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Adaptive Raman spectral unmixing method based on Voigt peak compensation for quantitative analysis of cellular
Xiang Chen1,2, Ping Tang1,2, Jianhui Wan1,2
1Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China.
Biomedical Optics Express
|March 20, 2025
Summary
This study introduces a new Raman spectroscopy method using a Voigt function for accurate cell biochemical analysis. The advanced technique improves spectral unmixing, enabling reliable quantification of molecular changes during cellular processes.
Area of Science:
- Biomedical Optics
- Spectroscopy
- Cell Biology
Background:
- Raman spectroscopy offers label-free, non-invasive cellular biochemical analysis by detecting molecular vibrations.
- Accurate quantification of cellular components is hindered by overlapping spectral signals, necessitating effective spectral unmixing.
- Existing unmixing methods struggle with interpretability (unsupervised) or sensitivity to environmental variations (supervised).
Purpose of the Study:
- To develop an improved spectral unmixing algorithm for Raman spectroscopy in cellular analysis.
- To enhance the accuracy and reliability of quantitative biochemical analysis in cells.
- To address the limitations of traditional unmixing methods in handling spectral distortions and environmental changes.
Main Methods:
- Incorporation of a parameterized Voigt function into a linear spectral mixing model for element spectrum compensation.
- Application of iterative least-squares optimization for adaptive unmixing and quantitative analysis.
- Validation through simulations and application to Raman spectra from Hela cell apoptosis and induced pluripotent stem cells (iPSCs) differentiation.
Main Results:
- The Voigt-compensated unmixing algorithm demonstrated improved spectral fitting accuracy and robustness in simulations.
- The method successfully tracked biochemical molecular changes during Hela cell apoptosis.
- Accurate monitoring of biochemical alterations during iPSCs differentiation was achieved.
Conclusions:
- The developed algorithm provides a precise, reliable, and versatile tool for quantitative biochemical analysis using Raman spectroscopy.
- This approach enhances the capability of Raman spectroscopy for in-depth cellular studies.
- The method overcomes key challenges in spectral unmixing for complex biological samples.
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