Journal
ANALYST
Volume 142, Issue 21, Pages 4067-4074Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c7an01371j
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Funding
- Innovate UK [132200]
- EPSRC [EP/M013375/1] Funding Source: UKRI
- Innovate UK [132200] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M013375/1] Funding Source: researchfish
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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.
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