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Study on the Mutton Freshness Using Multivariate Analysis Based on Texture Characteristics

Abstract : Aiming at discrimination and prediction of mutton freshness by texture profile, the texture parameters of mutton stored at 1°C, 4°C and Room temperature were analyzed. The analysis methods of Canonical Discriminant Analysis (CDA) and Principal component analysis (PCA) were used to analyze texture parameters of mutton. The results of PCA showed that mutton sample stored at three temperatures clustered into groups according to their freshness, changing along the direction of PC1. Better classification results were found by CDA. The changing trends of mutton freshness were described by Multiple Linear Regression (MLR) and Partial Least Square analysis (PLS), and effective predictive models were found for indices of days stored, TVB-N and pH using texture parameters. With optimum analysis methods, texture parameters could classify and predict freshness of mutton stored at three temperatures. Texture profiles were proved to be a fast and objective tool for the prediction of mutton freshness.
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Xiaojing Tian, Jun Wang, Jutian Yang, Shien Chen, Zhongren Ma. Study on the Mutton Freshness Using Multivariate Analysis Based on Texture Characteristics. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.143-154, ⟨10.1007/978-3-319-48354-2_15⟩. ⟨hal-01614168⟩



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