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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F18%3A00489532" target="_blank" >RIV/68081731:_____/18:00489532 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26620/17:PU125772 RIV/00216224:14110/18:00102429

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.sab.2017.11.004" target="_blank" >http://dx.doi.org/10.1016/j.sab.2017.11.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sab.2017.11.004" target="_blank" >10.1016/j.sab.2017.11.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10606 - Microbiology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Spectrochimica Acta Part B: Atomic Spectroscopy

ISSN

0584-8547

e-ISSN

—

Svazek periodika

139

Číslo periodika v rámci svazku

JAN

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

6-12

Kód UT WoS článku

000423897000002

EID výsledku v databázi Scopus

2-s2.0-85033551008