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T Ross - One of the best experts on this subject based on the ideXlab platform.
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Predictive Microbiology and food safety
Reference Module in Food Science#R##N#Encyclopedia of Food Microbiology (Second Edition), 2014Co-Authors: T Ross, Thomas A. Mcmeekin, Jozsef BaranyiAbstract:Predictive Microbiology is based on the premise that the responses of populations of microorganisms to environmental factors are reproducible and that by characterizing foods in terms of those factors, it is possible, from past observations, to predict the responses of those micro organisms in other analogous environments. This knowledge is summarized in mathematical models to enable prediction of the behavior of microbial populations in foods over time. Predictive Microbiology is a powerful tool to aid microbial food safety and quality assurance, both in its own right and to complement hazard analysis and critical control points programs, hurdle technology, and quantitative microbial risk assessment. This article considers the history, philosophy, and impetus of Predictive Microbiology; principles of mathematical modeling; types of Predictive Microbiology models; uses, strategies, and resources for ‘Predictive Microbiology’; and assessment of the performance of ‘Predictive Microbiology’ models.
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Predictive Microbiology theory and application is it all about rates
Food Control, 2013Co-Authors: T A Mcmeekin, J Olley, D A Ratkowsky, R Corkrey, T RossAbstract:Abstract We review early work on the microbial growth curve and the concept of balanced growth followed by commentary on the stringent response and persister cells. There is a voluminous literature on the effect of antibiotics on resistance and persistence and we call for a greater focus in food Microbiology on the effect of biocides in the same context. We also raise potential issues in development of resistance arising from “source–sink” dynamics and from horizontal gene transfer. Redox potential is identified as crucial in determining microbial survival or death, and the recently postulated role for reactive oxygen species in signalling also considered. “Traditional” Predictive Microbiology is revisited with emphasis on temperature dependence. We interpret the temperature vs growth rate curve as comprising 11 regions, some well-recognised but others leading to new insights into physiological responses. In particular we are intrigued by a major disruption in the monotonic rate of inactivation at a temperature, slightly below the actual maximum temperature for growth. This non-intuitive behaviour was earlier reported by other research groups and here we propose that it results from a rapid metabolic switch from the relaxed growth state to the stringent survival state. Finally, we envision the future of Predictive Microbiology in which models morph from empirical to mechanistic underpinned by microbial physiology and bioinformatics to grow into Systems Biology.
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using Predictive Microbiology to benefit the australian meat industry
Case studies in food safety and authenticity: lessons from real-life situations, 2012Co-Authors: John Sumner, Ian Jenson, T RossAbstract:Since 2002, Predictive food Microbiology models have become an everyday part of meat processing in Australia. In early February 2011, tropical cyclone Yasi, the most intense cyclone to land on the Australian continent in I 00 years, struck the north east coast of Australia. Many services were interrupted, including electricity supply. The integrity of perishable foods under refrigerated storage was compromised, including a large volume of fresh meat at a processing plant. Fortunately, the concept of Predictive Microbiology is well established and accepted in the Australian meat industry. This case describes how the operators of one plant used Predictive modelling to avert considerable economic loss and food wastage following the loss of electricity supply for refrigeration.
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the way forward with Predictive Microbiology in the dairy industry
Australian Journal of Dairy Technology, 2010Co-Authors: T A Mcmeekin, T Ross, M L Tamplin, Shigenobu KosekiAbstract:Herein we describe systems and technologies for the application of Predictive models and the development of growth boundary models. The latter are supported by systematic analysis of the literature and are now used in risk assessments including those cold tolerant pathogens in minimally processed foods including dairy products. To promote further application of Predictive models in the dairy industry and potential triple bottom line benefits we strongly advocate collaboration and integration of R&D at several levels.
