Premium
Machine learning tools formineral recognition and classification from Raman spectroscopy
Author(s) -
Carey C.,
Boucher T.,
Mahadevan S.,
Bartholomew P.,
Dyar M. D.
Publication year - 2015
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.4757
Subject(s) - preprocessor , artificial intelligence , pattern recognition (psychology) , smoothing , computer science , normalization (sociology) , support vector machine , classifier (uml) , dimensionality reduction , raman spectroscopy , metric (unit) , machine learning , data mining , engineering , optics , computer vision , physics , operations management , sociology , anthropology
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 © 2015 John Wiley & Sons, Ltd.