Effects of color on expectations of drug effects: A cross‐gender cross‐cultural study

To study the role of color in expectations of drug effects, 80 Chinese participants (40 females and 40 males) were asked to classify each of seven single colored capsules and six differently colored two‐piece capsules into one of four classifications of drug effects. The results from the Chinese sample were also compared with that from four other cultural groups studied elsewhere. The Chi‐square test results showed that all seven single colored capsules yielded non‐chance distributions in classifications of drug effects, with six showing specific effects; and that five two‐colored capsules had non‐chance distributions, with four significantly associated with specific effects. Notable gender differences were observed in the expectations of drug effects. While the cross‐group comparison revealed consistent red‐stimulant and blue‐depressant associations across the five cultural groups, disagreements existed for other colors among the groups. The findings emphasized the importance of color in drug design and administration in support of drug differentiation, medication adherence, and drug efficacy, and suggest gender and cultural implications on the basis of color to achieve better drug effects. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 124–130, 2017     

Effects of hue,saturation, and brightness on color preference in social networks: Gender‐based color preference on the social networking site Twitter

Account information for over 1 million Twitter users was downloaded and analyzed to determine color preference. Blues were found to be the most preferred color, whereas greens were least preferred. Distinct gender‐specific differences were found. Males preferred blues to a greater extent than females, whereas females preferred magentas to a much greater extent than males. Males preferred darker colors to a greater extent than women. Density plots within hue, saturation, and brightness space summarize the distribution of color choices and illustrate color preferences for both males and females. © 2011 Wiley Periodicals, Inc. Col Res Appl, 38, 196–202, 2013.     

Comparative studies on color preference in Japan and other Asian regions,with special emphasis on the preference for white

The results of three surveys are presented. The first survey was carried out in four large cities in Japan, and the findings were analyzed by factor analysis and cluster analysis. The second survey was carried out in Seoul, Korea and Tokyo, Japan to determine color preference in the two countries, focusing on the preference for white. The last survey compared color preference in Taipei and Tokyo, also with emphasis on the preference for white. In these successive studies on color preference in Japan and other Asian cities, the subjects were mainly asked to choose from a color chart the three colors they liked most and the three they liked least, and to state the reasons for their choices. The results of Survey 1 showed that color preference could be influenced by differences in age, sex, and geographical region. Also factor analysis and cluster analysis indicated some relation between color preference and the subjects' life styles. Dual scaling analysis of the results of Surveys 2 and 3 indicated that each Asian area has unique color preference tendencies and that there are statistically significant differences in the frequency of selection of colors of certain hues and tones. However, a high preference for white was common to all areas, along with preferences for some other colors. These results thus demonstrated a common strong preference for white in three neighboring Asian areas. The reasons given for the choices suggested that besides the factors of age and sex, associative images based on environmental and cultural aspects may be an important factor influencing color preference. © 1996 John Wiley & Sons, Inc.     

Hue,chroma, and lightness preference in Chinese adults: Age and gender differences

Recent studies have shown cultural differences in color preference. However, the color preference of people in China, which was found to have its own pattern, was yet to be studied in depth. The current study investigated color preference and the associated age and gender differences in an adult national sample (N = 1290) to provide a culture‐specific characteristic of color perception. Participants rated how much they liked each of 31 colors (four chroma‐lightness levels of red, orange, yellow, green, cyan, blue, and purple, plus three achromatic colors). We found a unique saturated color preference pattern characterized by red, cyan, and blue being preferred the most and orange as the least preferred chromatic color. The “red preference” phenomenon was observed in Chinese adults. Light colors were preferred the most in terms of chroma‐lightness level, followed by saturated, muted, and dark colors. The results of a principal component analysis of the 28 chromatic colors showed that blue‐green‐like colors (cool colors) constituted the largest proportion of color preference. The preference for orange and several dark colors increased with age, while that for bluish colors, purple, yellow, white, black, and light colors decreased. In terms of gender, women liked cyan, white, pink, and light colors and disliked red, orange, and dark colors more than men did. Our findings provide new empirical evidence about the color preference of Chinese and may offer some insight into the study of color preference and lay the foundations for future theoretical and practical research.     

Development of a comprehensive visual dataset based on a CIE blue color center: Assessment of color difference formulae using various statistical methods

The objectives of this work were to develop a comprehensive visual dataset around one CIE blue color center, NCSU‐B1, and to use the new dataset to test the performance of the major color difference formulae in this region of color space based on various statistical methods. The dataset comprised of 66 dyed polyester fabrics with small color differences ($\Delta E_{{\rm ab}}^* < 5$ ) around a CIE blue color center. The visual difference between each sample and the color center was assessed by 26 observers in three separate sittings using a modified AATCC gray scale and a total of 5148 assessments were obtained. The performance of CIELAB, CIE94, CMC(l:c), BFD(l:c), and CIEDE2000 (K_L:K_C:K_H) color difference formulae based on the blue dataset was evaluated at various K_L (or l) values using PF/3, conventional correlation coefficient (r), Spearman rank correlation coefficient (ρ) and the STRESS function. The optimum range for K_L (or l) was found to be 1–1.3 based on PF/3, 1.4–1.7 based on r, and 1–1.4 based on STRESS, and in these ranges the performances of CIEDE2000, CMC, BFD and CIE94 were not statistically different at the 95% confidence level. At K_L (or l) = 1, the performance of CIEDE2000 was statistically improved compared to CMC, CIE94 and CIELAB. Also, for NCSU‐B1, the difference in the performance of CMC (2:1) from the performance of CMC (1:1) was statistically insignificant at 95% confidence. The same result was obtained when the performance of all the weighted color difference formulae were compared for K_L (or l) 1 versus 2. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2011     

