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

作者：Pokrajac David^*; Lazarevic Aleksandar; Kecman Vojislav; Marcano Aristides; Markushin Yuri; Vance Tia; Reljin Natasa; McDaniel Samantha; Melikechi Noureddine

来源：Applied Spectroscopy, 2014, 68(9): 1067-1075.

DOI：10.1366/14-07488

• Automatic identification of organic solutions
• Laser-induced breakdown spectroscopy
• Neural networks
• Principal component analysis
• Adaptive local hyperplane
• K-nearest neighbors
• Support vector machines

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.

• 出版日期2014-9