Colour matching by principal component analysis‐based spectrophotometric technique

One of the most important aspects of the physics of colour is colour matching and recipe prediction. In this work, a new method is presented to evaluate the concentration of dyes in textile fabrics. The algorithm of the new technique is based on principal component analysis and single constant spectrophotometric matching methods. Using the normal spectrophotometric matching method, match prediction is carried out at three and 16 wavelengths. With the new algorithm, 3, 6, 9, 12 and 16 principal components are used in spectral match prediction. The performance of the new method improves with increasing numbers of principal components. In addition, the recipe prediction accuracy of the new method with three principal components is better than spectrophotometric matching at three wavelengths. The accuracy of principal component analysis–spectrophotometry with 16 principal components is comparable with the normal spectrophotometric matching method at 16 wavelengths.     

A New matching strategy: Trial of the principal component coordinates

A new matching strategy based on the equalization of the first three principal component coordinates of sample and target in a 3D eigenvector space is stated. Two series of databases including 1269 specimens of Munsell Color Book and a virtual sample population of textile materials were selected. Their first three basis functions were extracted and considered as axes of eigenvector space. The principal component coordinates of two different collections of textile samples were determined in these spaces and considered as matching criteria. The performance of the proposed algorithm is evaluated by the color difference values under different light sources as well as the root mean square differences of reflectance curves. Results indicate some types of improvements in comparison with previous algorithms in terms of spectral as well as colorimetric accuracy. © 2007 Wiley Periodicals, Inc. Col Res Appl, 33, 10–18, 2008     

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.     

Reconstruction of reflectance spectra using weighted principal component analysis

The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values. A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors. Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference. The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases. The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 360–371, 2008     

The effect of unit k/s calculation and spectral overlapping on the performance of color matching

There are several approaches to dyeing recipe formulation in the textile industry, because the accuracy of computer recipe prediction is not very high. There are several theories such as colorimetric and spectrophotometeric appraches on color reception. This work addressed the effect of the calibration method as the calculation of unit k/s and spectral overlapping of the components of the mixture on the accuracy of recipe prediction in dyeing nylon with acid dye. To this aim, the unit k/s was calculated using single and binary mixture samples. Two types unit k/s were used for the recipe prediction of ternary mixture dyed samples with low and high overlapping reflectance spectra. The obtained results indicated that the unit k/s of single component differed from the unit k/s of bicomponent mixture. The accuracy of recipe prediction using unit k/s of single component was less than that of the binary mixture. However, the performance of the recipe prediction of high level overlapping was found to be more than that of the low level overlapping mixture. Subsequently, the accuracy of color matching depended on the reflectance spectra similarity (spectral overlapping) of the components of the mixture.     

Recovering spectral reflectance based on natural neighbor interpolation with model‐based metameric spectra of extreme points

In this article, we proposed a new method based on natural neighbor interpolation to recover the spectral reflectance of objects from an image captured by a traditional Red‐Green‐Blue (RGB) digital camera. The concept of model‐based metameric spectra of eight extreme points in the standard RGB (sRGB) color gamut was further introduced to ensure that almost all test samples in the entire gamut can be simply and properly recovered without needing the extrapolation or any other auxiliary techniques. The quasi‐Newton method was used to estimate iteratively the optimal parameters of these metameric spectra, satisfying the constraints of the gamut extreme points. Several experiments were performed. The effectiveness of the method with and without the metameric spectra was evaluated, including some performance comparisons with the principal component analysis (PCA) method of transformational type (classic PCA and weighted PCA) and that of interpolation type. The results showed that the proposed method greatly enlarged the accurately applicable domain of the interpolation strategy and offered spectra with feasibility and naturalness much superior to the PCA‐based methods. The proposed method was obviously better than the conventional interpolation ones, and had a similar performance with the weighted PCA method in terms of color difference.     

Generation of virtual illuminants for a balanced colorimetric match

Colorimetric matching is employed under a series of virtual illuminants to produce more controllable and equalised colour‐difference results under multiple illuminants. A method based on weighted principal component analysis of different artificial lights and the weighted mean is used to create a series of virtual illuminants. The weights were selected in a manner to create the virtual illuminants that benefit from the impact of a given illuminant, while the effects of other light sources on the formation of desired illuminants were also considered. The generated virtual illuminants were implemented in a colorimetric colour‐matching trial. Using this method, by choosing suitable weights for different lighting conditions, more balanced colour‐difference values were presented in the data set than was expected. The advantages of the suggested method were evaluated by the matching of a collection of 135 woollen samples under different virtual illuminants.     

