Evidence map›Paper›PMID 33961103›Full record

ArticleAnalytical and bioanalytical chemistry2021

Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network.

Jingyu Ding, Qingqing Lin, Jiameng Zhang, Glenn M Young, Chun Jiang, Yaoguang Zhong, Jianhua Zhang

Abstract read
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In one paragraph

Article in Analytical and bioanalytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jingyu Ding *College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
Qingqing Lin *Key Laboratory of Ministry of Education of China for Research of Design and Electromagnetic Compatibility of High-Speed Electronic System, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jiameng ZhangKey Laboratory of Ministry of Education of China for Research of Design and Electromagnetic Compatibility of High-Speed Electronic System, Shanghai Jiao Tong University, Shanghai, 200240, China.
Glenn M YoungDepartment of Food Science and Technology, University of California, Davis, CA, 95616, USA.
Chun JiangKey Laboratory of Ministry of Education of China for Research of Design and Electromagnetic Compatibility of High-Speed Electronic System, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yaoguang ZhongCollege of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China. ygzhong@shou.edu.cn.
Jianhua ZhangSchool of Agriculture and Biology, Bor S. Luh Food Safety Research Center, Shanghai Jiao Tong University, Shanghai, 200240, China. zhangjh@sjtu.edu.cn.

Funding

the National Key Research and Development Program of China 2018YFC1603901
6 · The paper itself

Abstract

Salmonella is a prevalent pathogen causing serious morbidity and mortality worldwide. There are over 2600 serovars of Salmonella. Among them, Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi were reported to be the most common foodborne pathogenic serovars in the EU and China. In order to provide a more efficient approach to detect and distinguish these serovars, a new analytical method was developed by combining surface-enhanced Raman spectroscopy (SERS) with multi-scale convolutional neural network (CNN). We prepared 34-nm gold nanoparticles (AuNPs) as the label-free Raman substrate, measured 1854 SERS spectra of these three Salmonella serovars, and then proposed a multi-scale CNN model with three parallel CNNs to achieve multi-dimensional extraction of SERS spectral features. We observed the impact of the number of iterations and training samples on the recognition accuracy by changing the ratio of the number of the training and testing sets. By comparing the calculated data with experimental one, it was shown that our model could reach recognition accuracy more than 97%. These results indicate that it was not only feasible to combine SERS spectroscopy with multi-scale CNN for Salmonella serotype identification, but also for other pathogen species and serovar identifications.

Indexed as

GoldHumansMetal NanoparticlesNeural Networks, ComputerSalmonellaSalmonella InfectionsSpectrum Analysis, RamanTime FactorsGoldIdentificationMulti-scale convolutional neural networkSalmonella serovarsSurface-enhanced Raman scattering (SERS)

Identifiers

PMID33961103

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.