Particle size determination via supervised machine learning in microfluidic impedance spectroscopy

Douwe S. de Bruijn; Henricus R.A. ten Eikelder; Vasileios A. Papadimitriou; Wouter Olthuis; Albert van den Berg

Particle size determination via supervised machine learning in microfluidic impedance spectroscopy

Douwe S. de Bruijn^*, Henricus R.A. ten Eikelder, Vasileios A. Papadimitriou, Wouter Olthuis, Albert van den Berg

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

Impedance flow cytometers based on coplanar electrodes often have a significant signal dependency on the particle position. In this work we show that supervised machine learning can be employed to accurately predict the particle size of monodisperse polystyrene beads in an inhomogeneous electric field. This approach offers accurate results for the presented irregular signal shape (due to sensor geometry, particle position, and electrode alignment) without the need for signal template fitting and a compensation function.

Original language	English
Title of host publication	MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences
Publisher	The Chemical and Biological Microsystems Society
Pages	1653-1654
Number of pages	2
ISBN (Electronic)	9781733419031
Publication status	Published - 2021
Event	25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021 - Palm Springs, United States Duration: 10 Oct 2021 → 14 Oct 2021 Conference number: 25 https://microtas2021.org/

Conference

Conference	25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021
Abbreviated title	MicroTAS 2021
Country/Territory	United States
City	Palm Springs
Period	10/10/21 → 14/10/21
Internet address	https://microtas2021.org/

Keywords

Electrical Impedance Spectroscopy
Microfluidics
Neural Network
Particle Size
Supervised Machine Learning

Cite this

de Bruijn, D. S., ten Eikelder, H. R. A., Papadimitriou, V. A., Olthuis, W., & van den Berg, A. (2021). Particle size determination via supervised machine learning in microfluidic impedance spectroscopy. In MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences (pp. 1653-1654). The Chemical and Biological Microsystems Society.

@inproceedings{9c37e1c7166e4f79ba18703266aa19d9,

title = "Particle size determination via supervised machine learning in microfluidic impedance spectroscopy",

abstract = "Impedance flow cytometers based on coplanar electrodes often have a significant signal dependency on the particle position. In this work we show that supervised machine learning can be employed to accurately predict the particle size of monodisperse polystyrene beads in an inhomogeneous electric field. This approach offers accurate results for the presented irregular signal shape (due to sensor geometry, particle position, and electrode alignment) without the need for signal template fitting and a compensation function.",

keywords = "Electrical Impedance Spectroscopy, Microfluidics, Neural Network, Particle Size, Supervised Machine Learning",

author = "{de Bruijn}, {Douwe S.} and {ten Eikelder}, {Henricus R.A.} and Papadimitriou, {Vasileios A.} and Wouter Olthuis and {van den Berg}, Albert",

note = "Publisher Copyright: {\textcopyright} 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.; 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021, MicroTAS 2021 ; Conference date: 10-10-2021 Through 14-10-2021",

year = "2021",

language = "English",

pages = "1653--1654",

booktitle = "MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences",

publisher = "The Chemical and Biological Microsystems Society",

url = "https://microtas2021.org/",

}

de Bruijn, DS, ten Eikelder, HRA, Papadimitriou, VA, Olthuis, W & van den Berg, A 2021, Particle size determination via supervised machine learning in microfluidic impedance spectroscopy. in MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. The Chemical and Biological Microsystems Society, pp. 1653-1654, 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021, Palm Springs, California, United States, 10/10/21.

Particle size determination via supervised machine learning in microfluidic impedance spectroscopy. / de Bruijn, Douwe S.; ten Eikelder, Henricus R.A.; Papadimitriou, Vasileios A. et al.
MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. The Chemical and Biological Microsystems Society, 2021. p. 1653-1654.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Particle size determination via supervised machine learning in microfluidic impedance spectroscopy

AU - de Bruijn, Douwe S.

AU - ten Eikelder, Henricus R.A.

AU - Papadimitriou, Vasileios A.

AU - Olthuis, Wouter

AU - van den Berg, Albert

N1 - Conference code: 25

PY - 2021

Y1 - 2021

N2 - Impedance flow cytometers based on coplanar electrodes often have a significant signal dependency on the particle position. In this work we show that supervised machine learning can be employed to accurately predict the particle size of monodisperse polystyrene beads in an inhomogeneous electric field. This approach offers accurate results for the presented irregular signal shape (due to sensor geometry, particle position, and electrode alignment) without the need for signal template fitting and a compensation function.

AB - Impedance flow cytometers based on coplanar electrodes often have a significant signal dependency on the particle position. In this work we show that supervised machine learning can be employed to accurately predict the particle size of monodisperse polystyrene beads in an inhomogeneous electric field. This approach offers accurate results for the presented irregular signal shape (due to sensor geometry, particle position, and electrode alignment) without the need for signal template fitting and a compensation function.

KW - Electrical Impedance Spectroscopy

KW - Microfluidics

KW - Neural Network

KW - Particle Size

KW - Supervised Machine Learning

UR - http://www.scopus.com/inward/record.url?scp=85136045331&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85136045331

SP - 1653

EP - 1654

BT - MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences

PB - The Chemical and Biological Microsystems Society

T2 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021

Y2 - 10 October 2021 through 14 October 2021

ER -

Particle size determination via supervised machine learning in microfluidic impedance spectroscopy

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