SERS-based ssDNA composition analysis with inhomogeneous peak broadening and reservoir computing

Surface-enhanced Raman spectroscopy employed in conjunction with post-processing machine learning methods is a promising technique for effective data analysis, allowing one to enhance the molecular and chemical composition analysis of information rich DNA molecules. In this work, we report on a room temperature inhomogeneous broadening as a function of the increased adenine concentration and employ this feature to develop one-dimensional and two dimensional chemical composition classification models of 200 long single stranded DNA sequences. Afterwards, we develop a reservoir computing chemical composition classification scheme of the same molecules and demonstrate enhanced performance that does not rely on manual feature identification. (C) 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Surface-enhanced Raman spectroscopy combined with post-processing machine learning methods is used to enhance the analysis of molecular and chemical composition of DNA molecules. In this work, the inhomogeneous broadening caused by increasing adenine concentration is investigated, and one-dimensional and two-dimensional chemical composition classification models are developed for single stranded DNA sequences. Furthermore, a reservoir computing chemical composition classification scheme is proposed, which shows improved performance without relying on manual feature identification.