Prediction of optical efficacy of vital tooth bleaching using regression analysis

Establishing a colorimetric guideline to predict the effectiveness of tooth bleaching could produce a more reliable dental treatment. The purpose of this study was to evaluate the effectiveness of tooth bleaching and to test the predictability of tooth color changes. A 10% carbamide peroxide bleaching system was used in studies at Harvard University and at Iwate Medical University in Japan. L*, a*, and b* values (CIELAB) for pre‐ and postbleaching were obtained and color differences (ΔE) were calculated. The b* and L* values of the original tooth color indicated a relatively strong to moderate correlation with ΔE values, whereas a* showed a weak correlation. The multiple‐regression equation obtained from the color data of Harvard subjects performed better than the predictive model. The predicted ΔE correlated strongly with the observed ΔE (r = 0.78). The validation of this equation on data collected from Iwate confirmed the strong correlation (r = 0.74). © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 390–394, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20048     

CIELAB color space boundaries under theoretical spectra and 99 test color samples

A color space is a three-dimensional representation of all the possible color percepts. The CIE 1976 L*a*b* is one of the most widely used object color spaces. In CIELAB, lightness L* is limited between 0 and 100, while a* and b* coordinates have no fixed boundaries. The outer boundaries of CIELAB have been previously calculated using theoretical object spectral reflectance functions and the CIE 1931 and 1964 observers under the CIE standard illuminants D50 and D65. However, natural and manufactured objects reflect light smoothly as opposed to theoretical spectral reflectance functions. Here, data generated from a linear optimization method are analyzed to re-evaluate the outer boundaries of the CIELAB. The color appearance of 99 test color samples under theoretical test spectra has been calculated in the CIELAB using CIE 1931 standard observer. The lightness L* boundary ranged between 6 and 97, redness-greenness a* boundary ranged between −199 and 270, and yellowness-blueness b* boundary ranged between −74 and 161. The boundary in the direction of positive b* (yellowness) was close to the previous findings. While the positive a* (redness) boundary exceeded previously known limits, the negative a* (greenness) and b* (blueness) boundaries were lower than the previously calculated CIELAB boundaries. The boundaries found here are dependent on the color samples used here and the spectral shape of the test light sources. Irregular spectral shapes and more saturated color samples can result in extended boundaries at the expense of computational time and power.     

Unique hue judgments using saturated and desaturated Munsell samples under different light sources

Past studies investigating the unique hues only used samples with a relatively high saturation levels under standard illuminants. In this study, 10 observers selected the four samples with unique hues from 40 V6C8 (Value 6 Chroma 8) and 40 V8C4 (Value 8 Chroma 4) Munsell samples under six light sources, comprising three levels of D_uv (i.e., 0, ?0.02, and ?0.04) and two levels of correlated color temperature (i.e., 2700 and 3500 K). Significant differences were found between the two chroma levels for unique blue and yellow, with the hue angles of unique yellow and blue judged using the desaturated samples being significantly different from those defined in CIECAM02. The iso‐lines of unique yellow, blue, and green did not always go through the origin of the a*‐b* or a′‐b′ planes in CIELAB and CAM02‐UCS. Thus, the problems of CIECAM02, CIELAB, and CAM02‐UCS identified in this study need further investigations.     

Accuracy of approximations for CIELAB chroma and hue difference computation

The transformation in CIELAB from differences in the L^*, a^*, b^* coordinates to those in lightness, chroma, and hue, ΔL^*, ΔC_ab^*, ΔH_ab^*, can be approximated by a rotation in 3-space. Expressions for the error in the approximation of chroma and hue differences are developed. Significant errors are introduced if either the hue angle or chroma difference between reference and sample colors are large. A computed example illustrates the use of the analysis. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 61–64, 1997.     

