Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145682
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dc.contributor.authorOuyang, Tinghuien_US
dc.contributor.authorWang, Chongwuen_US
dc.contributor.authorYu, Zhangjunen_US
dc.contributor.authorStach, Roberten_US
dc.contributor.authorMizaikoff, Borisen_US
dc.contributor.authorLiedberg, Boen_US
dc.contributor.authorHuang, Guang-Binen_US
dc.contributor.authorWang, Qi-Jieen_US
dc.date.accessioned2021-01-05T01:47:02Z-
dc.date.available2021-01-05T01:47:02Z-
dc.date.issued2019-
dc.identifier.citationOuyang, 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/s19245535en_US
dc.identifier.issn1424-8220en_US
dc.identifier.urihttps://hdl.handle.net/10356/145682-
dc.description.abstractAdvanced 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.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF-CRP18-2017-02en_US
dc.relation.ispartofSensorsen_US
dc.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 (http://creativecommons.org/licenses/by/4.0/).en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleQuantitative analysis of gas phase IR spectra based on extreme learning machine regression modelen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.3390/s19245535-
dc.description.versionPublished versionen_US
dc.identifier.pmid31847409-
dc.identifier.issue24en_US
dc.identifier.volume19en_US
dc.subject.keywordsGas Sensingen_US
dc.subject.keywordsQuantitative Spectrum Analysisen_US
dc.description.acknowledgementThis work is supported by funding from National Research Foundation, Competitive Research Program (NRF-CRP18-2017-02).en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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