文献类型：期刊文献

英文题名：Independent Component Analysis-Based Baseline Drift Interference Suppression of Portable Spectrometer for Optical Electronic Nose of Internet of Things

作者：Wang, Heng[1];Zhao, Zhenzhen[2];Wang, Zhenfeng[1];Xu, Guangyin[1];Wang, Liang[1]

第一作者：Wang, Heng

通讯作者：Wang, ZF[1]

机构：[1]Henan Agr Univ, Coll Mech & Elect Engn, Zhengzhou 450002, Peoples R China;[2]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou 450002, Peoples R China

第一机构：Henan Agr Univ, Coll Mech & Elect Engn, Zhengzhou 450002, Peoples R China

通讯机构：[1]corresponding author), Henan Agr Univ, Coll Mech & Elect Engn, Zhengzhou 450002, Peoples R China.

年份：2020

卷号：16

期号：4

起止页码：2698-2706

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20200508114855);Scopus(收录号：2-s2.0-85078703054);WOS:【SCI-EXPANDED(收录号:WOS:000510901000052)】；

基金：This work was supported in part by the Henan Provincial Department of Science and Technology Research Project under Grant 182102210212 and Grant 192102310473, and in part by the Key Science Research Program of Henan Province under Grant 17A480004 and Grant 18B510004.

语种：英文

外文关键词：Absorption; Internet of Things; Gases; Interference; Optical sensors; Biomedical optical imaging; Baseline drift suppression; gas sensing; Internet of Things (IoT); optical electronic nose; spectrum analysis

摘要：The optical electronic nose (e-nose) is drawing more and more attention from the academia and industry in Internet of Things applications. Since the charge-coupled Device (CCD)-based portable spectrometer is commonly used in gas sensing system of e-nose, the baseline drift interference is easy to be introduced, which reduces the accuracy and sensitivity of the system and makes the sensing performance worse. For this issue, first, the baseline drift data are analyzed and the characteristics of the baseline drift interference brought by the portable spectrometer are found in this article. Then, according to the characteristics of the drift, a baseline drift suppression technique for portable spectrometer is proposed. Specifically, it mainly includes four steps: data preprocessing, dimension reduction using principal component analysis, sources separation using independent component analysis, and reconstruction after interference suppression. Finally, experimental results show that the proposed technique suppressed the interference effectively and even better compared with classical existing methods.