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Journal of Pathogens
Volume 2011 (2011), Article ID 310135, 11 pages
Review Article

Recent Advances in Molecular Technologies and Their Application in Pathogen Detection in Foods with Particular Reference to Yersinia

1College of Management and Technology, Walden University, 155 Fifth Avenue South, Minneapolis, MN 55401, USA
2Division of Molecular Biology, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 8301 Muirkirk Road, MOD 1 Facility, Laurel, MD 20708, USA

Received 22 June 2011; Accepted 8 September 2011

Academic Editor: Latiful Bari

Copyright © 2011 Jin Gui and Isha R. Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Yersinia enterocolitica is an important zoonotic pathogen that can cause yersiniosis in humans and animals. Food has been suggested to be the main source of yersiniosis. It is critical for the researchers to be able to detect Yersinia or any other foodborne pathogen with increased sensitivity and specificity, as well as in real-time, in the case of a foodborne disease outbreak. Conventional detection methods are known to be labor intensive, time consuming, or expensive. On the other hand, more sensitive molecular-based detection methods like next generation sequencing, microarray, and many others are capable of providing faster results. DNA testing is now possible on a single molecule, and high-throughput analysis allows multiple detection reactions to be performed at once, thus allowing a range of characteristics to be rapidly and simultaneously determined. Despite better detection efficiencies, results derived using molecular biology methods can be affected by the various food matrixes. With the improvements in sample preparation, data analysis, and testing procedures, molecular detection techniques will likely continue to simplify and increase the speed of detection while simultaneously improving the sensitivity and specificity for tracking pathogens in food matrices.