Predicting Animal Welfare Labels from Pork Fat Using Raman Spectroscopy and Chemometrics

The awareness of the origin of meat that people consume is rapidly increasing today and with that increases the demand for fast and accurate methods for its distinction. In this work, we present for the first time the application of Raman spectroscopy using a portable spectrometer for the classification of pork. Breeding conditions were distinguished from spectral differences of adipose tissues. The pork samples were obtained from Dutch vendors, from supermarkets with quality marks of 1 and 3 stars, and from a local butcher shop. In total, 60 fat samples were examined using a fiber-optic-coupled Raman spectrometer. Recorded spectra were preprocessed before being subjected to multivariate statistical analysis. An initial data exploration using Principal Component Analysis (PCA) revealed a separation of adipose tissue samples between the lower supermarket quality grade and the samples from the local butcher. Moreover, predictive modeling using Partial Least Squares Discriminant Analysis (PLS-DA) resulted in 96.67% classification accuracy for all three sources, demonstrating the suitability of the presented method for intraspecies meat classification and the potential on-site use.