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Classification of LIBS protein spectra using support vector machines and adaptive local hyperplanes

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9 Author(s)
Vance, T. ; CREOSA Center, Delaware State Univ., Dover, DE, USA ; Reljin, N. ; Lazarevic, A. ; Pokrajac, D.
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In recent years, the spectroscopy community has increasingly been using various techniques for automatic computer assisted quantitative and qualitative evaluation of specimen based on spectroscopy data. In this paper, an automated method for classification of four types of proteins (Bovine Serum Albumin (the most abundant protein in blood plasma), and Osteopontin, Leptin and Insulin-like Growth Factor II) (identified as potential biomarkers for ovarian cancer) from laser-induced breakdown spectroscopy (LIBS) data is proposed. Automatic classification of these complex proteins can lead to the identification of chemical components that are vital in the detection of certain diseases (i.e. ovarian cancer). The LIBS method consists of the laser ablation of the sample by using ultrashort and high energetic laser pulses that break the molecular bonds of the compound. The high intensity electromagnetic field induces multiphoton absorption and ionization, resulting in the formation of short living plasma in the vicinity of the sample. During cooling, this plasma reemits light collected by spectrometers. In this paper, the high dimensional spectroscopy data is preprocessed using linear dimensionality reduction technique of principal component analysis and then classification is performed using support vector machines (SVM) and adaptive local hyperplane (ALH). The influence of the number of extracted features as well as the parameters of the classification algorithms to the classification accuracy is also investigated. Our experiments performed on real life data suggest that both classification methods are quite efficient in distinguishing among four types of proteins and they have a fairly robust detection performance for a range of the numbers of extracted features as well as the algorithms' parameters.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010

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