Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy. (February 2020)

• Record Type: Journal Article
• Authors: Chen, Qingmin; Zhang, Yichi; Guo, Yahui; Cheng, Yuliang; Qian, He; Yao, Weirong; Xie, Yunfei; Ozaki, Yukihiro
• Is Part Of: Journal of food engineering. Volume 266(2020)
• Publication Date: 2020-02
• Subjects: Raw beef -- Frozen/thaw -- Texture -- Raman spectroscopy -- Non-destructive -- Prediction; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2019.109693
• Languages: English
• ISSNs: 0260-8774

Abstract

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 R 2 p of 0.81, 0.80, 0.81, 0.82 respectively, and weaker performance for springiness with R 2 p 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. Highlights: Paper firstly predicted frozen raw beef texture by Raman. Good texture prediction results were obtained based on PLS model. Hydrophobicity and structure composition of proteins are the main factors in texture prediction.