期刊论文

A Variable Selection Approach of Near Infrared Spectra Based on Window Competitive Adaptive Reweighted Sampling Strategy

Du Guo-rong

中文

光谱学与光谱分析

1000-0593

2019

39

5

1428-1432

10.3964/j.issn.1000-0593(2019)05-1428-05

31601551:国家自然科学基金 31671931:国家自然科学基金 15YJ08:湖南农业大学***科学基金 16QN24:湖南农业大学青年科学基金

本校为第一且通讯机构

通过消除光谱中的冗余信息变量,挑选出代表样品性质的特征变量代替全谱建立定量模型,可以提高近红外分析结果的准确性。基于进化论中适者生存原理的竞争性自适应重加权采样(CARS)算法因具有计算速度快、筛选得到的特征波长少等优点,在近红外特征变量筛选方面得到了广泛的应用。然而该方法在计算过程中容易出现校正集和验证集结果不一致情况。这是因为算法过于强调校正集交叉验证结果,且并未考虑相邻变量之间的协同作用。为了建立更加稳健的变量筛选方法,通过结合“窗口”以及CARS算法的优势,提出了一种基于窗口竞争性自适应重加权采样(WCARS)策略的近红外特征变量筛选方法,并将其应用于复杂植物...

Variable selection plays an important role in the quantitative analysis of near infrared spectra. The accuracy of near infrared spectroscopy can be improved by eliminating the redundant variables and selecting the characteristic variables. Competitive adaptive reweighted sampling (CARS) method is a newly developed strategy for wavelength selection by employing the principle 'survival of the fittest' on which Darwin's Evolution Theory is based. The number of selected wavelengths by CARS is much smaller than those of other methods with fast calcu...