Abstract Protein is an important nutrient for people diet, and protein content is one of the most vital properties in quality assessment of pork meat. The potential of line-scanning based hyperspectral imaging (HSI) has been proved to be able to assess the protein content of meat, however quality of images obtained from line-scanning HSI is affected by sample transformation conditions. A novel single shot (snapshot) HSI sensor was employed to evaluate the protein content of numerous processed pork meat by using back propagation - neural network (BP-NN) and partial least squares regression (PLSR) predictive models. The results of BP-NN were better than that of PLSR. The best spectral profile was selected from reflectance, absorbance, and Kubelka–Munk (K–M) spectra by comparing the performances of their BP-NN models. The BP-NN model combined with absorbance spectra showed the best performance for evaluating the protein content of various processed pork meats with determination coefficient of cross-validation set (R2CV) = 0.8318, and root mean square error of cross-validation set (RMSECV) = 8.38 mg/g, respectively. Results indicated the feasibility of determining the protein content of pork meats by means of single shot HSI.

中文翻译：

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基于新型单发（快照）高光谱成像传感器的加工猪肉蛋白质含量评估

摘要 蛋白质是人们饮食中的重要营养素，蛋白质含量是猪肉质量评价中最重要的指标之一。基于线扫描的高光谱成像 (HSI) 的潜力已被证明能够评估肉类的蛋白质含量，但是从线扫描 HSI 获得的图像质量受样品转换条件的影响。通过使用反向传播 - 神经网络 (BP-NN) 和偏最小二乘回归 (PLSR) 预测模型，采用新型单次（快照）HSI 传感器评估大量加工猪肉的蛋白质含量。BP-NN 的结果优于 PLSR。通过比较 BP-NN 模型的性能，从反射率、吸收率和 Kubelka-Munk (K-M) 光谱中选择了最佳光谱曲线。BP-NN 模型结合吸光度光谱在评估各种加工猪肉蛋白质含量时表现出最佳性能，交叉验证集的确定系数 (R2CV) = 0.8318，交叉验证集的均方根误差 (RMSECV) = 8.38 毫克/克，分别。结果表明单次HSI测定猪肉蛋白质含量是可行的。