Differential mobility spectrometry classification of bacteria

Lauri Hokkinen; Artturi Kesti; Jaakko Lepomäki; O Anttalainen; Anton Kontunen; Markus Karjalainen; Janne Aittoniemi; Risto Vuento; Terho Lehtimäki; Niku Oksala; Antti Roine

doi:10.2217/fmb-2019-0192

Differential mobility spectrometry classification of bacteria

Lauri Hokkinen
, Artturi Kesti
, Jaakko Lepomäki
, O Anttalainen
, Anton Kontunen
, Markus Karjalainen
, Janne Aittoniemi
, Risto Vuento
, Terho Lehtimäki
, Niku Oksala
, Antti Roine

Research output: Contribution to journal › Article › Scientific › peer-review

4 Citations (Scopus)

Abstract

<p>Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.</p>

Original language	English
Pages (from-to)	233-240
Number of pages	8
Journal	Future Microbiology
Volume	15
Issue number	4
DOIs	https://doi.org/10.2217/fmb-2019-0192
Publication status	Published - 2020
Publication type	A1 Journal article-refereed

Funding

This study was financially supported by the following agents: Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (9s045, 151B03, 9T044, 9U042, 150618, 9U042, 9V044, 9X040 and X51001); Academy of Finland: grant 286284; Finnish Foundation for Cardiovascular Research, Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association and EU Horizon 2020 (grant 755320 for TAXINOMISIS) and Tampere University Hospital Supporting Foundation. Anton Kontunen, Markus Karjalainen, Osmo Anttalainen, Niku Oksala and Antti Roine are shareholders of Olfactomics Ltd, which is about to commercialise proprietary technology for the detection of diseases by ion mobility spectrometry. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Keywords

DMS
IMS
bacteria
differential mobility spectrometry
eNose
ion mobility spectrometry

Publication forum classification

Publication forum level 1

Access to Document

10.2217/fmb-2019-0192

http://urn.fi/urn:nbn:fi:tuni-202006256225

Cite this

@article{51173e45b8de4dcb9c8942de7d40d96d,

title = "Differential mobility spectrometry classification of bacteria",

abstract = "Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials \& methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7\% for all bacterial species. For bacterial genera, the rate was 77.6\% and between Gram status, 89.1\%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.",

keywords = "DMS, IMS, bacteria, differential mobility spectrometry, eNose, ion mobility spectrometry, DMS, IMS, bacteria, differential mobility spectrometry, eNose, ion mobility spectrometry",

author = "Lauri Hokkinen and Artturi Kesti and Jaakko Lepom{\"a}ki and O Anttalainen and Anton Kontunen and Markus Karjalainen and Janne Aittoniemi and Risto Vuento and Terho Lehtim{\"a}ki and Niku Oksala and Antti Roine",

year = "2020",

doi = "10.2217/fmb-2019-0192",

language = "English",

volume = "15",

pages = "233--240",

journal = "Future Microbiology",

issn = "1746-0913",

publisher = "American School of Classical Studies at Athens",

number = "4",

}

TY - JOUR

T1 - Differential mobility spectrometry classification of bacteria

AU - Hokkinen, Lauri

AU - Kesti, Artturi

AU - Lepomäki, Jaakko

AU - Anttalainen, O

AU - Kontunen, Anton

AU - Karjalainen, Markus

AU - Aittoniemi, Janne

AU - Vuento, Risto

AU - Lehtimäki, Terho

AU - Oksala, Niku

AU - Roine, Antti

PY - 2020

Y1 - 2020

N2 - Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.

AB - Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.

KW - DMS

KW - IMS

KW - bacteria

KW - differential mobility spectrometry

KW - eNose

KW - ion mobility spectrometry

KW - DMS

KW - IMS

KW - bacteria

KW - differential mobility spectrometry

KW - eNose

KW - ion mobility spectrometry

U2 - 10.2217/fmb-2019-0192

DO - 10.2217/fmb-2019-0192

M3 - Article

AN - SCOPUS:85083385467

SN - 1746-0913

VL - 15

SP - 233

EP - 240

JO - Future Microbiology

JF - Future Microbiology

IS - 4

ER -

Differential mobility spectrometry classification of bacteria

Abstract

Funding

Keywords

Publication forum classification

Access to Document

Fingerprint

Cite this