Effective Raman spectra identification with tree-based methods
| Autor: | George Pavlidis, Vasileios Sevetlidis |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Archeology
Matching (statistics) Computational complexity theory Computer science Materials Science (miscellaneous) 02 engineering and technology Conservation Machine learning computer.software_genre 01 natural sciences symbols.namesake Tree based Spectroscopy business.industry 010401 analytical chemistry Supervised learning 021001 nanoscience & nanotechnology 0104 chemical sciences Cultural heritage Identification (information) Range (mathematics) Chemistry (miscellaneous) symbols Artificial intelligence 0210 nano-technology Raman spectroscopy business General Economics Econometrics and Finance computer |
| Zdroj: | Journal of Cultural Heritage. 37:121-128 |
| ISSN: | 1296-2074 |
| Popis: | Treatment of spectral information is an essential tool for the examination of various cultural heritage materials. Raman spectroscopy has become an everyday practice for compound identification due to its non-intrusive nature, but often it can be a complex operation. Spectral identification and analysis on artists’ materials is being done with the aid of already existing spectral databases and spectrum matching algorithms. We demonstrate that with a machine learning method called Extremely Randomised Trees, we can learn a model in a supervised learning fashion, able to accurately match an entire-spectrum range into its respective mineral. Our approach was tested and was found to outperform the state-of-the-art methods on the corrected RRUFF dataset, while maintaining low computational complexity and inherently supporting parallelisation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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