分子生物学技术在预测微生物学中的应用与展望

预测微生物学是食品微生物学的重要组成部分,其本质在于利用数学模型描述特定环境条件下微生物的生长和死亡规律。预测微生物模型既能应用于预测食品的货架期、控制腐败菌的滋生,又有助于完善食品微生物风险评估体系,减少致病菌的患病风险,对保障食品安全和改善公共卫生状况具有十分重要的意义。本文以综述的形式,概述预测微生物学的发展历史,并分析当前预测微生物学的研究热点。在此基础之上,着重介绍分子生物学技术在预测微生物学中应用的最新研究进展,阐述分子预测模型的概念和构建方法,并对其他分子生物学技术在预测微生物学中应用的可行性以及分子预测模型的应用前景进行展望,以期为全面推动预测微生物学这一学科的进步提供理论参考。     

Predictive modelling of the growth and survival of Listeria in fishery products

Predictive microbiology provides a powerful tool to aid the exposure assessment phase of 'quantitative microbial risk assessment'. Using predictive models changes in microbial populations on foods between the point of production/harvest and the point of eating can be estimated from changes in product parameters (temperature, storage atmosphere, pH, salt/water activity, etc.). Thus, it is possible to infer exposure to Listeria monocytogenes at the time of consumption from the initial microbiological condition of the food and its history from production to consumption. Predictive microbiology models have immediate practical application to improve microbial food safety and quality, and are leading to development of a quantitative understanding of the microbial ecology of foods. While models are very useful decision-support tools it must be remembered that models are, at best, only a simplified representation of reality. As such, application of model predictions should be tempered by previous experience, and used with cognisance of other microbial ecology principles that may not be included in the model. Nonetheless, it is concluded that predictive models, successfully validated in agreement with defined performance criteria, will be an essential element of exposure assessment within formal quantitative risk assessment. Sources of data and models relevant to assessment of the human health risk of L. monocytogenes in seafoods are identified. Limitations of the current generation of predictive microbiology models are also discussed. These limitations, and their consequences, must be recognised and overtly considered so that the risk assessment process remains transparent. Furthermore, there is a need to characterise and incorporate into models the extent of variability in microbial responses. The integration of models for microbial growth, growth limits or inactivation into models that can predict both increases and decreases in microbial populations over time will also improve the utility of predictive models for exposure assessment. All of these issues are the subject of ongoing research.     

荧光定量PCR在预测微生物学中的应用

食品微生物是影响食品安全的重要因素之一,快速准确预测食品加工和贮存过程中的微生物变化对食品风险评估具有重要意义。本文首先介绍了荧光定量PCR技术的历史及其发展,着重介绍了荧光染料法和水解探针法的基本原理,讨论了其优缺点并对其应用进行总结和展望。然后介绍了预测微生物学的历史及其发展,同时对一二三级模型进行了归纳和分类,并讨论预测模型的意义及在食品领域研究所需要注意的问题。最后介绍了荧光定量PCR技术在预测微生物学中的应用,归纳了当前国内外研究的现状,并指出发展缓慢的可能原因,提出荧光定量PCR技术只停留在检测层面并没有很好用于预测微生物学模型的构建。通过本综述以期推动荧光定量PCR技术在预测微生物学领域的全面应用,进而推动预测微生物学的进一步发展。     

Considering the complexity of microbial community dynamics in food safety risk assessment

The potential for competitive inhibition to limit the growth of microbial pathogens in food raises questions about the external validity of typical predictive microbiology studies and suggests the need to consider microbial community dynamics in food safety risk assessment. Ecological theory indicates, however, that community dynamics are highly complex and may be very sensitive to initial conditions and random variation. Seemingly incongruous empirical results for Escherichia coli O157:H7 in ground beef are shown to be consistent with a simple theoretical model of interspecific competition. A potential means of incorporating community-level microbial dynamics into the food safety risk assessment process is explored.     

Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries

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.     

Predictive microbiology models vs. modeling microbial growth within Listeria monocytogenes risk assessment: what parameters matter and why

Predictive microbiology models are essential tools to model bacterial growth in quantitative microbial risk assessments. Various predictive microbiology models and sets of parameters are available: it is of interest to understand the consequences of the choice of the growth model on the risk assessment outputs. Thus, an exercise was conducted to explore the impact of the use of several published models to predict Listeria monocytogenes growth during food storage in a product that permits growth. Results underline a gap between the most studied factors in predictive microbiology modeling (lag, growth rate) and the most influential parameters on the estimated risk of listeriosis in this scenario (maximum population density, bacterial competition). The mathematical properties of an exponential dose-response model for Listeria accounts for the fact that the mean number of bacteria per serving and, as a consequence, the highest achievable concentrations in the product under study, has a strong influence on the estimated expected number of listeriosis cases in this context.     

