期刊
ANALYTICA CHIMICA ACTA
卷 1105, 期 -, 页码 45-55出版社
ELSEVIER
DOI: 10.1016/j.aca.2020.01.018
关键词
Interval selection; Weighted bootstrap sampling; Soft shrinkage; Model population analysis; Surface-enhanced Raman scattering
资金
- National Natural Science Foundation of China [31901772, 31972154]
- Funding of 333 Project in Jiangsu Province of China [BRA2019087]
- Project of Faculty of Agricultural Equipment of Jiangsu University [4121680001]
- China Postdoctoral Science Foundation [2019M651748]
Quantitative analysis of surface-enhanced Raman scattering (SERS) spectra has been a critical step in trace level analysis. In this study, a novel variable selection method called interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) was proposed for simultaneously predicting the volume ratios of various pesticides by quantitative analysis of the SERS spectra of the compounds. Four strategies, including interval selection, model population analysis (MPA), weighted bootstrap sampling (WBS) and soft shrinkage were combined in the current designed ICIOA-SIMPLS approach. Firstly, the SERS spectra were split into a series of equal-width spectral intervals. Secondly, WBS, as a random sampling method was applied based on the initial weights of spectral intervals to generate random combinations of spectral intervals, namely sub-datasets. On this basis, multivariate calibration sub-models were developed by applying SIMPLS followed by MPA to statistically analyze the outputs of sub-models and update the weights of spectral intervals. Finally, using an iterative optimization procedure the optimal spectral interval combination with the lowest root mean squares error of cross-validation (RMSECV) was searched in a soft shrinkage manner. For the sake of investigating the performance of ICIOA-SIMPLS, four methods including SIMPLS, VCPA-SIMPLS, VISSA-SIMPLS and ICIOA-SIMPLS were tested on two groups of SERS spectra for comparison. The findings revealed that the best prediction performance was obtained with ICIOA-SIMPLS. Hence, this proposed method offers a robust and effective variable selection strategy for quantitative analysis of spectroscopic datasets. (C) 2020 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据