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Effective Raman spectra identification with tree-based methods
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DOI：10.1016/j.culher.2018.10.016.png)
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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. (C) 2018 Elsevier Masson SAS. All rights reserved.
Keyword:
Raman spectra identification
Mineral identification
Raman spectroscopy
Machine learning
Randomised trees
Random forest
Classification
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