ArticleApplied and environmental microbiology2016
Quantifying Variability in Growth and Thermal Inactivation Kinetics of Lactobacillus plantarum.
Article in Applied and environmental microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Oral Administration of Limosilactobacillus reuteri KBL346 Ameliorates Influenza Virus A/PR8 Infection in Mouse.Probiotics and antimicrobial proteins · 2025Article
- Modeling of Growth and Organic Acid Kinetics and Evolution of the Protein Profile and Amino Acid Content duringFoods (Basel, Switzerland) · 2022Article
- A Predictive Growth Model for Pro-technological and ProbioticFrontiers in microbiology · 2022Article
- Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling.Applied and environmental microbiology · 2021Article
- Bio-protective effects ofCurrent medical mycology · 2020Article
- A Bayesian non-parametric mixed-effects model of microbial growth curves.PLoS computational biology · 2020Article
- A quantitative study on growth variability and production of ochratoxin A and its derivatives by A. carbonarius and A. niger in grape-based medium.Scientific reports · 2018Article
- Modeling and Validation of the Ecological Behavior of Wild-Type Listeria monocytogenes and Stress-Resistant Variants.Applied and environmental microbiology · 2016Article
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3 authors.
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Abstract
unlabelledThe presence and growth of spoilage organisms in food might affect the shelf life. In this study, the effects of experimental, reproduction, and strain variabilities were quantified with respect to growth and thermal inactivation using 20 Lactobacillus plantarum strains. Also, the effect of growth history on thermal resistance was quantified. The strain variability in μmax was similar (P > 0.05) to reproduction variability as a function of pH, aw, and temperature, while being around half of the reproduction variability (P < 0.05) as a function of undissociated lactic acid concentration [HLa]. The cardinal growth parameters were estimated for the L. plantarum strains, and the pHmin was between 3.2 and 3.5, the aw,min was between 0.936 and 0.953, the [HLamax], at pH 4.5, was between 29 and 38 mM, and the Tmin was between 3.4 and 8.3°C. The average D values ranged from 0.80 min to 19 min at 55°C, 0.22 to 3.9 min at 58°C, 3.1 to 45 s at 60°C, and 1.8 to 19 s at 63°C. In contrast to growth, the strain variability in thermal resistance was on average six times higher than the reproduction variability and more than ten times higher than the experimental variability. The strain variability was also 1.8 times higher (P < 0.05) than the effect of growth history. The combined effects of strain variability and growth history on D value explained all of the variability as found in the literature, although with bias. Based on an illustrative milk-processing chain, strain variability caused ∼2-log10 differences in growth between the most and least robust strains and >10-log10 differences after thermal treatment. IMPORTANCE: Accurate control and realistic prediction of shelf life is complicated by the natural diversity among microbial strains, and limited information on microbiological variability is available for spoilage microorganisms. 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 Lactobacillus plantarum and to quantify the variability in thermal resistance attributed to growth history. The quantitative knowledge obtained on experimental, reproduction, and strain variabilities can be used to improve experimental designs and to adequately select strains for challenge growth and inactivation tests. Moreover, the integration of strain variability in prediction of microbial growth and inactivation kinetics will result in more realistic predictions of L. plantarum dynamics along the food production chain.
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