朴素贝叶斯分类器
栽培
橄榄油
作文(语言)
多酚
随机森林
人工智能
决策树
特征选择
选择(遗传算法)
食品科学
数学
机器学习
植物
生物
计算机科学
支持向量机
抗氧化剂
生物化学
语言学
哲学
作者
Fatima Zohra Issaad,Ala Abdessemed,Khalid Bouhedjar,Hani Bouyahmed,Mouna Derdour,Karima Ouffroukh,Ahmed Fellak,M. Dems,Salah Chihoub,Radouane Bechlem,Abdelkader Mahrouk,Mourad Houasnia,Amine Belaidi,Khaled Moumed,Zohir Sebai,Faiza Saidani,Houria Akmouche
标识
DOI:10.1016/j.jfca.2023.105812
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), 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.
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