文献类型：期刊文献

英文题名：Online updating of NIR model and its industrial application via adaptive wavelength selection and local regression strategy

作者：He, Kaixun[1,2];Cheng, Hui[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2014

卷号：134

起止页码：79

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242716633890);WOS:【SCI-EXPANDED(收录号:WOS:000336828100009)】；

基金：This study is supported by the Major State Basic Research Development Program of China (2012CB720500), the National Science Fund for Outstanding Young Scholars (61222303), the National Natural Science Foundation of China (61333010, 21276078), the Shanghai Rising-Star Program (13QH1401200), the New Century Excellent Talents in University (NCET-10-0885), and the Shanghai Leading Academic Discipline Project (B504). We thank the editor and anonymous reviewers for their valuable comments and suggestions to help improve the quality of our paper.

语种：英文

外文关键词：NIR model; Wavelength selection; Local regression; Gasoline blending

摘要：Near-infrared (NIR) spectroscopy has been widely used to estimate product quality or other key variables. The conventional updating strategy for an NIR model is based on new available samples. However, during a sampling interval, the model structure remains unchanged. To address this problem, in this article, a novel local regression strategy is proposed that can be adjusted according to process changes through wavelength selection and local regression approaches. The main idea of the presented algorithm is that for each query sample, a relevant calibration sample-set is selected, then the wavelength structure is updated and a local model is established. The performance of the method is demonstrated through an NIR dataset of gasoline, which was collected from a real gasoline blending and optimal control process. Compared with traditional partial least squares (PIS), locally weighted partial least squares (LW-PLS), and several other updating strategies, the proposed method is more accurate. (C) 2014 Elsevier B.V. All rights reserved.