Applying metamer sets to investigate data dependency of principal component analysis method in recovery of spectral data

Principal component analysis (PCA) has been widely studied for reconstruction of spectral reflectance of a color sample from its tristimulus values. One of the most important factors that influences the recovery performance is the characteristic of the data set used for obtaining principal vectors. In this article, we investigated the influence of color similarities or color differences between the recovered and principal component (PC) data sets on the reconstruction error. For this purpose, two metamer sets that have similar color differences with the recovered samples, are used. The results show that two metamer sets can make completely different performance in recovery of specific color samples. It was shown that the most important factor that influences the recovery of spectral reflectance by PCA method is the characteristics of the data set used for obtaining PC vectors independent of the recovered samples. Another factor that influences the performance of PCA for spectral recovery is the characteristic of the sample that would be recovered. Some spectral data cannot be recovered precisely even applying different PC data sets. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

Bayesian analysis of color preferences: An application for product and product line design

When choosing which colors to offer in their product lines, firms often rely upon consumer preference models that do not account for the heterogeneity of their target market and do not consider the trade‐offs consumers are willing to make for different color options. For this research we used visual conjoint analysis to assess preference for backpack color and then modeled respondent utilities with a Bayesian hierarchal multinomial logit model. This provided counter intuitive results in which product line color options are not additive but each color changes depending on the number of options the firm is willing to offer and that colors which seem to dominate secondary preferences within a target market may not be the best colors to choose for product line expansion. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 445–456, 2016     

Apparel color preferences for different regions in China: The connection to personal values

This article examines the associations between personal values and apparel color preferences, and deduces the apparel color preferences of consumers based on the mainstream values in different regions in China. Clustering analysis was used to classify the values, and a Chi‐square test was used to verify whether the different values had a significant effect on the consumer's color preferences. Finally, a corresponding analysis was conducted to clarify the associations between personal values and apparel color preferences. The associations reported in the study suggest the apparel color preferences in the 4 major urban agglomerations in China. It was found that consumers in the Pearl River Delta region were mainly ideal‐oriented and authority value types and preferred darker apparel colors than other city groups in China; consumers in the Yangtze River Delta region were mainly responsibility‐oriented and justice value types and preferred warmer and more contrasting colors; and consumers in the Beijing–Tianjin–Hebei and Chengdu–Chongqing regions were mainly benevolence‐oriented value types, preferring more highly saturated and brighter colors than the other regions. Self‐oriented consumers, who preferred cool and dark apparel colors were found to have no clear correspondence to any region. These results are important for fashion designers and fashion brands in China; it can assist the Chinese fashion industry in regionalizing their product offerings and in providing a theoretical reference for the development of the garment industry.     

Hue,saturation, lightness,and building exterior preference: An empirical study in Turkey comparing architects' and nonarchitects' evaluative and cognitive judgments

This study investigated architects' and nonarchitects' evaluative and cognitive judgments of color on building exteriors. Thirty architects and 30 high school teachers living in Izmir, Turkey participated in the study. The experiment had two phases. First, participants viewed eight images, in which the color of a building exterior was manipulated with hues selected from HSB (hue, saturation, and brightness) color space. Participants were then asked to rate each image on 7‐point semantic differential scales measuring preference (like–dislike), arousal (arousing–sleepy), naturalness (natural–artificial), and relaxation (relaxing–distressing). Second, participants viewed the same building in nine saturation and lightness levels for each hue and picked the most preferred lightness and saturation level for each hue. Findings showed that for a building exterior: (1) yellow and blue were the most liked colors, (2) some hues were rated as more arousing, more natural, and more relaxing over the others, (3) gender had an effect on color preference and semantic ratings of naturalness and relaxation, (4) architects and nonarchitects differed in their color preference and semantic ratings of arousal and naturalness, and (5) full bright and moderate to low saturated colors and full saturated and moderate to high bright colors were preferred more. The results have practical implications for architects and urban designers. A successful coloration of a building exterior may increase its use frequency and economical value. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 395–405, 2008     

Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method

The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data‐driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c‐means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab‐scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods.     

Prediction of dye concentrations in a three‐component dye mixture solution by a PCA‐derivative spectrophotometry technique

Derivative spectrophotometry is one of the most important techniques that can be used to determine the dye concentration. In addition, principal component analysis (PCA) is a linear method to condense the dimensionality of large numbers of absorbance spectra. In this work, PCA and derivative spectrophotometry techniques are used to improve the accuracy of Beer's law prediction of the concentrations in three‐component dye mixtures. The performance of the new method is compared with the normal Beer's law by calculation absolute error, relative error, and ternary relative error of prediction. As obtained results indicate, the prediction accuracy of dye concentration prediction in PCA‐derivative spectrophotometry method is higher than normal Beer's law method. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.