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.     

Effect of classification by competitive neural network on reconstruction of reflectance spectra using principal component analysis

The best way to describe a color is to study its reflectance spectrum, which provide the most useful information. Different methods were purposed for reflectance spectra reconstruction from CIE tristimulus values such as principal components analysis. In this study, the training samples were first divided into 3, 6, 9, and 12 subgroups by creating a competitive neural network. To do that, L*a*b*, L*C*h or L*a*b*C*h were introduced to neural network as input elements. In order to investigate the performance of reflectance spectra reconstruction, the color difference and RMS between actual and reconstructed data were obtained. The reconstruction of reflectance spectra were improved by using a six or nine‐neuron layer with L*a*b* input elements. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 182–188, 2017     

Methods of selecting a small reflectance set as a transfer standard for correcting spectrophotometers

The present article concerns the use of software and transfer standards of reflectance to correct a fielded spectrophotometer so that it behaves closely like a reference instrument. A method is described to choose from a large set of reflectances the best subset of a few reflectances to act as a transfer standard. A reflectance set is generated from the algorithm using each of two alternative metrics for instrument closeness: CIELAB ΔE* and a weighted sum of absolute differences over wavelength. Both metrics yield transfer standards that conspicuously exclude BCRA reflectances and also show improvement over the BCRA reflectances currently used for this purpose. © 2005 Wiley Periodicals, Inc. Col Res Appl, 31, 13–17, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20169     

Reconstruction of reflectance data by modification of Berns' Gaussian method

Berns' method for the synthesis of spectral reflectance curve from the tristimulus color coordinates is modified. Firstly, the Gaussian bell shape red primary is replaced with a sigmoidal one to solve the dissimilarity between the spectral curves at the end region of spectrum. Secondly, three predetermined Gaussian primaries used in the original Berns' method are replaced by the adaptive ones which their half‐height bandwidths vary with the tristimulus values of the desired color. The mentioned modifications are applied for the recovery of the reflectance curves of 1409 surface colors (including 1269 Munsell color chips and 140 samples of Colorchecker SG) and also 204 textile samples. Results of recovery are evaluated by the mean and the maximum color difference values under other standard light sources. The mean as well as the maximum of root mean squares between the reconstructed and the actual spectra are also calculated. The modifications are compared with the common principal component analysis (PCA) as well as Hawkyard's methods for recovery of reflectance factor. Although the PCA leads to the best results, the modifications significantly improve the recovery outcomes in comparison with the original Berns method. © 2008 Wiley Periodicals, Inc. Col Res Appl, 34, 26–32, 2009.     

Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi‐illuminants

A new spectral reflectance estimation method based on CIE XYZ values under multi‐illuminants was proposed to obtain multi‐spectral images accurately by using digital still cameras. CIE XYZ values under multi‐illuminants were initially predicted from raw RGB responses by using a polynomial model with local training samples. Then, spectral reflectance was constructed from the predicted CIE XYZ values via the pseudo‐inverse method. Experimental results indicated that the new spectral reflectance estimation method significantly outperformed the traditional colorimetric characterization method without requiring extra training samples or greatly increasing computational complexities. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 68–77, 2017     

Calibration of NIRS for the Estimation of Fatty Acids in Brassica Juncea

This study was done to test whether near infrared reflectance spectroscopy (NIRS) could be used as a quick substitute for measuring quality characteristics, i.e., a fatty acid profile over gas liquid chromatography. Near infrared reflectance spectroscopy is a multi‐trait technique. In the present study, the fatty acid profiles of seeds of 200 genotypes of Brassica juncea were analysed by gas liquid chromatography. Near infrared reflectance spectra of intact seeds of the same samples were collected (400–2500 nm) on a NIR systems Model 6500 spectrophotometer. The spectra were subjected to scanning, mathematical processing, and statistical analysis using Win ISI software. Data were scored to remove redundancy. Spectra were treated as outliers with H > 3.0 (global H) and similar samples with H < 0.6 (neighbourhood H). Cross validation of the spectra was done using a modified partial least‐square method to develop the calibration equation. The calibration equation had good R² values for oleic acid (R² = 0.91), linoleic acid (R² = 0.83), and erucic acid (R² = 0.88). The internal validation was done to test the goodness of fit of the developed equation. The equation provided reliable estimations of these traits in internal validation with R² values of 0.77, 0.68, 0.81 for the above quoted fatty acids, respectively. The external validation results also showed higher R² values for oleic acid (0.89), linoleic acid (0.69), and erucic acid (0.90). The equation was less reliable for linolenic acid, which had R² values of 0.53 in cross validation, 0.25 for internal validation, and 0.20 for external validation. The results indicated that NIRS could be used to rapidly determine oleic acid, linoleic acid, and erucic acid in intact B. juncea seeds.     

