Abstract
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.
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