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Raman spectroscopy provides a vibrational profile of the molecules and thuscan be used to uniquely identify different kind of materials. This sort offingerprinting molecules has thus led to widespread application of Ramanspectrum in various fields like medical dignostics, forensics, mineralogy,bacteriology and virology etc. Despite the recent rise in Raman spectra datavolume, there has not been any significant effort in developing generalizedmachine learning methods for Raman spectra analysis. We examine, experiment andevaluate existing methods and conjecture that neither current sequential modelsnor traditional machine learning models are satisfactorily sufficient toanalyze Raman spectra. Both has their perks and pitfalls, therefore we attemptto mix the best of both worlds and propose a novel network architectureRamanNet. RamanNet is immune to invariance property in CNN and at the same timebetter than traditional machine learning models for the inclusion of sparseconnectivity. Our experiments on 4 public datasets demonstrate superiorperformance over the much complex state-of-the-art methods and thus RamanNethas the potential to become the defacto standard in Raman spectra data analysis

RamanNet: A generalized neural network architecture for Raman Spectrum Analysis