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Researchers have developed a new method for analyzing Raman spectroscopy data in biological samples using group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework, providing a promising approach for interpreting Raman spectroscopy data in biological samples.
Researchers at The University of British Columbia–Okanagan in Kelowna, British Columbia, Canada, have developed a new method for analyzing Raman spectroscopy data in biological samples. The team, led by Kirsty Milligan, Kendra Scarrott, and Andrew Jirasek, utilized a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework to deconstruct the spectral data and obtain meaningful information related to the biochemical make up of cells and tissues (1).
GBR-NMF is a mathematical method that factors the spectral data into groups of biochemical components, each represented by a basis spectrum, and scores that represent their relative abundance. The method is restricted in that the basis spectra must be known and cannot be altered during the analysis, providing a more biologically interpretable model than other techniques. Additionally, GBR-NMF is non-negative, meaning that it only allows for additive combinations of the basis spectra, which is a property of many biological systems. The method has been shown to be robust and accurate for reconstructing mixture solutions of known concentrations, allowing for more accurate interpretation of Raman spectroscopy data in biological samples.
Raman spectroscopy is a powerful tool for obtaining biochemical information from biological samples, but interpreting the data can be difficult and even misleading if not done carefully. The GBR-NMF framework provides better biological interpretability of the Raman spectroscopy data compared to other commonly used dimensionality reduction techniques such as principal component analysis (PCA).
To ensure the most robust GBR-NMF model, the researchers evaluated and compared the accuracy of the model in reconstructing three mixture solutions of known concentrations. They assessed the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal-to-noise (S/N) thresholds, and how different groups of biochemicals compare to each other.
The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. The researchers also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component.
Overall, the researchers found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, as long as all biochemicals in the mixture were included as bases chemicals in the model.
The researchers also found that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely because of similarity in the individual basis spectra. The new method provides a promising approach for interpreting Raman spectroscopy data in biological samples, which could have important applications in biomedical research and clinical practice.
The findings of the study were published in the journal Applied Spectroscopy (1).
(1) Milligan, K.; Scarrott, K.; Andrews, J. L.; Brolo, A. G.; Lum, J. J.; Jirasek, A. Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization. Appl. Spectrosc. 2023, 37028231169971. DOI: 10.1177/00037028231169971