Abstract
Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.
Highlights
Soybean is one of the most important raw materials for oil and feed (Herman et al, 2018; Kumar et al, 2021; Wei et al, 2021a)
The objective of the study was to analyze the feasibility of THz frequency-domain spectroscopy and chemometrics to identify soybean varieties
Eighteen experimental samples were prepared for each batch of soybean samples
Summary
Soybean is one of the most important raw materials for oil and feed (Herman et al, 2018; Kumar et al, 2021; Wei et al, 2021a). The accuracy of the above methods is relatively high, and the sensitivity is relatively strong, and the application is relatively wide They have problems such as relatively long time consuming, relatively low efficiency, Variety Identification by Terahertz Spectroscopy and relatively complicated detection process. Compared to SSR molecular marker assays and the soybean component-based detection variety method, the NIRS technology has the advantage of not requiring pre-treatment of samples. It has limitations in detecting soybeans with surface defects (Zhu et al, 2010) and limited detection accuracy (Chen et al, 2019; Rong et al, 2020). It is essential to study a rapid and accurate identification method suitable for different varieties of soybeans
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