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the future of Predictive Microbiology strategic research innovative applications and great expectations
International Journal of Food Microbiology, 2008Co-Authors: T A Mcmeekin, T Ross, J P Bowman, O Mcquestin, Lundal Mellefont, M L TamplinAbstract:Abstract This paper considers the future of Predictive Microbiology by exploring the balance that exists between science, applications and expectations. Attention is drawn to the development of Predictive Microbiology as a sub-discipline of food Microbiology and of technologies that are required for its applications, including a recently developed biological indicator. As we move into the era of systems biology, in which physiological and molecular information will be increasingly available for incorporation into models, Predictive microbiologists will be faced with new experimental and data handling challenges. Overcoming these hurdles may be assisted by interacting with microbiologists and mathematicians developing models to describe the microbial role in ecosystems other than food. Coupled with a commitment to maintain strategic research, as well as to develop innovative technologies, the future of Predictive Microbiology looks set to fulfil “great expectations”.
T A Mcmeekin - One of the best experts on this subject based on the ideXlab platform.
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Predictive Microbiology theory and application is it all about rates
Food Control, 2013Co-Authors: T A Mcmeekin, J Olley, D A Ratkowsky, R Corkrey, T RossAbstract:Abstract We review early work on the microbial growth curve and the concept of balanced growth followed by commentary on the stringent response and persister cells. There is a voluminous literature on the effect of antibiotics on resistance and persistence and we call for a greater focus in food Microbiology on the effect of biocides in the same context. We also raise potential issues in development of resistance arising from “source–sink” dynamics and from horizontal gene transfer. Redox potential is identified as crucial in determining microbial survival or death, and the recently postulated role for reactive oxygen species in signalling also considered. “Traditional” Predictive Microbiology is revisited with emphasis on temperature dependence. We interpret the temperature vs growth rate curve as comprising 11 regions, some well-recognised but others leading to new insights into physiological responses. In particular we are intrigued by a major disruption in the monotonic rate of inactivation at a temperature, slightly below the actual maximum temperature for growth. This non-intuitive behaviour was earlier reported by other research groups and here we propose that it results from a rapid metabolic switch from the relaxed growth state to the stringent survival state. Finally, we envision the future of Predictive Microbiology in which models morph from empirical to mechanistic underpinned by microbial physiology and bioinformatics to grow into Systems Biology.
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the way forward with Predictive Microbiology in the dairy industry
Australian Journal of Dairy Technology, 2010Co-Authors: T A Mcmeekin, T Ross, M L Tamplin, Shigenobu KosekiAbstract:Herein we describe systems and technologies for the application of Predictive models and the development of growth boundary models. The latter are supported by systematic analysis of the literature and are now used in risk assessments including those cold tolerant pathogens in minimally processed foods including dairy products. To promote further application of Predictive models in the dairy industry and potential triple bottom line benefits we strongly advocate collaboration and integration of R&D at several levels.
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the future of Predictive Microbiology strategic research innovative applications and great expectations
International Journal of Food Microbiology, 2008Co-Authors: T A Mcmeekin, T Ross, J P Bowman, O Mcquestin, Lundal Mellefont, M L TamplinAbstract:Abstract This paper considers the future of Predictive Microbiology by exploring the balance that exists between science, applications and expectations. Attention is drawn to the development of Predictive Microbiology as a sub-discipline of food Microbiology and of technologies that are required for its applications, including a recently developed biological indicator. As we move into the era of systems biology, in which physiological and molecular information will be increasingly available for incorporation into models, Predictive microbiologists will be faced with new experimental and data handling challenges. Overcoming these hurdles may be assisted by interacting with microbiologists and mathematicians developing models to describe the microbial role in ecosystems other than food. Coupled with a commitment to maintain strategic research, as well as to develop innovative technologies, the future of Predictive Microbiology looks set to fulfil “great expectations”.
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Predictive Microbiology quantitative science delivering quantifiable benefits to the meat industry and other food industries
Meat Science, 2007Co-Authors: T A McmeekinAbstract:Predictive Microbiology is considered in the context of the conference theme “chance, innovation and challenge”, together with the impact of quantitative approaches on food Microbiology, generally. The contents of four prominent texts on Predictive Microbiology are analysed and the major contributions of two meat microbiologists, Drs. T.A. Roberts and C.O. Gill, to the early development of Predictive Microbiology are highlighted. These provide a segue into R&D trends in Predictive Microbiology, including the Refrigeration Index, an example of science-based, outcome-focussed food safety regulation. Rapid advances in technologies and systems for application of Predictive models are indicated and measures to judge the impact of Predictive Microbiology are suggested in terms of research outputs and outcomes. The penultimate section considers the future of Predictive Microbiology and advances that will become possible when data on population responses are combined with data derived from physiological and molecular studies in a systems biology approach. Whilst the emphasis is on science and technology for food safety management, it is suggested that decreases in foodborne illness will also arise from minimising human error by changing the food safety culture.
