title: Automatic classification of laser-induced breakdown spectroscopy (LIBS) data of protein biomarker solutions.
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.
authors.affiliation: Department of Information and Computer Sciences and Optical Science Center for Applied Research, Delaware State University, Dover, DE 19901 USA.
authors.forename: David
authors.initials: D
authors.lastname: Pokrajac
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Lazarevic
authors.forename: Vojislav
authors.initials: V
authors.lastname: Kecman
authors.forename: Aristides
authors.initials: A
authors.lastname: Marcano
authors.forename: Yuri
authors.initials: Y
authors.lastname: Markushin
authors.forename: Tia
authors.initials: T
authors.lastname: Vance
authors.forename: Natasa
authors.initials: N
authors.lastname: Reljin
authors.forename: Samantha
authors.initials: S
authors.lastname: McDaniel
authors.forename: Noureddine
authors.initials: N
authors.lastname: Melikechi
mesh_terms: D000818:Animals; D015415:Biomarkers / Q000032:analysis* / Q000737:chemistry; D002417:Cattle; D007834:Lasers; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D011506:Proteins / Q000737:chemistry; D012996:Solutions / Q000737:chemistry*; D013057:Spectrum Analysis / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D015415:Biomarkers; D011506:Proteins; D012996:Solutions