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Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging.

Eirik Almklov Magnussen1, Boris Zimmermann1, Simona Dzurendova2

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This study introduces a deep learning method for quantitative chemical analysis using infrared microspectroscopy. It enables accurate measurements on small samples by transferring calibration from larger samples.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Biotechnology

Background:

  • Infrared spectroscopy offers fast, low-cost analysis of macroscopic samples.
  • Quantitative analysis of microscopic infrared spectral data has been limited.
  • Spatially resolved chemical information from infrared microspectroscopy is challenging.

Purpose of the Study:

  • To develop a deep learning-based method for quantitative chemical analysis of hyperspectral infrared images.
  • To enable calibration transfer from macroscopic infrared spectroscopy models to microscopic pixel spectra.
  • To overcome limitations in spatially resolved quantitative analysis using infrared microspectroscopy.

Main Methods:

  • A deep learning-based calibration transfer technique was developed.
  • Infrared microspectroscopic data was adapted to the domain of macroscopic spectra.
  • Regression models established for bulk measurements were applied to hyperspectral images.

Main Results:

  • The method successfully adapted macroscopic calibration models for use with microscopic spectra.
  • Quantitative chemical analysis was achieved directly from infrared microspectroscopic measurements.
  • The approach was validated using oleaginous filamentous fungi, correlating with gas chromatography lipid profiles and glucosamine content.

Conclusions:

  • Deep learning enables calibration transfer for quantitative infrared microspectroscopy.
  • This method opens new possibilities for spatially resolved chemical analysis in imaging.
  • The approach is valuable for analyzing biological samples like fungi.