|
|
|
|
|
|
Study on the Optimization Method of Maize Seed Moisture Quantification Model Based on THz-ATR Spectroscopy |
WU Jing-zhu1, LI Xiao-qi1, SUN Li-juan2, LIU Cui-ling1, SUN Xiao-rong1, SUN Mei1, YU Le1 |
1. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
2. Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
|
|
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.
|
Received: 2021-02-20
Accepted: 2021-05-09
|
|
|
[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. |
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[3] |
WAN Mei, ZHANG Jia-le, FANG Ji-yuan, LIU Jian-jun, HONG Zhi, DU Yong*. Terahertz Spectroscopy and DFT Calculations of Isonicotinamide-Glutaric Acid-Pyrazinamide Ternary Cocrystal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3781-3787. |
[4] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[7] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
[8] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
[9] |
LI Yang1, LI Xiao-qi1, YANG Jia-ying1, SUN Li-juan2, CHEN Yuan-yuan1, YU Le1, WU Jing-zhu1*. Visualisation of Starch Distribution in Corn Seeds Based on Terahertz Time-Domain Spectral Reflection Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2722-2728. |
[10] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[11] |
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1. Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2894-2901. |
[12] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
[13] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[14] |
LI Hao-dong1, 2, LI Ju-zi1*, CHEN Yan-lin1, HUANG Yu-jing1, Andy Hsitien Shen1*. Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2252-2257. |
[15] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
|
|
|
|