PORK FAT FATTY ACID PROFILING WITH RAMAN SPECTROSCOPY AND PRINCIPAL COMPONENT ANALYSIS
DOI:
https://doi.org/10.48165/jms.2025.20.01.1Keywords:
Raman spectra, hidden patterns, significant” main component, chemometricsAbstract
Pork quality is largely influenced by the fat composition. Getting reliable data is critical for any successful analysis. Using multidimensional statistical analysis methods opens up the possibility to visualize Raman spectroscopy results of fatty acid (FA) profiles and identify animals by breed. Back fat of the Altai breed (sample 1), Duroc (sample 2) and Livenskaya (sample 3) was analyzed. At least 36 spectra were taken from each sample by the Renishaw inVia Reflex confocal Raman spectrometer and analyzed using the principal component method. The Cattell’s scree test was applied to determine the number of components “significant main components” to retain, namely – PC1, PC2 and PC3 (85%). It is shown samples 1 and 3 FA form clusters in all graphs of the "significant" main components. Sample 2 forms an area on the PC1 / PC2 graph and locates in the I, III and IV quadrants. Sample 1 - in the II quadrant, and Sample 3 - in the IV quadrant. For PC1 the most prominent variable is 1650 cm-1, responsible for the C = C molecular bond, for the saturated FA and conjugated linoleic acid. Spectrum 1650 cm-1 is important in the intraspecific classification of pork. For PC2 – main contribution of 868 cm-1 was marked, and 1368 cm-1 for PC3. Each spectrum characterized group of the pork backfat FA. PCA makes it possible to: (1) to evaluate pork fat lipid profile by groups - saturated, mono-, polyunsaturated, with a long carbon chain, etc.; (2) obtain reliable differences between breeds; (3) identify individual FA, via Raman spectra patterns.
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