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Exploring Feature Extraction Methods for Raman Spectroscopy: A Comparative Study
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DOI：10.1016/j.aca.2025.344755.png)
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Abstract
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Raman spectroscopy is a robust, non-destructive analytical technique that offers detailed insights into the chemical composition, molecular structure, and interactions of materials. However, the high-dimensional and complex nature of Raman spectral data requires effective feature extraction methods to reduce data volume and improve analysis. Efficient feature extraction methods are essential to reduce dimensionality while preserving critical spectral information. This study investigates and compares four feature extraction techniques, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Multivariate Curve Resolution (MCR), and Non-negative Matrix Factorization (NMF), in the context of Raman spectroscopy to assess their ability to reduce the dimensionality of high-dimensional spectral data while preserving critical chemical and biological information.
Keywords:
Feature extraction
Raman Spectroscopy
PCA
MCR
ICA
NMF
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