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Predictive Microbiology providing a knowledge based framework for change management
International Journal of Food Microbiology, 2002Co-Authors: T A Mcmeekin, T RossAbstract:Abstract This contribution considers Predictive Microbiology in the context of the Food Micro 2002 theme, “Microbial adaptation to changing environments”. To provide a reference point, the state of food Microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of Predictive Microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal Microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of Predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the “clean, green” aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on Predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. The challenge is to demonstrate the validity of such models and to develop applications of benefit to the food industry and consumers as was achieved with growth models to predict shelf life and the hygienic equivalence of food processing operations.
Pedro Antonio Gutiérrez - One of the best experts on this subject based on the ideXlab platform.
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SOCO - Multiobjective Pareto Ordinal Classification for Predictive Microbiology
Soft Computing, 2020Co-Authors: M. Cruz-ramírez, Juan Carlos Fernández, Alfonso Migueláñez Valero, Pedro Antonio Gutiérrez, César Hervás-martínezAbstract:This paper proposes the use of a Memetic Multiobjective Evolutionary Algorithm (MOEA) based on Pareto dominance to solve two ordinal classification problems in Predictive Microbiology. Ordinal classification problems are those ones where there is order between the classes because of the nature of the problem. Ordinal classification algorithms may take advantage of this situation to improve its classification. To guide the MOEA, two non-cooperative metrics have been used for ordinal classification: the Average of the Mean Absolute Error, and the Maximum Mean Absolute Error of all the classes. The MOEA uses an ordinal regression model with Artificial Neural Networks to classify the growth classes of microorganisms such as Listeria monocytogenes and Staphylococcus aureus.
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Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in Predictive Microbiology
Applied Soft Computing, 2020Co-Authors: Juan Carlos Fernández, Cesar Hervas, F. J. Martínez-estudillo, Pedro Antonio GutiérrezAbstract:The main objective of this work is to automatically design neural network models with sigmoid basis units for binary classification tasks. The classifiers that are obtained achieve a double objective: a high classification level in the dataset and a high classification level for each class. We present MPENSGA2, a Memetic Pareto Evolutionary approach based on the NSGA2 multiobjective evolutionary algorithm which has been adapted to design Artificial Neural Network models, where the NSGA2 algorithm is augmented with a local search that uses the improved Resilient Backpropagation with backtracking-IRprop+ algorithm. To analyze the robustness of this methodology, it was applied to four complex classification problems in Predictive Microbiology to describe the growth/no-growth interface of food-borne microorganisms such as Listeria monocytogenes, Escherichia coli R31, Staphylococcus aureus and Shigella flexneri. The results obtained in Correct Classification Rate (CCR), Sensitivity (S) as the minimum of sensitivities for each class, Area Under the receiver operating characteristic Curve (AUC), and Root Mean Squared Error (RMSE), show that the generalization ability and the classification rate in each class can be more efficiently improved within a multiobjective framework than within a single-objective framework.
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neural network ensembles to determine growth multi classes in Predictive Microbiology
Hybrid Artificial Intelligence Systems, 2012Co-Authors: Francisco Fernandeznavarro, Pedro Antonio Gutiérrez, Huanhuan Chen, Cesar HervasmartinezAbstract:This paper evaluates the performance of different ordinal regression, nominal classifiers and regression models when predicting probability growth of the Staphylococcus Aureus microorganism. The prediction problem has been formulated as an ordinal regression problem, where the different classes are associated to four values in an ordinal scale. The results obtained in this paper present the Negative Correlation Learning as the best tested model for this task. In addition, the use of the intrinsic ordering information of the problem is shown to improve model performance.
