Quantitative prediction of microbial behaviour during food processing using an integrated modelling approach: a review

Lebert, I.; Lebert, André

doi:10.1016/j.ijrefrig.2006.04.008

Cited by 44 publications

(14 citation statements)

References 59 publications

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“…(Zwietering et al . ; Isabelle and Andre ). Among these equations, the logistic equation and Gompertz equation can effectively describe the microbial growth and operate well.…”

Section: Resultsmentioning

confidence: 99%

Predictive Models for Growth of Leuconostoc citreum and Its Dynamics in Pickled Vegetables with Low Salinity

Weng

Lei

2012

J Food Process Engineering

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The strain of Leuconostoc citreum L‐33 (L. citreum L‐33) is a critical microorganism in the processing of pickled vegetables with low salinity. In this study, effects of temperature, initial pH and sodium chloride concentration on cell growth of L. citreum L‐33 and its growth prediction models were achieved in order to predict the growth situation of L. citreum L‐33 under definite conditions. By use of Man‐Rogosa‐Sharpe broth medium at different initial pH and sodium chloride concentration, the strain of L. citreum L‐33 was incubated at different temperature. Furthermore, first‐order and secondary growth prediction models of L. citreum were established, respectively, according to the fitting curve and data process by Origin software. The results demonstrated that in the experimental conditions, the growth prediction of L. citreum L‐33 can be described by the Gompertz equation with four parameters. High fitting accuracy values of most of the first‐order growth models were over 0.99 according to the Adjusted R‐squared. Relation between the maximum specific growth rate obtained by the Gompertz equation and temperature as well as initial pH can be described by the Ratkowsky equation. However, a relation between the maximum specific growth rate and sodium chloride concentration could be described by polynomial equations. The secondary model obtained was validated by the medium data. The bias and accuracy factors are between 0.9–1.0 and 1.0–1.1, respectively, within an acceptable range. Temperature, initial pH and sodium chloride concentration are important factors influencing the cell growth of L. citreum L‐33. The growth prediction models for the crucial microorganism L. citreum L‐33 in low‐salinity pickled vegetable processing could be conducted under definite conditions. Practical Applications The microorganisms play important role in the processing and preservation of pickled vegetables, especially in the conditions of low concentration of salt. A critical microorganism of Leuconostoc citreum L‐33 (L. citreum L‐33) in pickled vegetable processing involved, its growth prediction models under different environmental conditions were established. The established models can provide a more accurate estimation for growth of L. citreum and theoretical support for production processing optimization of pickled vegetables, as well as valuable data for developing mathematic models of food microbiology. These findings could be applied in pickled vegetable processing practice by means of growth prediction of crucial microorganisms impacting on quality and safety of the product.

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“…(Zwietering et al . ; Isabelle and Andre ). Among these equations, the logistic equation and Gompertz equation can effectively describe the microbial growth and operate well.…”

Section: Resultsmentioning

confidence: 99%

Predictive Models for Growth of Leuconostoc citreum and Its Dynamics in Pickled Vegetables with Low Salinity

Weng

Lei

2012

J Food Process Engineering

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“…This function permits one to classify the metabolites as 'primaries', if the rate of formation depends only on the growth rate of biomass (Y A/X ≠0; K=0), 'secondaries', if this relies on the biomass present (Y A/X =0; K≠0), and 'mixed', if this relies on both the rate and biomass present (Y A/X ≠0; K≠0) [11]. A response surface model was used to describe the combined influence of temperature and pH on cell growth rate (μ max ), growth-associated constant for bacteriocin production (Y A/X ) and non-growth-associated constant for bacteriocin production (K), as follows [12]:…”

Section: Modellingmentioning

confidence: 99%

Modelling Growth and Bacteriocin Production by Lactobacillus plantarum BC-25 in Response to Temperature and pH in Batch Fermentation

Zhou

Zeng

Han

et al. 2015

Appl Biochem Biotechnol

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The use of bacteriocin-producing probiotics to improve food fermentation processes seems promising. However, lack of fundamental information about their functionality and specific characteristics may hinder their industrial use. Predictive microbiology may help to solve this problem by simulating the kinetics of bacteriocin-producing strains and optimising the cell growth and production of beneficial metabolites. In this study, a combined model was developed which could estimate, from a given initial condition of temperature and pH, the growth and bacteriocin production of Lactobacillus plantarum BC-25 in MRS broth. A logistic model was used to model the growth of cells, and the Luedeking-Piret model was used to simulate the biomass and bacteriocin production. The parameters generated from these primary models were used in a response surface model to describe the combined influence on cell growth, biomass and bacteriocin production. Both the temperature and pH influenced cell and bacteriocin production significantly. The optimal temperature and pH for cell growth is 35 °C and 6.8, and the optimal bacteriocin production condition is a range dependent on two growth-associated constants (YA/X and K), where temperature is from 27 to 34 °C, and pH is 6.35 to 6.65. The developed model is consistent with similar studies and could be a useful tool to control and increase the production of lactic acid bacteria in bioreactors.

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“…The use of growth and inactivation models such as the Seafood Spoilage and Safety Predictor, the Pathogen Modeling Program or the Food MicroModel are often used and recommended to FBO, although outcomes have to be tested and compared with experimental results (Isabelle and André 2006;Scott 2005). Moreover, other systems can be realised by FBO without expensive software products with the aim of modelling obtained results (Parisi 2001(Parisi , 2004a.…”

Section: The Microbial Risk In Food Industries: Regulatory Norms and mentioning

confidence: 99%

Safety Protocols in the Food Industry and Emerging Concerns

Gurnari¹

2015

SpringerBriefs in Molecular Science

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Quantitative prediction of microbial behaviour during food processing using an integrated modelling approach: a review

Cited by 44 publications

References 59 publications

Predictive Models for Growth of Leuconostoc citreum and Its Dynamics in Pickled Vegetables with Low Salinity

Predictive Models for Growth of Leuconostoc citreum and Its Dynamics in Pickled Vegetables with Low Salinity

Modelling Growth and Bacteriocin Production by Lactobacillus plantarum BC-25 in Response to Temperature and pH in Batch Fermentation

Safety Protocols in the Food Industry and Emerging Concerns

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