Dynamics of low (1–4 cells) vs high populations of Listeria monocytogenes and Salmonella Typhimurium in fresh-cut salads and their sterile liquid or solidified extracts

Manios, Stavros G.; Konstantinidis, Nikolaos; Gounadaki, Antonia S.; Skandamis, Panagiotis

doi:10.1016/j.foodcont.2012.04.023

Cited by 31 publications

(18 citation statements)

References 41 publications

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“…Short-chain AHLs are generally diffusible throughout the bacterial membrane, while long-chain AHLs seem to be actively transported in and out of the cells via efflux and influx systems (133). Several factors may influence the concentration and type (i.e., the length and substitution of the C-3 of the acyl chain) of AHLs, including temperature, pH, NaCl, growth medium, inoculum size, and bacterial growth phase (36,76,77,79,142).…”

Section: Intraspecies Cell-to-cell Communicationmentioning

confidence: 99%

“…The food structure strongly affects the type (planktonic, colonial, immobilized) and the dynamics of growth and potentially the physiology of bacteria, due to accumulation of metabolic products (e.g., within a colony) compared to diffusion of metabolites away from cells in a liquid culture (57,76,125). Thus, considering that growth within a colony results in an inevitable proximity of cells and increase in cell density in a limited space, the release of QS compounds and their rapid diffusion within the colony might be more directly perceptible and thus, have greater impact on immobilized cells or cells within a colony than on cells growing planktonically in a liquid system (57,76). It should be noted that there are limited studies in which the above queries have been addressed.…”

Section: Quorum Sensing In the Context Of Food And Food Processingmentioning

confidence: 99%

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Quorum Sensing in the Context of Food Microbiology

Skandamis

Nychas

2012

Appl Environ Microbiol

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178

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ABSTRACTFood spoilage may be defined as a process that renders a product undesirable or unacceptable for consumption and is the outcome of the biochemical activity of a microbial community that eventually dominates according to the prevailing ecological determinants. Although limited information are reported, this activity has been attributed to quorum sensing (QS). Consequently, the potential role of cell-to-cell communication in food spoilage and food safety should be more extensively elucidated. Such information would be helpful in designing approaches for manipulating these communication systems, thereby reducing or preventing, for instance, spoilage reactions or even controlling the expression of virulence factors. Due to the many reports in the literature on the fundamental features of QS, e.g., chemistry and definitions of QS compounds, in this minireview, we only allude to the types and chemistry of QS signaling moleculesper seand to the (bioassay-based) methods of their detection and quantification, avoiding extensive documentation. Conversely, we attempt to provide insights into (i) the role of QS in food spoilage, (ii) the factors that may quench the activity of QS in foods and review the potential QS inhibitors that might “mislead” the bacterial coordination of spoilage activities and thus may be used as biopreservatives, and (iii) the future experimental approaches that need to be undertaken in order to explore the “gray” or “black” areas of QS, increase our understanding of how QS affects microbial behavior in foods, and assist in finding answers as to how we can exploit QS for the benefit of food preservation and food safety.

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Section: Intraspecies Cell-to-cell Communicationmentioning

confidence: 99%

Section: Quorum Sensing In the Context Of Food And Food Processingmentioning

confidence: 99%

Quorum Sensing in the Context of Food Microbiology

Skandamis

Nychas

2012

Appl Environ Microbiol

Self Cite

178

119

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“…Predictive microbiology involves the development of mathematical models to describe the effect of the most important environmental factors controlling the responses of microorganisms in foods (Ross et al, 2000). The majority of these models are based on experimental data derived from laboratory media, and limited information is available for assessing individual cell variability in foods (François et al, 2006;Manios et al, 2011). The models have immediate practical application to improve microbial food safety and quality and also can provide quantitative understanding of the microbial ecology of foods (Ross et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Microbial Growth Models in Gilthead Sea Bream (Sparus aurata) Stored in Ice

Carrascosa

Saavedra

Millán

et al. 2015

Journal of Aquatic Food Product Technology

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This study analyzes microbiological changes in whole, ungutted farmed gilthead sea bream (Sparus aurata) stored for an 18-day period in ice using traditional methods for mesophilic aerobic bacteria, psychrotrophic, Pseudomonas spp., Aeromonas spp., Shewanella putrefaciens, Enterobacteriaceae, sulphide-reducing Clostridium (Clostridia), and Photobacterium phosphoreum in muscle, skin, and gills, evaluating their seasonal differentiation. Two different statistical models were used to analyze microbiological growth.Simultaneously, physicochemical parameters such as the temperature, pH, biological oxygen demand (BOD 5 ), total dissolved solids, salinity, ammonia nitrogen, and total phosphorus content of growing waters were analyzed. The results showed that by the end of the storage time, specific spoilage bacteria (SSB) such as Pseudomonas spp., Aeromonas spp. and S. putrefaciens as H 2 S-producing bacteria were dominant in sea bream harvested in temperate water in the Canary Islands. Muscle tissue had least contamination, followed by skin and gills. The values of the analyzed seawater parameters were constant during the four seasons, except that the temperature showed a small difference between winter and summer. Seasonal effects were observed among the fish analyzed, suggesting that the lower levels of contamination detected in winter may have been due to the slight difference observed in water temperature in that season.

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“…More recently, studies also emphasized the need to take into account the single-cell growth probability when assessing the behavior of these food-borne bacteria (14-17). These observations were used to implement individual-based probabilistic approaches to assess the bacterial growth in food (18)(19)(20).Published microbial quantitative risk assessment studies take into account the variability of food characteristics by measuring physicochemical properties of foods, e.g., pH and water activity, on relatively large food portions, i.e., 10-or 25-g portions (21,22). These measurements expressing the between-and within-batch variability of product characteristics are then used to run stochastic models describing the growth of food-borne bacteria (19,22).…”

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confidence: 99%

mentioning

confidence: 99%

Combining Individual-Based Modeling and Food Microenvironment Descriptions To Predict the Growth of Listeria monocytogenes on Smear Soft Cheese

Ferrier

Hezard²,

Lintz³

et al. 2013

Appl Environ Microbiol

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An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability. Since risk analysis emerged as the internationally recognized framework to improve food control systems, many risk assessments were published that evaluated the probabilities and severities of adverse health effects resulting from the exposure of consumers to pathogenic microorganisms present in foods. This is especially the case for the food-borne pathogen bacterium Listeria monocytogenes. Its ubiquitous nature and its ability to multiply in many foods during chilled storage fostered the development of quantitative microbial risk assessment aimed at ranking foods according to risk or predicting the impact of management options (1-6).The assessment of the microbial behavior of the pathogen is of the highest importance when performing these quantitative assessments, since listeriosis cases are predominantly linked to the consumption of highly contaminated foods (2), and the variability of this behavior is of paramount importance in the context of exposure assessment (7, 8). The major sources of variability affecting microbial responses in foods are the initial contamination level, the variability in processing factors, the variability in food characteristics and in the storage conditions, and the biological variability, i.e., the variability of microbial behavior.For several years, it has...

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Dynamics of low (1–4 cells) vs high populations of Listeria monocytogenes and Salmonella Typhimurium in fresh-cut salads and their sterile liquid or solidified extracts

Cited by 31 publications

References 41 publications

Quorum Sensing in the Context of Food Microbiology

Quorum Sensing in the Context of Food Microbiology

Microbial Growth Models in Gilthead Sea Bream (Sparus aurata) Stored in Ice

Combining Individual-Based Modeling and Food Microenvironment Descriptions To Predict the Growth of Listeria monocytogenes on Smear Soft Cheese

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