Cold chain of chilled food in France

Derens-Bertheau, Evelyne; Osswald, Véronique; Laguerre, Onrawee; Álvarez, G.

doi:10.1016/j.ijrefrig.2014.06.012

Cited by 82 publications

(50 citation statements)

References 7 publications

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“…Inoculated commercial MPF samples, inoculated churned butter samples and noninoculated control samples were stored at 8C for up to 42 days in order to simulate a realistic worst-case scenario likely to have occurred in the cold chain (Augustin et al 2011;Derens-Bertheau et al 2015). After each analysis (microbiological and physico-chemical analyses), the samples were destroyed.…”

Section: Storage Conditionsmentioning

confidence: 99%

Growth Potential Assessment of Listeria in Milk Fat Products by Challenge Testing

Michelon

Leclercq

Garric

et al. 2015

Journal of Food Safety

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Milk fat products (MFP), including butter and low-fat dairy spreads, are a specific type of ready-to-eat food known as water-in-fat emulsions, in which the behavior of microbial foodborne pathogens such as Listeria monocytogenes is not clearly known. This study investigated the growth and survival of L. monocytogenes, and of Listeria innocua as a surrogate for L. monocytogenes, in these foods using challenge testing. Three commercial MFPs with various fat contents (butter, half butter and low-fat dairy spread) and two samples of traditional churned butter with various water droplet sizes were artificially contaminated with Listeria. Total mesophilic microflora including lactic acid bacteria, pH and Listeria were monitored throughout the shelf life. The growth potential of Listeria was calculated in the course of the shelf life and remains below the limit value of 0.5 log cfu/g during the whole shelf life in any of the butter. However, the concentration of Listeria remained stable during the shelf life in the tested MFPs (commercial and churned) except in the commercial low-fat dairy spread in which Listeria decreased gradually. PRACTICAL APPLICATIONSEuropean Union regulation No. 2073/2005 lays down the microbiological limit criteria for Listeria monocytogenes in ready-to-eat foods (RTE). According to this regulation, each RTE food has to be classified, with scientific justification, as able or unable to support the growth of L. monocytogenes during the shelf life. This challenge-test study on milk fat products (MFPs) provided some useful data needed to assess the behavior of Listeria in these particular RTE food products. This study showed that while MFPs do not promote growth, they do support Listeria survival. Water droplet size seems to be a key parameter in preventing the growth of both microflora and Listeria. The study also showed that, in order to determine whether a given MFP supports Listeria growth or not, it is crucial to investigate the impact of the variations in physico-chemical and microbiological parameters of each butter-making process used by companies.

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Section: Storage Conditionsmentioning

confidence: 99%

Growth Potential Assessment of Listeria in Milk Fat Products by Challenge Testing

Michelon

Leclercq

Garric

et al. 2015

Journal of Food Safety

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“…Applicability of RFID use among retailers, distributors, and customers was compared with barcode using return on investment as a (ROI) measure. Derens-Bertheau et al (2015) used the real-time-temperature profile of a Frisbee field test to measure the time-temperature performance of chilled food.…”

Section: Analyzing Real-time Data In the Cold Chain Perspectivementioning

confidence: 99%

“…However, most studies (Wang et al, 2015;Thakur and Forås, 2015;Gogou et al, 2015;Grunow and Piramuthu, 2013;Derens-Bertheau et al, 2015) have been conducted at an operational level and not at a planning level. The incorporation of perishability and deterioration at the planning stage would facilitate the design of a robust system and form a suitable framework for incorporating such technologies.…”

Section: Summary Of Research Gapsmentioning

confidence: 99%

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

et al. 2016

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The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation.Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain.Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver.The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.

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“…The temperature and its variability can be different according to the equipment. Several field studies were carried out on refrigerated products in the cold chain (Laguerre et al, 2002;Morelli and Derens, 2009;Rediers et al, 2009;Derens-Bertheau et al, 2014). Such data have already been combined with quality model evolution to identify the impact of the time temperature profile on the final quality, mostly in case-studies related with microbiological safety modelling (Afchain et al, 2005;Pouillot et al, 2007;Couvert et al, 2010;Ellouze et al, 2010;Koutsoumanis et al, 2010;De Cesare et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Identification of the significant factors in food quality using global sensitivity analysis and the accept-and-reject algorithm. Part III: Application to the apple cold chain

Duret

Gwanpua²,

Hoang³

et al. 2015

Journal of Food Engineering

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Cold chain of chilled food in France

Cited by 82 publications

References 7 publications

Growth Potential Assessment of Listeria in Milk Fat Products by Challenge Testing

Growth Potential Assessment of Listeria in Milk Fat Products by Challenge Testing

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

Identification of the significant factors in food quality using global sensitivity analysis and the accept-and-reject algorithm. Part III: Application to the apple cold chain

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