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综合比较不同的波长选择方法使用几个近红外光谱数据集与不同的维度

Tao Wang¹, Yun Zheng¹, Lilan Xu¹

¹School of Food Science and Engineering, Hainan University, Haikou 570228 PR China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|January 28, 2025

概括

本研究比较了近红外 (NIR) 光谱的波长选择方法. 模型人口分析 (MPA) 和波长间隔选择 (WIS) 方法为NIR光谱数据分析提供了卓越的性能和稳定性.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
化学测量化学测量化学测量

背景情况:

近红外 (NIR) 光谱在农业和食品安全等领域至关重要.
在NIR光谱中,高维度和数据冗余性带来了重大分析挑战.
有效的波长选择对于优化NIR光谱分析至关重要.

研究的目的:

评估和比较NIR光谱数据集的不同波长选择方法的性能.
为研究人员选择最佳波长选择技术提供全面的参考.
确定处理高维NIR数据的最有效方法.

主要方法:

波长选择方法分为四组:基于参数的部分最小平方 (PLS),基于智能优化算法 (IOA),基于模型人口分析 (MPA) 和波长间隔选择 (WIS) 方法.
基于包括R²_C,R²_P,RMSEC,RMSEP,选定变量,计算时间和iRMSEP在内的指标进行比较分析.
对20种特征波长选择方法的评估,包括启动软收缩 (BOSS) 和遗传算法间隔部分最小平方 (GA-iPLS).

主要成果:

与其他类别相比,使用基于MPA和WIS方法开发的模型在大多数数据集中表现出卓越的稳定性和性能.
在评估的20种方法中,启动软收缩 (BOSS) 和遗传算法间隔部分最小平方 (GA-iPLS) 显示出最好的整体性能.
该研究强调了特定先进方法在缓解与高维NIR数据相关的挑战方面的有效性.

结论:

基于MPA和WIS的波长选择方法是强烈推用于强大的NIR光谱分析.
BOSS和GA-iPLS成为复杂的NIR数据集中波长选择的高性能技术.
这项研究为优化NIR光谱应用中的分析策略提供了宝贵的见解.

关键词:

化学测量化学测量化学测量数据集是一组数据集.建模建模模型是什么近红外光谱学近红外光谱学波长选择波长的选择.

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