Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

Bovill, Richard; Bew, Jan; Baranyi, József

doi:10.1016/s0168-1605(01)00446-9

Cited by 54 publications

(28 citation statements)

References 15 publications

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“…To construct these models, the uncertainty of the predictions due to the lag phase was simulated with extremes of the initial physiological state of the microorganisms. Similar approach was also used by Bovill et al (2001). For steps in the exponential phase, often no (large) lag phases are observed as a result of temperature shift (Bovill et al, 2001;Zwietering et al, 1994b), and also in this study, no clear deviations were observed after growth was initiated.…”

Section: Discussionsupporting

confidence: 65%

“…Similar approach was also used by Bovill et al (2001). For steps in the exponential phase, often no (large) lag phases are observed as a result of temperature shift (Bovill et al, 2001;Zwietering et al, 1994b), and also in this study, no clear deviations were observed after growth was initiated. The validation of these models show that they can be used to successfully predict shrimp shelf-life under fluctuating temperature since the B f values obtained (Table 3) ranged between 0.75 and 1.25 (Dalgaard, 2000).…”

Section: Discussionsupporting

confidence: 65%

See 1 more Smart Citation

Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes

Dabadé

Azokpota

Nout

et al. 2015

International Journal of Food Microbiology

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Section: Discussionsupporting

confidence: 65%

Section: Discussionsupporting

confidence: 65%

Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes

Dabadé

Azokpota

Nout

et al. 2015

International Journal of Food Microbiology

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“…[9,20,21,49,184]). Zwietering et al [184] focused on modelling the relationship between temperature and bacterial growth rate.…”

Section: Temperaturementioning

confidence: 99%

Food storage in a changing world: implications of climate change for food-caching species

Sutton

Strickland²,

Norris

2016

Clim Chang Responses

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Food caching is a behavioural strategy used by a wide range of animals to store food for future use. When food is stored, it is susceptible to environmental conditions that can lead to spoilage via microbial proliferation or physical and chemical processes. Given that the nutrition gained from consuming cached food will almost always be less than consuming it immediately upon capture, the degree of degradation will play a central role in determining the ecological threshold at which caching is no longer profitable. Our framework proposes that the degree of susceptibility among caching species is based primarily on the duration of storage, and the perishability of stored food. We first summarize the degree of susceptibility of 203 vertebrate caching species. Thirty-eight percent (38%) of these species are long-term cachers (>10 days) but only 2% are both long-term cachers and store highly perishable food. We then integrate insights from the fields of applied food science and plant biology to outline potential mechanisms by which climate change may influence food-caching species. Four climatic factors (temperature, number of freeze-thaw events, deep-freeze events and humidity) have been shown to affect the degradation of food consumed by humans and are also expected to influence the quality of perishable food cached in the wild. Temperature and moisture are likely important factors influencing seemingly nonperishable seeds. Although we are able to provide broad classifications for caching species at risk of climate change, an improved understanding of how environmental conditions affect the quality and persistence of cached food may allow us to better predict the impact of changing climatic conditions on the fitness of food-caching animals.

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“…Bovill et al (2000) and Bovill, Bew, and Baranyi (2001) applied the model of Baranyi and Roberts (1994) to dynamic temperature profiles, when growing Salmonella in milk and meat products. Other dynamic approaches for the growth of Salmonella have been described by Dickson et al (1992), who applied their model during cooling.…”

Section: Modelling Growthmentioning

confidence: 99%

Predictive modelling of Salmonella: From cell cycle measurements to e-models

Muñoz-Cuevas

Métris

Baranyi

2012

Food Research International

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Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

Cited by 54 publications

References 15 publications

Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes

Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes

Food storage in a changing world: implications of climate change for food-caching species

Predictive modelling of Salmonella: From cell cycle measurements to e-models

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