Expanding Multivariate Analysis Principles in Conventional Chemometric Processes

Chemometrics encompasses numerous facets such as experimental design, data collection and analysis, and many others. This paper, in honor of Paul Geladi, provides our perspective on growing the scientific intuition of multivariate analysis in conventional chemometric directions not generally practicing multivariate principles. The motivation for this perspective is to express our opinion on the need for chemometrics to expand the role of the Rashomon effect beyond “many models predict well” by integrating a more comprehensive consideration of the multivariate nature of matrix effects. Described are multiple chemometric techniques that have already been enhanced by broadening the application the Rashomon effect including model selection and explanation, figures of merit (FOM), sample similarity assessment for model reliability, outlier detection, and classification—all recent research topics from the authors. This expository discussion revolves around spectroscopic data such as near infrared and fluorescence, but the concepts are relevant to other chemometric data structures.