Research and Application Validation of a Feature Wavelength Selection Method Based on Acousto-Optic Tunable Filter (AOTF) and Automatic Machine Learning (AutoML)

被引:7
作者
Ji, Zhongpeng [1 ,2 ]
He, Zhiping [1 ,2 ]
Gui, Yuhua [1 ,2 ]
Li, Jinning [1 ,2 ]
Tan, Yongjian [1 ,2 ]
Wu, Bing [1 ,2 ]
Xu, Rui [1 ,2 ]
Wang, Jianyu [1 ,2 ]
机构
[1] Chinese Acad Sci, Key Lab Space Act Optoelect Technol, Shanghai Inst Tech Phys, Shanghai 200083, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
AOTF; AutoML; feature wavelength selection; near infrared detection system; NEAR-INFRARED SPECTROSCOPY; QUALITY PARAMETERS; VARIABLE SELECTION; PLS REGRESSION; ONLINE; OLIVES; FRUIT;
D O I
10.3390/ma15082826
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Near-infrared spectroscopy has been widely applied in various fields such as food analysis and agricultural testing. However, the conventional method of scanning the full spectrum of the sample and then invoking the model to analyze and predict results has a large amount of collected data, redundant information, slow acquisition speed, and high model complexity. This paper proposes a feature wavelength selection approach based on acousto-optical tunable filter (AOTF) spectroscopy and automatic machine learning (AutoML). Based on the programmable selection of sub nm center wavelengths achieved by the AOTF, it is capable of rapid acquisition of combinations of feature wavelengths of samples selected using AutoML algorithms, enabling the rapid output of target substance detection results in the field. The experimental setup was designed and application validation experiments were carried out to verify that the method could significantly reduce the number of NIR sampling points, increase the sampling speed, and improve the accuracy and predictability of NIR data models while simplifying the modelling process and broadening the application scenarios.
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页数:12
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