title: Optimization of discriminant partial least squares regression models for the detection of animal by-product meals in compound feedingstuffs by near-infrared spectroscopy.
abstract: This paper evaluates two multivariate strategies for classifying near-infrared (NIR) spectroscopic data for the detection of animal by-product meals (henceforth generically termed AbP) as an ingredient in compound feedingstuffs. Classification models were developed to discriminate between the presence and absence of animal-origin meals in compound feeds using two forms of discriminant partial least squares (PLS) regression: the algorithms PLS1 and PLS2. The training set comprised 433 commercial feeds, of which 148 contained AbP and the other 285 were stated to be AbP-free. Since the initial set contained unequal numbers of each class, the effect of this imbalance was analyzed by applying the same algorithms to a training set containing equal numbers of AbP-free and AbP-containing samples. The best classification model (97.42% of samples correctly classified), obtained with PLS2, that showed less sensitivity to the use of class-unbalanced sets, was externally validated using a set of 18 samples (10 AbP-containing and 8 AbP-free); all samples were correctly classified, except for one AbP-free sample that was classified as containing AbP (false positive). The results suggest that the application of PLS discriminant analysis to NIR spectroscopic data enables detection of AbP, a feed ingredient banned since the bovine spongiform encephalopathy (BSE) crisis; this confirms the value of NIRS qualitative analysis for product authentication purposes.
authors.affiliation: Department of Animal Production, ETSIAM Universidad de Córdoba, Spain. pa2pemad@uco.es
authors.forename: D C
authors.initials: DC
authors.lastname: Pérez-Marín
authors.forename: A
authors.initials: A
authors.lastname: Garrido-Varo
authors.forename: J E
authors.initials: JE
authors.lastname: Guerrero
mesh_terms: D000465:Algorithms; D000821:Animal Feed / Q000032:analysis*; D000818:Animals; D003198:Computer Simulation; D016002:Discriminant Analysis; D005504:Food Analysis / Q000379:methods*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D011786:Quality Control; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013055:Spectrophotometry, Infrared / Q000379:methods*