Protein content in soybean is a key determinant of its nutritional and economic value. The paper investigated the feasibility of terahertz (THz) spectroscopy and dimensionality reduction algorithms for the determination of protein content in soybean. First of all, the THz sample spectrum was data processed by pre-processing or dimensionality reduction algorithms. Secondly, by calibration set, using partial least squares regression (PLSR), genetic algorithms-support vector regression (GA-SVR), grey wolf optimizer-support vector regression (GWO-SVR) and back propagation neural network (BPNN) were respectively used to model protein content determination. Afterwards, the model was validated by the prediction set. Ultimately, the BPNN model combined with linear discriminant analysis (LDA) for related coefficient of prediction set (Rp), root mean square error of prediction set (RMSEP), relative standard deviation (RSD), the time required for the operation was respectively 0.9677, 1.2467%, 3.3664%, and 53.51 s. The experimental results showed that the rapid and accurate quantitative determination of protein in soybean using THz spectroscopy is feasible after a suitable dimensionality reduction algorithm.

大豆中的蛋白质含量是其营养和经济价值的关键决定因素。本文研究了太赫兹（THz）光谱和降维算法用于测定大豆中蛋白质含量的可行性。首先，太赫兹采样频谱是通过预处理或降维算法处理的数据。其次，通过校正集，分别使用偏最小二乘回归（PLSR），遗传算法-支持向量回归（GA-SVR），灰太狼优化器-支持向量回归（GWO-SVR）和反向传播神经网络（BPNN）模拟蛋白质含量测定。之后，通过预测集对模型进行验证。最终，BPNN模型与线性判别分析（LDA）相结合，得出了相关的预测集系数（R p），预测集的均方根误差（RMSEP），相对标准偏差（RSD），操作所需的时间分别为0.9677、1.2467％，3.3664％和53.51 s。实验结果表明，采用合适的降维算法，使用太赫兹光谱法快速准确地测定大豆中的蛋白质是可行的。