Predictive microbiology: providing a knowledge-based framework for change management

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.     

预测食品微生物学概述及应用

为了解预测食品微生物学的基本内容,综述了预测微生物学在食品中的应用.预测食品微生物学通过数学模型来预测微生物在不同环境条件下生长或死亡的数据.预测模型的分类有多种方法,根据微生物生长或失活的情况将预测模型分为生长模型和失活/存活模型.预测微生物模型已经广泛应用于食品安全质量管理和生产工艺中.     

预测微生物学在禽肉质量安全控制中的应用

预测微生物学是运用数学、工程学、统计学和微生物学建立数学模型,对食品中微生物的生长和残存进行定量分析。本文对国内外的预测软件进行简介,并介绍了预测微生物学在禽肉中的研究进展及质量安全控制中的应用。     

Engineering modeling frameworks for microbial food safety at various scales

The landscape of mathematical model-based understanding of microbial food safety is wide and deep, covering interdisciplinary fields of food science, microbiology, physics, and engineering. With rapidly growing interest in such model-based approaches that increasingly include more fundamental mechanisms of microbial processes, there is a need to build a general framework that steers this evolutionary process by synthesizing literature spread over many disciplines. The framework proposed here shows four interconnected, complementary levels of microbial food processes covering sub-cellular scale, microbial population scale, food scale, and human population scale (risk). A continuum of completely mechanistic to completely empirical models, widely-used and emerging, are integrated into the framework; well-known predictive microbiology modeling being a part of this spectrum. The framework emphasizes fundamentals-based approaches that should get enriched over time, such as the basic building blocks of microbial population scale processes (attachment, migration, growth, death/inactivation and communication) and of food processes (e.g., heat and moisture transfer). A spectrum of models are included, for example, microbial population modeling covers traditional predictive microbiology models to individual-based models and cellular automata. The models are shown in sufficient quantitative detail to make obvious their coupling, or their integration over various levels. Guidelines to combine sub-processes over various spatial and time scales into a complete interdisciplinary and multiphysics model (i.e., a system) are provided, covering microbial growth/inactivation/transport and physical processes such as fluid flow and heat transfer. As food safety becomes increasingly predictive at various scales, this synthesis should provide its roadmap. This big picture and framework should be futuristic in driving novel research and educational approaches.     

Predictive Microbiology Models and Operational Readiness

A diverse field of predictive microbiology models has emerged in the past 30 years and has advanced our understanding of microbial behavior in foods. As most of published models have for objective to provide operationally relevant information to decision makers, predictive microbiology models have now found their place within both the academic, and the food industrial communities.Given the importance of these models to food safety, the decision makers are in need of evidence-based advices in order to assess confidence in the predictions provided by models they use. The objectives of this work were (i) to review current approaches in predictive microbiology used to build, verify and validate models, and (ii) to propose a categorization scheme that would tend to define a model's viability for use in an operational setting.     

Food engineering and predictive microbiology: on the necessity to combine biological and physical kinetics

Predictive microbiology is mainly applied in the area of risk assessment, but unusually regarded as a tool for the optimisation of processes, which needs the implementation of food engineering. Combination of predictive microbiology and food engineering allows both the assessment of a process in relation to risk and its optimisation. Intrinsic comparison between processes in relation to risk, on one hand, and the development of process optimisation tools on the other hand, necessitates the implementation of new concepts and the involvement of simplified and standard calculations based upon reference target strains and environmental conditions. Some conventional concepts related to heat treatments are extended, while some new ones related to bacterial growth are derived from the gamma concept of Marcel Zwietering.     

A retail and consumer phase model for exposure assessment of Bacillus cereus

An exposure assessment is conducted for psychrotrophic and mesophilic Bacillus cereus in a cooked chilled vegetable product. A model is constructed that covers the retail and consumer phase of the food pathway, using the output of a similar model on the industrial process as input. Microbial growth is the predominant process in the model. Variability in time and temperature during transport and storage is included in the model and different domestic refrigerator temperature distributions are compared. As an end point, probable levels of B. cereus colony forming units (cfu) in packages of vegetable purée are predicted at the moment the consumer takes the product from its refrigerator, that is prior to a cooking process. The psychrotrophic strain is predicted to end up above a threshold level of 10(5) cfu/g in 0.9% to 6.3% of the vegetable purée packages, depending on domestic refrigerator temperature. Accounting for spoilage this reduces to 0.3% to 2.4%. Even if the purée is stored at 4 degrees C in the domestic refrigerator and use-by-date (UBD) is respected, the threshold level may be passed. For the mesophilic strain the threshold level is rarely passed, but in contrast to the total viable count, the spore load at the end point is predicted to be higher than in the psychrotrophic strain. Our study illustrates how an exposure assessment model, which may be used in quantitative risk assessment, can integrate expertise in modelling, food processing and microbiology over the food pathway, and thus evaluate food safety, identify gaps in knowledge and compare risk management measures. As important gaps in knowledge, the lack of sporulation and germination models and data, validated non-isothermal growth models and a spoilage model useful for risk assessment are identified. Knowledge of the dose-response relationship is limited and does not allow a full risk assessment. It is shown that exposure can be lowered by lowering domestic refrigerator temperatures, and less so much by monitoring and withdrawing contaminated products at the end of industrial processing.     

