{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:57:33Z","timestamp":1784303853750,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,14]],"date-time":"2019-12-14T00:00:00Z","timestamp":1576281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation, Competitive Research Program","award":["NRF-CRP18-2017-02"],"award-info":[{"award-number":["NRF-CRP18-2017-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19245535","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"5535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model"],"prefix":"10.3390","volume":"19","author":[{"given":"Tinghui","family":"Ouyang","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongwu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangjun","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"},{"name":"Key Laboratory of In-fiber Integrated Optics, Ministry Education of China, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Stach","sequence":"additional","affiliation":[{"name":"Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris","family":"Mizaikoff","sequence":"additional","affiliation":[{"name":"Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liedberg","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang-Bin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9910-1455","authenticated-orcid":false,"given":"Qi-Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.3390\/s120302610","article-title":"Metal oxide nanostructures and their gas sensing properties: A review","volume":"12","author":"Sun","year":"2012","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9635","DOI":"10.3390\/s120709635","article-title":"A survey on gas sensing technology","volume":"12","author":"Liu","year":"2012","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1146\/annurev-anchem-071015-041507","article-title":"Advances in mid-infrared spectroscopy for chemical analysis","volume":"9","author":"Haas","year":"2016","journal-title":"Annu. 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