Deep Learning Assisted Microfluidic Impedance Flow Cytometry for Label-free Foodborne Bacteria Analysis and Classification

According to the urgent need for rapid detection and identification of foodborne bacteria to prevent public health event, a microfluidic electrical impedance flow cytometry assisted with convolutional neural network (ConvNet) based deep learning algorithm was proposed in this study to analyze the im...

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Bibliographic Details
Main Authors: Zhang, Shuaihua, Han, Ziyu, Feng, Zhe, Sun, Meiqing, Duan, Xuexin
Format: Conference Proceeding
Language:English
Subjects:
Bacteria
Convolutional neural networks
Data analysis
Deep Learning
Electric Impedance
Flow Cytometry
Impedance
Impedance measurement
Microfluidics
Microorganisms
Point of care
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Summary:According to the urgent need for rapid detection and identification of foodborne bacteria to prevent public health event, a microfluidic electrical impedance flow cytometry assisted with convolutional neural network (ConvNet) based deep learning algorithm was proposed in this study to analyze the impedance signals of bacteria. With the assistance of the deep learning algorithm, Escherichia coli (EPEC), Salmonella enteritidis (SE) and Vibrio parahaemolyticus (VP) were identified with an accuracy of 100%. The proposed impedance based analysis system can be potentially applied for pre-classification of different subtypes of bacteria in a label-free manner.Clinical Relevance-The whole platform can be miniaturized and applied for point-of-care testing (POCT) of pathogenic bacteria detection.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9630684