Establishment of multivariate specifications for food commodities with discriminant partial least squares.
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%.
Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, C/Marcel·lí Domingo, s/n. 43007 Tarragona, Spain.
Néstor F
NF
Pérez
Ricard
R
Boqué
Joan
J
Ferré
D002138:Calibration; D002623:Chemistry Techniques, Analytical; D005504:Food Analysis / Q000379:methods*; D005506:Food Contamination / Q000032:analysis*; D016018:Least-Squares Analysis; D008422:Materials Testing; D015233:Models, Statistical; D015999:Multivariate Analysis; D000069463:Olive Oil; D010938:Plant Oils / Q000032:analysis*; D015203:Reproducibility of Results
D000069463:Olive Oil; D010938:Plant Oils