油菜籽
橄榄油
渣
食用油
葵花籽油
食品科学
植物油
花生油
油磨机
支持向量机
大豆油
向日葵
化学
数学
人工智能
棕榈油
计算机科学
有机化学
原材料
组合数学
作者
Cong‐Hui Lu,Baoqiong Li,Quan Jing,Dong Pei,Xinyi Huang
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-04-14
卷期号:420: 136161-136161
被引量:11
标识
DOI:10.1016/j.foodchem.2023.136161
摘要
Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.
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