Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms

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 (Rp2) of 0.986 and 0.984 respectively. Good performances were achieved with Rp2 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.