Deep Learning Algorithms for Chemometric Analysis in MIR Gas Spectroscopy

dc.contributor.authorChin, Sanghoon
dc.contributor.authorVan Zaen, Jerome
dc.contributor.authorMuntane, Enric
dc.contributor.authorDenis, Severine
dc.contributor.authorLecomte, Steve
dc.contributor.authorBalet, Laurent
dc.date.accessioned2024-10-22T12:35:17Z
dc.date.available2024-10-22T12:35:17Z
dc.date.issued2024-07-17
dc.description.abstractWe have implemented machine learning techniques into a mid-infrared gas spectrometer for two specific goals: the improvement of chemometric analysis using artificial neural networks and geostatistical analysis over a geographic area using Kriging.
dc.description.sponsorshipThis project has received funding from Horizon 2020, the European Union’s Framework Program for Research and Innovation, under grant agreement No.101015825 (TRIAGE).
dc.identifier.citationOptica Sensing Congress 2024 (AIS, LACSEA, Sensors, QSM)
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1530
dc.language.isoen
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.titleDeep Learning Algorithms for Chemometric Analysis in MIR Gas Spectroscopy
dc.typeConference
dc.type.csemdivisionsBU-I
dc.type.csemresearchareasPhotonics
dc.type.csemresearchareasScientific Instrumentation
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