Automatic Classification of Laser-Induced Breakdown Spectroscopy (LIBS) Data of Protein Biomarker Solutions

We perform multi-class classification of laser-induced breakdown spectroscopy data of four commercial samples of proteins diluted in phosphate-buffered saline solution at different concentrations: bovine serum albumin, osteopontin, leptin, and insulin-like growth factor II. We achieve this by using...

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Bibliographic Details
Published in:Applied spectroscopy 2014-09, Vol.68 (9), p.1067-1075
Main Authors: Pokrajac, David, Lazarevic, Aleksandar, Kecman, Vojislav, Marcano, Aristides, Markushin, Yuri, Vance, Tia, Reljin, Natasa, McDaniel, Samantha, Melikechi, Noureddine
Format: Article
Language:English
Subjects:
Animals
Biomarkers - analysis
Biomarkers - chemistry
Cattle
Lasers
Neural Networks (Computer)
Principal Component Analysis
Proteins - chemistry
Solutions - chemistry
Spectrum Analysis - methods
Support Vector Machine
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Summary:We perform multi-class classification of laser-induced breakdown spectroscopy data of four commercial samples of proteins diluted in phosphate-buffered saline solution at different concentrations: bovine serum albumin, osteopontin, leptin, and insulin-like growth factor II. We achieve this by using principal component analysis as a method for dimensionality reduction. In addition, we apply several different classification algorithms (K-nearest neighbor, classification and regression trees, neural networks, support vector machines, adaptive local hyperplane, and linear discriminant classifiers) to perform multi-class classification. We achieve classification accuracies above 98% by using the linear classifier with 21–31 principal components. We obtain the best detection performance for neural networks, support vector machines, and adaptive local hyperplanes for a range of the number of principal components with no significant differences in performance except for that of the linear classifier. With the optimal number of principal components, a simplistic K-nearest classifier still provided acceptable results. Our proposed approach demonstrates that highly accurate automatic classification of complex protein samples from laser-induced breakdown spectroscopy data can be successfully achieved using principal component analysis with a sufficiently large number of extracted features, followed by a wrapper technique to determine the optimal number of principal components.
ISSN:0003-7028
1943-3530
DOI:10.1366/14-07488