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PyFasma: an open-source, modular Python package for preprocessing and multivariate analysis of Raman spectroscopy data.

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Bibliographic Details
Title: PyFasma: an open-source, modular Python package for preprocessing and multivariate analysis of Raman spectroscopy data.
Authors: Pavlou, Eleftherios, Kourkoumelis, Nikolaos
Source: Analyst; 7/21/2025, Vol. 150 Issue 4, p3112-3122, 11p
Subject Terms: RAMAN spectroscopy, MULTIVARIATE analysis, PYTHON programming language, FEATURE extraction, INTEGRATED software
Abstract: Raman spectroscopy is a versatile, label-free technique for probing molecular composition in biological samples. However, the detection of subtle biochemical traits in high-throughput spectral datasets requires careful preprocessing, dimensionality reduction, and statistically sound analytical strategies. We present PyFasma, an open-source Python package for Raman spectroscopy, integrating essential preprocessing tools (e.g., spike removal, smoothing, baseline correction, normalization), multivariate techniques (PCA, PLS-DA), and spectral deconvolution within a modular, Jupyter Notebook-friendly framework. In addition to describing the software, we demonstrate PyFasma's capabilities through a practical biomedical case study comparing Raman spectra from healthy and osteoporotic cortical bone samples. The results revealed statistically significant differences in mineral-to-matrix ratio and crystallinity between assigned groups, with PCA and PLS-DA successfully distinguishing pathological from normal bone spectra. PyFasma encourages best practices in model validation, including the powerful but often overlooked, repeated stratified cross-validation, enhancing the generalizability of multivariate analyses. It also offers an easy-to-use, extensible solution for Raman data analysis, enabling the reproducible and robust interpretation of complex spectra of biological samples. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Raman spectroscopy is a versatile, label-free technique for probing molecular composition in biological samples. However, the detection of subtle biochemical traits in high-throughput spectral datasets requires careful preprocessing, dimensionality reduction, and statistically sound analytical strategies. We present PyFasma, an open-source Python package for Raman spectroscopy, integrating essential preprocessing tools (e.g., spike removal, smoothing, baseline correction, normalization), multivariate techniques (PCA, PLS-DA), and spectral deconvolution within a modular, Jupyter Notebook-friendly framework. In addition to describing the software, we demonstrate PyFasma's capabilities through a practical biomedical case study comparing Raman spectra from healthy and osteoporotic cortical bone samples. The results revealed statistically significant differences in mineral-to-matrix ratio and crystallinity between assigned groups, with PCA and PLS-DA successfully distinguishing pathological from normal bone spectra. PyFasma encourages best practices in model validation, including the powerful but often overlooked, repeated stratified cross-validation, enhancing the generalizability of multivariate analyses. It also offers an easy-to-use, extensible solution for Raman data analysis, enabling the reproducible and robust interpretation of complex spectra of biological samples. [ABSTRACT FROM AUTHOR]
ISSN:00032654
DOI:10.1039/d5an00452g