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Tytuł:
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Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level.
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Autorzy:
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Yan S; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
Wang S; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
Qiu J; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
Li M; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
Li D; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
Xu D; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
Li D; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China. Electronic address: .
Liu Q; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, 266071, PR China. Electronic address: .
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Źródło:
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Talanta [Talanta] 2021 May 01; Vol. 226, pp. 122195. Date of Electronic Publication: 2021 Feb 05.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: Amsterdam : Elsevier
Original Publication: Oxford : Pergamon Press
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MeSH Terms:
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Machine Learning*
Spectrum Analysis, Raman*
Bacteria ; Food Contamination ; Prospective Studies
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Contributed Indexing:
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Keywords: Food-borne pathogens; Machine learning; Raman spectroscopy; Single cell
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Entry Date(s):
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Date Created: 20210307 Date Completed: 20210514 Latest Revision: 20210514
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Update Code:
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20240104
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DOI:
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10.1016/j.talanta.2021.122195
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PMID:
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33676719
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Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and more attention in the field of diagnosing food-borne pathogens in recent years. In the research, 15,890 single-cell Raman spectra of 23 common strains from 7 genera were obtained at the single cell level. Then, the nonlinear features of raw data were extracted by kernel principal component analysis, and the individual bacterial cell was evaluated and discriminated at the serotype level through the decision tree algorithm. The results demonstrated that the average correct rate of prediction on independent test set was 86.23 ± 0.92% when all strains were recognized by only one model, but there were high misjudgment rates for certain strains. Therefore, the four-level classification models were introduced, and the different hierarchies of the identification models achieved accuracies in the range of 87.1%-95.8%, which realized the efficient prediction of strains at the serotype level. In summary, Raman spectroscopy combined with machine learning based on fingerprint difference was a prospective strategy for the rapid diagnosis of pathogenic bacteria.
(Copyright © 2021 Elsevier B.V. All rights reserved.)
