Abstract
Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.