2015
DOI: 10.1016/j.ijfoodmicro.2015.05.006
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Quantifying strain variability in modeling growth of Listeria monocytogenes

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Cited by 82 publications
(77 citation statements)
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“…Recently, strain variability in the growth and thermal resistance of Listeria monocytogenes was quantified (8,9). The impact of strain variability on growth and thermal resistance was also reported for other pathogens such as, Salmonella enterica (10)(11)(12), Staphylococcus aureus (13,14), Bacillus cereus (15), and Escherichia coli (16)(17)(18).…”
mentioning
confidence: 94%
See 1 more Smart Citation
“…Recently, strain variability in the growth and thermal resistance of Listeria monocytogenes was quantified (8,9). The impact of strain variability on growth and thermal resistance was also reported for other pathogens such as, Salmonella enterica (10)(11)(12), Staphylococcus aureus (13,14), Bacillus cereus (15), and Escherichia coli (16)(17)(18).…”
mentioning
confidence: 94%
“…Therefore, the objectives of the present study were to quantify strain variability, reproduction (biological) variability, and experimental variability with respect to the growth and thermal inactivation kinetics of L. plantarum and to quantify the variability in thermal resistance attributed to growth history. Strain variability was defined as the difference between strains from the same species (8), reproduction variability was defined as the difference be-(ii) Preparation of media. Eight pH values, namely, 7.0, 6.0, 5.0, 4.0, 3.6, 3.5, 3.4, and 3.3, were selected to test the effect of pH on the maximum specific growth rate ( max ) of L. plantarum.…”
mentioning
confidence: 99%
“…For example, in the last years new predictive microbial models have been developed considering different factors like, the structural characteristics of the food matrix [16] and the cross-protection between different stresses [17]. In addition, the modelling approach followed in those studies that consider the inter-strain variability of the same microbial species has changed with respect to previous studies [18,19]. Latter developments in predictive modelling approaches have also resulted in new model equations [20], stochastic models [6,21,22] and models considering dynamic conditions [23].…”
Section: Recent Developments In Predictive Microbial Modellingmentioning
confidence: 99%
“…The focus was on the time period after the adoption of the previous Scientific Opinion of the BIOHAZ Panel, i.e. 2008-2015 (EFSA BIOHAZ Panel, 2008.…”
Section: Background and Terms Of Reference As Provided By The Requestormentioning
confidence: 99%
“…The focus was on the time period after the adoption of the previous Scientific Opinion of the BIOHAZ Panel, i.e. 2008-2015 (EFSA BIOHAZ Panel, 2008.Considering that the risk assessment was of particular interest to the public and scientific community, it is deemed appropriate to undertake a public consultation on the draft Scientific Opinion before its final adoption by the BIOHAZ Panel. The public consultation should last at least 6 weeks.…”
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confidence: 99%