Lu CH, Li BQ, Jing Q, Pei D, Huang XY. A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine.
Food Chem 2023;
420:136161. [PMID:
37080110 DOI:
10.1016/j.foodchem.2023.136161]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
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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