Some examples of, and some problems with, the use of nonlinear logistic regression in predictive food microbiology

A new technique, nonlinear logistic regression, is described for modelling binomially distributed data, i.e., presence/absence data where growth is either observed or not observed, for applications in predictive food microbiology. Some examples of the successful use of this technique are presented, where the controlling factors are temperature, water activity, pH and the concentration of lactic acid, a weakly dissociating organic acid. Generally speaking, good-fitting models were obtained, as evidenced using various performance measures and goodness-of-fit statistics. As may be expected with a new statistical technique, some problems were encountered with the implementation of the modelling approach and these are discussed.     

Status of Microbial Modeling in Food Process Models

ABSTRACT: Food process models are typically aimed at improving process design or operation by optimizing some physical or chemical outcome, such as maximizing processing yield, minimizing energy usage, or maximizing nutrient retention. However, in seeking to achieve these objectives, one of the critical constraints is usually microbiological. For example, growth of pathogens or spoilage organisms must be held below a certain level, or pathogen reduction for a kill step must achieve a certain target. Therefore, mathematical models for microbial populations subjected to food processing operations are essential elements of the broader field of food process modeling. However, the complexity of the underlying biological phenomena presents special challenges in formulating, validating, and applying microbial models to real‐world applications. In that context, the narrow purpose of this article is to (1) outline the general terminology and constructs of microbial models, (2) evaluate the state of knowledge/state of the art in application of these models, and (3) offer observations about current limitations and future opportunities in the area of predictive microbiology for food process modeling.     

Modelling bacterial growth in quantitative microbiological risk assessment: is it possible?

Quantitative microbiological risk assessment (QMRA), predictive modelling and HACCP may be used as tools to increase food safety and can be integrated fruitfully for many purposes. However, when QMRA is applied for public health issues like the evaluation of the status of public health, existing predictive models may not be suited to model bacterial growth. In this context, precise quantification of risks is more important than in the context of food manufacturing alone. In this paper, the modular process risk model (MPRM) is briefly introduced as a QMRA modelling framework. This framework can be used to model the transmission of pathogens through any food pathway, by assigning one of six basic processes (modules) to each of the processing steps. Bacterial growth is one of these basic processes. For QMRA, models of bacterial growth need to be expressed in terms of probability, for example to predict the probability that a critical concentration is reached within a certain amount of time. In contrast, available predictive models are developed and validated to produce point estimates of population sizes and therefore do not fit with this requirement. Recent experience from a European risk assessment project is discussed to illustrate some of the problems that may arise when predictive growth models are used in QMRA. It is suggested that a new type of predictive models needs to be developed that incorporates modelling of variability and uncertainty in growth.     

Predicting mycotoxins in foods: A review

The need to ensure the microbiological quality and safety of food products has stimulated interest in the use of mathematical models for quantifying and predicting microbial behaviour. For 20 years, predictive microbiology has been developed for predicting the occurrence of food-borne pathogens, although these tools are dedicated to bacteria. Recently, the situation has changed and a growing number of studies are available in the literature dealing with the predictive modelling approach of fungi. To our knowledge the present one is the first review focussed on predictive mycology and food safety, including mycotoxins; existing kinetic and probability models applied to mycotoxigenic fungi germination and growth, and mycotoxin production are reviewed.     

Microbial growth modelling with artificial neural networks

There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.     

储粮预测微生物模型的研究进展

粮食的安全储藏,关系到食品安全和人类健康。依据预测微生物学,构建储粮中微生物生长预测模型,可以快速对储粮中微生物的生长情况进行判断,对储粮中病原微生物和腐败微生物的控制有重要的意义。对实现"生态储粮",确保储粮安全也具有重要的理论和实际应用价值。以文献综述形式,简要概述了储粮微生物,根据不同的数学模型,综述了初级、二级和三级模型中常见的模型,并在此基础上,简述了储粮中主要有害霉菌模拟研究的最新研究进展。