Automatic Classification of Laser-Induced Breakdown Spectroscopy (LIBS) Data of Protein Biomarker Solutions. Issue 9 (September 2014)

• Record Type: Journal Article
• Authors: Pokrajac, David; Lazarevic, Aleksandar; Kecman, Vojislav; Marcano, Aristides; Markushin, Yuri; Vance, Tia; Reljin, Natasa; McDaniel, Samantha; Melikechi, Noureddine
• Is Part Of: Applied spectroscopy. Volume 68:Issue 9(2014)
• Page Start: 1067
• Page End: 1075
• Publication Date: 2014-09
• Subjects: Automatic identification of organic solutions -- Laser-induced breakdown spectroscopy -- Neural networks -- Principal component analysis -- Adaptive local hyperplane -- K-nearest neighbors -- Support vector machines; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1366/14-07488
• Languages: English
• ISSNs: 0003-7028

Abstract

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.