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

Jinchao Liu, Margarita Osadchy, Lorna Ashton, Michael Foster, Christopher J. Solomon, Stuart J. Gibson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)4067-4074
Number of pages8
JournalThe Analyst
Volume142
Issue number21
DOIs
StatePublished - 7 Nov 2017

Bibliographical note

Publisher Copyright:
© 2017 The Royal Society of Chemistry.

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Spectroscopy
  • Electrochemistry
  • Environmental Chemistry

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