of ΔE for Coca-Cola after polishing
Figure 78 shows that for Coca-Cola, polishing after staining caused ΔE in LU, VS and
CC to increase from ΔE0 = 0.25±0.11 to ΔE1 = 0.59±0.14, ΔE0 = 0.21±0.09 to ΔE1 =
0.60±0.21, and from ΔE0 = 0.95±0.55 to ΔE1 = 1.52±0.66, respectively.
Figure 79: Graph of change of ΔE for water after polishing
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Solution: Coca-Cola
LU
VS
CC
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
ΔE0 ΔE1
Solution: Water
LU
VS
CC
91
Figure 79 shows that for water, polishing after staining caused ΔE in LU, VS and CC to
increase from ΔE0 = 0.21±0.12 to ΔE1 = 0.53±0.24, ΔE0 = 0.30±0.20 to ΔE1 = 0.69±0.23, and
from ΔE0 = 0.60±0.42 to ΔE1 = 1.16±0.60, respectively.
3.2CIE L*a*b* and Chroma Descriptive Analysis
The comparison in the changes of L*, a*, b* and ΔC show that, for ΔL, a positive
value indicates an increase in value (becomes lighter). A negative ΔL indicates decrease in
value (becomes darker).
For Δa, a positive value indicates a shift toward a more red color. A negative value
indicates a shift toward a more green color.
For Δb, a positive value indicates a shift toward a more yellow color. A negative value
indicates a shift toward a more blue color.
For ΔC, a positive value indicates an increase in chroma compared to baseline. A
negative value indicates a decrease in chroma compared to baseline.
3.2.1 ΔL0 Results
For ΔL0, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 45).
92
Material Solution Mean S.D. Min Max
LU
Coffee -1.14 0.21 -1.50 -0.80
Wine -0.60 0.24 -1.12 -0.21
Coca-Cola -0.08 0.21 -0.66 0.20
Water 0.09 0.20 -0.18 0.61
VS
Coffee -3.04 0.53 -3.94 -2.33
Wine -0.97 0.22 -1.23 -0.61
Coca-Cola -0.03 0.20 -0.39 0.39
Water -0.02 0.13 -0.29 0.22
CC
Coffee -3.49 1.81 -6.03 -1.25
Wine -0.70 0.34 -1.31 -0.24
Coca-Cola 0.61 0.46 -0.33 1.30
Water 0.39 0.36 -0.08 1.21
Table 45: ΔL0 overall mean, standard deviation, minimum and maximum values
3.2.1.1 Overall Comparisons of ΔL0
3.2.1.1.1 Between Materials
-1.14
-3.04
-3.49
-0.60
-0.97
-0.70
-0.08
-0.03
0.61
0.09
-0.02
0.39
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
LU VS CC
ΔL0
Coffee
Wine
CocaCola
Water
93
Figure 80: Graph of mean ΔL0 by material for different solutions
Figure 80 shows that, in general, for the different materials tested, ΔL0 of LU was
negative (darker) for all solutions except water where it was positive (lighter). ΔL0 of VS was
negative (darker) for all solutions. ΔL0 of CC was negative (darker) for coffee and wine but
positive (lighter) for Coca-Cola and water.
3.2.1.1.2 Between Solutions
Figure 81: Graph of mean ΔL0 by solution for different materials
Figure 81 shows that, in general, for the different solutions tested, ΔL0 was negative
(darker) for coffee and wine for all materials. ΔL0 of Coca-Cola was negative (darker) for LU
and VS, but positive (lighter) for CC. ΔL0 of water was positive (lighter) for LU and CC, but
negative (darker) for VS.
3.2.2 ΔL1 Results
For ΔL1, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 46).
-1.14
-0.60
-0.08
0.09
-3.04
-0.97
-0.03
-0.02
-3.49
-0.70
0.61
0.39
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
Coffee Wine CocaCola Water
ΔL0
LU
VS
CC
94
Material Solution Mean S.D. Min Max
LU
Coffee -0.53 0.24 -0.90 -0.18
Wine -0.50 0.31 -1.20 0.04
Coca-Cola -0.16 0.33 -0.73 0.33
Water -0.39 0.35 -0.98 0.05
VS
Coffee -1.44 0.57 -2.38 -0.76
Wine -0.23 0.30 -0.87 0.35
Coca-Cola 0.53 0.32 0.05 1.36
Water 0.34 0.47 -0.45 1.34
CC
Coffee -1.49 1.89 -4.54 0.83
Wine 0.68 0.39 -0.01 1.19
Coca-Cola 1.48 0.46 0.76 2.14
Water 1.18 0.58 0.38 2.20
Table 46: ΔL1 overall mean, standard deviation, minimum and maximum values
3.2.2.1 Overall Comparisons of ΔL1
3.2.2.1.1 Between Materials
Figure 82: Graph of mean ΔL1 by material for different solutions
-0.53
-1.44
-1.49
-0.50
-0.23
0.68
-0.16
0.53
1.48
-0.39
0.34
1.18
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
LU VS CC
ΔL1
Coffee
Wine
CocaCola
Water
95
Figure 82 shows that, in general, for the different materials tested, ΔL1 of LU was
negative (darker) for all solutions. ΔL1 of VS was negative (darker) for coffee and wine, but
positive (lighter) for Coca-Cola and water. ΔL1 of CC was positive (lighter) for all solutions
except coffee where it was negative (darker).
3.2.2.1.2 Between Solutions
Figure 83: Graph of mean ΔL1 by solution for different materials
Figure 83 shows that, in general, for the different solutions tested, ΔL1 for coffee
was negative (darker) for all materials. ΔL1 for wine was negative (darker) for LU and VS but
positive (lighter) for CC. ΔL1 for Coca-Cola and water was positive (lighter) for VS and CC but
negative (darker) for LU.
3.2.3 Δa0 Results
For Δa0, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 47).
-0.53
-0.50
-0.16
-0.39
-1.44
-0.23
0.53
0.34
-1.49
0.68
1.48
1.18
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
Coffee Wine CocaCola Water
ΔL1
LU
VS
CC
96
Material Solution Mean S.D. Min Max
LU
Coffee 0.29 0.05 0.16 0.37
Wine 0.13 0.06 0.02 0.29
Coca-Cola -0.01 0.07 -0.18 0.12
Water -0.04 0.06 -0.14 0.11
VS
Coffee 0.32 0.24 -0.11 0.55
Wine 0.47 0.10 0.26 0.60
Coca-Cola -0.02 0.07 -0.14 0.11
Water -0.06 0.08 -0.20 0.09
CC
Coffee 0.70 0.18 0.43 0.95
Wine 0.43 0.11 0.29 0.67
Coca-Cola -0.12 0.11 -0.34 0.05
Water -0.13 0.09 -0.29 -0.01
Table 47: Δa0 overall mean, standard deviation, minimum and maximum values
3.2.3.1 Overall Comparisons of Δa0
3.2.3.1.1 Between Materials 0.29
0.32
0.70
0.13
0.47
0.43
-0.01
-0.02
-0.12
-0.04
-0.06
-0.13
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
LU VS CC
Δa0
Coffee
Wine
CocaCola
Water
97
Figure 84: Graph of mean Δa0 by material for different solutions
Figure 84 shows that, in general, for the different materials tested, Δa0 of LU, VS and
CC was positive (redder) for coffee and wine, and negative (greener) for Coca-Cola and
water.
3.2.3.1.2 Between Solutions
Figure 85: Graph of mean Δa0 by solution for different materials
Figure 85 shows that, in general, for the different solutions tested, Δa0 for coffee
and wine was positive (redder). Δa0 for Coca-Cola and water was negative (greener).
3.2.4 Δa1 Results
For Δa1, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 48). 0.29 0.13
-0.01
-0.04
0.32
0.47
-0.02
-0.06
0.70
0.43
-0.12
-0.13
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
Coffee Wine CocaCola Water
Δa0
LU
VS
CC
98
Material Solution Mean S.D. Min Max
LU
Coffee 0.14 0.05 0.05 0.23
Wine 0.03 0.07 -0.11 0.20
Coca-Cola 0.03 0.05 -0.06 0.14
Water -0.03 0.06 -0.11 0.11
VS
Coffee 0.11 0.31 -0.40 0.41
Wine 0.14 0.06 0.01 0.22
Coca-Cola -0.05 0.04 -0.13 0.02
Water 0.00 0.07 -0.12 0.14
CC
Coffee 0.15 0.22 -0.08 0.62
Wine -0.05 0.12 -0.25 0.18
Coca-Cola -0.14 0.12 -0.38 0.00
Water -0.14 0.10 -0.31 0.01
Table 48: Δa1 overall mean, standard deviation, minimum and maximum values
3.2.4.1 Overall Comparisons of Δa1
3.2.4.1.1 Between Materials
Figure 86: Graph of mean Δa1 by material for different solutions
0.14
0.11
0.15
0.03
0.14
-0.05
0.03
-0.05
-0.14
-0.03
0.00
-0.14
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
LU VS CC
Δa1
Coffee
Wine
CocaCola
Water
99
Figure 86 shows that, in general, for the different materials tested, Δa1 of LU was
positive (redder) except for water where it was negative (more green). Δa1 of VS was
positive (redder) except for Coca-Cola where it was negative (more green). Δa1 of CC was
negative (greener) except for coffee where it was positive (more red).
