Chemometrics in foodomics: Handling data structures from multiple analytical platforms

Abstract Foodomics studies are normally concerned with multifactorial problems and it makes good sense to explore and to measure the same samples on complementary, synergistic analytical platforms that comprise multifactorial sensors and separation methods. However, the challenge of exploring, extracting and describing the data increases exponentially. Moreover, the risk of becoming flooded with non-informative data increases concomitantly. Acquisition of data from different analytical platforms provides opportunities for checking the validity of the data, comparing analytical platforms and ensuring proper data (pre)processing – all in the context of correlation studies. We provide practical and pragmatic tools to validate and to deal advantageously with data from more than one analytical platform. We emphasize the need for complementary correlation studies within and between blocks of data to ensure proper data handling, interpretation and dissemination. Correlation studies are a preliminary step prior to multivariate data analysis or as an introduction to more advanced multi-block methods.