Rapid Detection of Protein and Starch Content in Brewing Wheat Using Hyperspectral Imaging Technology Combined with a Convolutional Neural Network Regression Model

Wheat is the main raw material in liquor brewing. However, the protein content (PC) and starch content (SC) in wheat will affect the quality and flavor of the final liquor. In this study, the rapid, non-destructive determination of the PC and SC in wheat was achieved by combining hyperspectral imaging (HSI) with a convolutional neural network regression (CNNR) model established using the original spectral data in the hyperspectral images. The best preprocessing method was first determined, and then the performance of CNNR, extreme gradient boosting (XGBoost) and partial least squares regression (PLSR) models were compared at full wavelength, and it was concluded that CNNR based on the original spectrum was the optimal model for predicting wheat PC (R-square (R2) = 0.9942, root mean square error (RMSE) = 0.1041, relative percentage difference (RPD) = 13.1306) and SC (R2 = 0.9329, RMSE = 0.8633, RPD = 3.8605) at full wavelength. Finally, to further explore the feature extraction capability of the CNN model, different feature selection and extraction methods are used to build the PLSR model, and the comparison revealed that the PLSR model built by feature extraction using convolutional neural network (CNN_F) performed best in predicting wheat PC and SC. These results showed that HSI combined with a CNNR model could enable the rapid and accurate analysis of the PC and SC in wheat, since the CNNR can extract features from samples better than the traditional machine learning models.

小麦是白酒酿造的主要原料。然而，小麦中的蛋白质含量（protein content, PC）与淀粉含量（starch content, SC）会影响最终白酒的品质与风味。本研究结合高光谱成像（hyperspectral imaging, HSI）与基于高光谱图像原始光谱数据构建的卷积神经网络回归（convolutional neural network regression, CNNR）模型，实现了小麦中蛋白质含量与淀粉含量的快速无损检测。首先确定最优预处理方法，随后在全波长范围内对比了卷积神经网络回归、极端梯度提升（extreme gradient boosting, XGBoost）以及偏最小二乘回归（partial least squares regression, PLSR）模型的性能，得出基于原始光谱的卷积神经网络回归模型为预测小麦蛋白质含量（决定系数R-square, R²=0.9942，均方根误差root mean square error, RMSE=0.1041，相对分析误差relative percentage difference, RPD=13.1306）与淀粉含量（R²=0.9329，RMSE=0.8633，RPD=3.8605）的最优模型。最后，为进一步探究卷积神经网络模型的特征提取能力，本研究采用不同特征选择与提取方法构建偏最小二乘回归模型，对比结果显示，通过卷积神经网络进行特征提取构建的偏最小二乘回归模型（CNN_F）在预测小麦蛋白质含量与淀粉含量时表现最优。研究结果表明，高光谱成像结合卷积神经网络回归模型可实现小麦中蛋白质与淀粉含量的快速精准分析，这是由于卷积神经网络回归相较于传统机器学习模型能够更有效地从样本中提取特征。