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 (21). ISSN 0003-2654

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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.

Item Type:
Journal Article
Journal or Publication Title:
Analyst
Additional Information:
© The Royal Society of Chemistry 2017
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1600/1603
Subjects:
ID Code:
88279
Deposited By:
Deposited On:
16 Oct 2017 09:36
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Nov 2020 04:58