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memetic pareto evolutionary artificial neural networks to determine growth no growth in Predictive Microbiology
Applied Soft Computing, 2011Co-Authors: J. C. Fernández, Cesar Hervas, F J Martinezestudillo, Pedro Antonio GutiérrezAbstract:The main objective of this work is to automatically design neural network models with sigmoid basis units for binary classification tasks. The classifiers that are obtained achieve a double objective: a high classification level in the dataset and a high classification level for each class. We present MPENSGA2, a Memetic Pareto Evolutionary approach based on the NSGA2 multiobjective evolutionary algorithm which has been adapted to design Artificial Neural Network models, where the NSGA2 algorithm is augmented with a local search that uses the improved Resilient Backpropagation with backtracking-IRprop+ algorithm. To analyze the robustness of this methodology, it was applied to four complex classification problems in Predictive Microbiology to describe the growth/no-growth interface of food-borne microorganisms such as Listeria monocytogenes, Escherichia coli R31, Staphylococcus aureus and Shigella flexneri. The results obtained in Correct Classification Rate (CCR), Sensitivity (S) as the minimum of sensitivities for each class, Area Under the receiver operating characteristic Curve (AUC), and Root Mean Squared Error (RMSE), show that the generalization ability and the classification rate in each class can be more efficiently improved within a multiobjective framework than within a single-objective framework.
Thomas Ross - One of the best experts on this subject based on the ideXlab platform.
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Shelf life prediction: Status and future possibilities
International Journal of Food Microbiology, 1996Co-Authors: Thomas A. Mcmeekin, Thomas RossAbstract:Although there is rapid progress in the field of chemical detection technology, little of this technology appears to have found application in estimation of the remaining shelf life of foods and early detection of spoilage. Predictive Microbiology aims to summarise the probable behaviour of specific spoilage organisms and the progression of spoilage processes in foods. The quantitative knowledge generated in the held of Predictive Microbiology provides a sound basis for the rational development of devices with which to monitor loss of product shelf life during storage, distribution and retail sale. To predict remaining shelf life accurately it is necessary, however, to consider the microbial ecology of the food system. Aspects of microbial ecology and physiology relevant to the spoilage of foods are briefly reviewed and the potential benefits of the use of Predictive Microbiology in shelf life estimation are described. These points are exemplified by reference to a modelling program undertaken to develop, validate and 'package' in an easily useable form, models of the effect of temperature, water activity and pH on the growth rate of psychrotrophic spoilage pseudomonads. Necessary properties of devices to monitor loss of shelf life are discussed. 'Bioindicators' are identified as potential monitors of spoilage and suggestions made for their development based on the concept of 'upper limiting bacterial growth' rates, for which preliminary evidence is presented.
J Kleer - One of the best experts on this subject based on the ideXlab platform.
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Predictive Microbiology and risk assessment
Deutsche Tierarztliche Wochenschrift, 2004Co-Authors: G Hildebrandt, J KleerAbstract:: Predictive Microbiology (Predictive modelling PM), in spite of its limits and short-comings, may often contribute to a reduction of the problems arising when HACCP systems are established or microbiological risk assessment is done. Having identified the agents which constitute a risk and the contamination rate and density in the raw material, the influences of production steps and storage on these microorganisms have to be examined. Finally, there should be an exposure assessment, i.e. an estimate of the contamination density in the final product at the time of consumption. Should the exposure assessment together with data from dose response assessments reveal a potential for intake of inacceptable numbers of organisms, the risk identified has to be characterized. As a consequence, risk management should result in a modification of the composition of the product and/or of the production process so that the risk does not surpass an acceptable limit. For this approach it is indispensable to have product- and process-specific information on the multiplication of pathogens prior to heat treatment, on reduction of their density by thermal treatment and on growth or dying of organisms having survived heat treatment or penetrated into the product after heat treatment as post-process contaminant. Commonly, challenge tests are conducted to provide such information. But they are time consuming and, as their results are only valid for the specific product tested and the conditions prevailing during the experiment, the have to be repeated if there is any modification of intrinsic or extrinsic factors. At least partially, the PM may replace the challenge tests. The efficiency of the models is rated particularly high if they are used already at the stage of product development when the question has to be answered whether a planned recipe or process of production are already save or have to be modified to become save.
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Bedeutung der Predictive Microbiology zur Risikominimierung bei der Lebensmittelherstellung
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 2002Co-Authors: J Kleer, G HildebrandtAbstract:From inoculation experiments in laboratory media, Predictive Microbiology (PM) derives equations to quantitatively describe the behavior of microorganisms in foods depending on intrinsic and extrinsic factors (controlling factors). Meanwhile, numerous growth, survival, and thermal inactivation (death) models have been elaborated for the most important foodborne pathogens. The Food MicroModel and the Pathogen Modeling Program are available as user-friendly software applications. Although all PM models are simplifications of biological mechanisms and the models actually available still have their limitations, comparisons with independent data from the literature indicate that predictions of most models are in the worst case fail-safe and that their systematic errors do not exceed those of inoculated pack experiments. Once a model has been validated for a specific type of food, it can be applied at all stages of food production and distribution. PM models are already used to conduct HACCP studies and are powerful tools for microbiological risk assessment in particular. Under both aspects, there are various applications for PM: determining necessary time-temperature combinations during heating processes, estimating the risk of pathogen growth during planned storage time and conditions, and examining new formulations for potential microbiological hazards. Traditionally, these tasks were done by using microbiological challenge testing (MCT, inoculated pack experiments). However, these experiments are expensive and time consuming. Moreover, their results are only valid for the product being tested and the conditions of its processing and storage. If changes are planned or occur occasionally, new MCTs have to be conducted under changed conditions. PM models are most effectively used at the stage of product development. As they allow a fast first estimation about the behavior of microorganisms, their application enables food microbiologists to recognize whether the product has to be modified in formulation or process without losing time waiting for MCT results, thus avoiding unacceptable levels of risk for consumers. Die Predictive Microbiology (PM) leitet aus Inokulationsversuchen in Labormedien mathematische Gleichungen ab, die das Verhalten von Keimen in Lebensmitteln in Abhängigkeit von inneren (intrinsic) und äußeren (extrinsic) Faktoren (Controlling Factors) quantitativ beschreiben. Es sind mittlerweile zahlreiche Wachstums-, Überlebens- und Absterbemodelle für die bedeutendsten Lebensmittelinfektions- und Intoxikationserreger erarbeitet worden. Mit dem Food MicroModel und dem Pathogen Modeling Program stehen benutzerfreundliche PC-Anwenderprogramme zur Verfügung. Der Vergleich mit unabhängigen Daten bei der Validierung zeigt, dass die Voraussagen in den meisten Fällen zumindest im sog. “fail-safe-Bereich” (unpräzise, aber sicher) liegen und die Fehler nicht größer ausfallen als die von mikrobiologischen Experimenten, die zur Beantwortung spezifischer Fragestellungen gezielt mit bestimmten Lebensmitteln durchgeführt werden. Obwohl den gegenwärtig angebotenen Varianten der PM Grenzen gesetzt sind, lassen sich die Modelle, wenn sie für eine bestimmte Produktgruppe validiert sind, auf allen Stufen von der Urproduktion bis zur Abgabe an den Verbraucher einsetzen. Die PM-Modelle werden bereits bei der Einrichtung von HACCP- (Hazard Analysis Critical Control Points-) Systemen genutzt und können die quantitative mikrobiologische Risikoabschätzung wesentlich unterstützen. Unter beiden Aspekten ergeben sich vielfältige Anwendungsmöglichkeiten: Festlegung erforderlicher Temperatur- und Zeitkombinationen bei Erhitzungsschritten, Ermittlung maximal tolerabler Standzeiten, Fixierung von Lagerbedingungen und -dauer sowie Überprüfung neuartiger Formulierungen auf potenzielle mikrobiologische Gefährdungen. Die genannten Aufgaben wurden traditionell durch so genannte mikrobiologische Challenge Tests (MCT) gelöst. Diese Experimente sind aber kostspielig und langwierig. Ihre Ergebnisse gelten nur für das im Test eingesetzte Erzeugnis und die Bedingungen seiner Herstellungsverfahren und Lagerung. Treten Änderungen ein, müssen die MCTs unter geänderten Bedingungen erneut durchgeführt werden. Mithilfe von PM-Modellen kann man hingegen bereits im Stadium der Produktentwicklung das geplante Erzeugnis und seinen Herstellungsprozess auf mikrobiologische Risiken hin beurteilen, ohne mit lang dauernden Inokulationsversuchen Zeit zu verlieren. Es besteht somit die Möglichkeit, frühzeitig zu erkennen, ob das Produkt in Rezeptur oder Prozess modifiziert werden muss, damit ein akzeptables Risiko für den Verbraucher nicht überschritten wird.