Color change due to surface state modification

The dependence of color on the surface state of an object is calculated in the context of spectrophotometric measurements in a back‐scattering configuration. A modification of the surface roughness leads to a vertical shift of the reflectance spectrum. This translation is related, on a physical basis, to the relevant characteristic of the topography: the ratio h/l (ie, r.m.s. roughness/surface correlation length). Changes in L*a*b* colorimetric coordinates associated with this translation are computed. Finally, the color change ΔE is related to the surface state modification via the ratio h/l. Computations show that the color becomes lighter and less saturated when the surface becomes rougher. The color change is more important for dark or saturated initial objects, and also more important for a yellow surface than for a blue one. Finally, the minimum roughness modification that can induce a visible color change is determined. These results could be applied for industrial needs (quality control), or in the artistic field of conservation or restoration (to follow the color of paintings). © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 45–49, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Spectral power distributions for the CIE stimuli

The CIE reference colour stimuli, X, Y, and Z, were derived by constructing a triangle outside the R,G,B triangle and outside the area bounded by the spectrum locus and the purple line. By this means, all colours, including monochromatic ones, have positive tristimulus values. The colour‐matching functions are the relative quantities of these stimuli required to be mixed additively to match the equal energy monochromatic colours. The stimuli are not realizable as light sources, and the CIE has not specified their spectral power distributions. There is an infinite number of spectral power distributions whose properties meet the prerequisites for X (X = 100, Y = 0, Z = 0), Y (0, 100, 0), and Z (0, 0, 100), and two possible sets have been calculated by different methods. These curves could be used as primary red, green, and blue lights in additive mixing to produce synthetic reflectance curves, which are useful in the specification of on‐screen colours, and as a means of producing colour constant standards. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 478–482, 2001     

Nondestructive Estimation of Fat Constituents of Sesame (Sesamum indicum L.) Seeds by Near‐Infrared Reflectance Spectroscopy

A rapid and efficient method for oil constituent estimation in intact sesame seeds was developed through near‐infrared reflectance spectroscopy (NIRS) and was used to evaluate a sesame germplasm collection conserved in China. A total of 342 samples were scanned by reflectance NIR in a range of 950–1650 nm, and the reference values for oil content and fatty acid (FA) profiles were measured by Soxhlet and gas chromatograph methods. Useful chemometric models were developed using partial least squares regression with full cross‐validation. The equations had low standard errors of cross‐validation, and high coefficient of determination of cross‐validation (R_c²) values (>0.8) except for stearic acid (0.794). In external validation, r² values of oil and FA composition equations ranged from 0.815 (arachidonic acid) to 0.877 (linoleic acid). The relative predictive determinant (RPD_v) values for all equations were more than 2.0. The whole‐seed NIR spectroscopy equations for oil content and FA profiles can be used for sesame seed quality rapid evaluation. The background information of the 4399 germplasm resources and accessions with high linoleic acid content identified in this study should be useful for developing new sesame cultivars with desirable FA compositions in future breeding programs.     

Prediction of the colorimetric parameters and mass loss of heat‐treated bamboo: Comparison of multiple linear regression and artificial neural network method

In this study, the colorimetric parameters (L*, a*, b*) and mass loss of heat‐treated bamboo were investigated, and the obtained results were modeled by using two methods: multiple linear regression (MLR) and artificial neural network (ANN). First, bamboo samples were exposed to heat treatment at different temperatures (110°C, 140°C, 170°C, and 200°C) and durations (15, 30, 45, 60, 75, 90, and 115 minutes) in a laboratory oven. Then, the colorimetric parameters (L*, a*, b*) and mass loss of each sample were measured after each period of heat treatment. All data were modeled by using two methods separately for each parameter and the performances of these proposed methods were compared. It was found that color change and mass loss increased with increasing temperature and duration of heat treatment. Mean absolute percentage error (MAPE) values of all obtained MLR ranged from 0.64% to 10.63%, while the all MAPE values of ANN were found to be lower than 1.5%. Based on these results, it can be said that MLR and ANN could be used to evaluate the changes on the selected properties of heat‐treated bamboo samples. On the other hand, it should be emphasized that the ANN gave more accurate results than the MLR method because of its learning capability.     

