Journal
FOOD CHEMISTRY
Volume 433, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2023.137306
Keywords
Camellia oil; Fatty acid profiles; Geographical origin traceability; One-class classification
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In this study, authentication models for Changshan camellia oil were developed using fatty acid profiles and one-class classification methods. The results showed that the one-class classification models were more effective in differentiating Changshan from non-Changshan camellia oils and provided a reference strategy for the geographical origin identification of other high-value oils or foods.
Geographical Indication (GI) agricultural products possess specific geographical origins and high qualities, which require an effective geographical origin traceability method for the important protective trademarks. In this study, authentication models for Changshan camellia oil were developed by fatty acid profiles and one-class classification methods including data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OCPLS), and compared with traditional two-class classification models. The results indicated that the prediction errors of three two-class classification models were 63.8%, 12.1%, and 65.2% for the samples out of targeted geographical origins, respectively. By contrast, the one-class classification models could completely differentiate Changshan from non-Changshan camellia oils, even from the adjacent counties. Moreover, compared with traditional indicators of mineral elements, the model built by fatty acid profiles possessed higher sensitivity and specificity. It also offered a reference strategy for the geographical origin identification of other high-value oils or foods.
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