Predicting heat process efficiency in thermal processes when bacterial inactivation is not log-linear

Desriac, Noémie; Vergos, M.; Achberger, Veronika; Coroller, Louis; Couvert, Olivier

doi:10.1016/j.ijfoodmicro.2018.09.028

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Over the years, models based on this concept of D and z-values, such as the botulinum cook, have been used extensively in the food industry, with the canning industry being the most notable example [ 46 ]. However, the success of this approach in the canning industry is mostly due to overprocessing rather than due to modelling accuracy [ 47 ]. The models assume loglinear behaviour according to Equation (1) [ 48 ].…”

Section: Historical Overview On the Inclusion Of Food Microstructure In Predictive Modelsmentioning

confidence: 99%

“…Therefore, early models (i.e., including both primary and secondary models) were accurate when describing the inactivation behaviour of microorganisms in simple systems, but were inaccurate in complex food environments because the influence of food microstructure was not taken into account [ 46 ]. Again, model predictions could be fail-safe or fail-dangerous, depending on the specific situation [ 47 , 54 , 55 ]. In order to solve the possible inaccuracy of loglinear predictive inactivation models, new models were developed to deal with common non-loglinear inactivation trends, with the most notable models dating from after 1988 (e.g., [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]).…”

Section: Historical Overview On the Inclusion Of Food Microstructure In Predictive Modelsmentioning

confidence: 99%

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The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Verheyen

Impe

2021

Foods

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Predictive microbiology has steadily evolved into one of the most important tools to assess and control the microbiological safety of food products. Predictive models were traditionally developed based on experiments in liquid laboratory media, meaning that food microstructural effects were not represented in these models. Since food microstructure is known to exert a significant effect on microbial growth and inactivation dynamics, the applicability of predictive models is limited if food microstructure is not taken into account. Over the last 10–20 years, researchers, therefore, developed a variety of models that do include certain food microstructural influences. This review provides an overview of the most notable microstructure-including models which were developed over the years, both for microbial growth and inactivation.

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Section: Historical Overview On the Inclusion Of Food Microstructure In Predictive Modelsmentioning

confidence: 99%

Section: Historical Overview On the Inclusion Of Food Microstructure In Predictive Modelsmentioning

confidence: 99%

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Verheyen

Impe

2021

Foods

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“…The acceptable prediction zones (APZ) method was developed to address limitations of traditional methods, it has been shown to outperform traditional methods, and is the only method that has criteria for test data, model performance, and model validation (Oscar, 2005a, 2005b, 2020b; ). Although it is not used by all predictive microbiologists, it is used by some (Desriac, Vergos, Achberger, Coroller, & Couvert, 2018; Jayeola et al, 2019; Luo, Hong, & Oh, 2015; Min & Yoon, 2010; Mohr et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method

Boleratz

Oscar

2022

Journal of Food Safety

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ComBase is a widely used microbial modeling database. ComBase data can be used to develop and validate models and to test novel modeling methods like artificial neural networks (ANN) and acceptable prediction zones (APZ), which have been shown to outperform traditional methods. Here, ComBase data were used to evaluate the ANN and APZ methods for modeling nonthermal inactivation of Campylobacter jejuni in milk and beef as a function of time, temperature (À20, 1, 10, 20, 30, and 40 C), and strain (18177, ATCC 29428). Four ANN were developed using Excel and Neu-ralTools, and the best-performing was a general regression neural network (GRNN) whose performance and data completeness were evaluated using the APZ method.Relative variable impacts in the GRNN model were 42.5%, 31.5%, 20.1%, and 5.9% for time, temperature, food, and strain, respectively. Nonthermal inactivation of C. jejuni was faster and greater at ambient than at cold temperatures and in milk than in beef except at 1 C where it was similar. The proportion of residuals in the APZ (pAPZ) ranged from 0.77 to 1 for individual nonthermal inactivation curves. Although the model had acceptable performance (pAPZ ≥0.7), it failed validation because of data gaps like one instead of four replicates per combination of independent variables and no data at À10 C. Thus, these and other data gaps identified need to be filled before the model can be used with confidence to predict behavior of C. jejuni in milk and beef. Nonetheless, results indicated that ANN and APZ methods can be used to model data for nonthermal inactivation of C. jejuni in food.

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The application and potential of postbiotics as sustainable feed additives in aquaculture

Tao,

Lu,

Xiong

et al. 2024

Aquaculture

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Predicting heat process efficiency in thermal processes when bacterial inactivation is not log-linear

Cited by 5 publications

References 28 publications

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method

The application and potential of postbiotics as sustainable feed additives in aquaculture

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