基于THz-ATR光谱的玉米种子水分定量模型优化方法研究

doi:10.3964/j.issn.1000-0593(2021)07-2005-07

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Abstract 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.

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

Received: 2021-02-20 Accepted: 2021-05-09

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O433.4

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)07-2005-07 OR http://www.gpxygpfx.com/EN/Y2021/V41/I07/2005

[1] LI Zhen-hua, WANG Jian-hua（李振华，王建华）. Sciences Agricultural Sinica（中国农业科学），2015，48(4)：646.
[2] WANG Jian-hua(王建华). Maize Seed Quality Evaluation Manual(玉米种子质量评价手册). Beijing: China Agricultural University Press(北京：中国农业大学出版社), 2015.
[3] YAO Jian-quan(姚建铨). Journal of Chongqing University of Posts and Telecommunications·Natural Science Edition(重庆邮电大学学报·自然科学版), 2010, 22(6): 703.
[4] ZHANG Cun-lin, MU Kai-jun (张存林，牧凯军). Progress in Laser and Optoelectronics(激光与光电子学进展), 2010，47(2): 1.
[5] Li Bin, Hua Kai, Shen Yin. IEEE Access, 2020, (8): 56092.
[6] Lian Feiyu, Xu Degang, Fu Maixia, et al. IEEE Transactions on Terahertz Science and Technology, 2017, 7(4): 378.
[7] Liu Wei, Liu Changhong, Hu Xiaohua, et al. Food Chemistry, 2016, 210: 415.
[8] Liu Jianjun，Li Zhi，Hu Fangrong，et al. Opt. Quant. Electron.，2015, 47：685.
[9] Sun X, Liu J. Journal of Infrared, Millimeter, and Teraherz Waves, 2020, 41: 307.
[10] Ge Hongyi, Jiang Yuying, Xu Zhaohui，et al. Optics Express，2014，22(10)：12533.
[11] LIU Li-ping, WANG Yu-fei, ZHANG Ya-zhou, et al(刘丽萍, 王煜斐, 张亚洲, 等). Advances in Analytical Chemistry(分析化学进展), 2018, 8(1): 1.
[12] BU Zheng-yan, LI Zhen-feng, SONG Fei-hu, et al(步正延, 李臻峰, 宋飞虎, 等). Acta Agriculturae Zhejiangensis(浙江农业学报), 2018, 30(8): 1420.
[13] LIANG Chuan, QI Shu-ye, LI Xi-ran, et al(梁川, 戚淑叶, 李曦染, 等). Food Safety and Quality Detection Technology(食品安全质量检测学报), 2014, 5(3): 730.
[14] SHEN Xiao-chen, LI Bin, LI Xia, et al(沈晓晨, 李斌, 李霞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(S1): 288.
[15] ZHANG Ji-yang, REN Jiao-jiao, CHEN Si-hong, et al(张霁旸，任娇娇，陈思宏，等). Chinese Journal of Lasers(中国激光)，2020，47(1)：326.
[16] Roberto K H G, Mario C U A, Gledson E J, et al. Talanta, 2005, 67(4): 736.
[17] Yun Xue, Bin Zou, Yimin Wen, et al. Sustainability, 2020, 12(11)：4441.
[18] Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita. LWT-Food Science and Technology, 2016, 66: 685.
[19] FAN Shu-ting, MA Ying-yu, SHU Guo-xiang, et al(范姝婷, 马莹玉, 舒国响, 等). Journal of Shenzhen University·Science and Engineering(深圳大学学报·理工版), 2019, 36(2): 200.