Numerical methods for smoothest reflectance reconstruction

Three numerical methods are presented for finding the smoothest reflectance curve associated with a given triplet of tristimulus values. The methods differ in how “smooth” is defined, and also differ in the domain of colors over which they are applicable. The first method is very quick and applies to any tristimulus values, but sometimes can yield reflectance curves with portions that fall outside the range 0 to 1. The second method applies to colors within the spectral locus (real colors) and guarantees that the reflectances produced are positive. The third method applies to colors within the object color solid (object colors) and guarantees that the reflectances fall within the range 0 to 1. The methods are shown to create reflectances that closely resemble those of real colors (natural and synthetic). Focus is given to implementing the numerical methods in very short MATLAB/Octave functions and to understanding the numerical behavior of the methods near the limits of their respective domains of applicability in terms of matrix conditioning and discretization artifacts.     

A novel method for color determination of edible oils in L*a*b* format

A simple method that uses visible spectrophotometer data and an artificial neural network (ANN) was developed to determine edible oil color based on the L*a*b* format. The 100 oil samples consisted of nine pure oils, a sesame oil blend and three heated oils. Binary, ternary and quaternary mixtures of these 13 oils in different ratios were prepared, and absorbance values of the samples were measured in the visible region (380–700 nm). The absorbance values at wavelengths of 416, 456, 483, 537, 611 and 672 nm were used to train, validate and test the network. Strong correlations between the instrumental L*a*b*ΔE and the estimated L*a*b*ΔE were found for the test samples, with correlation coefficients (R²) of 0.989, 0.984, 0.996 and 0.992 for L*, a*, b*, and ΔE, respectively. The effects of number and combination of the wavelengths used for training of the ANN on the estimation capability of the network for the test samples were also investigated. Although a good agreement, average R² of 0.991– 0 993 for L*a*b*, was obtained for combinations composed of three to six wavelengths with 483 and 537 nm in common, the best R² value was obtained when all six wavelengths were used to train the ANN. The developed method is objective, cost effective and simple, and allows the color measurement with a basic visible spectrophotometer and disposable cuvettes.     

A hybrid adaptation strategy for reconstruction reflectance based on the given tristimulus values

Recently, Cao et al proposed an adaptive weighting method for the training samples for reflectance reconstruction according to both colorimetric and spectral reflectance similarities for a given vector defined by tristimulus values. It was shown the Cao et al method outperforms the other methods including the regression estimation method in terms of multiple evaluation criteria. In this article, motivated by the work of Cao et al, a hybrid weight is introduced, which results in the size of the training samples selected is half of that used by the Cao et al method. Simulation results showed that the proposed method performs equally well as or slightly better than the Cao et al method, but uses less central processing unit time than that used by the Cao et al method. It was also found that about 100 training samples selected is good enough for the proposed method.     

A non‐PC look at principal components

Two principal‐component methods are used in color science. For a given data set of spectra, one method finds the best‐fitting subspace about the mean spectrum, and the other finds the best‐fitting subspace about the zero spectrum. The first of these was originally developed for illuminants and the second for reflectance analysis. Yet there seems to be no strong argument for choosing one method over the other, in either case. Hence it is urged that each of us declares which one we are using, even if making that discrimination is considered “non‐PC” (i.e., not “politically correct”). © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 69–71, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Improving the Hawkyard method for generating reflectance functions

An iterative method was developed by Hawkyard in 1993 for generating reflectance functions, based on a given set of tristimulus values. In a recent article by Dupont (Col Res Appl 2002;27:88–99), many methods for generating reflectance functions were compared, and it was shown that the Hawkyard method is one of the best methods. However, one of the weak points of the Hawkyard method is its iterative nature. In addition, one important issue for the Hawkyard method is its convergence, which has not been addressed. In this article, this issue is examined. The necessary and sufficient condition to achieve convergence, using the Hawkyard method, is given. The method is then modified to make it an analytical method. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 283–287, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20126     

Rapid Determination of Total Polar Compounds in Frying Oil Using ATR‐FTIR Combined with Extended Partial Least Squares Regression

