Chemometric application in foodomics: Nutritional quality parameters evaluation in milk-based infant formula

Abstract

A method to verify the differentiating characteristics of milk-based infant formula is proposed in this work. In order to evaluate a classification of the milks according to their nutritional profile, the concentration of 24 elements were determinated. Supervised methods PCA-LDA, PLS-DA, and SIMCA were contrasted. PCA-LDA and SIMCA provided significantly better results for milk classification of the two studied classes (infant formula and infant formula fortified). As an alternative approach SIMCA was capable to discriminate an overlapped group consisting of baby milks administrated during first 6 months of life. Chemometric methods employed highlight four metal concentrations (Zn, Mn, Cu, and S) which could be associated to relevant nutritional parameters in baby growth. Thus, proposed methodology provides a simpler, faster and more affordable classification for simple study on Foodomics in milk-based infant formula.