A novel method for determination of compatibility of dyes by means of principal component analysis

A novel method for determination of the compatibility of dyes in mixtures based on the application of principal component analysis is presented. The well known dip‐test method is used to dye samples in different binary combinations of cationic dyestuffs. The spectral reflectance of different samples of each mixture that dyed with a given set of dyestuffs by dip‐test method has been measured and the corresponding K/S values are calculated. The actual dimensional properties of each mixture are evaluated by using principal component analysis technique and determination of cumulative percentage variance of the eigenvalues of proposed datasets. Ideally, the K/S spectral data of fully compatible pairs scatter around one dimension, while proportional to the degree of incompatibility of dyes in the mixture, other dimensions should be taken into account and cannot be ignored. Strong correlations are found between the calculated percentage variance and the traditional compatibility values of dyes shown by K value for cationic dyestuffs. The validity of suggested technique is also reconfirmed by normalization of spectral K/S data obtained from different dye sets. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Non–destructive near–infra–red analysis for the identification of dyes on textiles

A pattern–recognition algorithm combined with near–infra–red reflectance spectroscopy has been modified to function as a non–destructive analysis technique for identifying dyes present on textiles. Samples of 261 dyes and textiles were measured in the 1100–2500 nm region to form a near–infrared (reflectance) spectral library. Principal component analysis (PCA) was used to generate an orthonormal reference library from the library of original spectra. The PCA algorithm treats the spectra in the library as an n component quantitative analysis problem in which each spectrum represents a standard mixture having a concentration of 1. 0 for that component. Spectra of dyed textiles were used as an unknown set in a library search. This new method saves time and materials in comparison with traditional methods of analysing dyes present on textile fibres. The library of dye spectra can be developed from measurements made directly on dye powder without interference from inorganic diluents. The method was successfully used to identify the dyes present on five textiles. The technique is particularly well suited for studying forensic, historic and archaeological textiles because of its non–destructive nature and ability to analyse small amounts of sample.     

基于加权统计局部核主元分析的非线性化工过程微小故障诊断方法

传统统计局部核主元分析（statistical local kernel principal component analysis, SLKPCA）在构造改进残差时未考虑样本的差异性,使得故障样本信息易于被其他样本所掩盖,针对该问题,提出一种基于加权统计局部核主元分析（weighted statistical local kernel principal component analysis, WSLKPCA）的非线性化工过程微小故障诊断方法。该方法首先利用KPCA获取过程的得分向量和特征值并构建初始残差。然后设计了一种基于测试样本与训练样本之间距离的加权策略构建加权改进残差,对含有较强微小故障信息的样本赋予较大权值,以增强故障样本的影响。最后,采用基于测量变量与监控统计量之间的加权互信息构建贡献图以识别故障源变量。在连续搅拌反应釜和田纳西伊斯曼（Tennessee Eastman, TE）化工过程上的仿真结果表明,所提方法具有良好的微小故障检测与识别性能。     

涤纶织物用分散染料的染色相容性的探讨

通过对五支分散染料的上染速率曲线、提升力曲线及色光变化分析,结合双拼色实验结果分析,结果表明,分散红3B、分散黄5GL和分散蓝2BLN组合在一起竭染时相容好。     

Feasibility of using digital color imaging devices for the determination of cationic dyes compatibility

In the present research, the compatibility of dyes was evaluated using a scanner by the application of the method proposed by Khalili and Amirshahi, which had been proved as a novel and accurate method for evaluating the compatibility behavior of dye mixtures. A dip‐test method was employed to dye samples, compatibility panels, in binary combinations of cationic dyes on acrylic fibers. In order to use the scanner, first the device was colorimetrically characterized using a regression technique. The tristimulus values obtained from the scanner were, then, used for the reconstruction of the reflectance of the compatibility panels by principal component analysis. Next, the reconstructed reflectance of the panels were transferred to the corresponding K/S spectra and the Khalili and Amirshahi proposed method applied to the spectra in order to obtain the percentage variance (PV), which is the criterion of the dye compatibility. The comparison between the PVs obtained from the scanner and the corresponding one calculated by the spectrophotometrically measured reflectance of the compatibility panels showed a very significant correlation of the compatibility results regarding Pearson correlation coefficients and their K values. It was concluded that due to the smoothness of the reconstructed spectra and the performance of the PCA method of the reconstruction, which manifest itself in the good spectral reconstruction, the scanner method joined with the Khalili and Amirshahi proposed method can reliably be used for the determination of dye mixtures compatibility by a dip test. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 337–345, 2017     