Color distribution of maxillary primary incisors in Korean children

The purpose of this study was to investigate the color distribution of maxillary primary incisors measured with a colorimeter. The subjects were 100 Korean children aged 2–5 with total number of 400 teeth. A spot measurement intraoral colorimeter was used to determine the color of maxillary primary central and lateral incisors at labial central area. The CIE L*, a*, b* value of each tooth and color difference (ΔE) among each other were calculated and analyzed. The range of L*, a*, and b* values, regardless of the type of teeth, was 72.7–84.9, ?0.6 to 4.9, and 4.7–15.0, respectively. Mean value (SD) of L*, a*, and b* for maxillary primary incisors was 78.6 (2.3), 1.2 (0.9), and 9.6 (1.8), respectively. Boys showed more red (higher a* value) and less yellow (lower b* value) hue than girls in the central incisors (P < 0.05). Mean color difference (ΔE) (SD) between two values which selected from overall 400 L*, a*, b* values measured (n = ₄₀₀C₂) was 3.9 (1.8) with 95% confidence interval range of 3.86–3.89, and most of them were found to be present around the previously reported clinical acceptability thresholds (ΔE = 2.7–6.8). Because mean intraperson ΔE (SD) was 3.0 (1.6) with 95% confidence interval range of 2.86–3.12, most colors among primary incisors in the same person were presumably difficult to discern by naked eye (ΔE < 3.7). Age influenced L* and b* values significantly, but the correlation coefficients were not high (r = ?0.182 for L* of central incisors, P < 0.01; r = 0.188 for b* of central incisors, P < 0.01; and r = 0.143 for b* of lateral incisors, P < 0.05). The present study showed somewhat higher color coordinates than the previous reports which based on primary anterior teeth in other ethnic groups. The results of this study could be used for the color modification of esthetic materials for primary teeth. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

Effects of cold plasma on the color parameters of Hyssop (Hyssopus officinalis L.) using color imaging instrumentation and spectrophotometer

This research was conducted to evaluate the effects of cold atmospheric plasma treatment on the color of Hyssop (Hyssopus officinalis L.) and also to compare the usage of the spectrophotometer vs the color imaging instrumentation for the evaluation of the treatment on the color parameters. The experiments were investigated at different treatment times of 1, 5, and 10 minutes and the voltage values of 17, 20, and 23 kV. Possible changes of color were evaluated by using CIE L*a*b* values obtained with HunterLab colorimeter and CIE L*a*b* values obtained with a digital still camera (DSC) using digital image processing (MATLAB software). The values of L*, a*, and b* of the samples were obtained using both the methods. The results revealed that the L*, a*, and b* values of the treated Hyssop samples changed with increasing the treatment time and the voltage applied. Evaluating the interaction effects revealed that there was a significant difference in the (−a^*/b^* ) ratio. In addition, the results showed that the effects of all variables on the color parameters were significantly different in the case of the DSC using digital image processing. However, these effects were not significantly different using HunterLab colorimeter except for time variable and interaction effects of a* and (−a^*/b^* ) ratio. The lightest green color and the maximum chlorophyll content loss were observed for 23 kV applied over 10 minutes. Based on the results, the digital image processing can be used as a practical tool to study the variations at the color of dried Hyssop leaves after cold plasma treatment.     

A shade guide model based on the color distribution of natural teeth

The objectives were to determine the color distribution of natural teeth sorted by the parameters of Value, Chroma, and hue angle measured with a colorimeter, and to suggest a shade guide model. The color of maxillary and mandibular 12 anterior teeth was measured with a tristimulus colorimeter for 47 subjects (n = 564). The color of teeth was grouped initially by Value (CIE L*) by the interval of 3.3 units. After then, within each main group, the color of teeth was subgrouped by Chroma by the interval of 3.3 units. Chroma was calculated as C*_ab = (a*2 + b*2)^1/2. Since the hue angles were in the first or fourth quadrant, subgroups were further sorted by the first or fourth quadrant hue angles. Hue angle was calculated as h° = arctan (b*/a*). Mean color difference (ΔE*_ab) between the color of an individual tooth and the mean color of each main group was 2.5–3.3, which was lower than acceptable limit (ΔE*_ab < 3.3), and that in each subgroup was 0.9–3.1. The number of subgroups was 22, which was comparable to those of conventional shade guides. A shade guide model based on the color distribution of natural teeth sorted by Value in six main groups, three or four subgroups within each main group sorted by Chroma, and further sorted by hue angle (first or fourth quadrant values) was suggested. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 278–283, 2007     

