Predicting the combinatorial effects of water activity, pH and organic acids on Listeria growth in media and complex food matrices

Nyhan, Laura; Begley, Máire; Mutel, A.; Qu, Yuejun; Johnson, Nicholas; Callanan, Michael

doi:10.1016/j.fm.2018.03.002

Cited by 38 publications

(23 citation statements)

References 48 publications

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“…In fresh beef, MAP with 60% CO 2 : 30% O 2 : 10% N 2 prevented growth at 4 C for more than 2 weeks of storage. Although regarding vacuum packaging, this preservation methodology seems not affecting the growth of L. monocytogenes as observed by [16]. With reference to [35], it was showed that L. monocytogenes survives better in vacuum packaging than in air-packed beef samples.…”

Section: Packagingmentioning

confidence: 84%

“…Its resistance depends on other ecological factors and its physiological condition. The influence of the pH in the growth of L. monocytogenes was largely studied [1,16]. With reference to [1], it was shown that ultimate pH (normal and DFD meats) influenced significantly the growth of L. monocytogenes inoculated on beef samples stored at two temperatures (4 and 9 C).…”

Section: Influence Of Phmentioning

confidence: 99%

“…The growth of L. monocytogenes is scarcely affected by anaerobic or oxygen-reduced atmosphere. According to [16,35], modified atmosphere packaging (MAP) systems may reduce the survival and growth of L. monocytogenes by the presence of carbon dioxide in modified atmosphere packaging (MAP). In fresh beef, MAP with 60% CO 2 : 30% O 2 : 10% N 2 prevented growth at 4 C for more than 2 weeks of storage.…”

Section: Packagingmentioning

confidence: 99%

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Modeling the Behavior of Listeria monocytogenes in Meat

Saraiva¹,

García‐Díez²,

Fontes³

et al. 2018

Listeria Monocytogenes

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This work was conducted to present some studies that show the behavior of Listeria monocytogenes in meat, according to intrinsic and extrinsic factors. The understanding of factors that affect the survival and growth of L. monocytogenes in meat, such as temperature, pH, acid, salt, water activity or modified atmosphere packaging, is crucial to develop strategies for food operators to reduce and prevent Listeria contamination and growth. The knowledge of L. monocytogenes behavior according to its physiological and ecological characteristics, under all probable conditions, will support risk assessors to find strategies to control this ubiquitous bacteria in food industry and food service. The Regulation (EC) No 2073/2005, reviewed by the Regulation (EC) No 1441/2007, does not establish the limits for L. monocytogenes in fresh meat. However, it is generally accepted a level of 100 cells on fresh meat, except for some risk groups. Food business operators and authorities can use predictive microbiology models as important tools to model bacterial growth in quantitative microbial risk assessments.The genus Listeria includes a group of Gram-positive psychrotrophic bacteria that can be isolated from a large variety of environmental sources such as water, soil, foodstuffs, animals or humans [2,3]. Also, Listeria can colonize various inert surfaces (e.g., surfaces of food machinery) [4]. Genus Listeria includes nonsporulating, catalase positive, Voges-Proskauer positive, indol and oxidase negative, facultative anaerobic rods that show motility at 25 C. Listeria can also grow in a large variety of conditions like high salt concentrations, low water activity, broad pH range (pH 4.5-9) and broad range of temperature (0-45 C, optimum 30-37 C) [3,5]. This genus Listeria includes several species such as L.

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Section: Packagingmentioning

confidence: 84%

Section: Influence Of Phmentioning

confidence: 99%

Section: Packagingmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Behavior of Listeria monocytogenes in Meat

Saraiva¹,

García‐Díez²,

Fontes³

et al. 2018

Listeria Monocytogenes

View full text Add to dashboard Cite

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“…The interest in this area has increased, and several models, based on the gamma concept proposed by Zwietering, Wijtzes, De Wit, and Riet (1992), have been devised to predict microbial behaviour considering extrinsic and intrinsic factors (such as temperature, pH, water activity, etc. ): Lambert and Bidlas (2007), Baka, Van Derlinden, Boon, Mertens, and Van Impe (2013) and Nyhan, Begley, Mutel, Qu, Johnson, and Callanan (2018), to mention some. The gamma concept is based on the assumptions that: (i) the factors affecting microbial growth are independent and can be represented by a discrete term that is multiplied by other terms describing the effect of additional growth rate affecting factors; and (ii) each factor can be expressed as a fraction of the maximum growth rate, μ max , and therefore each factor is normalised between 0 and 1 (Zwietering, Wijtzes, De Wit, & Riet, 1992).…”

Section: Introductionmentioning

confidence: 99%

Cardinal parameter meta-regression models describing Listeria monocytogenes growth in broth

