Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
This provides a more automated solution for chemists and materials scientists, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the problem of identifying chemical species from Raman spectra without requiring manual preprocessing steps, achieving superior classification performance compared to other machine learning methods like support vector machines on the RRUFF database.
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identification of chemical species in which a convolutional neural network is trained to automatically identify substances according to their Raman spectrum without the need of ad-hoc preprocessing steps. We evaluated our approach using the RRUFF spectral database, comprising mineral sample data. Superior classification performance is demonstrated compared with other frequently used machine learning algorithms including the popular support vector machine.