Classification of Algerian olive oils: Physicochemical properties, polyphenols and fatty acid composition combined with machine learning models

Abstract

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), Naïve 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 classification 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.