Study on the Optimization Method of Maize Seed Moisture Quantification Model Based on THz-ATR Spectroscopy

文献类型： 外文期刊

第一作者： Wu Jing-zhui

作者： Wu Jing-zhui;Li Xiao-qi;Liu Cui-ling;Sun Xiao-rong;Sun Mei;Yu Le;Sun Li-juan

作者机构：

关键词： Terahertz time-domain spectral; Attenuated total reflection; Seed moisture; Spectral region screening; Support vector machine

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2021 年 41 卷 7 期

页码：

收录情况： SCI

摘要： Characteristic Terahertz THz) bands of maize seed moisture were screened using the Terahertz time-domain spectroscopy technique combined with the interval partial least squares method. The support vector machine was used to construct a rapid quantitative analysis model of seed moisture based onthe characteristic spectral region against nonlinear interference. Take Zhengdan 958(Corn variety), for example, in this experiment, 40 sets of seed powder samples (3 samples from each set) with moisture content ranging from 9. 58% to 12. 71% were prepared. Terahertz time-domain spectra of 120 samples were collected by Terapluse 4 000 terahertz time-domain system with Attenuated Total Reflection (ATR) module. According to the SPXY method, 90 training set samples and 30 test set samples were obtained. Given the strong absorption of terahertz waves by seed moisture, the moving interval (mwPLS), independent interval (iPLS), backward interval (biPLS) and synergy interval (siPLS) methods based on partial least squares linear regression were firstly used to screen the optimal combination of the characteristic spectral regions. In view of the inevitable nonlinear interference of environmental moisture, other seed components and systematic noise on the terahertz spectrum of seed moisture, a nonlinear model for rapid quantitative analysis of seed moisture with optimal prediction performance was further constructed using support vector machine and grid search method based on RBF kernel function on the above spectral feature intervals. The optimal SVR model was obtained with a lower root mean square error of the training set (RMSEC) of 0. 021 2, a lower root mean square error of the prediction (RMSEP) of 0. 069 7 and a higher residual predictive deviation (RPD) of 12. 345 7. The model performance was significantly improved compared with the traditional partial least squares linear regression model. Seed moisture content is an important factor in seed storage safety and seed vigour. The experimental results show that THz time-domain spectroscopy combined with the chemometric method can effectively be used to screen the characteristic absorption spectral region of seed moisture and establish an interference-resistant and high-precision model for rapid quantitative analysis of seed moisture, which is expected to be a Promising complementary technology in the field of rapid seed quality determination.

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