文献类型：期刊文献

英文题名：Pattern-Coupled Baseline Correction Method for Near-Infrared Spectroscopy Multivariate Modeling

作者：Li, Yuqiang[1];Wang, Xinjie[1];Yu, Huijing[1];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：72

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20231714017300);WOS:【SCI-EXPANDED(收录号:WOS:000994647700018)】；

基金：This work was supported in part by the National Natural Science Foundation of China through Basic Science Center Program under Grant 61988101; in part by the Shanghai Committee of Science and Technology, China, under Grant 22DZ1101500; in part by the National Natural Science Foundation of China under Grant 61973124; in part by the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (CAST) under Grant 2022QNRC001; in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; and in part by the Shanghai AI Laboratory.

语种：英文

外文关键词：Baseline correction; multivariate modeling; near-infrared (NIR) spectroscopy; pattern-coupled model; sparse Bayesian learning (SBL)

摘要：In near-infrared (NIR) modeling, the baseline drift tends to affect the qualitative and quantitative performance of the multivariate calibration model. The baseline correction method based on the sparse representation of independent wavelength is a recent method for pure spectra estimation. In fact, the measured NIR generally exhibit local wavelength coupling sparse properties, and the existing baseline correction methods suffer from performance degradation due to ignoring the wavelength coupling effect on baseline correction. In this article, the pattern-coupled learning framework considering the local coupling property is proposed to achieve pure spectrum fitting and baseline correction simultaneously. Specifically, the pattern-coupled hierarchical Gaussian prior model is introduced to characterize the local sparse structure corresponding to characteristic peaks with wavelength coupling. The adaptive coupling method is further proposed to achieve robust learning of NIR under different noise interference. Unlike conventional frameworks where wavelength or hyperparameters are learned independently, the proposed mode coupling framework enables accurate baseline estimation without prior knowledge of spectral structure by characterizing wavelength coupling properties. Compared with the state-of-the-art methods, the root mean square error (RMSE) and R-2 of the proposed method are reduced and increased by 14.41% and 1.53V, 14.12% and 2.01%, 12.86% and 12.93% in three measured NIR datasets, respectively.