Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysis

翻译：拉曼光谱可提供分子的振动特征，从而能够唯一识别不同种类的材料。这种分子指纹特性使其在医学诊断、法医学、矿物学、细菌学与病毒学等领域得到广泛应用。尽管近年来拉曼光谱数据量激增，但在开发用于拉曼光谱分析的通用机器学习方法方面尚未取得显著进展。我们通过实验与评估现有方法，认为当前无论是序列模型还是传统机器学习模型，均不足以充分分析拉曼光谱，且两者各有优劣。因此，我们尝试融合二者优势，提出一种新型网络架构——RamanNet。RamanNet 既具备CNN对不变性的抗干扰能力，又通过引入稀疏连接性能优于传统机器学习模型。在4个公开数据集上的实验表明，该架构性能优于更为复杂的现有最优方法，因此RamanNet有潜力成为拉曼光谱数据分析的默认标准。