3.2.4.1.2 Between Solutions
Figure 87: Graph of mean Δa1 by solution for different materials
Figure 87 shows that, in general, for the different solutions tested, Δa1 for coffee
was positive (redder). Δa1 for wine was positive (redder) for LU and VS but was negative
(more green) for CC. Δa1 for Coca-Cola was negative (more green) for VS and CC but positive
(more red) for LU. Δa1 for water was negative (greener) for LU and CC, and was unchanged
in VS.
3.2.5 Δb0 Results
For Δb0, the overall results of the mean, standard deviation, minimum and
maximum values are shown below (Table 49). 0.140.03
0.03
-0.03
0.11
0.14
-0.05
0.00
0.15
-0.05
-0.14
-0.14
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
Coffee Wine CocaCola Water
Δa1
LU
VS
CC
100
Material Solution Mean S.D. Min Max
LU
Coffee 1.34 0.15 1.04 1.64
Wine 0.21 0.15 -0.14 0.45
Coca-Cola -0.26 0.09 -0.42 -0.11
Water 0.07 0.21 -0.30 0.43
VS
Coffee 1.55 1.94 -0.01 4.77
Wine -0.52 0.20 -0.95 -0.28
Coca-Cola -0.08 0.19 -0.35 0.21
Water -0.34 0.35 -1.36 0.04
CC
Coffee 4.85 4.02 -0.44 10.45
Wine -0.89 1.22 -2.53 0.78
Coca-Cola -0.91 1.02 -2.08 0.67
Water -0.38 0.79 -1.73 0.35
Table 49: Δb0 overall mean, standard deviation, minimum and maximum values
3.2.5.1 Overall Comparisons of Δb0
3.2.5.1.1 Between Materials 1.34
1.55
4.85
0.21
-0.52
-0.89
-0.26
-0.08
-0.91
0.07
-0.34
-0.38
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
LU VS CC
Δb0
Coffee
Wine
CocaCola
Water
101
Figure 88: Graph of mean Δb0 by material for different solutions
Figure 88 shows that, in general, for the different materials tested, Δb0 for LU was
positive (more yellow) for all solutions except Coca-Cola where it was negative (bluer). Δb0
showed negative values (bluer) for VS and CC for all solutions except coffee where it showed
positive values (more yellow).
3.2.5.1.2 Between Solutions
Figure 89: Graph of mean Δb0 by solution for different materials
Figure 89 shows that, in general, for the different solutions tested, Δb0 for coffee
was positive (more yellow). Δb0 for wine and water was negative (bluer) for VS and CC but
positive (more yellow) for LU. Δb0 for Coca-Cola was negative (bluer) for all materials.
3.2.6 Δb1 Results
For Δb1, the overall results of the mean, standard deviation, minimum and
maximum values are shown below (Table 50). 1.34 0.21
-0.26
0.07
1.55
-0.52
-0.08
-0.34
4.85
-0.89
-0.91
-0.38
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
Coffee Wine CocaCola Water
Δb0
LU
VS
CC
102
Material Solution Mean S.D. Min Max
LU
Coffee -0.06 0.25 -0.49 0.29
Wine -0.45 0.09 -0.61 -0.30
Coca-Cola -0.74 0.18 -1.02 -0.41
Water -0.55 0.21 -0.80 -0.10
VS
Coffee 0.16 1.83 -1.37 3.47
Wine -0.92 0.23 -1.35 -0.51
Coca-Cola -0.62 0.13 -0.79 -0.39
Water -0.78 0.37 -1.74 -0.12
CC
Coffee 2.62 4.16 -2.54 8.39
Wine -1.32 1.32 -3.17 0.45
Coca-Cola -1.38 1.08 -2.77 0.03
Water -0.87 0.91 -2.49 0.19
Table 50: Δb1 overall mean, standard deviation, minimum and maximum values
3.2.6.1 Overall Comparisons of Δb1
3.2.6.1.1 Between Materials
Figure 90: Graph of mean Δb1 by material for different solutions
-0.06
0.16
2.62
-0.45
-0.92
-1.32
-0.74
-0.62
-1.38
-0.55
-0.78
-0.87
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
LU VS CC
Δb1
Coffee
Wine
CocaCola
Water
103
Figure 90 shows that, In general, for the different materials tested, Δb1 for LU was
negative (bluer). Δb1 for VS and CC was negative (bluer) for all solutions except coffee
where it was positive (more yellow).
3.2.6.1.2 Between Solutions
Figure 91: Graph of mean Δb1 by solution for different materials
Figure 91 shows that, in general, for the different solutions tested, Δb1 for coffee
was positive (more yellow) except for LU where it was negative (bluer). Δb1 of wine, CocaCola and water was negative (bluer) for all materials.
3.2.7 ΔC0 Results
For ΔC0, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 51).
-0.06
-0.45
-0.74
-0.55
0.16
-0.92
-0.62
-0.78
2.62
-1.32
-1.38
-0.87
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
Coffee Wine CocaCola Water
Δb1
LU
VS
CC
104
Material Solution Mean S.D. Min Max
LU
Coffee 1.30 0.15 1.01 1.60
Wine 0.19 0.16 -0.16 0.43
Coca-Cola -0.26 0.09 -0.42 -0.11
Water 0.07 0.21 -0.31 0.41
VS
Coffee 1.56 1.94 -0.01 4.78
Wine -0.50 0.20 -0.93 -0.26
Coca-Cola -0.08 0.19 -0.35 0.22
Water -0.34 0.34 -1.36 0.03
CC
Coffee 4.65 4.02 -0.61 10.24
Wine -1.00 1.22 -2.61 0.68
Coca-Cola -0.83 1.01 -1.99 0.75
Water -0.32 0.78 -1.64 0.42
Table 51: ΔC0 overall mean, standard deviation, minimum and maximum values
3.2.7.1 Overall Comparisons of ΔC0
3.2.7.1.1 Between Materials 1.30
1.56
4.65
0.19
-0.50
-1.00
-0.26
-0.08
-0.83
0.07
-0.34
-0.32
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
LU VS CC
DC0
Coffee
Wine
CocaCola
Water
105
Figure 92: Graph of mean ΔC0 by material for different solutions
Figure 92 shows that, in general, for the different materials tested, ΔC0 for LU was
positive (more chromatic) for all solutions except Coca-Cola. ΔC0 for VS and CC was negative
(less chromatic) for all solutions except coffee where it was positive (more chromatic).
3.2.7.1.2 Between Solutions
Figure 93: Graph of mean ΔC0 by solution for different materials
Figure 93 shows that, in general, for the different solutions tested, ΔC0 for coffee was
positive (more chromatic) for all materials. ΔC0 for wine and water was negative (less
chromatic) for VS and CC and positive (more chromatic) for LU. ΔC0 for Coca-Cola was
negative (less chromatic) for all materials.
3.2.8 ΔC1 Results
For ΔC1, the overall results of the mean, standard deviation, minimum and maximum
values are shown below (Table 52). 1.30 0.19
-0.26
0.07
1.56
-0.50
-0.08
-0.34
4.65
-1.00
-0.83
-0.32
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
Coffee Wine CocaCola Water
DC0
LU
VS
CC
106
Material Solution Mean S.D. Min Max
LU
Coffee -0.08 0.25 -0.51 0.27
Wine -0.46 0.09 -0.61 -0.30
Coca-Cola -0.74 0.18 -1.02 -0.42
Water -0.55 0.21 -0.80 -0.10
VS
Coffee 0.16 1.82 -1.37 3.47
Wine -0.92 0.23 -1.34 -0.51
Coca-Cola -0.62 0.13 -0.79 -0.40
Water -0.78 0.37 -1.74 -0.12
CC
Coffee 2.55 4.13 -2.53 8.21
Wine -1.25 1.30 -3.07 0.52
Coca-Cola -1.28 1.06 -2.65 0.11
Water -0.78 0.90 -2.39 0.27
Table 52: ΔC1 overall mean, standard deviation, minimum and maximum values
3.2.8.1 Overall Comparisons of ΔC1
3.2.8.1.1 Between Materials
Figure 94: Graph of mean ΔC1 by material for different solutions
-0.08
0.16
2.55
-0.46
-0.92
-1.25
-0.74
-0.62
-1.28
-0.55
-0.78
-0.78
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
LU VS CC
DC1
Coffee
Wine
CocaCola
Water
107
Figure 94 shows that, in general, for the different materials tested, ΔC1 for LU was
negative (less chromatic) for all solutions. ΔC1 for VS and CC was negative (less chromatic)
for all solutions except coffee where it was positive (more chromatic).
