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| Development of Wheat Component Detector Based on Near Infrared
Spectrum |
| MAO Li-yu1, 2, BIN Bin1*, ZHANG Hong-ming2*, LÜ Bo2, 3*, GONG Xue-yu1, YIN Xiang-hui1, SHEN Yong-cai4, FU Jia2, WANG Fu-di2, HU Kui5, SUN Bo2, FAN Yu2, ZENG Chao2, JI Hua-jian2, 3, LIN Zi-chao2, 3 |
1. School of Electrical Engineering, University of South China, Hengyang 421001 China
2. Institute of Plasma Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
3. Science Island Branch Graduate School, University of Science and Technology of China, Hefei 230031, China
4. School of Physics and Materials Engineering, Hefei Normal University, Hefei 230601, China
5. Institute of Material Science and Information Technology, Anhui University, Hefei 230601, China
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Abstract Currently, the traditional measuring methods of grain quality are mainly the traditional separation and manual inspection, which take a long time and have low efficiency. Near Infrared (NIR, 780~2 500 nm) spectral analysis technology has the advantages of a wide range of applicable samples, high accuracy of quantitative measurement, high measurement efficiency, and non-destructive testing, which is widely used in agriculture online or rapid measurement. Currently, the existing NIR instruments measuring grain quality are expensive, which prevents a wider application of this kind of device. Moreover, the predicting model is limited in applicability due to the differences ingrains in different seasons and regions. To solve these problems, in this study, a new type of NIR spectrometer system is developed to measure wheat quality. The system uses a control system developed with Python. By setting and modifying the acquisition parameters, the three steering gears and weight sensors are integrated to control the spectra data acquisition. The spectral data are preprocessed and substituted into the model to calculate the quality parameters of the target wheat samples. The principal component analysis (PCA) method removes the outlier's spectral data. Then, the selected spectral data are preprocessed by recursive mean filtering and standard normal transformation (SNV). Finally, the optimized model is obtained with the partial least squares regression (PLS) method after competitive adaptive reweighting sampling (CARS) wavelength selection. The prediction model is currently developed for moisture, wet gluten, and whiteness of wheat. The results show that this model can effectively reduce the error caused by stray light, sample uniformity, and other effective factors. The developed NIR spectrometer system can satisfy the requirements of grain acquisition and storage.
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Received: 2023-05-29
Accepted: 2023-12-15
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Corresponding Authors:
BIN Bin, ZHANG Hong-ming, LÜ Bo
E-mail: hmzhang@ipp.ac.cn；22198265@qq.com；blu@ipp.ac.cn
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[1] SUN Hui，JIANG Wei-li，TIAN Xiao-hong, et al(孙 辉，姜薇莉，田晓红, 等). Science and Technology of Cereals, Oils and Foods(粮油食品科技), 2009，17(4): 6.
[2] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立，袁洪福，陆婉珍). Analytical Instrumentation(分析仪器), 2006，(2): 1.
[3] WANG Qing(王 清). Journal of Smart Agriculture(智慧农业导刊)，2022，16：63.
[4] CHEN Wei, WANG Wei-he(陈 巍，王卫和). Food Safety(食品安全)，2023，29(6)：143.
[5] Feng S L, Qiu X B, Guo G Q, et al. Analytical Chemistry, 2021, 93(10): 4552.
[6] Guo X Q, Zheng F J, Li C L, et al. Optics and Lasers in Engineering, 2019, 115: 243.
[7] Zang Z Z, Qiu X B, Guan Y M, et al. Optical and Quantum Electronics, 2019, 51: 133.
[8] WANG Jiao-jiao, LIU Hao, REN Gui-xing(王姣姣，刘 浩，任贵兴). Journal of Plant Genetic Resources(植物资源遗传学报)，2014，15(4)：779.
[9] Stoumpos C C, Malliakas C D, Kanatzidis M G. Inorganoic Chemistry, 2013, 52(15): 9019.
[10] HU Xin-xing, SHEN Xiao-mei, MA Lei, et al(胡心行，沈小梅，马 雷，等). Liquor Making(酿酒)，2017，44(5)：97.
[11] Ferrari M, Ouaresima V. Neuroimage, 2012, 63(2): 921.
[12] Shcherbakova D M, Verkhusha V V. Nature Methods, 2013, 10(8): 751.
[13] Barnes R J, Dhanoa M S, Lister S J. Applied Spectroscopy, 2016, 43(5): 772.
[14] SU Peng-fei, ZHANG Pan-feng, ZHANG Wu-gang, et al(苏鹏飞，张攀峰，张武岗，等). Liquor-Making Science & Technology(酿酒科技), 2021，(3)：31.
[15] ZHANG Yu-rong, FU Ling, ZHOU Xian-qing(张玉荣，付 玲，周显青). Journal of Henan University of Technology (Natural Science Edition)[河南工业大学学报(自然科学版)], 2013, 34(1)：18.
[16] ZHU Hong, CHEN Bin, FU Xi-guang(朱 虹，陈 斌，付西光). Food Science and Technology(食品科技), 2001, (6): 55.
[17] XU Lu-lu, MAO Xiao-dong, SUN Lai-jun(徐璐璐，毛晓东，孙来军). Quality and Safety of Agro-Products(农产品质量与安全), 2012，(B09): 62.
[18] Delwiche S R, Chen Y R, Hruschka W R. Cereal Chemistry, 1995, 72(3): 243.
[19] Li H D, Liang Y Z, Xu Q S, et al. Journal of Chemometrics, 2009, 24(7-8): 418.
[20] ZHENG Feng, LIU Li-ying, LIU Xiao-xi, et al(郑 峰, 刘丽莹, 刘小溪，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(11): 3523.
[21] ZHOU Xing-yu, JIANG Hong-zhe, JIANG Xue-song, et al(周星宇，姜洪喆，蒋雪松，等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2022, 37(3): 157.
[22] National Standards of the People's Republic of China(中华人民共和国国家标准). GB/T 29858—2013. Standard Guidelines for Molecular Spectroscopy Multivariate Calibration Quantitative Analysis(分子光谱多元校正定量分析通则). Issused by General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, National Stndardization Administration of the People's Republic of China(中华人民共和国国家质量监督检验检疫总局，中国国家标准化管理委员会发布).
[23] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立，袁洪福，陆婉珍). Progress in Chemistry(化学进展), 2004, 16(4): 528.
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