Discrimination of Camellia seed oils extracted by supercritical CO2 using electronic tongue technology

Analytical method which combines electronic tongue technique and chemometrics analysis is developed to discriminate oil types and predict oil quality. All the studied Camellia oil samples from pressing, n -hexane extraction and supercritical CO 2 extraction (SCCE), were successfully identified by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Furthermore, multi factor linear regression model (MLRM) was established to predict oil quality, which are indicated by acid value (AV) and peroxide value (POV). The practical potential of e-tongue for the discrimination and assessment of Camellia oils has shown promising application in the characterization of Camellia oils in the oil quality evaluation.