Design and performance of a color chart based in digitally processed images for sensory evaluation of piquillo peppers (Capsicum annuum)

A chart of color standards for visual color evaluation of the piquillo pepper (Capsicum annuum) has been designed. The chart comprises six rectangular regions of digitally processed images of piquillo peppers covering the observed visual range of variability in this product. Colorimetric characterization of piquillo peppers and the color chart has been made using instrumental color measurements. Both trained and untrained sensory panels tested the reliability of the designed color chart. The Pearson correlation coefficient between color chart scores and subjective color quality scores is 0.831 (P < 0.01). Correlation between all instrumental color coordinates, with the exception of CIELAB a*, and visual color chart scores are significant at P< 0.001 and Pearson correlation coefficients range from ?0.747 with CIELAB chroma C* to ?0.926 with CIELAB hue angle h. Repeatability of visual color chart scores is completely satisfactory, having found no statistically significant differences between color chart scores in samples evaluated twice by panelists. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 305–311, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20026     

Assessing the color of red wine like a taster's eye

Color of 33 commercial red wines and five‐color reference wines was measured in the same conditions in which visual color assessment is done by wine tasters. Measurements were performed in the two distinctive regions, center and rim, which are the regions assessed by wine tasters when the wine sampler is tilted. Commercial wines were classified into five color categories using the color specifications in their taste cards. The five color categories describe the spread of red hues found in red wines from the violet to brown nuances. The performance of CIELAB color coordinates in terms of their ability to reproduce the observed classification has been established using discriminant analysis. The CIELAB hue angle, h_ab, measured in the rim, where wine thickness is of the order of few millimeters, gives the best results classifying correctly 71.1% of the samples. Classification results are not significantly improved when additional color coordinates are considered. Moreover, ΔE* color differences with color reference wines do not provide good classification results. The analysis of reference and commercial wines supports the fact that hue is the main factor in the classification done by wine tasters. This is reinforced by the linear correlation found between h_ab in the rim and the wine age (R² = 0.795) in accordance with the fact that wines change their hues from violet to brown tints with ageing. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 153–162, 2009     

Optimization of color design for military camouflage in CIELAB color space

The assessment of military camouflage is a key consideration in the modern military field. Traditionally, the assessment relies on traditional human visual detection tests because a large scale multi‐level and multi‐factor experiments are time‐ and resource‐consuming. One aspect of camouflage assessment, to which this current study pertains, entails improving upon or “enhancing” an existing or “selected” design. The current study presents a new and practical approach for enhancing the selected military camouflage by utilizing response surface methodology (RSM) of %L*, %a*, and %b* in CIELAB color space. Ten participants were recruited to evaluate 35 variations of %L*, %a*, and %b* on camouflage similarity index (CSI) and reaction time (RT). Based on RSM, the optimum combination occurs at L*: 61.4966, a*: ?5.6505, and b*: 10.5114. In addition, a predictive algorithm to calculate the optimum shift of %L*, %a*, and %b* from the original camouflage to the improved camouflage derived from RSM is also proposed. The optimum shift occurs at ?25%L*, ?55%a*, and + 80%b*. In the end, a new design guideline is proposed for the enhancement of selected military camouflage, which adopts the present study's research findings.     

Use of microemulsions for removal of color and dyes from textile wastewater

One of the major environmental problems in the textile dyeing industry is the removal of color from effluents. The present study deals with color removal from effluents using microemulsions. The wastewater used in this study was the reactive exhausted dye liquor from a dyeing house containing Procion Yellow H‐E4R (CI Reactive Yellow 84), Procion Blue H‐ERD (CI Reactive Blue 160) and Procion Red H‐E3B (CI Reactive Red 120). Color removal was determined by CIE L*a*b* (CIELAB) color space, CIE L*a*b* color difference, ΔE*_ab, and absorbance. Color removal greater than 95% was achieved, attaining values lower than the consent requirements established by the Environmental Agency. It was observed that pH is an important parameter in color removal and effluent pH correction from 10.44 to 9 before extraction improved results. The results obtained were modeled using the Scheffé net method and evaluated through the construction of isoresponse diagrams by correlation graphics between experimental values and those obtained through use of model equations, providing an experimental error of less than 2%. The optimized method very efficiently removed all dyes contained in the effluent. The same microemulsion phase recovered after the extraction process can be used at least a further 14 times and all the extractions gave good color removal. Copyright © 2004 Society of Chemical Industry     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

