Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective

Background: The development of statistical software has enabled food scientists to perform a wide variety of mathematical/statistical analyses and solve problems. Therefore, not only sophisticated analytical methods but also the application of multivariate statistical methods have increased considerably. Herein, principal component analysis (PCA) and hierarchical cluster analysis (HCA) are the most widely used tools to explore similarities and hidden patterns among samples where relationship on data and grouping are until unclear. Usually, larger chemical data sets, bioactive compounds and functional properties are the target of these methodologies. Scope and approach: In this article, we criticize these methods when correlation analysis should be calculated and results analyzed. Key findings and conclusions: The use of PCA and HCA in food chemistry studies has increased because the results are easy to interpret and discuss. However, their indiscriminate use to assess the association between bioactive compounds and in vitro functional properties is criticized as they provide a qualitative view of the data. When appropriate, one should bear in mind that the correlation between the content of chemical compounds and bioactivity could be duly discussed using correlation coefficients.