特征选择
校准
多元统计
选择(遗传算法)
近红外光谱
仪表(计算机编程)
人工智能
生化工程
统计
变量(数学)
数据挖掘
机器学习
计算机科学
数学
工程类
物理
光学
数学分析
操作系统
作者
Yong‐Huan Yun,Hong‐Dong Li,Baichuan Deng,Dongsheng Cao
标识
DOI:10.1016/j.trac.2019.01.018
摘要
With the advances in innovative instrumentation and various valuable applications, near-infrared (NIR) spectroscopy has become a mature analytical technique in various fields. Variable (wavelength) selection is a critical step in multivariate calibration of NIR spectra, which can improve the prediction performance, make the calibration reliable and provide simpler interpretation. During the last several decades, there have been a large number of variable selection methods proposed in NIR spectroscopy. In this paper, we generalize variable selection methods in a simple manner to introduce their classifications, merits and drawbacks, to provide a better understanding of their characteristics, similarities and differences. We also introduce some hybrid and modified methods, highlighting their improvements. Finally, we summarize the limitations of existing variable selection methods, providing our remarks and suggestions on the development of variable selection methods, to promote the development of NIR spectroscopy.
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