Predictive Microbiology

2014 ◽  
pp. 997-1022
Author(s):  
E. Van Derlinden ◽  
L. Mertens ◽  
J. F. Van Impe
Keyword(s):  
Predictive Microbiology

scholarly journals From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 355
Author(s):  
Arícia Possas ◽  
Olga María Bonilla-Luque ◽  
Antonio Valero
Keyword(s):  
Production Process ◽  
Physicochemical Characteristics ◽  
Storage Conditions ◽  
Starter Cultures ◽  
Predictive Microbiology ◽  
Factors Affecting ◽  
Modeling Approaches ◽  
Modelling Studies ◽  
Foodborne Outbreaks ◽  
User Friendly

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, aw, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

Decomposition of Growth Curves into Growth Rate and Acceleration: a Novel Procedure to Monitor Bacterial Growth and the Time-Dependent Effect of Antimicrobials

2021 ◽  
Author(s):  
M. Luisa Navarro-Pérez ◽  
M. Coronada Fernández-Calderón ◽  
Virginia Vadillo-Rodríguez
Keyword(s):  
Growth Rate ◽  
Mathematical Models ◽  
Bacterial Growth ◽  
Time Course ◽  
Antimicrobial Agents ◽  
Numerical Procedure ◽  
Growth Curves ◽  
Predictive Microbiology ◽  
Insight Into ◽  
Over Time

In this paper, a simple numerical procedure is presented to monitor the growth of Streptococcus sanguinis over time in the absence and presence of propolis, a natural antimicrobial. In particular, it is shown that the real-time decomposition of growth curves obtained through optical density measurements into growth rate and acceleration can be a powerful tool to precisely assess a large range of key parameters [ i.e. lag time ( t 0 ), starting growth rate ( γ 0 ), initial acceleration of the growth ( a 0 ), maximum growth rate ( γ max ), maximum acceleration ( a max ) and deceleration ( a min ) of the growth and the total number of cells at the beginning of the saturation phase ( N s )] that can be readily used to fully describe growth over time. Consequently, the procedure presented provides precise data of the time course of the different growth phases and features, which is expected to be relevant, for instance, to thoroughly evaluate the effect of new antimicrobial agents. It further provides insight into predictive microbiology, likely having important implications to assumptions adopted in mathematical models to predict the progress of bacterial growth. Importance: The new and simple numerical procedure presented in this paper to analyze bacterial growth will possibly allow identifying true differences in efficacy among antimicrobial drugs for their applications in human health, food security, and environment, among others. It further provides insight into predictive microbiology, likely helping in the development of proper mathematical models to predict the course of bacterial growth under diverse circumstances.

scholarly journals A Combined Model To Predict the Functionality of the Bacteriocin-Producing Lactobacillus sakei Strain CTC 494

2003 ◽  
Vol 69 (2) ◽  
pp. 1093-1099 ◽  
Author(s):  
Frédéric Leroy ◽  
Luc De Vuyst
Keyword(s):  
Lactic Acid ◽  
Acid Production ◽  
Lactic Acid Production ◽  
Lactobacillus Sakei ◽  
Predictive Microbiology ◽  
Bacteriocin Activity ◽  
Combined Model ◽  
Food Fermentation ◽  
Dissociation Degree ◽  
Industrial Use

ABSTRACT The use of bacteriocin-producing lactic acid bacteria for improved food fermentation processes seems promising. However, lack of fundamental knowledge about the functionality of bacteriocin-producing strains under food fermentation conditions hampers their industrial use. Predictive microbiology or a mathematical estimation of microbial behavior in food ecosystems may help to overcome this problem. In this study, a combined model was developed that was able to estimate, from a given initial situation of temperature, pH, and nutrient availability, the growth and self-inhibition dynamics of a bacteriocin-producing Lactobacillus sakei CTC 494 culture in (modified) MRS broth. Moreover, the drop in pH induced by lactic acid production and the bacteriocin activity toward Listeria as an indicator organism were modeled. Self-inhibition was due to the depletion of nutrients as well as to the production of lactic acid. Lactic acid production resulted in a pH drop, an accumulation of toxic undissociated lactic acid molecules, and a shift in the dissociation degree of the growth-inhibiting buffer components. The model was validated experimentally.

scholarly journals Microrisk Lab: an online freeware for predictive microbiology

2020 ◽  
Author(s):  
Yangtai Liu ◽  
Xiang Wang ◽  
Baolin Liu ◽  
Qingli Dong
Keyword(s):  
Parameter Estimation ◽  
Bacterial Growth ◽  
Model Simulation ◽  
Parameter Determination ◽  
Predictive Microbiology ◽  
Isothermal Conditions ◽  
R Programming Language ◽  
Behavior Simulation ◽  
R Programming ◽  
And Behavior

AbstractMicrorisk Lab was designed as an interactive modeling freeware to realize parameter estimation and model simulation in predictive microbiology. This tool was developed based on the R programming language and ‘Shinyapps.io’ server, and designed as a fully responsive interface to the internet-connected devices. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/ inactivation under static and non-isothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/ inactivation under static and non-isothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate dataset and/ or parameter setting. In this research, six case studies were reproduced in Microrisk Lab and compared in parallel to DMFit, GInaFiT, IPMP 2013/ GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling system in most cases. Microrisk Lab allowed for uniform user experience to implement microbial predictive modeling by its friendly interfaces, high-integration, and interconnectivity. It might become a useful tool for the microbial parameter determination and behavior simulation. Non-commercial users could freely access this application at https://microrisklab.shinyapps.io/english/.

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