This study aimed to develop simplified models for rapid and nondestructive monitoring myoglobin contents (DeoMb, MbO₂ and MetMb) during refrigerated storage of Tan sheep based on a hyperspectral imaging (HSI) system in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were applied to correlate the spectral data with the reference values of myoglobin contents measured by a traditional method. In order to simplify the LSSVM models, competitive adaptive reweighted sampling (CARS) and Interval variable iterative space shrinkage approach (iVISSA) were used to select key wavelengths. The new CARS-LSSVM models of DeoMb and MbO₂ yielded good results, with R²p of 0.810 and 0.914, RMSEP of 1.127 and 2.598, respectively. The best model of MetMb was new iVISSA-CARS-LSSVM, with an R²p of 0.915 and RMSEP of 2.777. The overall results from this study indicated that it was feasible to predict myoglobin contents in Tan sheep using HSI.

这项研究的目的是建立一个简化的模型，用于基于400-1000 nm光谱范围内的高光谱成像（HSI）系统，在冰冻的棕褐色绵羊中快速，无损地监测肌红蛋白的含量（DeoMb，MbO ₂和MetMb）。应用偏最小二乘回归（PLSR）和最小二乘支持向量机（LSSVM）将光谱数据与通过传统方法测得的肌红蛋白含量的参考值进行关联。为了简化LSSVM模型，使用竞争性自适应加权采样（CARS）和间隔可变迭代空间收缩方法（iVISSA）选择关键波长。DeoMb和MbO ₂的新CARS-LSSVM模型产生了良好的结果，R ²p分别为0.810和0.914，RMSEP分别为1.127和2.598。MetMb的最佳模型是新的iVISSA-CARS-LSSVM，R ² p为0.915，RMSEP为2.777。这项研究的总体结果表明，使用HSI预测棕褐色绵羊肌红蛋白含量是可行的。