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| Effect of Wavelength Drift on PLSR Calibration Model of Near-Infrared Spectroscopy |
| LU Qi-peng1, WANG Dong-min2*, SONG Yuan1*, DING Hai-quan3, GAO Hong-zhi3 |
1. State Key Laboratory of Applied Optics Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2. School of Food Science and Technology, Henan University of Technology, Zhengzhou 450001, China
3. Guang Dong Spectrastar Instruments Co., Ltd., Guangzhou 510000, China
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Abstract Partial least squares regression (PLSR) calibration model will be effect by the wavelength change of a single instrument at a different time and the wavelength consistency of multiple instruments. The process of near-infrared spectroscopy analysis, this problems can be unified as the effect of wavelength drift on chemometric calibration model. In this paper, taking the analysis of crude protein in wheat flour as an example, two calibration models I and II were established by partial least squares regression (PLSR) method within different spectral regions. Different types and amplitudes of wavelength shift information were generated by computer and superimposed into the spectra of the validation set to produce wavelength shift information relative to the spectra of calibration set, the effect of wavelength drift on PLSR calibration model was studied by adding different types and amplitude of wavelength drift information to the spectra of the validation set samples. The results show that the RMSEP of every model is no more than 0.3% and the corresponding Rp is no less than 0.98 when there is no wavelength drift information in the spectra of validation set samples. When the wavelength drift at different wavelengths is constant, the RMSEP increases as the wavelength drift amplitude increases, the RMSEP increases to 3.69% when the wavelength drift is -32 cm-1, and the Rp is almost constant; When the wavelength drift varies randomly at different wavelengths, the prediction results of model II based on long wavelength regions are almost not affected. The model II is corrected by a series of spectra added to the calibration set with different wavelength drift information, the RMSEP of the corrected model is 0.3%, The influence of wavelength drift information on RMSEP has been almost eliminated, but the number of regression factors used to establish corredted model increases significantly from 3 to 8, the robustness of the model varies greatly. In general, the RMSEP can be polished by correcting the prediction results to ensure the accuracy of the analysis results if the amplitude of wavelength drift is slight. This study provides an experimental basis for determining the design instrument parameters and operating procedures to improve the reliability of NIR analysis results.
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Received: 2021-01-15
Accepted: 2021-04-06
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Corresponding Authors:
WANG Dong-min, SONG Yuan
E-mail: wdongmin@126.com；songyuan_show@126.com
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[1] LIU Cui-ling，WU Jing-zhu，SUN Xiao-rong（刘翠玲，吴静珠，孙晓荣）. Study on Near Infrared Spectroscopy in Food Quality Detection（近红外光谱技术在食品品质检测方法中的研究）. Beijing: China Machine Press（北京：机械工业出版社），2015.
[2] LIU Yi-lin，ZHANG Hai-yan，PENG Hai-gen，et al(刘艺琳，张海燕，彭海根，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2020, 40(10): 3260.
[3] Amodio M L, Ceglie F, Chaudhry M M, et al. Postharvest Biol. Technol., 2017，125（3）：112. [4] Yang Lei, Yang Qianhu, Yang Shihua，et al. Journal of Near Infrared Spectroscopy，2015，23（6）：391.
[5] Hazarika A K, Chanda S, Sabhapondit S, et al. J. Food Sci. Technol., 2018, 55(12): 4867.
[6] CHU Xiao-li，SHI Yun-ying，CHEN Pu，et al（褚小立，史云颖，陈 瀑，等）. Journal of Instrumental Analysis(分析测试学报)，2019，38(5)：603.
[7] ZHU Bin，SHI Yong，MA Shu-yan，et al（朱 斌，史 勇，马淑艳，等）. Pharm. J. Chin. PLA（解放军药学学报）, 2005，21（4）：280.
[8] Zhang Jin, Guo Cheng, Cui Xiaoyu, et al. Analytica Chimica Acta, 2018, (11): 13.
[9] Liu Yan，Cai Wensheng，Shao Xueguang. Analytica Chimica Acta, 2014，836(11)：18.
[10] WEI Yang，WANG Xu-quan，WEI Yong-chang，et al（魏 杨，王绪泉，魏永畅，等）. Infrared and Laser Engineering(红外与激光工程)，2019，48（9）：31.
[11] Fearn T. Journal of Near Infrared Spectroscopy，2001，9(4): 229.
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