The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Bacterial Separation and Concentration from Complex Sample Matrices: A Review
Abstract
The use of many rapid detection technologies could be expanded if the bacteria were separated, concentrated, and purified from the sample matrix before detection. Specific advantages of bacterial concentration might include facilitating the detection of multiple bacterial strains; removal of matrix-associated assay inhibitors; and provision of adequate sample size reduction to allow for the use of representative food sample sizes and/or small media volumes. Furthermore, bacterial concentration could aid in improving sampling techniques needed to detect low levels of pathogens or sporadic contamination, which may perhaps reduce or even eliminate the need for cultural enrichment prior to detection. Although bacterial concentration methods such as centrifugation, filtration, and immunomagnetic separation have been reported for food systems, none of these is ideal and in many cases a technique optimized for one food system or microorganism is not readily adaptable to others. Indeed, the separation and subsequent concentration of bacterial cells from a food sample during sample preparation continues to be a stumbling block in the advancement of molecular methods for the detection of foodborne pathogens. The purpose of this review is to provide a detailed understanding of the science, possibilities, and limitations of separating and concentrating bacterial cells from the food matrix in an effort to further improve our ability to harness molecular methods for the rapid detection of foodborne pathogens.
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Cites
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Cited by
Pathogen detection: A perspective of traditional methods and biosensors
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Current and Developing Technologies for Monitoring Agents of Bioterrorism and Biowarfare
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Metadata
- Authors
- Stevens, K. A., & Jaykus, L.-A.
- Venue
- Critical Reviews in Microbiology
- Publisher
- Informa UK Limited
- Published
- 2004-01
- Volume
- 30
- Issue
- 1
- Pages
- 7-24
- ISSN
- 1040-841X (ISSN-L)
- 1549-7828
- OpenAlex
- W2131567632
- Cited by
- 305
- References
- 38
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- closed
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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.