Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy

• Published in: Talanta (Oxford) Vol. 222; p. 121511
• Main Authors: Pérez-Marín, Dolores, Fearn, Tom, Riccioli, Cecilia, De Pedro, Emiliano, Garrido, Ana
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.01.2021
• Subjects: acorns; Animals; Bayes Theorem; Carcass authentication; Discriminant Analysis; feeds; finishing; fraud; ham; Iberian (swine breed); Iberian pig classification; In situ NIRS analysis; near-infrared spectroscopy; pig carcasses; Portable microspectrometer; prices; Probabilistic discrimination; regression analysis; slaughterhouses; Spectroscopy, Near-Infrared; Swine; swine feeding; uncertainty; Probabilistic discrimination; Iberian pig classification; Carcass authentication; Portable microspectrometer; In situ NIRS analysis
• ISSN: 0039-9140; 1873-3573; 1873-3573
• DOI: 10.1016/j.talanta.2020.121511

Iberian pig ham is one of several high value European food products that are the subject of significant attempts at fraud because of the high price differences between commercial categories. Iberian pig products are classified by the Spanish regulations into different categories, mainly depending on the feeding regime during the fattening phase and the race involved, being of Premium quality those products obtained from the animals fed with acorns and other natural resources. Most of the previous NIRS studies related to the Iberian pig have involved the use of at-line instruments to predict quantitative quality parameters. This paper explores the use of the NIR spectra (369 for training and 199 for validation) to classify samples according to the categories Premium (animals fed with acorn) and Non Premium (animals fed with compound feeds), using a MicroNIR™ Pro1700 microspectrometer to analyse individual carcasses in situ at the slaughterhouse line. Four discriminant methods were explored: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Kernel Bayes and Logistic Regression. These are all discriminant methods that naturally produce classification probabilities to quantify the uncertainty of the results. Rules were tuned and methods compared using both classification error rates and a probability scoring rule. LDA gave the best results, attaining an overall accuracy of 93% and providing well-calibrated classification probabilities. [Display omitted] •NIRS can successfully authenticate pig carcasses at slaughtering line speed.•Probabilistic classification models show the uncertainty of the predictions.•LDA provides the best discrimination between categories for Iberian pig carcasses.