Thermal destruction ofListeria monocytogenes in liver sausage slurry

Bhaduri, Saumya; Smith, Patrick W.; Palumbo, Samuel A.; Turner-Jones, C.; Smith, James L.; Marmer, Benne S.; Buchanan, Robert L.; Zaika, Laura L.; Williams, Aaron C.

doi:10.1016/0740-0020(91)90019-x

Cited by 105 publications

(55 citation statements)

References 10 publications

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“…Available scientific publications have indicated that L. monocytogenes may be of higher heat resistance than other non-spore-forming pathogenic bacteria (Mackey and Bratchell, 1989;Boyle et al, 1990;Schoeni et aI., 1991). Heat resistance increases with increasing temperature of culture propagation (Bhaduri et al, 1991); the presence of curing ingredients (Yen et aI., 1991); heat shocking or tempering (e.g. 48°C) before thermal processing (Farber and Brown, 1990;Linton et al, 1992); or after slow temperature rise (Quintavalla and Campanini, 1991).…”

Section: Pathogens Of Emerging Concernmentioning

confidence: 99%

Microbial growth and its control in meat, poultry and fish

Sofos¹

1994

Quality Attributes and Their Measurement in Meat, Poultry and Fish Products

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Section: Pathogens Of Emerging Concernmentioning

confidence: 99%

Microbial growth and its control in meat, poultry and fish

Sofos¹

1994

Quality Attributes and Their Measurement in Meat, Poultry and Fish Products

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“…The mathematical model used to describe inactivation of microorganisms was based on modifications of the Gompertz equation and described by Zwietering et al [91]. Badhuri et al (1991) [92] were the first to demonstrate that the modified equation of the Gompertz model describes the nonlinear survival curves of Listeria monocytogenes heated in sausage slurry. Later, it was successfully tested to describe bacterial heat inactivation [93].…”

Section: Effects On Microorganisms and Virusesmentioning

confidence: 99%

Effect of High Pressure Processing on the Microbial Inactivation in Fruit Preparations and Other Vegetable Based Beverages

2017

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Abstract:The purpose of this study is to review the effects of High Hydrostatic Pressure Processing (HPP) on the safety of different fruit derivatives (juices, nectars, jams, purees, pastes . . . ), considering the types established in the European legislation and some other vegetable-based beverages (mainly juices and smoothies). The main inactivation processes and mechanisms on microorganisms are reviewed. Studies have revealed that HPP treatment is capable of destroying most microorganisms, depending on the application conditions (amplitude of the pressure, duration time, temperature, and the mode of application), the properties of the fresh and processed fruit/vegetables (pH, nutrient composition, water activity, maturity stage), and the type of microorganisms or viruses.

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“…Commonly used empirical models are the Gompertz model (Gibson et al 1994;McKellar and Lu 2004), the logistic model (Zwietering et al 1990), and the three-phase linearized growth or Buchanan model (Ingraham et al 1983;Buchanan et al 1997). These models and their modifications (e.g., Bhaduri et al 1991;Peleg and Corradini 2011) have a small number of mostly intuitive parameters (three to four), but usually do not cover all phases of microbial growth or do not account for specific properties such as long lag phases. Typical parameters are the relative growth rate, the time to reach maximum growth rate, initial population size, or the asymptotic population size.…”

Section: Microbial Growth Models In Food Safety Researchmentioning

confidence: 98%

“…Typical parameters are the relative growth rate, the time to reach maximum growth rate, initial population size, or the asymptotic population size. Inactivation or survival models are designed to model survival over time and are often based on classical primary growth models, for example the modified Gompertz model (Bhaduri et al 1991). In complex survival models, more than one level of heat resistance is included, e.g., in the Whiting-Buchanan Model (Whiting and Buchanan 1993) and the Xiong et al Fig.…”

Section: Microbial Growth Models In Food Safety Researchmentioning

confidence: 99%

Modeling microbial growth and dynamics

Esser

Leveau

Meyer

2015

Appl Microbiol Biotechnol

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Modeling has become an important tool for widening our understanding of microbial growth in the context of applied microbiology and related to such processes as safe food production, wastewater treatment, bioremediation, or microbe-mediated mining. Various modeling techniques, such as primary, secondary and tertiary mathematical models, phenomenological models, mechanistic or kinetic models, reactive transport models, Bayesian network models, artificial neural networks, as well as agent-, individual-, and particle-based models have been applied to model microbial growth and activity in many applied fields. In this mini-review, we summarize the basic concepts of these models using examples and applications from food safety and wastewater treatment systems. We further review recent developments in other applied fields focusing on models that explicitly include spatial relationships. Using these examples, we point out the conceptual similarities across fields of application and encourage the combined use of different modeling techniques in hybrid models as well as their cross-disciplinary exchange. For instance, pattern-oriented modeling has its origin in ecology but may be employed to parameterize microbial growth models when experimental data are scarce. Models could also be used as virtual laboratories to optimize experimental design analogous to the virtual ecologist approach. Future microbial growth models will likely become more complex to benefit from the rich toolbox that is now available to microbial growth modelers.

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Thermal destruction ofListeria monocytogenes in liver sausage slurry

Cited by 105 publications

References 10 publications

Microbial growth and its control in meat, poultry and fish

Microbial growth and its control in meat, poultry and fish

Effect of High Pressure Processing on the Microbial Inactivation in Fruit Preparations and Other Vegetable Based Beverages

Modeling microbial growth and dynamics

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