Identification of single bacteria using micro Raman spectroscopy

M. de Biasio; G. McGunnigle; R. Leitner; J. Popp; P. Rösch; D. Balthasar

doi:10.1109/ICSensT.2013.6727612

Identification of single bacteria using micro Raman spectroscopy

M. de Biasio, G. McGunnigle, R. Leitner, J. Popp, P. Rösch, D. Balthasar

Publikation: Konferenzband/Beitrag in Buch/Bericht › Konferenzartikel › Begutachtung

Abstract

We present Raman micro-spectroscopic measurements of Escherichia coli, Bacillus thuringiensis and Staphylococcus cohnii. Several authors have reported successful methods for classifying single bacteria using Raman micro-spectroscopy. The aim of this paper is to evaluate which features of the spectra are useful for the classification. PCA obtained the best performance with 3 PCs. We also tested classifiers based on two and three narrow band filters chosen by exhaustive search. These gave accuracy comparable or greater than that of the PCA based classifier.

Originalsprache	Deutsch (Österreich)
Titel	2013 Seventh International Conference on Sensing Technology (ICST)
Herausgeber (Verlag)	IEEE Computer Society
Seiten	34-39
Seitenumfang	6
ISBN (Print)	978-1-4673-5220-8
DOIs	https://doi.org/10.1109/ICSensT.2013.6727612
Publikationsstatus	Veröffentlicht - 5 Dez. 2013
Extern publiziert	Ja
Veranstaltung	2013 Seventh International Conference on Sensing Technology (ICST) - Wellington, New Zealand Dauer: 3 Dez. 2013 → 5 Dez. 2013

Konferenz

Konferenz	2013 Seventh International Conference on Sensing Technology (ICST)
Zeitraum	3/12/13 → 5/12/13

Schlagwörter

Microorganisms
Principal component analysis
Accuracy
Smoothing methods
Support vector machines
Fingerprint recognition
Band-pass filters

Zugriff auf Dokument

10.1109/ICSensT.2013.6727612

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6727612

Andere Dateien und Links

https://ieeexplore.ieee.org/document/6727612/

Dieses zitieren

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title = "Identification of single bacteria using micro Raman spectroscopy",

abstract = "We present Raman micro-spectroscopic measurements of Escherichia coli, Bacillus thuringiensis and Staphylococcus cohnii. Several authors have reported successful methods for classifying single bacteria using Raman micro-spectroscopy. The aim of this paper is to evaluate which features of the spectra are useful for the classification. PCA obtained the best performance with 3 PCs. We also tested classifiers based on two and three narrow band filters chosen by exhaustive search. These gave accuracy comparable or greater than that of the PCA based classifier.",

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author = "Biasio, {M. de} and G. McGunnigle and R. Leitner and J. Popp and P. R{\"o}sch and D. Balthasar",

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Biasio, MD, McGunnigle, G, Leitner, R, Popp, J, Rösch, P & Balthasar, D 2013, Identification of single bacteria using micro Raman spectroscopy. in 2013 Seventh International Conference on Sensing Technology (ICST)., 6727612, IEEE Computer Society, S. 34-39, 2013 Seventh International Conference on Sensing Technology (ICST), 3/12/13. https://doi.org/10.1109/ICSensT.2013.6727612

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AU - McGunnigle, G.

AU - Leitner, R.

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AU - Rösch, P.

AU - Balthasar, D.

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N2 - We present Raman micro-spectroscopic measurements of Escherichia coli, Bacillus thuringiensis and Staphylococcus cohnii. Several authors have reported successful methods for classifying single bacteria using Raman micro-spectroscopy. The aim of this paper is to evaluate which features of the spectra are useful for the classification. PCA obtained the best performance with 3 PCs. We also tested classifiers based on two and three narrow band filters chosen by exhaustive search. These gave accuracy comparable or greater than that of the PCA based classifier.

AB - We present Raman micro-spectroscopic measurements of Escherichia coli, Bacillus thuringiensis and Staphylococcus cohnii. Several authors have reported successful methods for classifying single bacteria using Raman micro-spectroscopy. The aim of this paper is to evaluate which features of the spectra are useful for the classification. PCA obtained the best performance with 3 PCs. We also tested classifiers based on two and three narrow band filters chosen by exhaustive search. These gave accuracy comparable or greater than that of the PCA based classifier.

KW - Microorganisms

KW - Principal component analysis

KW - Accuracy

KW - Smoothing methods

KW - Support vector machines

KW - Fingerprint recognition

KW - Band-pass filters

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DO - 10.1109/ICSensT.2013.6727612

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