外文摘要：The regulation of olive cultivar and geographical origin is a requirement for the global extra virgin olive oil market, due to its significant impact on consumer choice. Our work involves obtaining a promising marker parameter for cultivar and geographical origin that can be used to verify declared labels. The effects of these factors on the physicochemical parameters and composition of monovarietal extra virgin olive oil (MEVOO) from Algeria were studied. Thirteen olive fruit varieties were analyzed using different physicochemical methods, including phenolic and fatty acid composition. Five classification techniques, random forests (RForest), gradient boosted trees (GBoost), Naive Bayes (NBayes), logistic regression (LRegression) and decision tree (DTree), were applied and their results were compared. The best validation accuracy of 91.7 % was achieved with DT classi-fication through a feature selection procedure using a genetic algorithm (GA). These results demonstrate the effective use of machine learning techniques to rapidly classify different Algerian varieties based on their compositional fingerprints.
外文关键词：machine learning;Polyphenols;Monovarietal extra virgin olive oil;Physicochemical analyses;Free fatty acid;Fingerprint
作者：Issaad, Fatima Zohra;Abdessemed, Ala;Bouhedjar, Khalid;Bouyahmed, Hani;Derdour, Mouna;Ouffroukh, Karima;Fellak, Ahmed;Dems, Mohamed Abd Salem;Chihoub, Salah;Bechlem, Radouane;Mahrouk, Abdelkader;Houasnia, Mourad;Belaidi, Amine;Moumed, Khaled;Sebai, Zohir;Saidani, Faiza;Akmouche, Houria
作者单位：Biotechnol Res Ctr;Inst Tech Arboriculture Fruitiere & Vigne ITAFV Si
期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS
期刊影响因子：0.0
出版年份：2024
出版刊次：125
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台