Discrimination of Four Species with Physico-Functional Properties and Chemometric Techniques: Application of PCA and MDA Models.

Discrimination of highly valued and non-hepatotoxic species () from hepatotoxic (. , . , and . ) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of species to prevent food fraud.