3.2.8.1.2 Between Solutions
Figure 95: Graph of mean ΔC1 by solution for different materials
Figure 95 shows that, in general, for the different solutions tested, ΔC1 for coffee was
positive (more chromatic) for VS and CC and negative (less chromatic) for LU. ΔC1 for wine,
Coca-Cola and water was negative (less chromatic) for all materials.
3.3Translucency Analysis
The Kolmogorov-Smirnov and Shapiro-Wilk tests demonstrated that the data for
ΔTP1 was not normally distributed (p<0.05) within the groups of material (Table 53) and
solution (Table 54), while ΔTP2 was not normally distributed (p<0.05) within the groups of
material (Table 55) but was normally distributed (p>0.05) within the groups of solution
(Table 56). However, Kolmogorov-Smirnov and Shapiro-Wilk test of normality for all ΔTP2
outcome data across groups demonstrated that the data for ΔTP2 was not normally
distributed (p<0.05)(Table 57).
-0.08
-0.46
-0.74
-0.55
0.16
-0.92
-0.62
-0.78
2.55
-1.25
-1.28
-0.78
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
Coffee Wine CocaCola Water
DC1
LU
VS
CC
108
Therefore, it was decided that parametric analysis of the ΔTP data was not
conducted to allow for consistent statistical analysis across different groups and for more
conservative estimation of statistical significance. The Kruskall-Wallis test was employed to
compare between more than two groups (⍺=0.05).
The results show that when ΔTP was positive, the more transparent the material
became. When ΔTP was negative, the opaquer the material became.
Tests of Normality
Material Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔTP1 LU .089 60 .200* .978 60 .365
VS .207 60 <.001 .845 60 <.001
CC .160 60 <.001 .907 60 <.001
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 53: Test for normality ΔTP1 for groups of material
Tests of Normality
Staining
Solution
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔTP1 Coffee .138 45 .031 .930 45 .010
Wine .084 45 .200* .974 45 .410
Cola .081 45 .200* .969 45 .270
Water .087 45 .200* .972 45 .355
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 54: Test for normality ΔTP1 for groups of solution
109
Tests of Normality
Material Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔTP2 LU .089 60 .200* .965 60 .087
VS .136 60 .008 .895 60 <.001
CC .140 60 .005 .932 60 .002
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 55: Test for normality ΔTP2 for groups of material
Tests of Normality
Staining
Solution
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔTP2 Coffee .076 45 .200* .973 45 .383
Wine .104 45 .200* .977 45 .499
Cola .106 45 .200* .971 45 .314
Water .083 45 .200* .982 45 .704
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
Table 56: Test for normality ΔTP2 for groups of solution
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ΔTP2 .111 180 <.001 .943 180 <.001
a. Lilliefors Significance Correction
Table 57: Test for normality ΔTP2 across all groups
110
Based on the work of Salas and colleagues109, the 50:50% perceptibility and
acceptability thresholds for ΔTP values used in our present study were 1.33 and 4.43,
respectively.
3.3.1 ΔTP1 Analysis
For ΔTP1, the overall results of the mean, standard deviation, minimum and
maximum values are shown below (Table 58).
Material Solution Mean S.D. Min Max
LU
Coffee -0.46 0.25 -0.94 0.19
Wine -0.42 0.18 -0.72 -0.14
Coca-Cola -0.25 0.28 -0.75 0.19
Water -0.07 0.21 -0.41 0.28
VS
Coffee -1.96 0.33 -2.59 -1.30
Wine -0.66 0.22 -1.21 -0.23
Coca-Cola -0.22 0.17 -0.50 0.20
Water -0.28 0.22 -0.82 0.12
CC
Coffee -1.70 0.35 -2.26 -1.25
Wine -0.77 0.20 -1.17 -0.54
Coca-Cola 0.01 0.17 -0.29 0.23
Water -0.07 0.19 -0.39 0.35
Table 58: ΔTP1 overall mean, standard deviation, minimum and maximum values
3.3.1.1 Overall Comparisons of ΔTP1
3.3.1.1.1 Between Materials
111
Figure 96: Graph of mean ΔTP1 by material for different solutions
Figure 96 shows that, for ΔTP1, all materials had ΔTP1 values within the acceptability
thresholds.
For LU, none of the solutions caused a perceptible translucency change.
For VS, only staining with coffee resulted in perceptible translucency change of ΔTP1
= -1.96±0.33.
For CC, only staining with coffee resulted in perceptible translucency change of ΔTP1
= -1.70±0.35.
-0.46
-1.96
-1.70
-0.42
-0.66
-0.77
-0.25
-0.22
0.01
-0.07
-0.28
-0.07
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
LU VS CC
ΔTP1
Coffee
Wine
CocaCola
Water
112
Figure 97: Boxplot of ΔTP1 of materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 10.402a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
.006
a. The test statistic is adjusted for ties.
Table 59: Kruskall-Wallis test summary ΔTP1 across materials
The Kruskall-Wallis test showed that the differences in ΔTP1 between the materials
were significant (p=0.006) (Table 59).
113
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-CC -14.533 9.513 -1.528 .127 .380
VS-LU 30.667 9.513 3.224 .001 .004
CC-LU 16.133 9.513 1.696 .090 .270
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 60: Pairwise comparison of ΔTP1 of different materials
Figure 98: Pairwise comparison of ΔTP1 of different materials
Pairwise comparisons of ΔTP1 of the materials showed a significant difference
between VS-LU (p=0.004), but no significant differences between VS-CC (p=0.380) and CCLU (p=0.270) (Table 60, Figure 98).
114
3.3.1.1.2 Between Solutions
Figure 99: Graph of mean ΔTP1 by solution for different materials
Figure 99 shows that, for ΔTP1, all solutions except coffee caused ΔTP1 values to be
within the perceptibility thresholds.
For coffee, only VS and CC had perceptible ΔTP1 values of -1.96±0.33 and -1.70±0.35,
respectively.
-0.46
-0.42
-0.25
-0.07
-1.96
-0.66
-0.22
-0.28
-1.70
-0.77
0.01
-0.07
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
Coffee Wine CocaCola Water
ΔTP1
LU
VS
CC
115
Figure 100: Boxplot of ΔTP1 of solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 107.075a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
.000
a. The test statistic is adjusted for ties.
Table 61: Kruskall-Wallis test summary ΔTP1 across solutions
The Kruskall-Wallis test showed that the differences in ΔTP1 between the solutions
were significant (p=0.000) (Table 61).
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -27.200 10.984 -2.476 .013 .080
Coffee-Coca-Cola -90.633 10.984 -8.251 .000 .000
Coffee-Water -92.611 10.984 -8.431 .000 .000
Wine-Coca-Cola -63.433 10.984 -5.775 <.001 .000
Wine-Water -65.411 10.984 -5.955 <.001 .000
Coca-Cola-Water -1.978 10.984 -.180 .857 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
116
Table 62: Pairwise comparison of ΔTP1 of different solutions
Figure 101: Pairwise comparison of ΔTP1 of different solutions
Pairwise comparisons of ΔTP1 of the solutions showed significant differences
between coffee-Coca-Cola (p=0.000), coffee-water (p=0.000), wine-Coca-Cola (p=0.000) and
wine-water (p=0.000), but no significant differences between coffee-wine (p=0.080) and
Coca-Cola-water (p=1.000) (Table 62, Figure 101).
117
3.3.1.2 LU Comparisons Between Solutions
Figure 102: Boxplot of ΔTP1 in LU for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 19.321a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 63: Kruskall-Wallis test summary ΔTP1 in LU across solutions
The Kruskall-Wallis test showed that the differences in ΔTP1 in LU between the
solutions were significant (p<0.001) (Table 63).
118
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -2.900 6.376 -.455 .649 1.000
Coffee-Coca-Cola -14.033 6.376 -2.201 .028 .166
Coffee-Water -25.067 6.376 -3.932 <.001 .001
Wine-Coca-Cola -11.133 6.376 -1.746 .081 .485
Wine-Water -22.167 6.376 -3.477 <.001 .003
Coca-Cola-Water -11.033 6.376 -1.731 .084 .501
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 64: Pairwise comparison of ΔTP1 in LU for different solutions
Figure 103: Pairwise comparison of ΔTP1 in LU for different solutions
When ΔTP1 was compared for LU between different solutions, pairwise comparisons
of solutions showed significant differences between coffee-water (p=0.001) and wine-water
119
(p=0.003), but no significant differences between coffee-wine (p=1.000), coffee-Coca-Cola
(p=0.166), wine-Coca-Cola (p=0.485) and Coca-Cola-water (p=0.501) (Table 64, Figure 103).
3.3.1.3 VS Comparisons Between Solutions
Figure 104: Boxplot of ΔTP1 in VS for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 45.878a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 65: Kruskall-Wallis test summary ΔTP1 in VS across solutions
The Kruskall-Wallis test showed that the differences in ΔTP1 in VS between the
solutions were significant (p<0.001) (Table 65).
120
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -17.067 6.376 -2.677 .007 .045
Coffee-Water -34.633 6.376 -5.432 <.001 .000
Coffee-Coca-Cola -38.300 6.376 -6.007 <.001 .000
Wine-Water -17.567 6.376 -2.755 .006 .035
Wine-Coca-Cola -21.233 6.376 -3.330 <.001 .005
Water-Coca-Cola 3.667 6.376 .575 .565 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 66: Pairwise comparison of ΔTP1 in VS for different solutions
Figure 105: Pairwise comparison of ΔTP1 in VS for different solutions
When ΔTP1 was compared for VS between different solutions, pairwise comparisons
of solutions showed significant differences between coffee-wine (p=0.045), coffee-water
121
(p=0.000), coffee-Coca-Cola (p=0.000), wine-water (p=0.035), and wine-Coca-Cola (p=0.005),
but no significant differences between water-Coca-Cola (p=1.000) (Table 66, Figure 105).
3.3.1.4 CC Comparisons Between Solutions
Figure 106: Boxplot of ΔTP1 in CC for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 50.159a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 67: Kruskall-Wallis test summary ΔTP1 in CC across solutions
The Kruskall-Wallis test showed that the differences in ΔTP1 in CC between the
solutions were significant (p<0.001) (Table 67).
122
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -15.000 6.376 -2.353 .019 .112
Coffee-Water -35.633 6.376 -5.589 <.001 .000
Coffee-Coca-Cola -39.367 6.376 -6.174 <.001 .000
Wine-Water -20.633 6.376 -3.236 .001 .007
Wine-Coca-Cola -24.367 6.376 -3.822 <.001 .001
Water-Coca-Cola 3.733 6.376 .586 .558 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 68: Pairwise comparison of ΔTP1 in CC for different solutions
Figure 107: Pairwise comparison of ΔTP1 in CC for different solutions
When ΔTP1 was compared for CC between different solutions, pairwise comparisons
of solutions showed significant differences between coffee-water (p=0.000), coffee-CocaCola (p=0.000), wine-water (p=0.007), and wine-Coca-Cola (p=0.001), but no significant
123
differences between coffee-wine (p=0.112) and water-Coca-Cola (p=1.000) (Table 68, Figure
107).
3.3.1.5 Coffee Comparisons Between Materials
Figure 108: Boxplot of ΔTP1 of coffee for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 31.137a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 69: Kruskall-Wallis test summary ΔTP1 in coffee across materials
The Kruskall-Wallis test showed that the differences in ΔTP1 in coffee between the
materials were significant (p<0.001) (Table 69).
124
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-CC -6.400 4.795 -1.335 .182 .546
VS-LU 25.700 4.795 5.360 <.001 .000
CC-LU 19.300 4.795 4.025 <.001 .000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 70: Pairwise comparison of ΔTP1 of coffee for different materials
Figure 109: Pairwise comparison of ΔTP1 of coffee for different materials
When ΔTP1 was compared for coffee between different materials, pairwise
comparisons of materials showed significant differences between VS-LU (p=0.000) and CCLU (p=0.000), but no significant difference between VS-CC (p=0.546) (Table 70, Figure 109).
125
3.3.1.6 Wine Comparisons Between Materials
Figure 110: Boxplot of ΔTP1 of wine for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 16.682a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 71: Kruskall-Wallis test summary ΔTP1 in wine across materials
The Kruskall-Wallis test showed that the differences in ΔTP1 in wine between the
materials were significant (p<0.001) (Table 71).
126
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
CC-VS 6.300 4.793 1.315 .189 .566
CC-LU 19.200 4.793 4.006 <.001 .000
VS-LU 12.900 4.793 2.692 .007 .021
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 72: Pairwise comparison of ΔTP1 of wine for different materials
Figure 111: Pairwise comparison of ΔTP1 of wine for different materials
When ΔTP1 was compared for wine between different materials, pairwise
comparisons of materials showed significant differences between CC-LU (p=0.000) and VSLU (p=0.021), but no significant difference between CC-VS (p=0.566) (Table 72, Figure 111).
127
3.3.1.7 Coca-Cola Comparisons Between Materials
Figure 112: Boxplot of ΔTP1 of Coca-Cola for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 10.890a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
.004
a. The test statistic is adjusted for ties.
Table 73: Kruskall-Wallis test summary ΔTP1 in Coca-Cola across materials
The Kruskall-Wallis test showed that the differences in ΔTP1 in Coca-Cola between
the materials were significant (p=0.004) (Table 73).
128
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-LU .067 4.794 .014 .989 1.000
VS-CC -13.733 4.794 -2.865 .004 .013
LU-CC -13.667 4.794 -2.851 .004 .013
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 74: Pairwise comparison of ΔTP1 of Coca-Cola for different materials
Figure 113: Pairwise comparison of ΔTP1 of Coca-Cola for different materials
When ΔTP1 was compared for Coca-Cola between different materials, pairwise
comparisons of materials showed significant differences between VS-CC (p=0.013) and LUCC (p=0.013), but no significant difference between VS-LU (p=1.000) (Table 74, Figure 113).
129
3.3.1.8 Water Comparisons Between Materials
Figure 114: Boxplot of ΔTP1 of water for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 7.816a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
.020
a. The test statistic is adjusted for ties.
Table 75: Kruskall-Wallis test summary ΔTP1 in water across materials
The Kruskall-Wallis test showed that the differences in ΔTP1 in water between the
materials were significant (p=0.020) (Table 75).
130
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-LU 11.367 4.794 2.371 .018 .053
VS-CC -11.833 4.794 -2.468 .014 .041
LU-CC -.467 4.794 -.097 .922 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 76: Pairwise comparison of ΔTP1 of water for different materials
Figure 115: Pairwise comparison of ΔTP1 of water for different materials
When ΔTP1 was compared for water between different materials, pairwise
comparisons of materials showed a significant difference between VS-CC (p=0.041), but no
significant difference between VS-LU (p=0.053) and LU-CC (p=1.000) (Table 76, Figure 115).
131
3.3.2 ΔTP2 Analysis
For ΔTP2, the overall results of the mean, standard deviation, minimum and
maximum values are shown below (Table 77).
Material Solution Mean S.D. Min Max
LU
Coffee -0.54 0.25 -0.91 0.03
Wine -0.41 0.33 -1.14 0.06
Coca-Cola -0.36 0.29 -0.86 0.09
Water -0.50 0.44 -1.41 0.01
VS
Coffee -1.80 0.45 -2.43 -1.09
Wine -0.82 0.22 -1.18 -0.49
Coca-Cola -0.67 0.34 -1.24 0.02
Water -0.56 0.20 -0.94 -0.22
CC
Coffee -1.34 0.35 -1.90 -0.85
Wine -0.67 0.44 -1.50 -0.09
Coca-Cola -0.13 0.24 -0.54 0.22
Water -0.03 0.25 -0.43 0.46
Table 77: ΔTP2 overall mean, standard deviation, minimum and maximum values
132
3.3.2.1 Overall Comparisons of ΔTP2
3.3.2.1.1 Between Materials
Figure 116: Graph of mean ΔTP2 by material for different solutions
Figure 116 shows that, for ΔTP2, all materials had ΔTP values within the acceptability
thresholds.
For LU, none of the solutions caused a perceptible translucency change.
For VS, only staining with coffee resulted in perceptible translucency change of ΔTP2
= -1.80±0.45.
For CC, only staining with coffee resulted in perceptible translucency change of ΔTP2
= -1.34±0.35.
-0.54
-1.80
-1.34
-0.41
-0.82
-0.67
-0.36
-0.67
-0.13
-0.50
-0.56
-0.03
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
LU VS CC
ΔTP2
Coffee
Wine
CocaCola
Water
133
Figure 117: Boxplot of ΔTP2 of materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 28.562a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 78: Kruskall-Wallis test summary ΔTP2 across materials
The Kruskall-Wallis test showed that the differences in ΔTP2 between the materials
were significant (p<0.001) (Table 78).
134
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-CC -41.967 9.513 -4.412 <.001 .000
VS-LU 45.833 9.513 4.818 <.001 .000
CC-LU 3.867 9.513 .406 .684 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 79: Pairwise comparison of ΔTP2 of different materials
Figure 118: Pairwise comparison of ΔTP2 of different materials
Pairwise comparisons of ΔTP2 of the materials showed significant differences
between VS-CC (p=0.000) and VS-LU (p=0.000), but no significant difference between CC-LU
(p=1.000) (Table 79, Figure 118).
135
3.3.2.1.2 Between Solutions
Figure 119: Graph of mean ΔTP2 by solution for different materials
Figure 119 shows that, coffee resulted in perceptible color change of ΔTP2 =
1.80±0.45 and ΔE0 = 1.34±0.35 in VS and CC respectively. Coffee resulted in non-perceptible
translucency change in LU.
