Abstract
Raman spectroscopy has emerged as a pivotal technology in modern scientific research and industrial applications, offering nondestructive, high-resolution analysis with robust molecular fingerprinting capabilities. The extraction of Raman spectral features is a critical step in spectral data analysis, directly influencing sample identification, classification, and quantitative outcomes. However, integrating important data features from machine learning models with context-specific biosignatures to derive meaningful insights into spectral analysis remains a significant challenge. Herein, the Raman Peak Feature Matching (RPFM) method is proposed, which matches protein peak features with salient breast cell data features extracted from the machine learning models. Feature augmentation is subsequently applied to the matching-retained breast cell features, thereby enhancing spectral analysis capabilities. The RPFM method is applied to breast cell spectra for feature augmentation with a reclassification accuracy of 97.12% using a linear support vector machine model, achieving an 8.34% improvement over the model's performance without feature augmentation. The RPFM method has also been successfully implemented in generalized linear logistic regression and tree-based eXtreme gradient boosting, demonstrating its versatility across diverse machine learning algorithms. The RPFM method leverages data-driven machine learning models while compensating for the inability to take into account specific specialized background knowledge. This methodology significantly advances the accuracy and efficacy of spectral analysis in biological and medical applications, offering a novel framework for machine learning algorithms to perform augmented Raman spectral analysis.