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Automated identification of class-separating principal component subspaces in biomedical Raman and Fourier Transform
Dorota Jakubczyk1, Jan Jakub Kęsik2, Piotr Terlecki2
1Department of Physics and Medical Engineering, Rzeszow University of Technology, Rzeszów, Poland.
Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|November 4, 2025
Summary
PCA AutoExplorer automates the identification of key spectral features for disease diagnosis using Principal Component Analysis (PCA) triplets. This tool enhances biomarker discovery in clinical vibrational spectroscopy by rigorously ranking spectral data.
Area of Science:
- Biomedical Engineering
- Spectroscopy
- Data Science
Background:
- Clinical vibrational spectroscopy offers powerful biomarker discovery potential.
- Automated analysis is crucial for handling complex spectral datasets and improving diagnostic accuracy.
Purpose of the Study:
- To introduce PCA AutoExplorer, an open-source tool for automated identification of Principal Component (PC) triplets that maximize class separation in clinical vibrational spectroscopy.
- To develop a statistically rigorous framework for prioritizing spectral biomarkers and enhancing diagnostic reliability.
Main Methods:
- Exhaustive evaluation of all PC triplets using Mahalanobis distance and Linear Discriminant Analysis (LDA) accuracy.
- Development of marker strength plots and PC loading heatmaps for prioritizing diagnostic bands.
- Validation using Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy data from acute coronary syndrome patients and controls.
Main Results:
- PCA AutoExplorer successfully identified PC triplets yielding high class separation (Mahalanobis distance up to ~2.03, LDA accuracy 96%-100%).
- Raman spectroscopy (second derivative) and FTIR (intensity/first derivative) showed optimal performance depending on spectral range.
- Key diagnostic bands were highlighted, including specific wavenumbers for FTIR and Raman Spectroscopy.
Conclusions:
- PCA AutoExplorer provides a reproducible and statistically sound method for biomarker discovery in clinical vibrational spectroscopy.
- The tool enhances the reliability of spectral analysis and is adaptable to other omics-related spectral data.
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