Noise Reduction in Nano-Raman Spectroscopy Using Principal Component Analysis

Abstract

Tip-enhanced Raman spectroscopy (TERS) combined with principal component analysis (PCA) offers a robust approach for enhancing signal quality and uncovering spectroscopic features otherwise concealed by noise. This study demonstrates that integrating TERS with PCA in large-scale datasets effectively reduces noise and enhances the extraction of weak Raman signals that are often obscured by random spectral fluctuations. The methodology was applied to hyperspectral datasets acquired from MoSe2 monolayers exhibiting nanoscale surface features. Through this approach, previously hidden nano-Raman peaks were successfully isolated, enabling reliable chemical identification at the nanoscale. The combined use of TERS and PCA significantly improves sensitivity and resolution in the spectroscopic analysis of 2D materials, advancing their characterization with respect to interfacial and environmental effects.