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Hyperspectral imaging (HSI), a non-destructive method combining image and spectral techniques to obtain image and spectral information of target objects, and qualitative and quantitative analyses using spectral data, has been widely used in agricultural product detection. This study was conducted to assess the feasibility of using HSI (900–1700 nm) for rapid determination of myoglobin contents (DeoMb, OxyMb and MetMb) in nitrite-cured mutton during refrigerated storage. The data were analyzed using evolutionary computing methods, including partial least squares regression (PLSR) and least squares support vector machines (LSSVM). Corresponding feature wavelengths were then selected by using competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and interval variable iterative space shrinkage approach (iVISSA) respectively. The simplified iVISSA-PLSR model showed the best performance for determining DeoMb with R p of 0.9017 and RMSEP of 2.3509. In addition, the best model for OxyMb was CARS-PLSR, with R p of 0.9661 and RMSEP of 2.3762. Moreover, the CARS-LSSVM model showed a better performance with a higher R p (0.8931) and a lower RMSEP (3.2743) for MetMb. Furthermore, the overall performance of wavelength selection methods in terms of CARS (R pDeoMb ≥ 0.8943, R pOxyMb ≥ 0.9607, R pMetMb ≥ 0.8910) and iVISSA (R pDeoMb ≥ 0.8976, R pOxyMb ≥ 0.9568, R pMetMb ≥ 0.8880) were equivalent and slightly better than VCPA (R pDeoMb ≥ 0.8711, R pOxyMb ≥ 0.9573, R pMetMb ≥ 0.8808). The results demonstrated that effective wavelength selection method can improve the performance of HSI system for the determination of myoglobin contents in comparison to full wavelength. • Feature wavelengths were selected by CARS, VCPA, iVISSA and iVISSA-CARS. • PLSR and LSSVM models were developed to measure myoglobin contents (DeoMb, OxyMb and MetMb) of nitrite-cured mutton. • The correlation coefficient of simpled model is acceptable for measuring myoglobin contents. [ABSTRACT FROM AUTHOR]

• The NIR-HSI was used for the first time to evaluate the lipid oxidation in mutton. • G2D-CS was used to determine the sequence of the changes in chemical bonds. • The CARS-LSSVM model showed good result to assess TBARS content in mutton (R p 2 = 0.83). This study aimed to investigate the feasibility of a rapid and non-destructive detecting method for lipid oxidation degree in mutton by near-infrared hyperspectral imaging (NIR-HSI). The hyperspectral images of the samples were collected in the spectral range of 900–1700 nm based on line-scanning. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were established to correlate the full spectra with measured TBARS values. To reduce the complexity of the calibration models, eight new PLSR and LSSVM models based on the four sets of key variables extracted by interval variable iterative space shrinkage approach (iVISSA), interval random frog (IRF), variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were developed and compared. The optimized LSSVM model based on the feature wavebands selected by CARS methods was found to be good in predicting TBARS content, with R p 2 of 0.83, RMSEP of 0.11 mg kg−1, RPD of 2.82 and RER of 10.36. In summary, this study showed that it was feasible to non-destructively and rapidly evaluate lipid oxidation degree in mutton using NIR-HSI. [ABSTRACT FROM AUTHOR]