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Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications.

Shuxia Guo1,2, Achim Kohler3, Boris Zimmermann3

  • 1Institute of Physical Chemistry and Abbe Center of Photonics , Friedrich Schiller University of Jena , Helmholtzweg 4 , D-07743 Jena , Germany.

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Summary
This summary is machine-generated.

Extended multiplicative signal correction (EMSC) enables robust model transfer for Raman spectroscopy. This preprocessing method improves bacterial spore classification across different instruments without requiring extra data.

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Microbiology

Background:

  • Raman spectral variations from instrumental differences hinder chemometric analysis of biological samples.
  • Established statistical models often fail when applied to data from different devices, necessitating model transfer strategies.

Purpose of the Study:

  • To introduce and evaluate a novel model transfer approach for Raman spectroscopy based on extended multiplicative signal correction (EMSC).
  • To demonstrate that the EMSC approach can improve the transferability of chemometric models across different Raman spectrometers without requiring additional sample measurements or group information.

Main Methods:

  • Developed an EMSC-based preprocessing procedure for Raman spectral data model transfer.
  • Applied the EMSC method to Raman spectra of three Bacillus bacteria spore species acquired on four different spectrometers.
  • Evaluated model performance using partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV).
  • Benchmarked model transferability using mean sensitivity, relative Pearson's correlation coefficient, and relative Fisher's discriminant ratio.

Main Results:

  • The EMSC-based model transfer approach significantly improved the mean sensitivity of bacterial spore classification on independent devices.
  • The newly defined numeric markers (relative Pearson's correlation coefficient and relative Fisher's discriminant ratio) provided consistent conclusions regarding model transferability.
  • EMSC preprocessing enhanced the objective and effective evaluation of model transferability, independent of specific classification models.

Conclusions:

  • EMSC is an effective preprocessing technique for transferring chemometric models in Raman spectroscopy, overcoming instrumental variations.
  • The proposed method enhances classification accuracy and model robustness across different measurement devices.
  • The developed numeric markers offer a reliable and objective means to assess model transferability in spectroscopic data analysis.