Rapid determination of pork sensory quality using Raman spectroscopy

Existing objective methods to predict sensory attributes of pork in general do not yield satisfactory correlation to panel evaluations, and their applications in meat industry are limited. In this study, a Raman spectroscopic method was developed to evaluate and predict tenderness, juiciness and chewiness of fresh, uncooked pork loins from 169 pigs. Partial Least Square Regression models were developed based on Raman spectroscopic characteristics of the pork loins to predict the values of the sensory attributes. Furthermore, binary barcodes were created based on spectroscopic characteristics of the pork loins, and subjected to multivariate statistical discriminant analysis (i.e., Support Vector Machine) to differentiate and classify pork loins into quality grades (“good” and “bad” in terms of tenderness and chewiness). Good agreement (> 83% correct predictions) with sensory panel results was obtained. The method developed in this report has the potential to become a rapid objective assay for tenderness and chewiness of pork products that may find practical applications in pork industry.

► Raman spectroscopy provides chemical fingerprints for raw meat. ► Raman spectral signatures correlate well with meat sensory attributes. ► Classification using Raman data provides good prediction of meat quality.