Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning

Abstract

Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hindered by spectral interference from citrate ions, which overlap with key DNA features. This study introduces an innovative, ultrasensitive SERS platform utilizing thiol-modified silver nanoparticles (Ag@SDCNPs) that overcomes this challenge by eliminating citrate interference. This platform enables direct, interference-free detection and structural characterization of a wide range of DNA conformations, including single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), i-motif, hairpin, G-quadruplex, and triple-stranded DNA (tsDNA). Employing calcium ions as aggregating agents and deuterated methanol as an internal standard, the system achieved high spectral quality and reproducibility. Machine learning (ML) techniques, such as linear discriminant analysis (LDA) and t-distributed stochastic neighbor embedding (t-SNE), were utilized for spectral classification, alongside support vector machines (SVM) for predictive modeling, yielding accuracies above 99%. These findings establish a robust and versatile platform for DNA structural analysis, offering transformative potential for applications in clinical diagnostics and biomedical research.