ResearchArticle Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy Aimen El Orche , 1 Amine Mamad, 2 Omar Elhamdaoui, 2 Amine Cheikh, 3 Miloud El Karbane, 2 and Mustapha Bouatia 2 1 Team of Analytical and Computational Chemistry,Nanotechnology and Environment, Faculty of Sciences and Techniques, University of Sultan Moulay Slimane, Beni Mellal, Morocco 2 Laboratory of Analytical Chemistry, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco 3 Faculty of Medicine, Abulcasis University, Rabat, Morocco Correspondence should be addressed to Aimen El Orche; aimen.elorche@gmail.com Received 1 October 2021; Revised 22 November 2021; Accepted 26 November 2021; Published 8 December 2021 Academic Editor: Ana Domi nguez Vidal Copyright © 2021 Aimen El Orche et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. One of the significant challenges in the food industry is the determination of the geographical origin, since products from different regions can lead to great variance in raw milk. erefore, monitoring the origin of raw milk has become very relevant for producers and consumers worldwide. In this exploratory study, midinfrared spectroscopy combined with machine learning classification methods was investigated as a rapid and nondestructive method for the classification of milk according to its geographical origin. e curse of dimensionality makes some classification methods struggle to train efficient models. us, principal component analysis (PCA) has been applied to create a smaller set of features. e application of machine learning methods such as PLS-DA, PCA-LDA, SVM, and PCA-SVM demonstrates that the best results are obtained using PLS-DA, PCA- LDA, and PCA-SVM methods which show a correct classification rate (CCR) of 100% for PLS-DA and PCA-LDA and 94.95% for PCA-SVM, whereas the application of SVM without feature extraction gives a low CCR of 66.67%. ese findings demonstrate that FT-MIR spectroscopy, combined with machine learning methods, is an efficient and suitable approach to classify the geographical origins of raw milk. 1. Introduction Consumers are increasingly demanding guarantees con- cerning the quality and safety of food products, especially when they are of animal origin. Food authentication consists of checking that the product is consistent with the state- ments made on the label [1]. Falsification or willful mis- labeling is usually used to reduce production costs [2]. In the milk industry especially, mislabeling can be used to confuse the industry and consumers about the origin of the milk, since products of different origins can have different qual- ities [1]. e European Union (EU) promotes dairy product quality programs designed to support farmers and safeguard their product names from abuse and imitation [3]. Partic- ularly, the EU promotes two principal quality regimes that are based on the valuation of the geographical origins of foods, called Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI), which respectively identify food products that are produced or closely asso- ciated with a given geographical area [4]. Food scientists support such programs by developing analytical techniques to enhance the capacity to identify the geographic origin of food [5]. ese techniques are classified into two types: those based on targeted approaches and those based on untargeted approaches [6]. Targeted approaches are usually the most suitable for regulatory purposes, as they are very specific and sensitive Hindawi Journal of Spectroscopy Volume 2021, Article ID 5845422, 9 pages https://doi.org/10.1155/2021/5845422