Color of natural tooth crown in Japanese people

The aim of this study was to investigate the color of the natural maxillary incisor tooth from Japanese people of all age groups. These results were compared with the Trubyte Bioblend shade guide. The subjects of this study were in the age range of 13–84, 42 male and 45 female making 87 people in total. Areas with 1.0‐mm diameter at five sites were measured along the tooth axis for L*,a*,b*, according to CIELAB color spaces using a Spectroradiometric Color Computer. At the incisal site, two significant positive correlations were found between age and a* (r = 0.376, p < 0.001), and b* (r = 0.483, p < 0.001). At the center site, a significant negative correlation (r = −0.418, p < 0.001) was found between age and L*, but positive correlation (r = 0.497, p < 0.001) was found between age and b*. At the cervical site, a significant negative correlation (r = −0.326, p < 0.01) was found between age and L*, but positive correlation (r = 0.702, p < 0.001) was found between age and b*. Near the root, particularly, the values of a* were greater than those suggested by the Trubyte Bioblend shade guide. In conclusion, as the Trubyte Bioblend shade guide does not match the natural tooth color in red‐green chromaticity near the root, it is significant for us in dentistry to develop new shade guides that match the Japanese people based on the data collected. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 43–48, 2000     

Improving the compatibility of disperse dye mixtures using levelling agent — assessment through colour coordinates

The addition of a nonionic levelling agent to a dyebath containing a mixture of three disperse dyes in equal proportions and having similar hues (all in the red—yellow sector of colour space) significantly improved their compatibility, especially at higher applied depths of 3.0% and 4.5%. The dyed samples were measured for the differences in their colour coordinates with respect to the undyed substrate on a spectrophotometer attached to an IBM personal computer. The plots of ΔL* vs ΔC*_ab, ΔL* vs K/S, Δb* vs Δa*, Δa* vs K/S and Δ6* vs K/S clearly indicated the improvement in compatibility of the dye mixture.     

Effect of orange fiber addition on yogurt color during fermentation and cold storage

Orange fiber obtained from orange juice by‐products was added to yogurt. Fiber (0%, 0.6%, 0.8%, and 1% doses and different fiber size: 0.417–0.701 and 0.701–0.991 mm) effects on color during yogurt fermentation and cold storage were studied. Overall composition, pH, acidity, syneresis, L*, a*, and b* values were determined. Sensory evaluation of yogurts was carried out. Fiber addition did not cause changes in yogurt acidification and color during fermentation process, though decreased L* value and increased b* value of the milk. Color evaluation along fermentation is pH dependent (R > 0.870). pH decreased and syneresis increased along cold storage. Because of the acidification process, L* value decreased and a* and b* values increased in all yogurts. Yogurts with 1% fiber were significantly different from the others along cold storage, presenting lower L*, higher a* and b* values, and lower syneresis. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 457–463, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20158     

A Euclidean color space in high agreement with the CIE94 color difference formula

Many consider it futile to try to create color spaces that are significantly more uniform than the CIELAB space, and, therefore, efforts concentrate on developing estimates of perceived color differences based on non‐Euclidean distances for this color space. A Euclidean color space is presented here, which is derived from the CIELAB by means of a simple adjustment of the a* and b* axes, and in which small Euclidean distances agree to within 10.5% with the non‐Euclidean distances given by the CIE94 formula. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 64–65, 2000     

Evaluation of performance of various color‐difference formulae using an experimental black dataset