Wine, Coca-Cola and water resulted in non-perceptible translucency change in all
materials.
-0.54
-0.41
-0.36
-0.50
-1.80
-0.82
-0.67
-0.56
-1.34
-0.67
-0.13
-0.03
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
Coffee Wine CocaCola Water
ΔTP2
LU
VS
CC
136
Figure 120: Boxplot of ΔTP2 of solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 180
Test Statistic 58.112a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 80: Kruskall-Wallis test summary ΔTP2 across solutions
The Kruskall-Wallis test showed that the differences in ΔTP2 between the solutions
were significant (p<0.001) (Table 80).
137
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -41.178 10.984 -3.749 <.001 .001
Coffee-Coca-Cola -71.078 10.984 -6.471 <.001 .000
Coffee-Water -73.167 10.984 -6.661 <.001 .000
Wine-Coca-Cola -29.900 10.984 -2.722 .006 .039
Wine-Water -31.989 10.984 -2.912 .004 .022
Coca-Cola-Water -2.089 10.984 -.190 .849 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 81: Pairwise comparison of ΔTP2 of different solutions
Figure 121: Pairwise comparison of ΔTP2 of different solutions
Pairwise comparisons of ΔTP2 of the solutions showed a significant difference
between coffee-wine (p=0.001), coffee-Coca-Cola (p=0.000), coffee-water (p=0.000), wine-
138
Coca-Cola (p=0.039) and wine-water (p=0.022), but no significant difference between CocaCola-water (p=1.000) (Table 81, Figure 121).
3.3.2.2 LU Comparisons Between Solutions
Figure 122: Boxplot of ΔTP2 in LU for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 3.166a,b
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
.367
a. The test statistic is adjusted for ties.
b. Multiple comparisons are not performed because the
overall test does not show significant differences across
samples.
139
Table 82: Kruskall-Wallis test summary ΔTP2 in LU across solutions
The Kruskall-Wallis test showed that the differences in ΔTP2 in LU between the
solutions were not significant (p=0.367). Therefore, a pairwise comparison was not
conducted (Table 82).
3.3.2.3 VS Comparisons Between Solutions
Figure 123: Boxplot of ΔTP2 in VS for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 36.800a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
140
Table 83: Kruskall-Wallis test summary ΔTP2 in VS across solutions
The Kruskall-Wallis test showed that the differences in ΔTP2 in VS between the
solutions were significant (p<0.001) (Table 83).
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -22.533 6.376 -3.534 <.001 .002
Coffee-Coca-Cola -30.000 6.376 -4.705 <.001 .000
Coffee-Water -36.133 6.376 -5.667 <.001 .000
Wine-Coca-Cola -7.467 6.376 -1.171 .242 1.000
Wine-Water -13.600 6.376 -2.133 .033 .197
Coca-Cola-Water -6.133 6.376 -.962 .336 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 84: Pairwise comparison of ΔTP2 in VS for different solutions
141
Figure 124: Pairwise comparison of ΔTP2 in VS for different solutions
When ΔTP2 was compared for VS between different solutions, pairwise comparisons
of solutions showed significant differences between coffee-wine (p=0.002), coffee-CocaCola (p=0.000) and coffee-water (p=0.000), but no significant differences between wineCoca-Cola (p=1.000), wine-water (p=0.197) and Coca-Cola-water (p=1.000) (Table 84, Figure
124).
3.3.2.4 CC Comparisons Between Solutions
Figure 125: Boxplot of ΔTP2 in CC for different solutions
Independent-Samples Kruskal-Wallis Test
Summary
Total N 60
Test Statistic 41.451a
Degree Of Freedom 3
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
142
Table 85: Kruskall-Wallis test summary ΔTP2 in CC across solutions
The Kruskall-Wallis test showed that the differences in ΔTP2 in CC between the
solutions were significant (p<0.001) (Table 85).
Pairwise Comparisons of Solution
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
Coffee-Wine -13.600 6.376 -2.133 .033 .197
Coffee-Coca--Cola -32.267 6.376 -5.061 <.001 .000
Coffee-Water -35.867 6.376 -5.626 <.001 .000
Wine-Coca-Cola -18.667 6.376 -2.928 .003 .020
Wine-Water -22.267 6.376 -3.493 <.001 .003
Coca-Cola-Water -3.600 6.376 -.565 .572 1.000
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 86: Pairwise comparison of ΔTP2 in CC for different solutions
143
Figure 126: Pairwise comparison of ΔTP2 in CC for different solutions
When ΔTP2 was compared for CC between different solutions, pairwise comparisons
of solutions showed significant differences between coffee-Coca-Cola (p=0.000) and coffeewater (p=0.000), wine-Coca-Cola (p=0.020) and wine-water (p=0.003), but no significant
differences between coffee-wine (p=0.197), and Coca-Cola-water (p=1.000) (Table 86,
Figure 126).
3.3.2.5 Coffee Comparisons Between Materials
Figure 127: Boxplot of ΔTP2 of coffee for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 32.583a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
144
Table 87: Kruskall-Wallis test summary ΔTP2 in coffee across materials
The Kruskall-Wallis test showed that the differences in ΔTP2 in coffee between the
materials were significant (p<0.001) (Table 87).
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-CC -8.933 4.794 -1.863 .062 .187
VS-LU 26.867 4.794 5.604 <.001 .000
CC-LU 17.933 4.794 3.741 <.001 .001
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 88: Pairwise comparison of ΔTP2 of coffee for different materials
Figure 128: Pairwise comparison of ΔTP2 of coffee for different materials
145
When ΔTP2 was compared for coffee between different materials, pairwise
comparisons of materials showed significant differences between VS-LU (p=0.000) and CCLU (p=0.001), but no significant difference between VS-CC (p=0.187) (Table 88, Figure 128).
3.3.2.6 Wine Comparisons Between Materials
Figure 129: Boxplot of ΔTP2 of wine for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 10.338a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
.006
a. The test statistic is adjusted for ties.
Table 89: Kruskall-Wallis test summary ΔTP2 in wine across materials
The Kruskall-Wallis test showed that the differences in ΔTP2 in wine between the
materials were significant (p=0.006) (Table 89).
146
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-CC -8.300 4.795 -1.731 .083 .250
VS-LU 15.400 4.795 3.212 .001 .004
CC-LU 7.100 4.795 1.481 .139 .416
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 90: Pairwise comparison of ΔTP2 of wine for different materials
Figure 130: Pairwise comparison of ΔTP2 of wine for different materials
When ΔTP2 was compared for wine between different materials, pairwise
comparisons of materials showed a significant difference between VS-LU (p=0.004), but no
significant differences between VS-CC (p=0.250) and CC-LU (p=0.416) (Table 90, Figure 130).
147
3.3.2.7 Coca-Cola Comparisons Between Materials
Figure 131: Boxplot of ΔTP2 of Coca-Cola for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 15.898a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 91: Kruskall-Wallis test summary ΔTP2 in Coca-Cola across materials
The Kruskall-Wallis test showed that the differences in ΔTP2 in Coca-Cola between
the materials were significant (p<0.001) (Table 91).
148
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-LU 10.733 4.795 2.239 .025 .076
VS-CC -19.067 4.795 -3.977 <.001 .000
LU-CC -8.333 4.795 -1.738 .082 .247
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 92: Pairwise comparison of ΔTP2 of Coca-Cola for different materials
Figure 132: Pairwise comparison of ΔTP2 of Coca-Cola for different materials
When ΔTP2 was compared for Coca-Cola between different materials, pairwise
comparisons of materials showed a significant difference between VS-CC (p=0.000), but no
significant differences between VS-LU (p=0.076) and LU-CC (p=0.247) (Table 92, Figure 132).
149
3.3.2.8 Water Comparisons Between Materials
Figure 133: Boxplot of ΔTP2 of water for different materials
Independent-Samples Kruskal-Wallis Test
Summary
Total N 45
Test Statistic 20.012a
Degree Of Freedom 2
Asymptotic Sig.(2-sided
test)
<.001
a. The test statistic is adjusted for ties.
Table 93: Kruskall-Wallis test summary ΔTP2 in water across materials
The Kruskall-Wallis test showed that the differences in ΔTP2 in water between the
materials were significant (p<0.001) (Table 93).
150
Pairwise Comparisons of Material
Sample 1-Sample 2 Test Statistic Std. Error Std. Test
Statistic
Sig. Adj. Sig.a
VS-LU 6.133 4.795 1.279 .201 .602
VS-CC -20.867 4.795 -4.352 <.001 .000
LU-CC -14.733 4.795 -3.073 .002 .006
Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is .050.
a. Significance values have been adjusted by the Bonferroni correction for multiple tests.
Table 94: Pairwise comparison of ΔTP2 of water for different materials
Figure 134: Pairwise comparison of ΔTP2 of water for different materials
When ΔTP2 was compared for water between different materials, pairwise
comparisons of materials showed significant differences between VS-CC (p=0.000) and LUCC (p=0.006), but no significant difference between VS-LU (p=0.602) (Table 94, Figure 134).
