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

Content Provider	IEEE Xplore Digital Library
Author	Vance, T. Reljin, N. Lazarevic, A. Pokrajac, D. Kecman, V. Melikechi, N. Marcano, A. Markushin, Y. McDaniel, S.
Copyright Year	2010
Description	Author affiliation: CREOSA Center, Delaware State University, Dover, 19901, USA (Vance, T.; Reljin, N.; Pokrajac, D.; Melikechi, N.; Marcano, A.; Markushin, Y.; McDaniel, S.) \|\| Virginia Commonwealth University, 401 West Main Street, E4244, P.O. Box 843019, Richmond, 23284-3019, USA (Kecman, V.) \|\| United Technologies Research Center, 411 Silver Lane, East Hartford, CT 06106, USA (Lazarevic, A.)
Abstract	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.
Starting Page	1
Ending Page	7
File Size	1248994
Page Count	7
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596575
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Classification algorithms Feature extraction Support vector machines Kernel Proteins Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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