PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication. Finally, fine performance metrics around of 0.95 were obtained for both multivariate classification methods.

Automated summary

Conventional PLS-DA and sparse sPLS-DA were effectively used to authenticate avocado samples by analyzing lipid chromatographic fingerprints. The concatenated classification models successfully resolved multiclass problems in food authentication, with performance metrics around 0.95 for both multivariate classification methods.