151
3.3.3 Translucency Change After Polishing
Figure 135: Graph of change in ΔTP for LU after polishing
Figure 135 shows that, for LU, polishing after staining with coffee, Coca-Cola and
water caused ΔTP to decrease from ΔTP1 = -0.46±0.25 to ΔTP2 = -0.54±0.25 for coffee, ΔTP1
= -0.25±0.28 to ΔTP2 = -0.36±0.29 for Coca-Cola and ΔTP1 = -0.07±0.21 to ΔTP2 = -0.50±0.44
for water. Polishing after staining with wine caused ΔTP to increase from ΔTP1 = -0.42±0.18
to ΔTP2 = -0.41±0.33.
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
ΔTP1 ΔTP2
Material: LU
Coffee
Wine
CocaCola
Water
152
Figure 136: Graph of change in ΔTP for VS after polishing
Figure 136 shows that, for VS, polishing after staining with wine, Coca-Cola and
water caused ΔTP to decrease from ΔTP1 = -0.66±0.22 to ΔTP2 = -0.82±0.22 for wine, ΔTP1 =
-0.22±0.17 to ΔTP2 = -0.67±0.34 for Coca-Cola and ΔTP1 = -0.28±0.22 to ΔTP2 = -0.56±0.20
for water. Polishing after staining with coffee caused ΔTP to increase from ΔTP1 = -
1.96±0.33 to ΔTP2 = -1.80±0.45.
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
ΔTP1 ΔTP2
Material: VS
Coffee
Wine
CocaCola
Water
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
ΔTP1 ΔTP2
Material: CC
Coffee
Wine
CocaCola
Water
153
Figure 137: Graph of change in ΔTP for CC after polishing
Figure 137 shows that, for CC, polishing after staining with Coca-Cola caused ΔTP to
decrease from ΔTP1 = 0.01±0.17 to ΔTP2 = -0.13±0.22. Polishing after staining with coffee,
wine and water caused ΔTP to increase from ΔTP1 = -1.70±0.35 to ΔTP2 = -1.34±0.35 for
coffee, ΔTP1 = -0.77±0.20 to ΔTP2 = -0.67±0.44 for wine, and ΔTP1 = -0.07±0.19 to ΔTP2 = -
0.03±0.25 for water.
Figure 138: Graph of change of in ΔTP for coffee after polishing
Figure 138 shows that, for coffee, polishing caused ΔTP in LU to decrease from ΔTP1
= -0.46±0.25 to ΔTP2 = -0.54±0.25. For VS and CC, polishing caused ΔTP to increase from
ΔTP1 = -1.96±0.33 to ΔTP2 = -1.80±0.45, and from ΔTP1 = -1.70±0.35 to ΔTP2 = -1.34±0.35,
respectively.
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
ΔTP1 ΔTP2
Solution: Coffee
LU
VS
CC
154
Figure 139: Graph of change in ΔTP for wine after polishing
Figure 139 shows that, for wine, polishing caused ΔTP in VS to decrease from ΔTP1 =
-0.66±0.22 to ΔTP2 = -0.82±0.22. For LU and CC, polishing caused ΔTP to increase from ΔTP1
= -0.42±0.18 to ΔTP2 = -0.41±0.33, and from ΔTP1 = -0.77±0.20 to ΔTP2 = -0.67±0.44,
respectively.
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
ΔTP1 ΔTP2
Solution: Wine
LU
VS
CC
155
Figure 140: Graph of change in ΔTP for Coca-Cola after polishing
Figure 140 shows that, for Coca-Cola, polishing caused ΔTP in LU, VS and CC to
decrease from ΔTP1 = -0.25±0.28 to ΔTP2 = -0.36±0.29, ΔTP1 = -0.22±0.17 to ΔTP2 = -
0.67±0.34, and from ΔTP1 = 0.01±0.17 to ΔTP2 = -0.13±0.24, respectively.
Figure 141: Graph of change in ΔTP for water after polishing
ΔTP1 ΔTP2
Solution: Coca-Cola
LU
VS
CC
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
0.50
ΔTP1 ΔTP2
Solution: Water
LU
VS
CC
156
Figure 141 shows that, for water, polishing caused ΔTP in LU and VS to decrease
from ΔTP1 = -0.07±0.21 to ΔTP2 = -0.50±0.44, and from ΔTP1 = -0.28±0.22 to ΔTP2 = -
0.56±0.20, respectively. For CC, polishing caused ΔTP to increase from ΔTP1 = -0.07±0.19 to
ΔTP2 = -0.03±0.25.
157
4. Chapter 4: Discussion
4.1 Null Hypotheses
Color change
1. The first null hypothesis that different resin ceramic materials are not different in
staining susceptibility when stained with different staining solutions was rejected since
there was a significant difference in color change (ΔE) between materials.
2. The second null hypothesis that different solutions are not different in staining ability for
different resin ceramic materials was rejected since there was a significant difference in
color change (ΔE) brought about by different solutions.
Translucency
3. The third null hypothesis that different rein ceramic materials are not different in
translucency change when stained with different staining solutions was rejected since
there was a significant difference in translucency change (ΔTP) between materials.
4. The fourth null hypothesis that different solutions are not different in staining ability for
different resin ceramic materials was rejected since there was a significant difference in
translucency change (ΔTP) brought about by different solutions.
4.2 Color Stability of Materials
Color change in resin-based materials has been shown to be related to endogenous
reasons such as material composition, degradation of the matrix with time, degree of
conversion and water sorption,67, 68 or exogenous reasons such as staining from solutions,
which results in both extrinsic as well as intrinsic staining. ΔE0 was calculated to represent
the color change of both extrinsic as well as intrinsic staining, and ΔE1 was calculated after
polishing of the specimens to represent the color change of only the intrinsic staining of the
material.
The results of our present study showed that LU had the least change in color among
solutions tested. LU was used in our present study as it represented a material that is used
in CAD/CAM dentistry by the subtractive manufacturing method. This is in contrast with the
tested materials VS and CC which are more newly introduced materials used in CAD/CAM
dentistry by the additive manufacturing method.
158
While additive manufacturing of resin-based materials has better controlled the
consistency and uniformity of polymerization of the material by removing the operator
influence in the polymerization process, there may still be a difference between
polymerization with a 3D printer compared to in an industrial process of block fabrication
for subtractive CAD/CAM manufacturing.
LU consists of three different fillers: silica and zirconia nanoparticles combined in
clusters, zirconia nanoparticles, and silica nanoparticles. These fillers make up about 79% by
weight of the material, which is dispersed within a highly cross-linked polymeric matrix by
an appropriate manufacturing process.110
VS and CC are resin-based materials filled with ceramic filler particles. The
manufacturing process used in this present study was additive manufacturing using a digital
light processing (DLP) printer that cured the material layer by layer.
Perhaps the light curing by layer in the additive manufacturing process still does not
result in as high a degree of conversion compared to the industrial fabrication of a resin
ceramic block for subtractive manufacturing, where high temperature and high pressure
polymerization modes can result in higher degree of conversion and better physical
properties of resin-based materials.111 As a high degree of conversion has been advocated
to achieve low stainability of a resin-based material,16, 17 this could explain why the printable
resin based definitive CAD/CAM materials in general had more staining compared to the
milled LU block. Interestingly, when LU was compared to other CAD/CAM ceramic materials
like lithium disilicate, there seems to be a significant difference between LU and ceramic
materials, with more staining seen in LU compared to the ceramic materials.68 Furthermore,
when LU was compared with laboratory processed composite resins, it was shown to also
have more staining.65 The results of our present study did not show any unacceptable color
changes in LU throughout the experiment. This could be related to the length of time of
immersion of specimens in the staining solutions. However, even with such a short period of
time, unacceptable changes in color were seen with the printable resin based definitive
CAD/CAM materials.
CC had the largest color change of all the materials. It was interesting to see that
after printing of CC, there was a thin film of white powder on the material, which could be
attributed to the ceramic filler particles. This disappeared upon extensive polishing.
However, what is not known is the quality of bond between the filler particles and the
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matrix, nor the filler particle size. The optimal coupling of filler particles to the resin matrix
has been shown to be a factor in the staining susceptibility of a resin-based material.81
Perhaps the difference in the quality of coupling of the filler particles to the respective resin
matrices in VS and CC could have contributed to the significant differences in color change
between them. Filler particle size could have affected color changes as well, as
dislodgement of filler particles from the surface could leave voids of varying sizes which
affects the degree of surface roughness and the ease to which chromogens adhere.23
The results of our present study show that the printable resin based definitive
CAD/CAM materials discolor significantly more than the milled resin based definitive
CAD/CAM material. This is in agreement with previous work by Shin and colleagues
comparing milled vs printed CAD/CAM materials.112 In their study, unacceptable color
change was seen after storage of printed CAD/CAM materials in staining solutions for 7
days. Another publication by Gruber and colleagues also stands in agreement with the
findings of our present study.113 They found that the additive manufactured resins
consistently were above the threshold for acceptability for color change compared to the
conventional resin and subtractive manufactured resin groups. As mentioned earlier, the
postulation that increased water sorption and lower degree of conversion in these printable
resin-based CAD/CAM materials were mentioned in Shin’s and Gruber’s publications. The
need for printable resins to be as low viscosity as possible to allow for printing limits their
filler loading which may affect the eventual physical properties. There are evidently
differences in material composition between printable resin-based CAD/CAM materials and
milled resin-based CAD/CAM materials.
