Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging

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dc.contributor.authorMagnussen, Eirik Almklovcs
dc.contributor.authorZimmermann, Boriscs
dc.contributor.authorDzurendová, Simonacs
dc.contributor.authorSlany, Ondrejcs
dc.contributor.authorTafintseva, Valeriacs
dc.contributor.authorLiland, Kristian Hovdecs
dc.contributor.authorTondel, Kristincs
dc.contributor.authorShapaval, Volhacs
dc.contributor.authorKohler, Achimcs
dc.coverage.issue40cs
dc.coverage.volume97cs
dc.date.accessioned2025-10-31T10:04:49Z
dc.date.available2025-10-31T10:04:49Z
dc.date.issued2025-10-06cs
dc.description.abstractInfrared spectroscopy of macroscopic samples can be calibrated against reference analysis, such as lipid profiles acquired by gas chromatography, and serve as a fast, low-cost, quantitative analytical method. Calibration of infrared microspectroscopic images against reference data is in general not feasible, and thus spatially resolved quantitative analysis from infrared spectral data has not been possible so far. In this work, we present a deep learning-based calibration transfer method to adapt regression models established for macroscopic infrared spectroscopic data to apply to microscopic pixel spectra of hyperspectral IR images. The calibration transfer is accomplished by transferring microspectroscopic infrared spectra to the domain of macroscopic spectra, which enables the use of models obtained for bulk measurements. This allows us to perform quantitative chemical analysis in the imaging domain based on infrared microspectroscopic measurements. We validate the suggested microcalibration approach on microspectroscopic data of oleaginous filamentous fungi, which is calibrated toward lipid profiles obtained by gas chromatography and measurements of glucosamine content to perform quantitative infrared microspectroscopy.en
dc.description.abstractInfrared spectroscopy of macroscopic samples can be calibrated against reference analysis, such as lipid profiles acquired by gas chromatography, and serve as a fast, low-cost, quantitative analytical method. Calibration of infrared microspectroscopic images against reference data is in general not feasible, and thus spatially resolved quantitative analysis from infrared spectral data has not been possible so far. In this work, we present a deep learning-based calibration transfer method to adapt regression models established for macroscopic infrared spectroscopic data to apply to microscopic pixel spectra of hyperspectral IR images. The calibration transfer is accomplished by transferring microspectroscopic infrared spectra to the domain of macroscopic spectra, which enables the use of models obtained for bulk measurements. This allows us to perform quantitative chemical analysis in the imaging domain based on infrared microspectroscopic measurements. We validate the suggested microcalibration approach on microspectroscopic data of oleaginous filamentous fungi, which is calibrated toward lipid profiles obtained by gas chromatography and measurements of glucosamine content to perform quantitative infrared microspectroscopy.en
dc.formattextcs
dc.format.extent21947-21955cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationANALYTICAL CHEMISTRY. 2025, vol. 97, issue 40, p. 21947-21955.en
dc.identifier.doi10.1021/acs.analchem.5c03049cs
dc.identifier.issn0003-2700cs
dc.identifier.orcid0000-0002-7796-8170cs
dc.identifier.orcid0000-0001-8286-2349cs
dc.identifier.orcid0000-0001-5759-1989cs
dc.identifier.other199141cs
dc.identifier.urihttps://hdl.handle.net/11012/255617
dc.language.isoencs
dc.relation.ispartofANALYTICAL CHEMISTRYcs
dc.relation.urihttps://pubs.acs.org/doi/10.1021/acs.analchem.5c03049cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0003-2700/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectspectroscopyen
dc.subjectftiren
dc.subjectmicrospectroscopyen
dc.subjectpredictionen
dc.subjectspectroscopy
dc.subjectftir
dc.subjectmicrospectroscopy
dc.subjectprediction
dc.titleCalibration for Quantitative Chemical Analysis in IR Microscopic Imagingen
dc.title.alternativeCalibration for Quantitative Chemical Analysis in IR Microscopic Imagingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-199141en
sync.item.dbtypeVAVen
sync.item.insts2025.10.31 11:04:49en
sync.item.modts2025.10.31 10:33:09en
thesis.grantorVysoké učení technické v Brně. Fakulta chemická. Ústav chemie potravin a biotechnologiícs
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