Machine learning for composition analysis of ssDNA using chemical enhancement in SERS.

Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. In this manuscript we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train a linear regression as well as neural network models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for PCA (Principal Component Analysis) and NN (Neural Network) models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Furthermore, we show that NN model provides superior performance in the presence of complex noise and data dispersion factors that affect SERS signals collected from metal substrates fabricated on different days.