multivariate curve resolution as a pretreatment step in multivariate analysis of variance

In analytical chemistry, it is common to study the effect of changes in one or more factors (liketemperature or type of catalyst) on a measured response for several possible purposes (likeoptimizing the yield of a reaction). usually, in such studies, data is collected on the basis of anexperimental design to guarantee that it will contain the information targeted by the study. analysisof variance (anova) is a statistical model that aims to decompose the total response variation totest the significance of each factor effect. anova is utilized to determine the effect of the studiedfactors, as well as, to quantify the effect of different levels of each factor [1]. with the advancementof technology, it is possible to study the effect of the desired factors on multiple responses. as aresult, anova, due to its univariate nature, is not applicable to these datasets. anovasimultaneous component analysis (asca), which is an extension of anova, is applicable in suchcases [2]. in a dataset coming from an experimentally designed study, various chemicalcomponents can be influenced by the factors under investigation. anova based methodsdecompose the dataset into matrices that represent the effects of each factor and their potentialinteractions, allowing for the examination of the effect of factors. however, in this decomposition,the effects of factors on the chemical components cannot be examined separately. since the studiedfactors may have different effects on chemical components, investigating the effects of each factoron the chemical components can be crucial in various studies like metabolomics or food industry.mcr-asca approach has been proposed in this work to achieve the above-mentioned goal. inthis approach, data is decomposed into the contributions of chemical components, and then thecontribution of each component is decomposed with asca model for obtaining furtherinformation about the desired components. several simulated and experimental data sets areapplied for illustrating the advantages of applying this approach.