Effectiveness of discriminant analysis and varimax rotation as food forensic tools for authentication of skin gelatine sources

Abstract

This study aims at (1) authenticating sources of skin gelatine via the incorporation of putative amino acid (AA) analysis via Ultra-High-Performance Liquid Chromatography Diode-Array Detector (UHPLC-DAD) with multivariate data analysis and (2) developing the amino acid profiles in skin gelatines. The classification ability of MDA, such as partial least square-discriminant analysis (PLS-DA) and discriminant analysis (DA) was compared to choose the best discriminating model. Principal component analysis (PCA) with Varimax rotation was executed to ensure the correct grouping of the skin gelatine clusters and thus, facilitate assigning significantly contributing AA to the skin gelatine clusters. The DA was superior over the PLS-DA since it had successfully classified 96.7% porcine, bovine and fish skin gelatines using 17 AAs. The backbone of chemical structure may render the correlations among AAs in the skin gelatines. The DA identified 15 significant AAs (p < 0.01) in the skin gelatines via PCA with Varimax rotation. Four Varimax rotations had successfully grouped the porcine, bovine and fish skin gelatines into correct clusters. L-Tyrosine, L-Phenylalanine and L-Valine were dominant in porcine gelatine; L-Methionine, L-Threonine, L-Serine, L-Histidine, L-Arginine and Glycine were dominant in fish gelatine, while L-Proline, L-Leucine and L-Hydroxyproline were in moderate content in bovine. This study anticipated that the authority might adopt this approach to establish an authentication standard for skin gelatine samples.

Discrimination and authentication of lard blending with palm oil in cosmetic soap formulations

“…In order to verify the models' capacity to quantify the adulterant lard in the tested dataset, the relative error between the quantified and actual lard was calculated. Based on the lowest range and average relative error, the optimal model to quantify the adulterant lard in the cosmetic soap was chosen [23]. Another verification dataset with 93 data points and 20 replicates for each unknown soap sample ( n = 5) was used to validate regression models on cosmetic soap.…”

“…In order to verify the models' capacity to quantify the adulterant lard in the tested dataset, the relative error between the quantified and actual lard was calculated. Based on the lowest range and average relative error, the optimal model to quantify the adulterant lard in the cosmetic soap was chosen [23].…”