Detail publikačního výsledku

Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging

MAGNUSSEN, E.; ZIMMERMANN, B.; DZURENDOVÁ, S.; SLANY, O.; TAFINTSEVA, V.; LILAND, K.; TO̷NDEL, K.; SHAPAVAL, V.; KOHLER, A.

Originální název

Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging

Anglický název

Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging

Druh

Článek WoS

Originální abstrakt

Infrared 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.

Anglický abstrakt

Infrared 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.

Klíčová slova

spectroscopy; ftir; microspectroscopy; prediction

Klíčová slova v angličtině

spectroscopy; ftir; microspectroscopy; prediction

Autoři

MAGNUSSEN, E.; ZIMMERMANN, B.; DZURENDOVÁ, S.; SLANY, O.; TAFINTSEVA, V.; LILAND, K.; TO̷NDEL, K.; SHAPAVAL, V.; KOHLER, A.

Vydáno

06.10.2025

Periodikum

ANALYTICAL CHEMISTRY

Svazek

97

Číslo

40

Stát

Spojené státy americké

Strany od

21947

Strany do

21955

Strany počet

9

URL

https://pubs.acs.org/doi/10.1021/acs.analchem.5c03049

Plný text v Digitální knihovně

http://hdl.handle.net/11012/255617

BibTex

@article{BUT199141,
  author="{} and  {} and Simona {Dzurendová} and  {} and  {} and  {} and  {} and  {} and  {}",
  title="Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging",
  journal="ANALYTICAL CHEMISTRY",
  year="2025",
  volume="97",
  number="40",
  pages="21947--21955",
  doi="10.1021/acs.analchem.5c03049",
  issn="0003-2700",
  url="https://pubs.acs.org/doi/10.1021/acs.analchem.5c03049"
}