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Detection of Salmonella in Food Matrices, from Conventional Methods to Recent Aptamer-Sensing Technologies

by Nathalie Paniel 1,2,* and Thierry Noguer 1,3,*
Laboratoire BAE, Université de Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
Unité EMaiRIT’S, Centre Technique de la Conservation des Produits Agricoles (CTCPA), Site Agroparc, 449 Avenue Clément Ader, BP21203, 84911 Avignon, France
Laboratoire de Biodiversité et Biotechnologies Microbiennes, USR 3579, Sorbonne Universités (UPMC) Paris 6 et CNRS, Observatoire Océanologique, 66650 Banyuls-sur-Mer, France
Authors to whom correspondence should be addressed.
Foods 2019, 8(9), 371;
Received: 16 July 2019 / Revised: 20 August 2019 / Accepted: 26 August 2019 / Published: 1 September 2019
(This article belongs to the Section Food Analytical Methods)
Rapid detection of the foodborne pathogen Salmonella in food processing is of crucial importance to prevent food outbreaks and to ensure consumer safety. Detection and quantification of Salmonella species in food samples is routinely performed using conventional culture-based techniques, which are labor intensive, involve well-trained personnel, and are unsuitable for on-site and high-throughput analysis. To overcome these drawbacks, many research teams have developed alternative methods like biosensors, and more particularly aptasensors, were a nucleic acid is used as biorecognition element. The increasing interest in these devices is related to their high specificity, convenience, and relative rapid response. This review aims to present the advances made in these last years in the development of biosensors for the detection and the quantification of Salmonella, highlighting applications on meat from the chicken food chain. View Full-Text
Keywords: Salmonella; food matrices; aptamers; biosensors; standard methods Salmonella; food matrices; aptamers; biosensors; standard methods
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Paniel, N.; Noguer, T. Detection of Salmonella in Food Matrices, from Conventional Methods to Recent Aptamer-Sensing Technologies. Foods 2019, 8, 371.

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