Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton

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.