Deep convolutional neural networks for Raman spectrum recognition: a unified solution
| Autor: | Christopher J. Solomon, Michael Foster, Lorna Ashton, Margarita Osadchy, Jinchao Liu, Stuart J. Gibson |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science Machine Learning (stat.ML) 02 engineering and technology 01 natural sciences Biochemistry Convolutional neural network Machine Learning (cs.LG) Analytical Chemistry symbols.namesake Statistics - Machine Learning Electrochemistry Environmental Chemistry Spectroscopy business.industry 010401 analytical chemistry Pattern recognition 021001 nanoscience & nanotechnology Sample (graphics) 0104 chemical sciences Support vector machine Computer Science - Learning Identification (information) symbols Artificial intelligence 0210 nano-technology Raman spectroscopy business |
| Zdroj: | The Analyst. 142:4067-4074 |
| ISSN: | 1364-5528 0003-2654 |
| Popis: | 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 of ad-hoc preprocessing steps. 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. |
| Databáze: | OpenAIRE |
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