4.3 Staining Ability of Different Solutions
The results of our present study showed that coffee caused the most color change
among materials tested. This is in contrast with other studies that found that red wine
caused the highest ΔE.64, 68, 84, 110, 114 In one study by Quek 2018,84 the specimens were
immersed into the staining solutions for 7 days at 37°C, with solutions being refreshed every
2 days. In our present study, a protocol of staining only for 15 minutes at a time, twice a day
for 14 days was used, for a total contact time of 30 minutes a day. This was thought to
mimic the consumption patterns of the beverages more closely. Furthermore, the coffee
160
used in our present study was at 60°C, which also was chosen to reflect the temperature
that the beverage is normally consumed at. The increased temperature, and difference in
the contact time, could explain the differences seen between our present study and that of
Quek 2018 regarding the staining ability of different solutions. The results of our present
study is in agreement with another study115 that investigated the relationship of
temperature of beverages and color stability of composite resins which showed that higher
temperature staining solutions (in particular, coffee) resulted in higher color changes. It
would be interesting to see in future publications the influence of temperature on the
staining ability of resin-based definitive CAD/CAM materials.
Coffee contains more than 20 types of acids which can cause chemical corrosion of
the matrix which then increase the material’s surface roughness and hence extrinsic
staining.116 Chromogens in coffee has also been shown to penetrate and be absorbed into
the organic phase of the material.117 This could explain why the results of our present study
showed significant differences between coffee and other solutions in its staining ability. The
reduction in ΔE after polishing also showed the large extent of extrinsic staining ability of
coffee.
Wine contains chromogens, acids, alcohol, and tannins. Alcohols and acids can
degrade the matrix of the resin-based material which leads to increased surface roughness,
and extrinsic staining. A previous study showed that alcohol content was a critical factor
that affected the color change of nanohybrid composite resin materials.118 Tannins are also
able to enhance staining by enhancing chromogen adherence to the material.84 Chromogens
can also penetrate the matrix of a resin-based material to cause intrinsic staining. Despite
this, our present study was in contrast with others that showed that wine resulted in the
most amount of color change compared to coffee.114 Perhaps the contact time of wine in
our present study was insufficient to allow for the degradation of the matrix and sufficient
adherence of chromogens to result in color changes to similar degrees as in other previous
studies.
The effect on acidity and its effect on resin based materials have been documented
by Karatas.119 Their study utilized atomic force microscopy and profilometry to support the
findings that beverages affect the surface roughness of composite resins depending on the
pH, alcohol content, and the organic and inorganic structure of the resin based material.
They did recognize that although more acidic pH could result in surface wear and roughness,
161
the surface roughness of the composite resins depended more on the composite resin
matrix structure and its interaction with the staining solution, rather than just relating to the
staining solution itself.119
The rationalization in other studies for 1 day of in-vitro immersion corresponding to
1 month of in-vivo contact of the solution was from the assumption of a 15 minute contact
time for one beverage, with a daily consumption of 3.2 beverages.114 By this measure, our
present study would have only investigated up to 2 weeks of in-vivo contact with the
solutions. It is concerning, that even with such a short testing period, non-acceptable color
changes were already seen in VS and CC when coffee was used. More investigation into
contact time of staining solutions should be conducted, and accurate estimation of in-vivo
representation of in-vitro testing conditions should be validated.
Coca-Cola seemed to cause the least amount of color change compared to coffee
and wine. Interestingly, Coca-Cola was the most acidic of the solutions tested in our present
study. As mentioned, acidity is thought to affect matrix degradation which can affect color
stability of the material. Matrix degradation could be related to the function of contact
time. In our present study, contact time with the solutions were 15 minutes at a time, twice
a day for two weeks. There might not have been sufficient time for the effects of matrix
degradation due to the acids in Coca-Cola to show effects in color change in this present
study. However, the lack of staining ability of Coca-Cola despite its high acidity is consistent
with other studies on the lack of staining chromogens and low polarity of Coca-Cola.84, 117, 120
Um and Ruyter97 postulated that while Coca-Cola is more acidic, it does not contain the
yellow colorants present in coffee and other solutions, which seem to interact readily with
the resin-based material. This was validated in our present study by the fact that the color
change as represented by the b* values after staining and after polishing for Coca-Cola
showed negative values which represented a shift toward a bluer color instead of a more
yellow color in the CIELAB color space.
4.4 Surface Roughness of Materials
An interesting result in our present study was an increase in ΔE after polishing for
specimens in the Coca-Cola and Water groups. While the increase in ΔE after polishing was
within the perceptibility threshold of ΔE=0.8 and hence not perceptible, this was seen
162
consistently for all 3 materials tested. A look at the L*a*b* values showed that in general, ΔL
had positive values for Coca-Cola and water, and ΔL1 was in general higher than ΔL0. This
meant that the L* value increased after polishing of the specimens. a* values also showed a
shift towards the greener color (negative values) when both Coca-Cola and water were
used, in contrast to a shift towards the redder color (positive values) when both coffee and
wine were used.
Visual inspection of the specimens after staining and polishing showed an increase in
surface roughness from baseline. The increased surface roughness of the specimens stained
with Coca-Cola also could be related to the high acidity of Coca-Cola, which could lead to
degradation of the surface of the material as previously mentioned. Also, the use of flour of
pumice to polish the specimens could have increased the surface roughness of the
specimens that were in both the Coca-Cola and water groups.
Increased surface roughness leads to increased scatter of light which can lead to
increase in value of the specimen. Together with the fact that Coca-Cola and water led to
slight greener changes in the specimen, and the fact that green light scatters more than red
light because it is of shorter wavelength, these factors could have led to the numerical
increase of ΔE after polishing of the samples stained with Coca-Cola and water. This is in
contrast with studies that show an increase in surface roughness is usually related to
increase in extrinsic staining and greater color change after immersion in staining
solutions.121 However, given that the solutions in our present study (Coca-Cola and water)
did not contain the chromogens/colorants for severe staining of the material, the increase
in surface roughness could then have led to the increase in ΔE after polishing for the reasons
mentioned above.
The evidence for surface roughness affecting translucency is divided, with evidence
that it is important regarding the translucency of materials,53 and contrasting evidence
reporting no correlation with surface roughness and translucency.122 The results of our
present study demonstrate the below perceptible change of translucency after polishing
was done. Presumably, the influence of light polishing did not roughen surfaces enough to
result in perceptible changes in translucency. Furthermore, the action of the solutions on
material surface roughness, while visible, did not result in translucency changes that would
be non-acceptable based on our data in the present study.
163
Future investigations could incorporate measurement of surface roughness of
specimens before and after staining and polishing and relate the findings to changes in color
and translucency with different staining solutions.
4.5 Thickness
While translucency has been linked to inherent material compositions, the thickness
of the material is known to also affect the translucency of the material.123, 124 This was
controlled for in our present study by ensuring that all specimens were 2.0 mm thick, with a
tolerance of ±0.1 mm as measured by a digital caliper. The effects of varying thicknesses on
the change in translucency would be valuable information in future studies, as restorations
vary in thicknesses in different clinical scenarios and indications. Translucency is an
important optical property of teeth, and definitive restorations should also possess similar
optical properties to teeth if we are to utilize them successfully in the long term. The
challenge with matching natural teeth translucency is that the translucency of teeth have
been shown to change with natural changes within the tooth in size of the pulp, increasing
dimensions of dentine, and reduction of enamel from external wear factors.48, 49 It has been
shown that aging of teeth causes a corresponding increase in translucency.50 The reverse
trend was seen in our present study, where staining and polishing caused a decrease in
translucency. While this was only perceptible when coffee was used as a staining medium,
longer term immersion in the staining solutions may cause more perceivable and even nonacceptable changes in translucency of these materials. Future longer-term studies would be
able to validate this hypothesis.
4.6 Devices
There are different devices on market that enable one to measure CIE L*a*b* values.
A spectrophotometer (CrystalEye) was used in our present study because it has previously
shown good reliability and accuracy for color matching in dentistry.61, 62 Colorimeters are
another type of device to measure color. They do not measure spectral reflectance but
rather, tristimulus values, and aging of the filters within the device can affect the accuracy.57
The use of digital cameras are a basic way of measuring color and this is can be assisted with
the use of a gray card of known CIE L*a*b* values, and post processing of the image in an
164
image processing software.125 However, factors such as varying lighting conditions can affect
the result gained from the use of digital cameras to compare LAB values of the specimens.
