Application of a Model Based on the Central Limit Theorem to Predict Growth of Pseudomonas spp. in Fish Meat

Robazza, Weber da Silva; Teleken, Jhony Tiago; Galvão, Alessandro Cazonatto; Miorelli, Suzane; Stolf, Denise Ortigosa

doi:10.1007/s11947-017-1939-7

Cited by 10 publications

(5 citation statements)

References 29 publications

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“…In the Baranyi model, the adaptation function quantifies the physiological state of the microbial cells, 12 while in the Huang model the adaptation function is only an empirical expression (logistic function). 24 Robazza et al 25 also used a semi-mechanistic model based on the Central Limit Theorem to model the same experimental data. This model provides a link between the physiological state of individual cells and the specific growth rate of the population.…”

Section: Resultsmentioning

confidence: 99%

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Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Teleken

Galvão

Robazza

2018

Brazilian Journal of Microbiology

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Mathematical models are often used to predict microbial growth in food products. An important class of these models involves the adaptation of classical sigmoid functions, such as the Gompertz and logistic functions. This study aimed to validate the use of the modified Richards model in various situations, which have not previously been tested. The model was obtained through solving a system of two differential equations and could be applied to both isothermal and non-isothermal environments. To test and validate this model, we used published datasets containing data for the growth of Pseudomonas spp. in fish products. The results obtained after fitting the model showed that it could be effectively used to describe and predict the Pseudomonas growth curves under various temperature regimens. However, the influence of the shape parameter on the growth curve is an issue that needs further evaluation.

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Section: Resultsmentioning

confidence: 99%

“…Based on the prediction parameters (BF and AF), the performance of the modified Richards model was similar to that of the Baranyi and Huang semi-mechanistic models 23 and Central Limit Theorem model. 25 …”

Section: Discussionmentioning

confidence: 99%

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Teleken

Galvão

Robazza

2018

Brazilian Journal of Microbiology

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“…In which the noise

\varepsilon

is often assumed to be Gaussian (Halter et al, 2009; Robazza et al, 2017). It should be noted that, in certain applications, the observed noise can be non‐Gaussian and as a consequence degrade the performance of BO, in which case alternative regressors with a less stringent noise assumption (e.g., Student‐t processes) could be used (Shah et al, 2014).…”

Section: Bioprocess Optimizationmentioning

confidence: 99%

Bayesian cell therapy process optimization

Claes,

Heck,

Coddens

et al. 2024

Biotech & Bioengineering

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Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource‐intensive experimentation, are two contributing factors that make the development of these next‐generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state‐of‐the‐art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state‐of‐the‐art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

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“…There are numerous studies that describe the growth of microorganisms as a function of a single environmental factor, e.g. temperature (Gil et al 2017;Robazza et al 2017) or a w (Patriarca et al 2001;Zimmermann et al 2011). Research works modelling the impact of both temperature and water availability on the kinetic parameters of the primary model were also undertaken.…”

Section: Overall Predictive Model Of Mould Growth As a Function Of Tementioning

confidence: 99%

Model of Fungal Development in Stored Barley Ecosystems as a Prognostic Auxiliary Tool for Postharvest Preservation Systems

Wawrzyniak

2021

Food Bioprocess Technol

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Postharvest preservation and storage have a crucial impact on the technological quality and safety of grain. The important threat to stored grain quality and nutritional safety of cereal products is mould development and their toxic metabolites, mycotoxins. Models based on predictive microbiology, which are able to estimate the kinetics of fungal growth, and thus, the risks of mycotoxin accumulation in a mass of grain are promising prognostic tools that can be applied in postharvest management systems. The study developed a modelling approach to describe total fungal growth in barley ecosystems stored at different temperatures (T = 12–30 °C) and water activity in grain (aw = 0.78–0.96). As the pattern of fungal growth curves was sigmoidal, the experimental data were modelled using the modified Gompertz equation, in which constant coefficients reflecting biological parameters of mould development (i.e. lag phase duration (τlag), maximum growth rate (μmax) and the maximum increase in fungal population level (Δmaxlog(CFU)) were expressed as functions of storage conditions, i.e. aw and T. The criteria used to evaluate the overall model performance indicated its good precision (R2 = 0.95; RMSE = 0.23) and high prediction accuracy (bias factor and accuracy factor Bf = 1.004, Af = 1.035). The formulated model is able to estimate the extension of fungal contamination in a bulk of grain versus time by monitoring temperature and intergranular relative humidity that are readily measurable in practice parameters; therefore, it may be used as a prognostic support tool in modern postharvest management systems.

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Application of a Model Based on the Central Limit Theorem to Predict Growth of Pseudomonas spp. in Fish Meat

Cited by 10 publications

References 29 publications

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Bayesian cell therapy process optimization

Model of Fungal Development in Stored Barley Ecosystems as a Prognostic Auxiliary Tool for Postharvest Preservation Systems

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