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This study was conducted to determine the correlation among serum biochemical indices, slaughter traits, texture characteristics and water-holding capacity in Tan sheep. Eighty Tan sheep aged 4–6 months were selected. Blood samples were collected from live animals to analyse serum biochemical indices. The slaughter traits, texture characteristics and water-holding capacity of meat were assessed after slaughter. There were highly significant correlations (p<.01) and high linear regression R values among serum biochemical indices, slaughter traits, meat texture characteristics and water-holding capacity in Tan sheep. Therefore, this study has demonstrated that the meat qualities of Tan mutton may be evaluated by the serum biochemical indices of live Tan sheep. This study was conducted to determine the correlation among serum biochemical indices, slaughter traits, texture characteristics and water-holding capacity in Tan sheep. This study has demonstrated that meat qualities of Tan mutton may be assessed by the serum biochemical indices of live Tan sheep. It is necessary to determine slaughter traits, meat texture characteristics and water-holding capacity of meat, which are major indicators for assessing meat and meat products by consumers. [ABSTRACT FROM AUTHOR]

The detection of meat texture is of great value because it is the key factor that drives consumer purchasing decisions. In this study, a hyperspectral imaging (HSI) system was utilized to determine the texture parameters of Tan mutton. In order to observe the influence of mutton spectra during different refrigeration periods for modeling, hyperspectral images of the Tan mutton samples were collected in the 900–1700 nm spectral range, and the correction models of Tan mutton texture parameters were established. The four machine learning algorithms, such as partial least squares regression (PLSR), least squares support vector machine (LSSVM), random forest (RF), and decision trees (DT), were developed to establish the spectral models based on the characteristic bands selected by different extraction strategies including interval variable iterative space shrinkage approach (iVISSA), competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and variable combination population analysis (VCPA). The results showed that the LSSVM-iVISSA-CARS models exhibited excellent performance in predicting hardness and gumminess with root-mean-square errors (RMSEP) of 5.259 and 3.051 as well as the coefficient of determination for the prediction data set (R p 2) of 0.986 and 0.984 respectively. Good performances were achieved with R p 2 of 0.987 and RMSEP of 4.970 with the LSSVM-iVISSA-SPA model for chewiness, respectively. Therefore, HSI has potential for the evaluation and prediction of texture parameters in Tan mutton. • Spectra at 900–1700 nm was applied for detecting texture parameters of Tan mutton. • The OSC and MC methods were both utilized to preprocess X-block and Y-block. • Informative wavelengths were selected using iVISSV-CARS and iVISSV-SPA approaches. • The LSSVM model yielded the best result in predicting hardness, gumminess and chewiness of Tan mutton. [ABSTRACT FROM AUTHOR]