Establishment of multivariate specifications for food commodities with discriminant partial least squares

Abstract

A novel method for establishing multivariate specifications of food commodities is proposed. The specifications are established for discriminant partial least squares (DPLS) by setting limits on the predictions of the DPLS model together with Hotelling T(2) and square error of prediction (SPE). These limits can be tuned depending on whether type I error (i.e. a correct sample is declared out-of-specification) or type II error (i.e. an out-of-specification sample is declared within specifications) need to be minimized. The methodology is illustrated with a set of NIR spectra of Italian olive oils, corresponding to five regions and the class Liguria is the class of interest. The results demonstrate the possibility of establishing multivariate specification for olive oils from the Liguria region on the basis of spectral data obtaining type I and type II errors lower than 5%.