Chemometrics and innovative multidimensional data analysis (MDA) based on multi-element screening to protect the Italian porcino (Boletus sect. Boletus) from fraud

In this study, a statistical model, combining principal components analysis (PCA), stepwise-canonical discriminant analysis (stepwise-CDA), classification and regression tree (CART), partial least squares-discriminant analysis (PLS-DA) and an innovative multidimensional analysis (MDA), was build up to predict the geographical origin of edible porcini (Boletus sect. Boletus). To this purpose, the elemental signatures of 180 commercial and manually harvested samples from different Italian production areas, China and Poland, were chemometrically elaborated. PCA differentiated Italian products from Chinese and Polish mushrooms. Based on the fusion of PCA and hierarchical cluster analysis (HCA), MDA identified elements such as Na, Mn, Fe, Cu and Cd as powerful discriminating variables. Finally, highly accurate and trained stepwise-CDA, CART and PLS-DA models, were able to predict the geographical origin of a survey of commercial porcini, through few metals (Mg, Mn, and Fe). The provenance reported on the labelling of these products was confirmed. Nevertheless, both models revealed that a commercial sample, with a claimed Italian origin, consisted of Chinese mushrooms. Overall, the combination of standard and innovative chemometric techniques demonstrated to support a reliable authentication of the geographical origin of porcini, thus, protecting the Italian production from fraud.