采用KPCA特征提取的近红外煤炭发热量预测模型

近红外光谱分析技术通过搭建基于GA-BP神经网络方法的定量分析模型,实现煤炭发热量的快速评估。为了提高模型的学习速度和精度,必须对光谱信息进行数据处理。该过程属于复杂的非线性问题,经典的线性主成分分析方法具有一定的局限性,因此采用了一种基于多项式核主成分分析特征提取方法。通过分析主成分的特征值筛选异常样本。实验结果表明,该方法提取的特征信息主成分集中度高、降维效果明显、与输出变量间的相关性好,且能够准确判断出异常样本,大幅度提高了模型的准确性,为近红外煤质分析模型提供了一种分析速度快、准确率高的有效数据处理方法。     

基于WPRKPCA的非线性化工过程微小故障检测

传统核主元分析法（KPCA）是一种广泛应用的非线性化工过程故障检测方法,但是其未充分利用过程数据的概率分布信息,往往难以有效检测过程中的微小故障。针对传统KPCA方法的局限性,本文提出了一种基于加权概率相关核主元分析（WPRKPCA）的非线性化工过程微小故障检测方法。与传统KPCA方法监控核成分的变化不同,该方法利用Kullback Leibler散度（KLD）度量核成分的概率分布变化,进而建立基于KLD成分的统计监控模型,以充分挖掘过程数据所包含的概率信息。进一步考虑到不同KLD成分承载故障信息的差异性,该方法设计了一种基于核密度估计的指数加权策略,根据KLD成分描述故障信息程度的差异分配相应的权值,以加强监控模型对微小故障检测的灵敏性。在一个数值例子和连续搅拌反应器（CSTR）系统上的仿真结果表明,本文所提方法具有比传统KPCA方法更好的微小故障检测性能。     

基于KPLS模型的间歇过程产品质量控制

针对间歇过程所具有的非线性特性,提出了一种基于核偏最小二乘(KPLS)模型的最终产品质量控制策略。利用初始条件、批次展开后的过程数据以及最终产品质量建立了间歇过程的KPLS模型;采用基于主成分分析(PCA)映射的预估方法对未知的过程数据进行补充,实现了最终产品质量的在线预测。为了解决最终产品质量的控制,利用T2统计量确定KPLS模型的适用范围,并作为约束引入产品质量控制问题,提高控制策略的可行性;采用粒子群优化(PSO)算法实现了优化问题的高效求解。仿真结果表明,与基于偏最小二乘(PLS)模型的控制策略相比,所提出的方法具有更高的预测精度,且能有效解决产品质量控制中出现的各种问题。     

A contribution to the study of color of fabrics

An expansion of the Allen and Goldfinger approach to the prediction of the color of absorbing-scattering substrates is carried out taking into account the effect of optical anisotropy of fibers and definite values of the indices of refraction in the visible spectral range. Using this approach and a computer program the spectral reflectance curves, based on the shape of the spectral transmittance curves of the dye-fiber polymer and Cauchy's dispersion formula, are calculated for an array of dyed nylon fibers. Also, the spectral reflectance curves for the same dyed fiber is calculated using Allen and Goldfinger model. Both spectra are compared with those experimentally measured. Spectra for dyed fibers with different dye concentrations are calculated.     

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     

立方体溴化银颗粒光谱增感的电子转移超快动力学研究

本文利用皮秒时间分辨条纹相机技术检测了3种染料在立方颗粒溴化银上吸附后形成聚集体的荧光光谱,分析了3种染料在不同染料浓度下对染料聚集体到溴化银导带的超快电子转移过程的影响,进而分析其对增感效率的影响关系,并探讨了增感过程的微观机理.实验结果表明,荧光衰减的动力学曲线与一个双指数函数拟合得相当好,存在一快一慢两个衰减成分,快衰减成分占拟合较大比例,表明其源于与荧光衰减相竞争的从激发态染料聚集体到AgBr导带的电子转移.光致电子转移的速率及增感效率随着染料相对浓度的增加表现出一定的变化趋势,染料浓度增加,增感效率减小.     

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.     

Neuro–fuzzy modelling of spectroscopic data. Part B – Dye concentration prediction

An adaptive network–based fuzzy inference system, ANFIS, has been used for predicting dye concentrations using spectroscopic absorbance data in the visible region. The samples were two–component red/yellow dye solutions with a concentration range of 0–900 mg/l for the one component (red) while the concentration of the other component (yellow) was kept constant. The modelled system had two inputs (wavelength and absorbance) with the concentration values as output. Generalised bell–shaped membership functions were used for the inputs. The inference system used was a first–order Sugeno fuzzy model. The ANFIS models gave concentration prediction results with approximately the same standard error of prediction as artificial neural network (ANN) models. However, the ANFIS model building runs faster than in the case of ANN.