The objective of this study was to develop a specific visual dataset comprising black‐appearing samples with low lightness (L* ranging from approximately 10.4 to 19.5), varying in hue and chroma, evaluating their visual differences against a reference sample, and testing the performance of major color difference formulas currently in use as well as OSA‐UCS‐based models and more recent CAM02 color difference formulas including CAM02‐SCD and CAM02‐UCS models. The dataset comprised 50 dyed black fabric samples of similar structure, and a standard (L*= 15.33, a* = 0.14, b* = ?0.82), with a distribution of small color differences, in ΔE^*_ab, from 0 to approximately 5. The visual color difference between each sample and the standard was assessed by 19 observers in three separate sittings with an interval of at least 24 hours between trials using an AATCC standard gray scale for color change, and a total of 2850 assessments were obtained. A third‐degree polynomial equation was used to convert gray scale ratings to visual differences. The Standard Residual Sum of Squares index (STRESS) and Pearson's correlation coefficient (r), were used to evaluate the performance of various color difference formulae based on visual results. According to the analysis of STRESS index and correlation coefficient results CAM02 color difference equations exhibited the best agreement against visual data with statistically significant improvement over other models tested. The CIEDE2000 (1:1:1) equation also showed good performance in this region of the color space. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 589–598, 2014     

What can we learn from a dress with ambiguous colors?

We performed objective spectroradiometric measurements on an LCD image of the recently famous Tumblr dress which is typically perceived by people as blue/black or white/gold. The average ± standard deviation of the CIELAB coordinates was as follows: For a set of 33 points in the areas considered as blue/white, L* = 46 ± 6, C*_ab = 33 ± 6, and h_ab = 282 ± 3°, and for a set of 36 points in the areas considered as black/gold, L* = 29 ± 6; C*_ab = 10 ± 4; h_ab = 16 ± 34°. Initially, this first set of values has low variability and corresponds to a blue color, whereas the second set of values has a very large hue‐angle range, including points which can be considered as both gold and black colors. We also performed spectrophotometric measurements on an original model of this dress, and, assuming D65 illuminant and CIE 1931 colorimetric standard observer, the average results were L* = 26, C*_ab = 39, and h_ab = 289°, and L* = 10, C*_ab = 1, and h_ab = 290° for the blue/white and black/gold points, respectively. We discuss the influence of different factors on the blue/black and white/gold perceptions of different people, including observers' variability in color‐matching functions, Bezold–Brücke and Abney effects, background influence, and illumination assumptions. Although more research on the effect shown in this dress is needed, we think that from this example we can learn that objects do not have specific colors; that is, color is a human perception, and many times the answer of the human visual system is not simple and relies on assumptions of unknown, and variable, origin. © 2015 The Authors Color Research & Application Published by Wiley Periodicals, Inc., 40, 525–529, 2015     

Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

High dynamic range (HDR) and wide color gamut imagery has an established video ecosystem, spanning image capture to encoding and display. This drives the need for evaluating how image quality is affected by the multitudes of ecosystem parameters. The simplest quality metrics evaluate color differences on a pixel‐by‐pixel basis. In this article, we evaluate a series of these color difference metrics on four HDR and three standard dynamic range publicly available distortion databases consisting of natural images and subjective scores. We compare the performance of the well‐established CIE L*a*b* metrics (ΔE₀₀ , ΔE₉₄ ) alongside two HDR‐specific metrics (ΔE_Z [J_za_zb_z], ΔE_ITP [IC_TC_P]) and a spatial CIE L*a*b* extension (). We also present a novel spatial extension to ΔE_ITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.     

Color appearance of a large homogenous visual field

In this article, the color appearance of a large (85°) homogeneous self‐luminous visual stimulus was studied in a psychophysical experiment. Large stimuli were displayed on a plasma display panal (PDP) monitor. The large stimuli were viewed with a fixed viewing time (2 s). They were compared with 2° and 10° stimuli presented on a grey background on a CRT monitor. The so‐called “color size effect” was found to be significant. The color stimulus was perceived to be lighter when it was large compared with the 2° and 10° situation. But we did not find the general increase of chroma claimed in previous literature. We found only small hue changes. A model of the color appearance of large‐field stimuli is presented in terms of the CIELAB L*, a*, and b* values of the corresponding 2° and 10° stimuli. © 2007 Wiley Periodicals, Inc. Col Res Appl, 33, 45–54, 2008