Identification and classification of organic and inorganic components of particulate matter via Raman spectroscopy and chemometric approaches

In this paper, a novel method for developing a tree-like classifier which differentiates between organic and inorganic particulate matter by means of Raman spectroscopy is introduced. The algorithm is fully automatic and optimises itself without any human interaction. This method uses a tree-like structure to classify Raman spectra as a decision tree. On every knot of this tree, the optimal classifier is automatically obtained, tested and trained. The optimal classifier is an artificial neural network, linear discriminant analysis or a support vector machine, where different kernels are possible. The support vector machine is optimised by the simulated annealing method to achieve the best possible classifier. After the training, a hold-out experiment with two completely independent sets of Raman spectra was tried to show the abilities of this method for real-world application. Copyright © 2010 John Wiley & Sons, Ltd.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[3]  Jürgen Popp,et al.  A comprehensive study of classification methods for medical diagnosis , 2009 .

[4]  J. Popp,et al.  Effect of supplementary manganese on the sporulation of Bacillus endospores analysed by Raman spectroscopy , 2009 .

[5]  Jürgen Popp,et al.  A comparative Raman and CARS imaging study of colon tissue , 2009, Journal of biophotonics.

[6]  K. Baumann,et al.  Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy , 2009 .

[7]  Jürgen Popp,et al.  Localizing and identifying living bacteria in an abiotic environment by a combination of Raman and fluorescence microscopy. , 2008, Analytical chemistry.

[8]  P. Matousek,et al.  Advanced transmission Raman spectroscopy: a promising tool for breast disease diagnosis. , 2008, Cancer research.

[9]  Jürgen Popp,et al.  Quantitative online detection of low-concentrated drugs via a SERS microfluidic system. , 2007, ChemPhysChem.

[10]  R. Niessner,et al.  Raman Microspectroscopic Analysis of Size-Resolved Atmospheric Aerosol Particle Samples Collected with an ELPI: Soot, Humic-Like Substances, and Inorganic Compounds , 2007 .

[11]  R. Zimmermann,et al.  Atmosphärische Aerosole: Quellen, Vorkommen, Zusammensetzung , 2007 .

[12]  Jürgen Popp,et al.  On-line monitoring and identification of bioaerosols. , 2006, Analytical chemistry.

[13]  Michael Schmitt,et al.  Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations , 2005, Applied and Environmental Microbiology.

[14]  J Popp,et al.  Identification of biotic and abiotic particles by using a combination of optical tweezers and in situ Raman spectroscopy. , 2004, Chemphyschem : a European journal of chemical physics and physical chemistry.

[15]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[16]  N. Siddique,et al.  Raman spectroscopy of carbon-containing particles , 2001 .

[17]  J. Popp,et al.  Observation of a phase transition in an electrodynamically levitated NH4NO3 microparticle by Mie and Raman scattering , 2000 .

[18]  J. Laureyns,et al.  Microchemical investigations of dust emitted by a lead smelter , 1999 .

[19]  G. Schweiger,et al.  Dispersive raman spectroscopy on soot particles , 1998 .

[20]  W. Kiefer,et al.  Investigations of Radical Polymerization and Copolymerization Reactions in Optically Levitated Microdroplets by Simultaneous Raman Spectroscopy, Mie Scattering, and Radiation Pressure Measurements , 1998 .

[21]  W. Kiefer,et al.  High-Precision Determination of Size, Refractive Index, and Dispersion of Single Microparticles from Morphology-Dependent Resonances in Optical Processes , 1998 .

[22]  J. Popp,et al.  SIMULATION OF MORPHOLOGY-DEPENDENT RESONANCES IN THE RAMAN SPECTRA OF OPTICALLY LEVITATED MICROSPHERES , 1997 .

[23]  M. Schmitt,et al.  Resonant Light Scattering: from Diatomic Molecules to Laser-Trapped Microparticles , 1996 .

[24]  D. R. Cousens,et al.  SNIP, A STATISTICS-SENSITIVE BACKGROUND TREATMENT FOR THE QUANTITATIVE-ANALYSIS OF PIXE SPECTRA IN GEOSCIENCE APPLICATIONS , 1988 .

[25]  T. Novakov,et al.  Raman scattering and the characterisation of atmospheric aerosol particles , 1977, Nature.

[26]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[27]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[28]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .