Open AccessA neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry
Author(s) -
Carlos Honrado,
John S. McGrath,
Riccardo Reale,
Paolo Bisegna,
Nathan S. Swami,
Federica Caselli
Publication year - 2020
Publication title -
analytical and bioanalytical chemistry/analytical and bioanalytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.86
H-Index - 166
eISSN - 1618-2650
pISSN - 1618-2642
DOI - 10.1007/s00216-020-02497-9
Subject(s) - microfluidics , sorting , biological system , artificial neural network , electrical impedance , computer science , cytometry , characterization (materials science) , particle (ecology) , lab on a chip , tracking (education) , artificial intelligence , nanotechnology , materials science , electronic engineering , chemistry , algorithm , engineering , electrical engineering , cell , psychology , pedagogy , biochemistry , oceanography , geology , biology
Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.