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Differential mobility spectrometry classification of bacteria

Tutkimustuotosvertaisarvioitu

Standard

Differential mobility spectrometry classification of bacteria. / Hokkinen, Lauri; Kesti, Artturi; Lepomäki, Jaakko; Anttalainen, Osmo; Kontunen, Anton; Karjalainen, Markus; Aittoniemi, Janne; Vuento, Risto; Lehtimäki, Terho; Oksala, Niku; Roine, Antti.

julkaisussa: FUTURE MICROBIOLOGY, Vuosikerta 15, Nro 4, 01.03.2020, s. 233-240.

Tutkimustuotosvertaisarvioitu

Harvard

Hokkinen, L, Kesti, A, Lepomäki, J, Anttalainen, O, Kontunen, A, Karjalainen, M, Aittoniemi, J, Vuento, R, Lehtimäki, T, Oksala, N & Roine, A 2020, 'Differential mobility spectrometry classification of bacteria', FUTURE MICROBIOLOGY, Vuosikerta. 15, Nro 4, Sivut 233-240. https://doi.org/10.2217/fmb-2019-0192

APA

Hokkinen, L., Kesti, A., Lepomäki, J., Anttalainen, O., Kontunen, A., Karjalainen, M., ... Roine, A. (2020). Differential mobility spectrometry classification of bacteria. FUTURE MICROBIOLOGY, 15(4), 233-240. https://doi.org/10.2217/fmb-2019-0192

Vancouver

Hokkinen L, Kesti A, Lepomäki J, Anttalainen O, Kontunen A, Karjalainen M et al. Differential mobility spectrometry classification of bacteria. FUTURE MICROBIOLOGY. 2020 maalis 1;15(4):233-240. https://doi.org/10.2217/fmb-2019-0192

Author

Hokkinen, Lauri ; Kesti, Artturi ; Lepomäki, Jaakko ; Anttalainen, Osmo ; Kontunen, Anton ; Karjalainen, Markus ; Aittoniemi, Janne ; Vuento, Risto ; Lehtimäki, Terho ; Oksala, Niku ; Roine, Antti. / Differential mobility spectrometry classification of bacteria. Julkaisussa: FUTURE MICROBIOLOGY. 2020 ; Vuosikerta 15, Nro 4. Sivut 233-240.

Bibtex - Lataa

@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 = "bacteria, differential mobility spectrometry, DMS, eNose, IMS, ion mobility spectrometry",
author = "Lauri Hokkinen and Artturi Kesti and Jaakko Lepom{\"a}ki and Osmo 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",
month = "3",
day = "1",
doi = "10.2217/fmb-2019-0192",
language = "English",
volume = "15",
pages = "233--240",
journal = "FUTURE MICROBIOLOGY",
issn = "1746-0913",
publisher = "Future Medicine",
number = "4",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Differential mobility spectrometry classification of bacteria

AU - Hokkinen, Lauri

AU - Kesti, Artturi

AU - Lepomäki, Jaakko

AU - Anttalainen, Osmo

AU - Kontunen, Anton

AU - Karjalainen, Markus

AU - Aittoniemi, Janne

AU - Vuento, Risto

AU - Lehtimäki, Terho

AU - Oksala, Niku

AU - Roine, Antti

PY - 2020/3/1

Y1 - 2020/3/1

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 - bacteria

KW - differential mobility spectrometry

KW - DMS

KW - eNose

KW - IMS

KW - ion mobility spectrometry

U2 - 10.2217/fmb-2019-0192

DO - 10.2217/fmb-2019-0192

M3 - Article

VL - 15

SP - 233

EP - 240

JO - FUTURE MICROBIOLOGY

JF - FUTURE MICROBIOLOGY

SN - 1746-0913

IS - 4

ER -