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Advances in Rapid Detection Methods for Foodborne Pathogens
Open accessAbstract
Food safety is increasingly becoming an important public health issue, as foodborne diseases present a widespread and growing public health problem in both developed and developing countries. The rapid and precise monitoring and detection of foodborne pathogens are some of the most effective ways to control and prevent human foodborne infections. Traditional microbiological detection and identification methods for foodborne pathogens are well known to be time consuming and laborious as they are increasingly being perceived as insufficient to meet the demands of rapid food testing. Recently, various kinds of rapid detection, identification, and monitoring methods have been developed for foodborne pathogens, including nucleic-acid-based methods, immunological methods, and biosensor-based methods, etc. This article reviews the principles, characteristics, and applications of recent rapid detection methods for foodborne pathogens.
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Citations
Cited by
A review of modern methods for the detection of foodborne pathogens
Aladhadh, M. (2023) · Microorganisms · doi:10.3390/microorganisms11051111 · Cited by 233
Chen, X., Peng, M., Peng, Z., & Zhou, Y. (2026) · Chemistry – A European Journal · doi:10.1002/chem.202503631 · Cited by 1
Metadata
- Authors
- Zhao, X., Lin, C.-W., Wang, J., & Oh, D. H.
- Venue
- Journal of Microbiology and Biotechnology
- Published
- 2014-03-28
- Volume
- 24
- Issue
- 3
- Pages
- 297-312
- ISSN
- 1017-7825 (ISSN-L)
- 1738-8872
- OpenAlex
- W2058007140
- Cited by
- 714
- References
- 0
- FWCI
- 17.71
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Zhao, X., Lin, C.-W., Wang, J., & Oh, D. H. (2014). Advances in Rapid Detection Methods for Foodborne Pathogens. Journal of Microbiology and Biotechnology. https://doi.org/10.4014/jmb.1310.10013
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.