Incorporation of Partial Least Squares-Discriminant Analysis with Ultra-High-Performance Liquid Chromatography Diode-Array Detector for Authentication of Skin Gelatine Sources

Abstract

This research seeks to (1) authenticate sources of skin gelatine by combining putative 17 aminoacids (AAs) analysis with chemometrics by Ultra-High-Performance Liquid ChromatographyDiode-Array Detector (UHPLC-DAD) and (2) create AA profiles in skin gelatines. Theclassification capability of partial least square-discriminant analysis (PLS-DA) models wasassessed to determine the most effective discriminant model. Principal component analysis(PCA) with quartimax rotation was utilised to accurately organise gelatine clusters and assign thesignificantly contributing AAs to each cluster. The PLS-DA model with 13 AAs (PLS-DAVIPAA)outperformed the PLS-DA model with 17 AAs (PLS-DAAA) because its R2Y (0.938), R2X (0.881),and Q2 (0.929) values were greater. With 13 significant AAs, the PLS-DAVIPAA model obtainedcluster classification accuracy of 100% on training and cross-validation datasets and 93.3% ontesting and verification datasets. The chemical structure of gelatines may shed light on theinteractions between AAs. Following six quartimax rotations, the gelatines were groupedcorrectly. The PCA showed the dominant presence of these AAs: L-Valine, L-Phenylalanine andL-Tyrosine in porcine gelatine; Glycine, L-Threonine, L-Arginine, L-Methionine, L-Histidine andL-Serine in fish gelatine; and L-Hydroxyproline, L-Leucine and L-Proline in bovine gelatine. Theauthority could use this technique to set a standard for authenticating skin gelatine samples.