Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
By Jinchao Liu, Margarita Osadchy, Lorna Ashton, Michael Foster, Christopher J. Solomon, Stuart James Gibson. In Analysis, 2017
367 citations recorded by the source corpus.
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 for preprocessing. 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 method.
Subfields: Analytical Chemistry, Biophysics, Media Technology
View Paper: https://www.semanticscholar.org/paper/b0ebf91b31c0d491af6f269d1e3c9a9a9b6d4cd8
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Citations in the corpus
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