Journal
JOURNAL OF RAMAN SPECTROSCOPY
Volume 46, Issue 10, Pages 894-903Publisher
WILEY
DOI: 10.1002/jrs.4757
Keywords
Raman; mineral identification; spectral library search; machine learning
Categories
Funding
- NSF [DUE-1140312]
- NASA [NNA14AB04A]
- Division Of Undergraduate Education
- Direct For Education and Human Resources [1140312] Funding Source: National Science Foundation
- NASA [NNA14AB04A, 684981] Funding Source: Federal RePORTER
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Tools for mineral identification based on Raman spectroscopy fall into two groups: those that are largely based on fits to diagnostic peaks associated with specific phases, and those that use the entire spectral range for multivariate analyses. In this project, we apply machine learning techniques to improve mineral identification using the latter group. We test the effects of common spectrum preprocessing steps, such as intensity normalization, smoothing, and squashing, and found that the last is superior. Next, we demonstrate that full-spectrum matching algorithms exhibit excellent performance in classification tasks, without requiring time-intensive dimensionality reduction or model training. This class of algorithms supports both vector and trajectory input formats, exploiting all available spectral information. By combining these insights, we find that optimal mineral spectrum matching performance can be achieved using careful preprocessing and a weighted-neighbors classifier based on a vector similarity metric. Copyright (c) 2015 John Wiley & Sons, Ltd.
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