Discrimination on potential adulteration of extra virgin olive oils consumed in China by differential scanning calorimeter combined with dimensionality reduction classification techniques. (30th March 2023)

• Record Type: Journal Article
• Authors: Huang, Kaiyuan; Zhong, Peihao; Xu, Baojun
• Is Part Of: Food chemistry. Volume 405:Part B(2023)
• Publication Date: 2023-03-30
• Subjects: Extra virgin olive oil -- Edible oil -- Adulteration -- Principal component analysis -- K-nearest neighbors model; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.134996
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Thermal properties of edible oils collected by differential scanning calorimetry. Adulterations were evidenced through dimension reduction techniques. K-nearest neighbors model effectively identify even 5% canola oil adulteration. Abstract: Thermal properties of extra virgin olive oil (EVOO) consumed in China were investigated through differential scanning calorimetry (DSC), Principal Component Analysis (PCA) dimension reduction analysis combined with K-nearest neighbors (KNN) and support vector machine (SVM) models were used to determine whether oil samples would be EVOO. Both models exhibited a 100% ability to distinguish EVOO from non-EVOO. Additionally, the discrimination test of canola oil was still limited from EVOO adulteration due to its similarity of cooling profiles with EVOO. Canola oil was artificially added into EVOO in 5%, 10%, 20% and 30% and DSC test results collected were detected in computer models. Both models effectively identified even 5% canola oil adulterated in EVOO; however, KNN (93.75%) model exhibited much higher accuracy than SVM (43.92%). In conclusion, DSC combined with KMN model analysis was more available for the adulteration detection of EVOO.