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Deep convolutional neural networks for Raman spectrum recognition: a unified solution

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Abstract

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.

Graphical abstract: Deep convolutional neural networks for Raman spectrum recognition: a unified solution

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Publication details

The article was received on 17 Aug 2017, accepted on 27 Sep 2017 and first published on 28 Sep 2017


Article type: Paper
DOI: 10.1039/C7AN01371J
Citation: Analyst, 2017, Advance Article
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    Deep convolutional neural networks for Raman spectrum recognition: a unified solution

    J. Liu, M. Osadchy, L. Ashton, M. Foster, C. J. Solomon and S. J. Gibson, Analyst, 2017, Advance Article , DOI: 10.1039/C7AN01371J

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