Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans

Protein content in soybean is a key determinant of its nutritional and economic value. The paper inves-tigated 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 soy-bean using THz spectroscopy is feasible after a suitable dimensionality reduction algorithm. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper explored the feasibility of using terahertz spectroscopy and dimensionality reduction algorithms for the determination of protein content in soybean, achieving rapid and accurate results through different regression models.