The CrystalEye spectrophotometer used in our present study had a sleeve that allowed for
more consistent recording of measurement of CIE L*a*b* values by controlling the distance
from the sensor to the specimen, and by blocking out ambient lighting during measurement.
In addition, to control the testing conditions to be as consistent as possible, a custom-made
measurement chamber was made to ensure accuracy and consistency of measurements.
The CrystalEye spectrophotometer was also calibrated prior to measurement of each
individual sample.
4.7 CIELAB Formulas
The original CIELAB formula was introduced by CIE, but there were still issues with
differences between the computed color difference and the actual perceived color
difference. Corrections to this came in the form of updated formulas. The CIE94 was
introduced in 1995,31 and then the CIEDE2000 in 2001.32 The non-uniformity in the CIELAB
color space was corrected in the CIEDE2000 formula using the weighting functions for
lightness (SL), chroma (SC) and hue (SH). The influence of lighting and viewing conditions
when evaluating color differences was also accounted for with the parametric factors (kL, kC,
kH). In addition, a rotation term was introduced to control the difference between chroma
and hue in the blue region of the color space and a modification of the a* axis which
predominantly affects neutral colors with low chroma. In contrast, the CIE94 only
introduced corrections to the hue and chroma weighting functions, while keeping the SL
weighting function as 1.0 and leaving out the rotation term.33
In our present study, the CIEDE2000 formula was chosen to calculate ΔE, because a
significant difference between CIEDE2000 and CIE94 formulas was previously
demonstrated,33 and there have been multiple studies that demonstrate better
performance of CIEDE2000 formula over CIELAB when reflecting the color difference that is
perceived by the human eye.34-36
Previous studies have demonstrated using the older CIELAB formula that the
threshold for perceptibility is ΔE = 2.6, and the threshold for clinical tolerability was ΔE =
5.6.44 More recently, values for perceptibility and acceptability using the 50:50% thresholds
165
was published for the CIEDE2000 ΔE formula.45 The ΔE values for perceptibility and
acceptability was reported to be 0.8 and 1.8, respectively. These were the values that were
used to evaluate the results in our present study as the CIEDE2000 formula was used to
calculate ΔE in this study.
4.8 Limitations and Future Considerations
There are a few limitations in our present study. First, was the lack of a pellicle
formation from the interaction of saliva with the materials. Nathoo documented the main
mechanism of extrinsic staining in 1997 and reported that this is via the formation of a
salivary pellicle via electrostatic forces and calcium bridges. Chromogens from foods can
then be directly deposited on to the tooth/restorative material via an ion exchange
mechanism with the salivary pellicle.89
The importance of saliva, its enzymes and proteins in the interaction of salivary
pellicle and restorative material has also been documented.90 Surface roughness and
surface free energy of the material also affects the pellicle formation and plaque growth on
a restorative material,92, 93 which can affect the extrinsic staining effects on these materials.
Salivary esterases94 and organic acids from plaque95 can also cause degradation of resin
based materials and affect its stain resistance ability.
Our present study did not use saliva or its substitutes and was unable to relate the
color and translucency change to pellicle formation. Future investigations that use saliva or
artificial saliva to form a pellicle may give further information on the staining of these
materials in an environment more closely simulating the oral environment.
Second, the effect on surface roughness after immersion in staining solutions was
also only qualitatively described and not quantitatively recorded. Future investigations
would provide more information on the effect of surface roughness on the eventual color
and translucency change noted by including measurement of surface roughness of the
tested materials.
Third, the thickness of the materials in our present study was kept consistent, but in
clinical practice, different applications of definitive restorations would mandate varying
thicknesses of material that is used. Future investigations on staining of varying thickness of
166
resin based definitive CAD/CAM materials could provide more influence on material
thickness as it relates to color and especially translucency change.
Finally, our present study did not use natural teeth, and we were unable to relate
the color and translucency change of the resin based definitive CAD/CAM materials in
different solutions with that of natural teeth, which should be the reference/benchmark.
The color change of natural teeth with time should ideally be mimicked by definitive
restorations, and future investigations should also attempt to quantify the changes in
natural teeth under the same testing conditions as the restorative materials.
167
5. Chapter 5: Conclusions
The following conclusions could be made based on the findings and limitations of our
present study:
Color change appeared to be material and staining solution dependent.
1. Coffee caused VS and CC to stain significantly more compared to LU. Clinically, the
color change in LU was perceptible. The color change in VS and CC were nonacceptable.
2. Wine caused VS and CC to stain significantly more compared to LU. Clinically, the
color change in VS and CC were perceptible. The color change in LU was not
perceptible.
3. Coca-Cola caused CC to stain significantly more compared to LU and VS. Clinically,
the color change in CC was perceptible. The color change in LU and VS were not
perceptible.
4. Distilled water did not cause perceptible color changes in all materials.
5. Polishing of coffee-stained specimens resulted in improved but still perceptible color
change in VS, and still non-acceptable color change in CC.
6. Polishing of wine-stained specimens resulted in improved and non-perceptible color
change in VS, but still perceptible color change in CC.
7. Polishing of Coca-Cola and water-stained specimens resulted in a non-perceptible
increase in color change in all 3 materials.
Translucency change
8. Coffee caused significant decrease in translucency in VS and CC compared to LU.
Clinically, the decrease in translucency in VS and CC were perceptible.
9. All other solutions did not cause perceptible translucency changes in all materials.
10. Polishing of coffee-stained specimens resulted in improved but still perceptible
translucency changes in VS and CC that were significantly different compared to LU.
168
Disclaimer
The author reports no conflicts of interest.
169
Funding
This study was funded by the Advanced Operative and Adhesive Dentistry (AOAD)
program, Restorative Sciences, at the Herman Ostrow School of Dentistry of the University
of Southern California (USC).
170
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Abstract (if available)
Abstract
Aim: To assess the effect of different commonly consumed beverages (coffee, red wine, Coca-Cola, and distilled water) on the color and translucency change in resin based definitive CAD/CAM materials.
Materials and Methods: A total of 180 specimens (length 14.50 mm x width 7.10 mm x thickness 2.00 mm) of 3 different resin based definitive CAD/CAM materials of shade A1, Lava Ultimate (LU; 3M, St. Paul, MN, USA), VarseoSmile Crown plus (VS; Bego, Bremen, Germany) and Ceramic Crown (CC; SprintRay, Los Angeles, CA, USA) were immersed in 3 different staining solutions (coffee, wine and Coca-Cola) twice a day, for 15 minutes each time, for 2 weeks. Specimens were immersed in distilled water throughout the testing period as a control. CIE L*a*b* values were measured against both white and black backgrounds. Measurements were done at baseline, after staining and after polishing. The color and translucency changes ΔE and ΔTP, respectively, were calculated. Data was statistically analyzed by employing the Kolmogorov-Smirnov and Shapiro-Wilk tests to test for normality, and the Kruskall-Wallis test with Bonferroni correction at α = 0.05.
Results: For ΔE0, overall comparisons showed significant differences between materials, except for LU and VS (p=0.155). Overall comparisons also showed significant differences between solutions, except for distilled water and Coca-Cola (p=1.000). For ΔE1, overall comparisons showed significant differences between all materials (p<0.05). Overall comparisons also showed significant differences between coffee and wine (p=0.038) and coffee and water (p=0.040). Polishing showed a general decrease in ΔE for all materials, except for when Coca-Cola and water where an increase in ΔE was seen after polishing.
For ΔTP1, overall comparisons showed a significant difference in materials only between LU and VS (p=0.004). Overall comparisons also showed significant differences between solutions except for coffee and wine (p=0.080) and Coca-Cola and water (p=1.000). For ΔTP2, overall comparisons showed significant differences between materials, except for CC and LU (p=1.000). Overall comparisons also showed significant differences between all solutions, except for water and Coca-Cola (p=1.000).
Conclusions: Different staining solutions influenced the color and translucency of resin based CAD/CAM materials. Coffee produced the most significant color and translucency changes. Polishing improved the color change of coffee and wine-stained resin based CAD/CAM materials. Printable resin based definitive CAD/CAM materials had more significant color and translucency changes compared to the milled resin based definitive CAD/CAM material.
Clinical significance: Differences in color and translucency changes in the new printable resin based definitive CAD/CAM materials compared to the milled resin based definitive CAD/CAM materials should be considered prior to their clinical use, as optical properties are important in definitive materials that are expected to remain serviceable intra-orally for an extended period. Caution should be employed when deciding on the new printable resin based definitive CAD/CAM materials for definitive restorations due to the non-acceptable color changes noted in this study.
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Lim, Xin En Andrew
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Effect of staining solutions on the color and translucency change of various resin based definitive CAD/CAM materials
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School of Dentistry
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Master of Science
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Biomaterials and Digital Dentistry
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2023-12
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3D printing
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color stability
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