Efficiently handling high‐dimensional data from multifactorial designs with unequal group sizes using Rebalanced ASCA (RASCA).

A novel chemometric approach is proposed to analyze high‐dimensional data from unbalanced designs of experiments. It combines a rebalancing strategy based on averages with the ASCA method under the name Rebalanced ASCA (RASCA). The ability of RASCA to handle unbalanced designs was compared with standard ASCA, as well as state‐of‐the‐art methods, such as ASCA+ and WE‐ASCA. For that purpose, a controlled framework was designed to provide a systematic comparison of the various approaches. It included two real datasets obtained from initially balanced designs, which were gradually unbalanced by removing observations belonging to specific combinations of factor levels. The results illustrate that all methods considered led to identical solutions when the initial balanced design was kept. Nevertheless, increasing differences appeared when the design was gradually unbalanced. The proposed benchmark showed that RASCA and ASCA+ provided overall similar results for all effects with high agreement with the balanced solutions in comparison to classical ASCA and WE‐ASCA. RASCA was found to be a suitable chemometric tool to tackle unbalanced designs by ensuring unbiased parameter estimators with the added benefit of producing rigorously orthogonal effect matrices, thus facilitating interpretation. [ABSTRACT FROM AUTHOR]