Combination of laser-induced breakdown spectroscopy and Raman
spectroscopy for multivariate classification of bacteria

J 2018

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

PROCHAZKA, David, Martin MAZURA, Ota SAMEK, Katarína REBROŠOVÁ, Pavel PORIZKA et. al.

Basic information

Original name

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

Authors

PROCHAZKA, David (203 Czech Republic, guarantor), Martin MAZURA (203 Czech Republic), Ota SAMEK (203 Czech Republic), Katarína REBROŠOVÁ (703 Slovakia, belonging to the institution), Pavel PORIZKA (203 Czech Republic), J. KLUS (203 Czech Republic), Petra PROCHAZKOVÁ (203 Czech Republic, belonging to the institution), J. NOVOTNY (203 Czech Republic), Karel NOVOTNÝ (203 Czech Republic) and J. KAISER (203 Czech Republic)

Edition

Spectrochimica Acta, Part B: Atomic Spectroscopy, Oxford, PERGAMON-ELSEVIER SCIENCE LTD, 2018, 0584-8547

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

21100 2.11 Other engineering and technologies

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

full text

Impact factor

Impact factor: 3.101

RIV identification code

RIV/00216224:14110/18:00102429

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1016/j.sab.2017.11.004

UT WoS

000423897000002

Keywords in English

Laser-induced breakdown spectroscopy; Raman spectroscopy; Chemometrics; Bacteria

Abstract

V originále

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods. (C) 2017 Elsevier B.V. All rights reserved.

Links

LQ1601, research and development project

Name: CEITEC 2020 (Acronym: CEITEC2020)

Investor: Ministry of Education, Youth and Sports of the CR

Citovat

PROCHAZKA, David, Martin MAZURA, Ota SAMEK, Katarína REBROŠOVÁ, Pavel PORIZKA, J. KLUS, Petra PROCHAZKOVÁ, J. NOVOTNY, Karel NOVOTNÝ and J. KAISER. Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria. Spectrochimica Acta, Part B: Atomic Spectroscopy. Oxford: PERGAMON-ELSEVIER SCIENCE LTD, 2018, vol. 139, JAN 2018, p. 6-12. ISSN 0584-8547. Available from: https://dx.doi.org/10.1016/j.sab.2017.11.004.

@article{1411768,
   author = {Prochazka, David and Mazura, Martin and Samek, Ota and Rebrošová, Katarína and Porizka, Pavel and Klus, J. and Prochazková, Petra and Novotny, J. and Novotný, Karel and Kaiser, J.},
   article_location = {Oxford},
   article_number = {JAN 2018},
   doi = {http://dx.doi.org/10.1016/j.sab.2017.11.004},
   keywords = {Laser-induced breakdown spectroscopy; Raman spectroscopy; Chemometrics; Bacteria},
   language = {eng},
   issn = {0584-8547},
   journal = {Spectrochimica Acta, Part B: Atomic Spectroscopy},
   title = {Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria},
   url = {https://www.sciencedirect.com/science/article/pii/S058485471730109X},
   volume = {139},
   year = {2018}
}

TY  - JOUR
ID  - 1411768
AU  - Prochazka, David - Mazura, Martin - Samek, Ota - Rebrošová, Katarína - Porizka, Pavel - Klus, J. - Prochazková, Petra - Novotny, J. - Novotný, Karel - Kaiser, J.
PY  - 2018
TI  - Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria
JF  - Spectrochimica Acta, Part B: Atomic Spectroscopy
VL  - 139
IS  - JAN 2018
SP  - 6-12
EP  - 6-12
PB  - PERGAMON-ELSEVIER SCIENCE LTD
SN  - 05848547
KW  - Laser-induced breakdown spectroscopy
KW  - Raman spectroscopy
KW  - Chemometrics
KW  - Bacteria
UR  - https://www.sciencedirect.com/science/article/pii/S058485471730109X
N2  - In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods. (C) 2017 Elsevier B.V. All rights reserved.
ER  -

PROCHAZKA, David, Martin MAZURA, Ota SAMEK, Katarína REBROŠOVÁ, Pavel PORIZKA, J. KLUS, Petra PROCHAZKOVÁ, J. NOVOTNY, Karel NOVOTNÝ and J. KAISER. Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria. \textit{Spectrochimica Acta, Part B: Atomic Spectroscopy}. Oxford: PERGAMON-ELSEVIER SCIENCE LTD, 2018, vol.~139, JAN 2018, p.~6-12. ISSN~0584-8547. Available from: https://dx.doi.org/10.1016/j.sab.2017.11.004.

Detailed Information on Publication Record