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Abstract Surface-enhanced Raman scattering (SERS) process results in a tremendous increase of Raman scattering cross section of molecules adsorbed to plasmonic metals and influenced by numerous physico-chemical factors such as geometry and optical properties of the metal surface, orientation of chemisorbed molecules and chemical environment. While SERS holds promise for single molecule sensitivity and optical sensing of DNA sequences, more detailed understanding of the rich physico-chemical interplay between various factors is needed to enhance predictive power of existing and future SERS-based DNA sensing platforms. In this work, we report on experimental results indicating that SERS spectra of adsorbed single-stranded DNA (ssDNA) isomers depend on the order on which individual bases appear in the 3-base long ssDNA due to intramolecular interaction between DNA bases. Furthermore, we experimentally demonstrate that the effect holds under more general conditions when the molecules do not experience chemical enhancement due to resonant charge transfer effect and also under standard Raman scattering without electromagnetic or chemical enhancements. Our numerical simulations qualitatively support the experimental findings and indicate that base permutation results in modification of both Raman and chemically enhanced Raman spectra. 

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.