Silva

Cadavez

Teixeira

et al. 2020

Food Research International

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Since Listeria monocytogenes has a high case-fatality rate, substantial research has been devoted to estimate its growth rate under different conditions of temperature, pH and water activity (a w). In this study, published findings on L. monocytogenes growth in broth were extracted and unified by constructing meta-regression models based on cardinal models for (i) temperature (CM[T]), (ii) temperature and pH (CM[T][pH]), and (iii) temperature, pH and a w (CM[T][pH][a w ]). After assessing all the sources retrieved between 1988 until 2017, fortynine primary studies were considered appropriate for inclusion. Apart from the modelling variables, study characteristics such as: type of broth (BHI, TSB, TPB), reading method (colony-forming-units, CFU; or binarydilution optical density methods, OD), inoculum concentration and strain serotype, were also extracted. Metaregressions based on CM[T] and CM[T][pH] were fitted on subsets of the 2009 growth rate measures and revealed that type of broth and reading method significantly modulated the cardinal parameter estimates. In the most parsimonious CM[T][pH][a w ] meta-regression model, whereby the variability due to type of broth was extracted in a nested random-effects structure, the optimum growth rate μ opt of L. monocytogenes was found to be lower when measured as CFU (0.947 log CFU/h; SE = 0.094 log CFU/h) than when measured as OD (1.289 log CFU/h; SE = 0.092 log CFU/h). Such a model produced the following cardinal estimates: T min =-1.273°C (SE = 0.179°C), T opt = 37.26°C (SE = 0.688°C), T max = 45.12°C (SE = 0.013°C), pH min = 4.303 (SE = 0.014), pH opt = 7.085 (SE = 0.080), pH max = 9.483 (SE = 0.080), a w min = 0.894 (SE = 0.002) and a w opt = 0.995 (SE = 0.001). Integrating the outcomes from numerous L. monocytogenes growth experiments, this meta-analysis has estimated pooled cardinal parameters that can be used as reference values in quantitative risk assessment studies. the most serious foodborne illnesses under EU surveillance (EFSA & ECDC, 2019). Moreover, among the cases with information on hospitalisation status, 97% were hospitalised (EFSA & ECDC, 2019). Occurrence data in ready-to-eat (RTE) food samples has revealed that, in the EU, in 2017, L. monocytogenes occurrence was highest in fish and fishery products (6%), RTE salads (4.2%), RTE meat and meat products (1.8%), soft and semi-soft cheeses (0.9%), fruits and vegetables (0.6%) and hard cheeses (0.1%) (EFSA & ECDC, 2018), while in 2018, the occurrence of L. monocytogenes ranged from 0.09% for "hard cheeses made from pasteurised milk" up to 3.1% for RTE bovine meat (EFSA & ECDC, 2019). Even though general, healthy population can be susceptible to listeriosis infection, the main groups of risk are people over 65 years-old, women aged 25-44 and children below 1 year of age (believed to be mainly pregnancy-related), and immunocompromised patients or patients with a higher number of severe comorbidities (EFSA BIOHAZ Panel, 2018). Since one of the main goals and responsibilities of the food industry

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“…But the majority of the developed models are based on the growth of two bacterial species in a food matrix (Vereecken et al, 2000;Giuffrida et al, 2007), most often to study the interaction between spoilage and pathogenic bacteria (Lebert et al, 2000;Mejlholm and Dalgaard, 2007;Giuffrida et al, 2009;Cornu et al, 2011;Ye et al, 2014;Correia Peres Costa et al, 2019;Pedrozo et al, 2019). Moreover, these models often describe the growth of the SSOs depending on the storage temperature (Dominguez and Schaffner, 2007;Gospavic et al, 2008;Kreyenschmidt et al, 2010;Psomas et al, 2011;Longhi et al, 2013;Antunes-Rohling et al, 2019) or the packaging conditions (Devlieghere et al, 1999;Chaix et al, 2015;Guillard et al, 2016;Couvert et al, 2019;Kapetanakou et al, 2019), but do not always consider the interaction of these storage conditions for the growth of spoilage bacteria (Rosso et al, 1995;Augustin and Carlier, 2000;Le Marc et al, 2002;Pinon et al, 2004;Dalcanton et al, 2018;Kakagianni et al, 2018;Nyhan et al, 2018;Correia Peres Costa et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

et al. 2020

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The aim of this study was to obtain the growth parameters of specific spoilage microorganisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono-and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO 2 30%/O 2 70%)] at three isothermal conditions (4, 8, and 12 • C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey-predator) model. The modified Jameson-effect model showed slightly better performances, with 40-86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14-48% for the prey-predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three-or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.

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Predicting the combinatorial effects of water activity, pH and organic acids on Listeria growth in media and complex food matrices

Cited by 38 publications

References 48 publications

Modeling the Behavior of Listeria monocytogenes in Meat

Modeling the Behavior of Listeria monocytogenes in Meat

Cardinal parameter meta-regression models describing Listeria monocytogenes growth in broth

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

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