Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC–MS fingerprints

• Published in: Food chemistry Vol. 352; p. 129422
• Main Authors: Shi, Ting, Wu, Gangcheng, Jin, Qingzhe, Wang, Xingguo
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.08.2021
• Subjects: accuracy; adulterated products; Adulteration; calcium oxide; Camellia; Camellia - chemistry; Camellia oil; Chemometrics; corn oil; detection; Discriminant Analysis; Fatty acid; fatty acids; Fatty Acids - analysis; food chemistry; Food Contamination - analysis; Fraud - prevention & control; Gas Chromatography-Mass Spectrometry; Informatics; Least-Squares Analysis; olive oil; peanut oil; Phytosterols; Phytosterols - analysis; Plant Oils - chemistry; prediction; Principal Component Analysis; rapeseed oil; rice bran oil; rice oil; sesame oil; soybean oil; squalene; sunflower oil; triterpenoids; vegetables; Adulteration; Fatty acid; Camellia oil; Chemometrics; Phytosterols
• ISSN: 0308-8146; 1873-7072; 1873-7072
• DOI: 10.1016/j.foodchem.2021.129422

•Fatty acids and sterols with chemometrics were used to authenticate camellia oil.•PCA can differentiate camellia oil using triterpene alcohols.•PLS-DA can help discriminate the specific adulterated oil types.•PLS can be successfully applied for the adulterated level prediction.•Less than 22 key markers with VIP scores were useful for PLS optimization. The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%–100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO.