Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
5.2
Journals
Foods
Special Issues
Molecular Detection and Characterization of Foodborne Bacterial Pathogens
share announcement

Molecular Detection and Characterization of Foodborne Bacterial Pathogens

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Microbiology".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 2391

Special Issue Editor

Prof. Dr. Domenico Meloni

E-Mail Website
Guest Editor
Department of Veterinary Medicine, University of Sassari, Sassari, Italy
Interests: food microbiology; food safety; foodborne pathogens; molecular methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue of Foods is to present the state of the art of molecular methods used for the detection and characterization of foodborne bacterial pathogens. Although conventional microbial control techniques are currently significantly employed, their major drawbacks are mainly related to the long time they require and their limited accuracy. In recent years there has been a strong and increasing demand for high-quality molecular methods for the detection and characterization of Listeria monocytogenes, Yersinia enterocolitica, Salmonella spp., E.coli STEC, Campylobacter, Vibrio parahaemolyticus and Vibrio cholerae. This has led to the development of several modern and innovative molecular methods of foodborne pathogen control in food processing. Original and review papers dealing with all aspects of the “Molecular Detection and Characterization of Foodborne Bacterial Pathogens” are welcome for inclusion in this Special Issue of Foods.

Prof. Dr. Domenico Meloni
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • food safety
  • foodborne pathogens
  • molecular methods
  • Listeria monocytogenes
  • Yersinia enterocolitica
  • Salmonella spp.
  • E.coli STEC
  • Campylobacter
  • Vibrio parahaemolyticus
  • Vibrio cholerae

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2669 KiB  
Article
Point-of-Care Lateral Flow Detection of Viable Escherichia coli O157:H7 Using an Improved Propidium Monoazide-Recombinase Polymerase Amplification Method
by Alka Rani, Charles Chinyere Dike, Nitin Mantri and Andrew Ball
Foods 2022, 11(20), 3207; https://doi.org/10.3390/foods11203207 - 14 Oct 2022
Cited by 5 | Viewed by 2034
Abstract
The detection of both viable and viable but non-culturable (VBNC) Escherichia coli O157:H7 is a crucial part of food safety. Traditional culture-dependent methods are lengthy, expensive, laborious, and unable to detect VBNC. Hence, there is a need to develop a rapid, simple, and [...] Read more.
The detection of both viable and viable but non-culturable (VBNC) Escherichia coli O157:H7 is a crucial part of food safety. Traditional culture-dependent methods are lengthy, expensive, laborious, and unable to detect VBNC. Hence, there is a need to develop a rapid, simple, and cost-effective detection method to differentiate between viable/dead E. coli O157:H7 and detect VBNC cells. In this work, recombinase polymerase amplification (RPA) was developed for the detection of viable E. coli O157:H7 through integration with propidium monoazide (PMAxx). Initially, two primer sets, targeting two different genes (rfbE and stx) were selected, and DNA amplification by RPA combined with PMAxx treatment and the lateral flow assay (LFA) was carried out. Subsequently, the rfbE gene target was found to be more effective in inhibiting the amplification from dead cells and detecting only viable E. coli O157:H7. The assay’s detection limit was found to be 102 CFU/mL for VBNC E. coli O157:H7 when applied to spiked commercial beverages including milk, apple juice, and drinking water. pH values from 3 to 11 showed no significant effect on the efficacy of the assay. The PMAxx-RPA-LFA was completed at 39 °C within 40 min. This study introduces a rapid, robust, reliable, and reproducible method for detecting viable bacterial counts. In conclusion, the optimised assay has the potential to be used by the food and beverage industry in quality assurance related to E. coli O157:H7. Full article
(This article belongs to the Special Issue Molecular Detection and Characterization of Foodborne Bacterial Pathogens)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop