Separation-free bacterial identification in arbitrary media via deep neural network-based SERS analysis

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Universal and fast bacterial detection technology is imperative for food safety analyses and diagnosis of infectious diseases. Although surface-enhanced Raman spectroscopy (SERS) has recently emerged as a powerful solution for detecting diverse microorganisms, its widespread application has been hampered by strong signals from surrounding media that overwhelm target signals and require time-consuming and tedious bacterial separation steps. By using SERS analysis boosted with a newly proposed deep learning model named dual-branch wide-kernel network (DualWKNet), a markedly simpler, faster, and effective route to classify signals of two common bacteria E. coli and S. epidermidis and their resident media without any separation procedures is demonstrated. With outstanding classification accuracies up to 98%, the synergistic combination of SERS and deep learning serves as an effective platform for “separation-free“ detection of bacteria in arbitrary media with short data acquisition times and small amounts of training data.
Publisher
ELSEVIER ADVANCED TECHNOLOGY
Issue Date
2022-04
Language
English
Article Type
Article
Citation

BIOSENSORS & BIOELECTRONICS, v.202

ISSN
0956-5663
DOI
10.1016/j.bios.2022.113991
URI
http://hdl.handle.net/10203/296967
Appears in Collection
MS-Journal Papers(저널논문)CS-Journal Papers(저널논문)
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