Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution

Liu, Jinchao and Osadchy, Margarita and Ashton, Lorna and Foster, Michael and Solomon, Christopher J. and Gibson, Stuart J. (2017) Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution. Analyst, 142 . pp. 4067-4074. ISSN 0003-2654. E-ISSN 1364-5528. (doi:https://doi.org/10.1039/C7AN01371J) (Full text available)

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http://dx.doi.org/10.1039/C7AN01371J

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.

Item Type: Article
Divisions: Faculties > Sciences > School of Physical Sciences > Forensic Imaging Group
Faculties > Sciences > School of Physical Sciences
Depositing User: Stuart Gibson
Date Deposited: 28 Sep 2017 15:17 UTC
Last Modified: 28 Sep 2018 23:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63657 (The current URI for this page, for reference purposes)
Gibson, Stuart J.: https://orcid.org/0000-0002-7981-241X
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