Color memory in children

The methods of simultaneous and memory color matching have been studied for a set of five Munsell color samples by 50 children, 25 boys and 25 girls (ranging in age from 9 to 11 years). By comparison between this group and one of 50 young adult observers, we can deduce the following: (a) In children, as in young adults, the mean CIELAB total color difference, ΔE*_ab, in simultaneous color matching is lower than the ΔE*_ab by memory color matching. (b) Children matched reference test worse than young adults for orange, bluish green (only boys and men) and yellow green (only girls and women). (c) While men remember, independently of age and delay time, violet reference test worse than women (P = 0.02), boys remember, independently of delay time, reference test worse than girls for orange (P = 0.026) and pink (P = 0.049). (d) In short‐term memory, boys remember the reference test better than girls for bluish green (P = 0.022); girls remember yellow green reference test worse than women (P = 0.034). (e) Chroma is the perceptual color attribute that best explains sex differences, although that depends upon the reference color test considered. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 372–380, 2008     

Color memory matching: Time effect and other factors

The methods of simultaneous and successive, or memory, color matching have been compared for 10 color reference samples distributed in two groups each performed by 50 observers (25 men and 25 women). Our results, obtained with a total of two hundred Munsell color chips arrayed on ten gray cardboard panels, indicate that: (a) while by simultaneous matching the mean color differences obtained are, in most cases, lower than 1 CIELAB unit, those obtained by memory are generally higher; (b) the worst remembered colors are yellow, light green, blue, and pink, and the best remembered color is orange; (c) the influence of the delay time (15 s, 15 min, and 24 h) is significant for the remembered mean color (p < 0.03); (d) we find significant men-women differences for the remembered mean color (p < 0.05). © 1998 John Wiley & Sons, Inc. Col Res Appl, 23, 234–247, 1998     

Color variability for a wine sample poured into a standard glass wine sampler

Spectroradiometric color measurements were performed at 26 regularly spaced points of a standard wine sampler into which 100 cc of wine were poured. Our goal is to describe the color changes occurring in this system, but not to propose a new method for wine‐color measurement. Three samples of three different wines (red, rosé and white) were studied. From experimental measurements, lines of constant lightness (L), chroma (C_,10) and hue‐angle (h_ab,10) were plotted for each wine poured into the wine sampler, as well as lines of constant CIELAB color differences (ΔE_,10), with respect to a reference point placed at the axis of the wine sampler and at the zone with the greatest diameter. Considering different points of the wine sampler, the color attribute undergoing the greatest change was lightness (ΔL about 16.0, 15.0 and 11.0 for the red, rosé and white wines, respectively), followed by chroma (ΔC_,10 about 2.8, 3.8 and 2.6 for the red, rosé and white wines, respectively) and hue(ΔH_,10 lower than 1.0 for all the wines). Lightness variations were related mainly to the thickness differences between various zones of the wine sampler. Large color differences were found among the different points of the wine poured into the wine sampler (about 20.0, 21.0 and 14.0 CIELAB units, for the red, rosé and white wines, respectively). Panels should be aware of these large color variations when wine is visually assessed using standard wine samplers. It should be concluded that a single color specification for a wine poured into a wine sampler gives incomplete information, but hue, which is the main color attribute considered by observers, is nearly constant in the wine sampler. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 473–479, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10200     

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     

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     

Are chroma tolerances dependent on hue‐angle?

Relationships between suprathreshold chroma tolerances and CIELAB hue‐angles have been analyzed through the results of a new pair‐comparison experiment and the experimental combined data set employed by CIE TC 1–47 for the development of the latest CIE color‐difference formula, CIEDE2000. Chroma tolerances have been measured by 12 normal observers at 21 CRT‐generated color centers L*₁₀ = 40, C*_ab,10 = 20 and 40, and h_ab,10 at 30° regular steps). The results of this experiment lead to a chroma‐difference weighting function with hue‐angle dependence W_CH, which is in good agreement with the one proposed by the LCD color‐difference formula [Color Res Appl 2001;26:369–375]. This W_CH function is also consistent with the experimental results provided by the combined data set employed by CIE TC 1–47. For the whole CIE TC 1–47 data set, as well as for each one of its four independent subsets, the PF/3 performance factor [Color Res Appl 1999;24:331–343] was improved by adding to CIEDE2000 the W_CH function proposed by LCD, or the one derived by us using the results of our current experiment together with the combined data set employed by CIE TC 1–47. Nevertheless, unfortunately, from the current data, this PF/3 improvement is small (and statistically nonsignificant): 0.3 for the 3657 pairs provided by CIE TC 1–47 combined data set and 1.6 for a subset of 590 chromatic pairs (C*_ab,10>5.0) with color differences lower than 5.0 CIELAB units and due mainly to chroma. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 420–427, 2004; Published online in Wiley Interscience (www.interscience.wiley.com). DOI 10.1002/col.20057     

Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

The sizes for the perceptible or acceptable color difference measured with instruments vary by factors such as instrument, material, and color‐difference formula. To compensate for disagreement of the CIELAB color difference (ΔE^*_ab) with the human observer, the CIEDE2000 formula was developed. However, since this formula has no uniform color space (UCS), DIN99 UCS may be an alternative UCS at present. The purpose of this study was to determine the correlation between the CIELAB UCS and DIN99 UCS using dental resin composites. Changes and correlations in color coordinates (CIE L*,a*, and b* versus L₉₉, a₉₉, and b₉₉ from DIN99) and color differences (ΔE^*_ab and ΔE₉₉) of dental resin composites after polymerization and thermocycling were determined. After transformation into DIN99 formula, the a value (red–green parameter) shifted to higher values, and the span of distribution was maintained after transformation. However, the span of distribution of b values (yellow–blue parameter) was reduced. Although color differences with the two formulas were correlated after polymerization and thermocycling (r = 0.77 and 0.68, respectively), the color coordinates and color differences with DIN99 were significantly different from those with CIELAB. New UCS (DIN99) was different from the present CIELAB UCS with respect to color coordinates (a and b) and color difference. Adaptation of a more observer‐response relevant uniform color space should be considered after visual confirmation with dental esthetic materials. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 168–173, 2006     

How strong metamerism disturbs color spaces

Systems for arranging and describing color include “color spaces” and “color order systems.” In a color space, tristimulus values R, G, and B are computable for every light (every point in the space). In familiar color spaces, such computation makes use of three functions of wavelength (the color-matching functions that define one of the CIE Standard Observers), one function corresponding to each of R, G, and B. In the presence of strong metamerism (marked spectral difference between the spectral power distributions of a pair of visually matching lights), the color-matching functions may report that one light of the pair has an entirely different color from that of the other member of the visually matching pair of lights. The CIE Standard Observer embodying those color-matching functions “sees” the two visually matching lights as entirely different in color, that is, it reports entirely different sets of R, G, and B for the two visually matching lights, and, thus, an entirely different chromaticity. In an example given here, each of the CIE Standard Observers assigns a strong green color to lights that are seen by normal human observers as a visual match to a hueless reference white. On the other hand, color order systems comprising sets of real objects in a specified illuminant, and which are assembled (visually arranged) by normal observers, as are the Munsell and OSA sets, do not suffer from the type of trouble discussed here. Color spaces depending on mathematical functions of R, G, and B are at risk: both Standard Observers are shown to plot visually identical lights at widely varying points in familiar color spaces (e.g., delta E*_ab = 40–50). © 1998 John Wiley & Sons, Inc. Col Res Appl, 23: 402–407, 1998     

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     

Color evolution during a coating process of pharmaceutical tablet cores by random spraying

Placebo white tablet cores (lactose anhydrous [47.6%], corn starch [23.8%], microcrystalline cellulose [19.1%], polyvinylpyrrolidone [7.9%], magnesium stearate [0.8%], and talcum powder [0.8%]) were coated with a colorant (hydroxypropyl methylcellulose [8% w/v], titanium dioxide [0.2% w/v], FD&C yellow No. 6 with aluminum lacquer [0.8% w/v], polyethylene glycol 4000 [0.4% w/v], and purified water [q.s.p. 100 mL]) using a random spraying method during 130 minutes. During the coating process, batches of 21 samples were extracted every 10 minutes and measured with a DigiEye imaging system. The initial cores showed very similar and uniform colors (Mean Color Difference from the Mean [MCDM] of 0.8 CIELAB units), but partially coated tablets showed lower uniformity (MCDM below 2.0 CIELAB units). There was a high color variability (MCDM about 4.0 CIELAB units) among tablets of the same batch in the period between 10 and 30 minutes, which decreased as the coating process progressed, until achieving a final acceptable value (MCDM below 2.0 CIELAB units). During the coating process, L^* decreased, C^*_ab strongly increased, and h _ab remained nearly constant (disregarding results at 0 and 10 minutes). CIELAB and CIEDE2000 color differences (mainly chroma differences) with respect to the initial color of the tablets were modeled as a function of time by exponential functions with three coefficients. The color change in the interval from 90 to 130 minutes (4.3 CIELAB units, or 2.6 CIEDE2000 units), may be considered negligible bearing in mind the color variability in the batches of 21 samples and typical values of visual color thresholds.     

