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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]

• The R spectra were transformed into two spectral units, A and K-M. • IVISSA algorithm was used to select characteristic wavelengths. • PLS-DA and SVM algorithms were used to identify sound and bruised jujubes. • A-Raw-iVISSA-PLS-DA was the optimal classification discriminant model. Bruising is one of the major challenges appearing in the postharvest grading and processing of Lingwu long jujubes, which leads to quality deterioration and microbial infection. In the present work, the spectra of reflectance (R), absorbance (A) and Kubelka-Munk (K-M) in Lingwu were obtained by hyperspectral imaging, and applied to non-destructively detect bruising symptoms. Spectra were preprocessed and characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and the interval variable iterative space shrinkage approach (iVISSA). Classification discriminant models were constructed by partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). By comparison, the results revealed that the A-raw-iVISSA-PLS-DA model showed the lowest errors in cross-validation, while the number of feature variables was the lowest accounting for 28.8 %, and the accuracies of the calibration and cross validation were 88.9 % and 100.0 %, respectively. In particular, this study demonstrated the feasibility to detect the early bruising degree in Lingwu long jujubes based on absorbance spectrum. Consequently, it also laid a foundation for future studies about detecting early bruising in small fruit with a rapid and non-destructive spectral-optical measurement. [ABSTRACT FROM AUTHOR]