Intelligent convolution neural network-assisted SERS to realize highly accurate identification of six pathogenic <i>Vibrio</i>-Reference-Cited by-同舟云学术

Intelligent convolution neural network-assisted SERS to realize highly accurate identification of six pathogenic Vibrio

Published:2023 Issue:38 Volume:59 Page:5779-5782
ISSN:1359-7345
Container-title:Chemical Communications
language:en
Short-container-title:Chem. Commun.

Author:

Yu Hui¹,Yang Zhilan¹,Fu Shiying¹,Zhang Yuejiao¹,Panneerselvamc Rajapandiyan²^ORCID,Li Baoqiang³^ORCID,Zhang Lin³,Chen Zehui⁴,Wang Xin¹,Li Jianfeng¹^ORCID

Affiliation:

1. College of Materials, State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China

2. Department of Chemistry, SRM University AP, Amaravati, Andhra Pradesh 522502, India

3. State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China

4. Xiamen City Center for Disease Control and Prevention, Xiamen 361005, China

Abstract

Based on the label-free SERS technology, we successfully developed a deep learning CNN model, achieving low-cost and high-accuracy identification of six representative pathogenic Vibrio within 15 minutes.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Metals and Alloys,Surfaces, Coatings and Films,General Chemistry,Ceramics and Composites,Electronic, Optical and Magnetic Materials,Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CC/D3CC01129A

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5. Occurrence of the three major Vibrio species pathogenic for human in seafood products consumed in France using real-time PCR

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