title: [Study of Rapid Species Identification of Bacteria in Water].
abstract: Multi-wavelength ultraviolet visible (UV-Vis) transmission spectra of bacteria combined the forward scattering and absorption properties of microbes, contains substantial information on size, shape, and the other chemical, physiological character of bacterial cells, has the bacterial species specificity, which can be applied to rapid species identification of bacterial microbes. Four different kinds of bacteria including Escherichia coli, Staphylococcus aureus, Salmonella typhimurium and Klebsiella pneumonia which were commonly existed in water were researched in this paper. Their multi-wavelength UV-Vis transmission spectra were measured and analyzed. The rapid identification method and model of bacteria were built which were based on support vector machine (SVM) and multi-wavelength UV-Vis transmission spectra of the bacteria. Using the internal cross validation based on grid search method of the training set for obtaining the best penalty factor C and the kernel parameter g, which the model needed. Established the bacteria fast identification model according to the optimal parameters and one-against-one classification method included in LibSVM. Using different experimental bacteria strains of transmission spectra as a test set of classification accuracy verification of the model, the analysis results showed that the bacterial rapid identification model built in this paper can identification the four kinds bacterial which chosen in this paper as the accuracy was 100%, and the model also can identified different subspecies of E. coli test set as the accuracy was 100%, proved the model had a good stability in identification bacterial species. In this paper, the research results of this study not only can provide a method for rapid identification and early warning of bacterial microbial in drinking water sources, but also can be used as the microbes identified in biomedical a simple, rapid and accurate means.
authors.forename: Jiu-yue
authors.initials: JY
authors.lastname: Wang
authors.forename: Nan-jing
authors.initials: NJ
authors.lastname: Zhao
authors.forename: Jing-bo
authors.initials: JB
authors.lastname: Duan
authors.forename: Li
authors.initials: L
authors.lastname: Fang
authors.forename: De-shuo
authors.initials: DS
authors.lastname: Meng
authors.forename: Rui-fang
authors.initials: RF
authors.lastname: Yang
authors.forename: Xue
authors.initials: X
authors.lastname: Xiao
authors.forename: Jian-guo
authors.initials: JG
authors.lastname: Liu
authors.forename: Wen-qing
authors.initials: WQ
authors.lastname: Liu
mesh_terms: D001419:Bacteria / Q000145:classification*; D004926:Escherichia coli; D007711:Klebsiella pneumoniae; D008962:Models, Theoretical; D012486:Salmonella typhimurium; D013057:Spectrum Analysis; D013211:Staphylococcus aureus; D060388:Support Vector Machine; D014871:Water Microbiology