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Title: Quantitative analysis of gas phase IR spectra based on extreme learning machine regression model
Authors: Ouyang, Tinghui
Wang, Chongwu
Yu, Zhangjun
Stach, Robert
Mizaikoff, Boris
Liedberg, Bo
Huang, Guang-Bin
Wang, Qi-Jie
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ouyang, T., Wang, C., Yu, Z., Stach, R., Mizaikoff, B., Liedberg, B., . . . Wang, Q.-J. (2020). Quantitative analysis of gas phase IR spectra based on extreme learning machine regression model. Sensors, 19(24), 5535-. doi:10.3390/s19245535
Project: NRF-CRP18-2017-02
Journal: Sensors
Abstract: Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed model makes two contributions to the field of advanced chemometrics. First, an ELM-based autoencoder (AE) was developed for reducing the dimensionality of spectral signals and learning important features for regression. Second, the fast regression ability of ELM architecture was directly used for constructing the regression model. In this contribution, nitrogen oxide mixtures (i.e., N2O/NO2/NO) found in vehicle exhaust were selected as a relevant example of a real-world gas mixture. Both simulated data and experimental data acquired using Fourier transform infrared spectroscopy (FTIR) were analyzed by the proposed chemometrics model. By comparing the numerical results with those obtained using conventional principle components regression (PCR) and partial least square regression (PLSR) models, the proposed model was verified to offer superior robustness and performance in quantitative IR spectral analysis.
ISSN: 1424-8220
DOI: 10.3390/s19245535
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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