From design of experiments to analysis of variance of multivariate data: a tutorial review on ANOVA simultaneous component analysis

TL;DR

This paper reviews ANOVA Simultaneous Component Analysis (ASCA), a leading chemometric method for analyzing high-dimensional experimental data from Design of Experiments, providing best practices and illustrative examples.

Contribution

It offers a comprehensive tutorial review with practical recommendations for using ASCA, grounded in literature and exemplified through a guiding case.

Findings

ASCA effectively analyzes high-dimensional DoE data.

Best practices improve the reliability of ASCA results.

The review consolidates theoretical and practical insights for chemometric analysis.

Abstract

ANOVA Simultaneous Component Analysis (ASCA) is the current state-of-theart chemometric tool for analyzing and interpreting high-dimensional experimental data from a Design of Experiment (DoE). Being a multivariate extension of the ANOVA, ASCA makes a perfect tandem with DoE. This tutorial review recommends best practices for using ASCA, building upon the long-established combination of ANOVA and DoE theory developed over the last century. These recommendations are grounded in a comprehensive literature review and illustrated through a guiding example.