  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.