Correspondence Between CIELAB and CIELUV Color Differences

The CIE presently recommends two uniform color spaces, the CIE 1976 (L*u*v*)-space (CIELUV) and the CIE 1976 (L*a*b*)-space (CIELAB). With each of these spaces is associated a color-difference formula. Color differences calculated by one formula cannot readily be converted to color differences calculated by the other formula. A conversion factor such as ρ = ΔE_uv*/ΔE_ab* cannot be determined uniquely. However, for any given location in color space, it is possible to determine a range, ρ_min ? ρ ? ρ_max, within which ρ must lie. Lines of constant ρ_min and ρ_max can be plotted in (L*a*b*)-space which indicate the range of ρ in (L*u*v*)-space.     

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     

Color assessment of granitic rocks and implications for their ornamental utilization

The purpose of this study is to evaluate the factors affecting the assessment of granite color, such as grain size and texture, and to propose a methodology for this task which would reduce the margin of error associated with this procedure. For this purpose, an evaluation was carried out on the color of several granites with different textures used in the dimensional stone industry to highlight the importance of the sample area, the number of measurements per sample and the aperture of the equipment. A colorimeter was used to measure the granite color according to the CIE‐L*a*b* and CIE‐L*C*_abh_ab systems, in both large slabs and small samples selected in a processing plant of ornamental granites. Granite color characteristics from large slabs had to be obtained with at least 60 shots due to the variation between different slabs. Therefore, several samples are needed for granite characterization. The color of gray granites does not vary significantly. Nevertheless, the more weathered granites show significant differences which are more evident in the b*‐parameter, or the yellow–blue component, which allow the use of the colorimeter for quality control. By doing so significant differences among the rock pieces used in a single building can be avoided. There were no significant differences found in the color parameters from distinct apertures. However, due to the heterogeneity of the granite the color is evaluated better with larger apertures. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2012     

Deriving instrumental tolerances from pass-fail and colorimetric data

Equations such as CIE94 and CMC are now in common use to set instrumental tolerances for industrial color control. A visual experiment was performed to generate a data set to be used in evaluating typical industrial practices. Twenty-two observers performed a pass-fail color tolerance experiment for a single high-chroma yellow color. Thirty-two glossy samples varying in all three CIE-LAB dimensions were compared with a single standard. A near-neutral anchor pair was used to define the quality of match criterion. The pooled pass data were used to fit a 95% confidence ellipsoid. The chromaticness dimension was well estimated by either CMC or CIE94. The lightness dimension was poorly estimated by either equation. Evaluating the sampling distribution of the 32 test samples via a covariance matrix revealed a poor sampling, particularly in the ΔL*Δb* plane. This sampling may have biased the visual experiment. The visual data were used to optimize various color-difference equations based on CIE94 and CMC, where the l:c and total color difference were adjustable parameters. Several methods of optimization are described including minimizing the number of instrumental wrong decisions and logistic multiple-linear regression. Some methods require only pass response data, while others require both pass and fail data. Because industrial tolerances are usually based on a single observer, ellipsoids were fitted for three observers to demonstrate the large variability between observers in judging color differences. It was concluded that when tolerances need to be set based on a single observer's visual responses of samples not well distributed about the standard, typical industrial occurences, one should only adjust the tolerance magnitude based on a statistically valid equation such as CIE94. One should not change l:c or derive a new ellipsoid. © 1996 John Wiley & Sons, Inc.     

Are we able to distinguish color attributes?

The perception and understanding of the three color attributes have been analyzed from two experiments using pairs of Munsell samples, where only one of the three color attributes were changed/unchanged (Experiment I/II) at a time. In each experiment, 36 pairs with color differences of 3 different sizes (average values of 15.8 and 21.7 CIELAB units for Experiments I and II, respectively) were assessed under standardized conditions by 40 normal observers, 20 of them with previous knowledge and experience in colorimetry. At a 95% confidence level, the results from the two experiments were not significantly different, indicating that color attributes were not easily distinguished: for example, for experienced observers, the percentage of correct answers for identifying the color attribute responsible for a color difference was only 72.4%, the random probability being 33.3%. There were no significant differences between the results found by men and women. The worst distinguished attribute was Chroma, that is, the least frequent confusion was between Hue and Value or vice versa. Value differences were more easily detected for achromatic than for chromatic pairs, both for experienced and inexperienced observers. With respect to the size of the color differences, we observed that large hue differences were more easily identifiable than smaller ones, and a constant Hue was more identifiable when the entire color difference was small. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 356–367, 2000     

Influence of Turbidity Grade on Color and Appearance of Virgin Olive Oil

The appearance of most of the commercialized olive oils involves both their color and turbidity depending on the different technologies used for their elaboration. This research has been carried out to study the filtration impact on the colorimetric changes of virgin olive oils. Naturally turbid olive oils were blended at different proportions (100, 80, 60, 40, 20 and 0%) with their corresponding filtered replicates to obtain a scale of six levels of turbidity and simulating different turbidity grades. Tristimulus colorimetry, particularly the CIELAB uniform color, was used to follow color changes. As turbidity of the oil increased in the blend, yellowish oils, darker, and less saturated were obtained. Univariate correlations between the colorimetric parameters and turbid content were achieved with second degree polynomial equations, being chroma ( C_\textab ^* C_{{_{\text{ab}} }}^{ *} ) and hue (h _ab) the best correlated parameters. The color differences ( \Updelta E_\textab ^* \Updelta E_{{_{\text{ab}} }}^{ *} ) calculated between turbid oils (100%) and the consecutively decreasing turbid oils blends ranged from 3.18 to 18.72 CIELAB units, revealing differences in color visually perceptible to the human eye.     

