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Recursive feature elimination in Raman spectra with support vector machines

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Abstract

The presence of irrelevant and correlated data points in a Raman spectrum can lead to a decline in classifier performance. We introduce support vector machine (SVM)-based recursive feature elimination into the field of Raman spectroscopy and demonstrate its performance on a data set of spectra of clinically relevant microorganisms in urine samples, along with patient samples. As the original technique is only suitable for two-class problems, we adapt it to the multi-class setting. It is shown that a large amount of spectral points can be removed without degrading the prediction accuracy of the resulting model notably.

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Acknowledgements

Funding of the research project InterSept (13N13852) from the Federal Ministry of Education and Research, Germany (BMBF) is gratefully acknowledged.

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Authors and Affiliations

  1. Institute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Helmholtzweg 4, D-07743, Jena, Germany

    Bernd Kampe, Sandra Kloß, Thomas Bocklitz, Petra Rösch & Jürgen Popp

  2. InfectoGnostics Research Campus Jena, Center for Applied Research, Philosophenweg 7, 07743, Jena, Germany

    Sandra Kloß, Thomas Bocklitz, Petra Rösch & Jürgen Popp

  3. Leibniz-Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745, Jena, Germany

    Jürgen Popp

Authors
  1. Bernd Kampe
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  2. Sandra Kloß
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  3. Thomas Bocklitz
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  4. Petra Rösch
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  5. Jürgen Popp
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Correspondence to Jürgen Popp.

Additional information

Bernd Kampe studied bioinformatics at the Friedrich Schiller University Jena, Germany. He subsequently joined the work group of Jürgen Popp in July 2010 to start his Ph.D. studies focused on the identification of microorganisms with micro-Raman spectroscopy and chemometric methods, especially support vector machines. He is currently with the Jena University Language & Information Engineering Lab (JULIE Lab), where his research is aimed at the extraction of information about protein-protein interactions from text.

Sandra Kloß studied chemistry in Jena. In 2015 she received her Ph.D. at the Friedrich-Schiller-University Jena. Currently she is working as a post-doctoral researcher in the work group of Jürgen Popp. Her main research interests are the isolation of microorganisms from complex matrices and their subsequent Raman spectroscopic and molecular biological investigation.

Thomas Bocklitz studied physics at the Friedrich-Schiller-University. He received his diploma in theoretical physics in 2007 and the Ph.D. in chemometrics in 2011. Dr. Bocklitz is a junior research group leader for statistical data analysis and image analysis mostly for biophotonic applications. Dr. Bocklitz research agenda is closely connected with the translation of physical information, measured by AFM, TERS, Raman-spectroscopy, CARS, SHG, TPEF, into medical or biological relevant information. This research led to over 60 publications in peerreviewed journals and his habilitation, which he completed in 2016.

Petra Rösch studied chemistry at the University of Würzburg. Actually she is research associate at the chair of Jürgen Popp at the University of Jena. Her research interests are focused on the investigation of all kind of biological, medical, and pharmaceutical relevant problems with various vibrational spectroscopic methods. Her main focus lays on the characterization and identification of microorganisms with Raman spectroscopy.

Jürgen Popp studied chemistry at the universities of Erlangen and Würzburg. After his Ph.D. in Chemistry he joined Yale University for postdoctoral work. He subsequently returned to Würzburg University where he finished his habilitation in 2002. Since 2002 he holds a chair for Physical Chemistry at the Friedrich-Schiller University Jena. Since 2006 he is also the scientific director of the Leibniz Institute of Photonic Technology, Jena. His research interests are mainly concerned with biophotonics. In particular his expertise in the development and application of innovative Raman techniques for biomedical diagnosis should be emphasized.

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Kampe, B., Kloß, S., Bocklitz, T. et al. Recursive feature elimination in Raman spectra with support vector machines. Front. Optoelectron. 10, 273–279 (2017). https://doi.org/10.1007/s12200-017-0726-4

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  • DOI: https://doi.org/10.1007/s12200-017-0726-4

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