This study provided a step-by-step procedure to investigate the distribution of 17 amino acids (AAs) in 50 fish, 50 bovine and 54 porcine gelatines using Ultra-High-Performance Liquid Chromatography Diode-Array Detector (UHPLC–DAD) with the incorporation of principal component analysis (PCA). Dataset pre-processing step, including outlier removal, analysis of variance (ANOVA), dataset adequacy test, dataset transformation and correlation test was performed before the PCA. The method rendered linearity range of 37.5–1000 pmol/µL and accuracy of 85–111% recovery. The bovine and porcine gelatines showed a similar ranking while the l-Alanine (Ala), l-Arginine (Arg) and l-Glutamic acid (Glu) concentrations had differed the fish gelatine from the bovine and porcine gelatines. The PCA, which explained 77.013% cumulative variability at eigenvalue of 5.436, showed AAs with strong FL in PC1 had polar and nonpolar side chains while AAs with strong FL in PC2 had polar side chain. The AAs with moderate and weak FL in PC1 had a nonpolar side chain. The AAs with strong FL of in PC1 were also the same AAs with 7, 6 and 5 strong CMs as determined in the correlation test. The second PCA showed that the l-Serine (Ser), Arg, Glycine (Gly), l-Threonine (Thr), l-Methionine (Met), l-Histidine (His) and L-Hydroxyproline (Hyp) were significant in fish gelatine; Hyp, Met, Thr, Ser, His, Gly, and Arg in bovine gelatine; and l-Proline (Pro), l-Tyrosine (Tyr), l-Valine (Val), l-Leucine (Leu), and l-Phenylalanine (Phe) in porcine gelatine. The 100% fish, bovine and porcine gelatines accommodated grouping 1, 2 and 3, respectively, which proved that AAs with strong FL (Hyp, His, Ser, Arg, Gly, Thr, Pro, Tyr, Met, Val, Leu and Phe) were the significant AAs and becomes the biomarkers to identify the gelatine source. From this study, the PCA was a useful tool to analyse a multivariate dataset that could provide an in-depth understanding of AA distributions as compared to ANOVA and correlation test.

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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.