Media‐dependent color appearance modeling based on artificial neural networks

In this study, we tried to consider various color appearance factors and device characterization together by visual experiment to simplify the across‐media color appearance reproduction. Two media, CRT display (soft‐copy) and NCS color atlas (hard‐copy), were used in our study. A total of 506 sample pairs of RGB and HVC, which are the attributes of NCS color chips, were obtained according to psychophysical experiments by matching soft copy and hard copy by a panel of nine observers. In addition, a set of error back‐propagation neural networks was used to realize experimental data generalization. In order to get a more perfect generalizing effect, the whole samples were divided into four parts according to different hues and the conversion between HVC and R_HVCG_HVCB_HVC color space was implemented. The current results show that the displays on the CRT and the color chips can match well. In this way, a CRT‐dependent reproduction modeling based on neural networks was formed, which has strong practicability and can be applied in many aspects. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 218–228, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20209     

Precision and accuracy in the color specification of virgin olive oils from the bromthymol blue method

Twenty experienced observers with nondefective color vision judged 27 virgin olive oil samples within an acceptable color range, using the bromthymol blue (BTB) method, under controlled observation conditions (daylight source with a correlated color temperature of 6500 K, and standard gray back-ground). On the average, 44.8% of the observers agreed in their selections of the BTB standard solution matching a given oil sample, and this percentage increased to 88.2% considering ±one step in the two dimensions (pH and concentration) of the BTB scale. On the average, the lowest color difference between oil samples and available BTB solutions was 6.6 Commission Internationale de l'éclairage 1976-(L^*a^*b^*) (CIELAB) units, but this color difference was approximately two times greater for the color difference between oil samples and BTB solutions selected by our observers. The colors of the BTB standard solutions in the CIELAB space are not uniformly distributed, and thus one step in pH or concentration is equivalent to CIELAB color differences varying in a wide range (1.7–13.5 and 1.7–26.3 CIELAB units, respectively). From these values, indicating low precision, accuracy, and uniformity, some suggestions are made for future improvements of the current BTB method.     

Formulating Pantone colors by unused base inks,formulation software,and a spectrophotometer

Unused base inks that are not going to be used for printing production are considered to be hazardous materials. Their disposal is expensive, and strict environmental regulations should be followed for their disposal. As an alternative, this article describes how spectral data of unused base inks can be gathered and mixed to generate new colors to incorporate them back to print production for small‐volume jobs. In this study, 30 different Pantone colors were selected as target colors. The CIE L*a*b* spectral data of Pantone colors and unused base inks were gathered via a spectrophotometer. A commercial formulation software, based on multiflux theory and CIE L*a*b* color space, was used to formulate ink recipes that contained the base inks. To quantify the performance of ink recipes, they were mixed and printed using an offset printability tester. The CIELAB ΔE*_ab metric, developed by CIE, was used to detect the visual differences between the target Pantone Color and printed colors.     

Influence of different disinfecting solutions on the color change of artificial irises used in ocular prostheses

Making an artificial iris with an aesthetically acceptable color is an important aspect of ocular rehabilitation. This work evaluated the influence of different disinfecting solutions on changes to the color of artificial irises used in ocular prostheses. Fifty samples simulating ocular prostheses were produced with cobalt blue artificial irises and divided (n = 10) according to the disinfectant used: neutral soap, Opti‐free, Efferdent, 1% hypochlorite, and 4% chlorhexidine. The samples were disinfected for 120 days and subjected to a color readings by spectrophotometry, using the CIE L*a*b* system, before the disinfection period (B), after 60 days of disinfectant exposure (T₁), and after 120 days of disinfectant exposure (T₂). Color differences (ΔE) were calculated for the intervals between T₁ and B (T₁B), and between T₂ and B (T₂B). The data were evaluated by analysis of variance and the Tukey Honestly Significantly Different (α = 0.05). All disinfectant groups exhibited color changes. The mean color change observed for all groups overall during T₂B (ΔE = 3.51) was significantly greater than that observed during T₁B (ΔE = 2.10). All groups exhibited greater color change for the b* values when compared to the a* and L* values. There were no significant differences between the disinfectant groups. It can be concluded that the time period of disinfection and storage significantly affected the stability of artificial iris color, independent of the disinfectant used. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 56–62, 2014