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In this study, the ENVI 4.6 software was used to obtain the spectral reflection value of samples. The outlier samples were eliminated by the Monte Carlo method, and then SPXY (sample set partitioning based on be x–y distances) was used to divide the calibration set and prediction set. The spectral images were pretreated and characteristic wavelengths were extracted. The spectral models of full and pretreated spectra and characteristic bands were established by partial least squares regression (PLSR) and principle component regression (PCR), and the optimal modeling combination was selected. The results showed that the modeling effect of the original spectrum was the best. In full‐PLSR model, the determination coefficient of the calibration set (Rc2), the determination coefficient of prediction set (Rp2), and the determination coefficient of interactive verification set (Rcv2) were 0.8804, 0.7375, and 0.7422, and root‐mean‐square error of calibration set (RMSEC), root‐mean‐square error of prediction (RMSEP), and root mean square error of interactive validation set (RMSECV) were 2.3630, 2.9607, and 3.4209, respectively. PLSR and PCR models were established to obtain the optimal models of CARS‐PLSR and PCR‐PLSR. In the CARS‐PLSR model, the Rc2, Rp2, and Rcv2 were 0.9135, 0.7654, and 0.8171, respectively, while RMSEC, RMSEP, and RMSECV were 2.0275, 2.9306, and 2.9262, respectively. In the iRF‐PCR model, Rc2, Rp2, and Rcv2 were 0.7952, 0.7372, and 0.7280, respectively, while RMSEC, RMSEP, and RMSECV were 3.0207, 2.8278, and 3.4288, respectively. This study has demonstrated that visible and near‐infrared hyperspectral imaging system can rapidly predict the content of metmyoglobin in cooked tan mutton. Practical Application: This study has demonstrated that visible and near‐infrared (Vis/NIR) hyperspectral imaging system can rapidly predict the content of MetMb in cooked tan mutton. With the advantages of nondestructive, rapid, real‐time, Vis/NIR, hyperspectral imaging system can be widely expanded and applied to the detection of myoglobin in meat to evaluate the color of meat. [ABSTRACT FROM AUTHOR]

• Make a quantitative damage device for Lingwu long jujube. • Spectral of samples was scanned by VIS/NIR hyperspectral system. • Using SPA, UVE, CARS, iVISSA and 2D-COS to select the characteristic wavelength. • Establish the PLS-DA classification models. Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube. [ABSTRACT FROM AUTHOR]