Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy

Abstract

To date, the main methods of texture measurement are sensory testing and instrumental testing. These two testing methods are time-consuming and often destructive respectively. In this study, Raman spectroscopy was used to predict the texture of different frozen/thaw raw beef from continuous freezing and repeated freeze-thaw treatments. The effect of repeated freeze-thaw treatment on beef texture was significantly different (p < 0.05) when the number of freeze-thaw cycles exceeded three times. Quantitative models were developed with optimized spectra and texture parameters of the samples based on partial least squares analysis. The result showed that Raman spectroscopy exhibited good performance in predicting tenderness, chewiness, firmness, and hardness with R2p of 0.81, 0.80, 0.81, 0.82 respectively, and weaker performance for springiness with R2p of 0.53. Therefore, Raman spectroscopy has potential for the quantification of texture parameters of frozen/thaw beef. Besides, the PCA loadings plots for PC1 and PC2 revealed that the main variables of prediction equations were located at approximately 960–1060 cm−1, 1370–1490 cm−1, and 1550–1680 cm−1. These regions are significantly influence by changes in hydrophobic properties and secondary structure composition of meat protein.