Interpreting and Reporting Principal Component Analysis in Food Science Analysis and Beyond

Principal component analysis (PCA) is one of the most widely used data mining techniques in sciences and applied to a wide type of datasets (e.g. sensory, instrumental methods, chemical data). However, several questions and doubts on how to interpret and report the results are still asked every day from students and researchers. This brief communication is inspired in relation to those questions asked by colleagues and students. Please note that this article is a focus on the practical aspects, use and interpretation of the PCA to analyse multiple or varied data sets. In summary, the application of the PCA provides with two main elements, namely the scores and loadings. The scores provide with a location of the sample where the loadings indicate which variables are the most important to explain the trends in the grouping of samples.