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bedeutung der Predictive Microbiology zur risikominimierung bei der lebensmittelherstellung
Bundesgesundheitsblatt-gesundheitsforschung-gesundheitsschutz, 2002Co-Authors: J Kleer, Goetz HildebrandtAbstract:Die Predictive Microbiology (PM) leitet aus Inokulationsversuchen in Labormedien mathematische Gleichungen ab, die das Verhalten von Keimen in Lebensmitteln in Abhangigkeit von inneren (intrinsic) und auseren (extrinsic) Faktoren (Controlling Factors) quantitativ beschreiben. Es sind mittlerweile zahlreiche Wachstums-, Uberlebens- und Absterbemodelle fur die bedeutendsten Lebensmittelinfektions- und Intoxikationserreger erarbeitet worden. Mit dem Food MicroModel und dem Pathogen Modeling Program stehen benutzerfreundliche PC-Anwenderprogramme zur Verfugung. Der Vergleich mit unabhangigen Daten bei der Validierung zeigt, dass die Voraussagen in den meisten Fallen zumindest im sog. “fail-safe-Bereich” (unprazise, aber sicher) liegen und die Fehler nicht groser ausfallen als die von mikrobiologischen Experimenten, die zur Beantwortung spezifischer Fragestellungen gezielt mit bestimmten Lebensmitteln durchgefuhrt werden. Obwohl den gegenwartig angebotenen Varianten der PM Grenzen gesetzt sind, lassen sich die Modelle, wenn sie fur eine bestimmte Produktgruppe validiert sind, auf allen Stufen von der Urproduktion bis zur Abgabe an den Verbraucher einsetzen. Die PM-Modelle werden bereits bei der Einrichtung von HACCP- (Hazard Analysis Critical Control Points-) Systemen genutzt und konnen die quantitative mikrobiologische Risikoabschatzung wesentlich unterstutzen. Unter beiden Aspekten ergeben sich vielfaltige Anwendungsmoglichkeiten: Festlegung erforderlicher Temperatur- und Zeitkombinationen bei Erhitzungsschritten, Ermittlung maximal tolerabler Standzeiten, Fixierung von Lagerbedingungen und -dauer sowie Uberprufung neuartiger Formulierungen auf potenzielle mikrobiologische Gefahrdungen. Die genannten Aufgaben wurden traditionell durch so genannte mikrobiologische Challenge Tests (MCT) gelost. Diese Experimente sind aber kostspielig und langwierig. Ihre Ergebnisse gelten nur fur das im Test eingesetzte Erzeugnis und die Bedingungen seiner Herstellungsverfahren und Lagerung. Treten Anderungen ein, mussen die MCTs unter geanderten Bedingungen erneut durchgefuhrt werden. Mithilfe von PM-Modellen kann man hingegen bereits im Stadium der Produktentwicklung das geplante Erzeugnis und seinen Herstellungsprozess auf mikrobiologische Risiken hin beurteilen, ohne mit lang dauernden Inokulationsversuchen Zeit zu verlieren. Es besteht somit die Moglichkeit, fruhzeitig zu erkennen, ob das Produkt in Rezeptur oder Prozess modifiziert werden muss, damit ein akzeptables Risiko fur den Verbraucher nicht uberschritten wird.