A method of rapidly determining the total polar compounds (TPCs) in frying oils using attenuated total reflectance‐Fourier transform infrared spectroscopy combined with partial least squares (PLS) regression is developed. Oils of various types and geographic origins are used to ensure that the proposed model is robust. The first derivative spectrum is selected as the spectral processing method. The interval PLS, forward interval PLS, and backward interval PLS algorithms are compared in terms of their performance. A correlation coefficient (R²) of 0.9942, a root mean square error of calibration (RMSEC) of 1.1, a root mean square error of prediction (RMSEP) of 2.30, a residual predictive deviation (RPD) of 4.1, and a limit of detection (LOD) of 1.65% are obtained by the fiPLS33 model with fewer latent variables and a lower spectral interval number. In addition, sub‐models using a single type of oil showed higher performance (R² 0.9957–0.9998, RMSEC 0.12–0.92, RMSEP 0.79–1.58, RPD 4.79–9.64, LOD 0.66–1.26%) than the general model. The TPC models developed are accurate, stable, and adaptable, and they can be used to analyze general frying oil samples quickly, regardless of the oil type, and to analyze samples of specific oil types accurately. Practical applications: The content of TPCs is an important indicator of whether the oil has been overused and whether it will be harmful during the frying process. However, traditional chemical methods are time‐consuming, and they have not been used to determine large‐sized samples. In addition, due to a lack of regional optimization, most studies on determining TPCs with FTIR give unsatisfactory model performance. A general TPC model that incorporates several oil types and regional optimization is expected to improve prediction performance. Therefore, the proposed method represents a rapid and accurate tool for measuring TPCs in edible fats and oils.     

A novel analysis of color component for top dyed melange yarn with support vector machine

In view of the difficulty of distinguishing the color component in top dyed melange yarn due to the spectral overlap of the component colors, a novel color component analysis method based on support vector machine is presented. With this method, spectra data can be distinguished more accurately and effectively than with the traditional method—human‐eye detection—and therefore, the method will be very helpful for accurate color matching. In our work, the core idea was to convert the overlapped spectra data into linearly separable ones in a high dimension space, followed by recognition and determination of the composition of melange yarn by trained support vector machine classifier. The effects of four kernel functions, i.e., linear, radial basis kernel, sigmoid, and polynomial, as well as five spectral preprocessing methods, including amplification, first derivative, second derivative, principal components analysis, and L*a*b* values were studied. The results demonstrated that with the amplification factor of 100 of reflectance spectra coupled with L*a*b* as input data, and using radial basis kernel as kernel function, the highest recognition rate was achieved, with an average recognition rate of eight colors of 96.5%, indicating that it was a better color component analysis method for top dyed melange yarn. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 636–641, 2016     

Modeling color change after spinning process using feedforward neural networks

This article is concerned with the reflectance spectra prediction based on a neural network developed for yarn from the roving reflectance spectra. The neural network developed is a multilayer feed‐forward network. The first system is wavelength dependent, but its performance is not very satisfactory. The scaled conjugate gradient algorithm is incorporated into the backpropagation procedure to reduce the training phase. Once the wavelength independence of the transformation is established, a second system, whose performances agree with the experimental curves, is proposed. Finally, this system is completed by the introduction of a yarn parameter: the count. The results of this later model are quite promising. © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 50–58, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

New biomaterial based on cotton with incorporated Biomolecules

The aim of this study was to investigate a method of embedding l ‐cysteine (l ‐cys), an antimicrobial agent, between layers of chitosan (CH) and sodium alginate (ALG) onto cotton samples obtained via a layer‐by‐layer electrostatic deposition technique via several embedding methods. The results show that the best way to incorporate l ‐cys into the layers was the one that used the property of gelling ALG. To monitor the l ‐cys embedding into the CH/ALG multilayer film, different methods were used: energy‐dispersive X‐ray spectrometry analysis to assess the presence of sulfur on the sample, Ellman's reagent method to analyze l ‐cys release from the sample, and attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FTIR) to compare the ATR–FTIR spectra of the pure l ‐cys and l ‐cys embedded in the CH/ALG multilayer film to study the interaction between the l ‐cys and the CH/ALG multilayer films. Functionalized CH/ALG cotton samples were also investigated for their antibacterial properties toward Staphylococcus aureus and Klebsiella pneumonia with the Japanese Industrial Standard method JIS L 1902:2002, and the results show an enhancement of the antibacterial effect due to the presence of l ‐cys. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40519.