The future of predictive microbiology: strategic research, innovative applications and great expectations

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".     

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.     

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。     

Modelling Salmonella concentration throughout the pork supply chain by considering growth and survival in fluctuating conditions of temperature, pH and a(w)

We aim to predict the population density of Salmonella spp. through the pork supply chain under dynamic environmental conditions (pH, a(w) and temperature) that fluctuate from growth to survival/slow inactivation. To do this, the dependence of the probability of growth, and of the growth and inactivation rate on the temperature, pH and a(w) were modelled. Probabilistic and kinetic measurements, i.e. growth and survival curves, were collected from the ComBase database (www.combase.cc). Conditions at which selected data used to fit the models were generated covered wide ranges that are relevant to the pork supply chain. Probabilistic and kinetic models were combined to give predictions on the concentration of Salmonella spp. at any stage of the pork supply chain under fluctuating pH, a(w) and/or temperature. Models were implemented in a user-friendly computing tool freely available from http://www.ifr.ac.uk/safety/SalmonellaPredictions/. This program provides estimates on the population dynamics of Salmonella spp. at any stage of the pork supply chain and its predictive performance has been validated in several pork products.     

食品预测微生物软件的分析与比较

近年来,微生物预测和风险评估软件取得了一定的发展。微生物预测是利用所建模型来预测和描述处在特定食品环境下微生物的生长和死亡。文章概述了16款微生物预测软件,并且依据不同的标准对其做了比较分析,如建模方法,功能模块,研究过程中的环境变量(温度、酸碱度、水活性),不同的基质类型和不同的微生物种类等。对食品微生物研究领域有一定的研究和参考价值,并且可以满足不同用户对不同微生物研究的需要。     

Microbial systems biology: new frontiers open to predictive microbiology

The field of Systems Biology is a rapidly evolving area of research. It follows on from the previous experimental and theoretical 'omics' revolution in biology. Now that we have through the use of these tools many 'indices' of biological systems available the next step is to actually start composing the systems that these indices specify. In this paper we will discuss the developments in the field of Systems Biology as they pertain to predictive food microbiology and give an example of state of the art current approaches. The data discussed in the case study deal with the resistance of the yeast Saccharomyces cerevisiae towards environmental temperature changes through adaptation of its metabolism, protein signalling and gene-expression. The results are integrated and its implications for the definition of new experiments discussed; the iteration between experiment driven model definition and model driven experimentation being characteristic for contemporary Systems Biology approaches. The stress condition discussed represents in no way a practical situation in food microbiology but what it teaches may well be applied in such cases. We will indicate how the latter may be achieved.     

Methods to determine the growth domain in a multidimensional environmental space

Data from a database on microbial responses to the food environment (ComBase, see www.combase.cc) were used to study the boundary of growth several pathogens (Aeromonas hydrophila, Escherichia coli, Listeria monocytogenes, Yersinia enterocolitica). Two methods were used to evaluate the growth/no growth interface. The first one is an application of the Minimum Convex Polyhedron (MCP) introduced by Baranyi et al. [Baranyi, J., Ross, T., McMeekin, T., Roberts, T.A., 1996. The effect of parameterisation on the performance of empirical models used in Predictive Microbiology. Food Microbiol. 13, 83–91.]. The second method applies logistic regression to define the boundary of growth. The combination of these two different techniques can be a useful tool to handle the problem of extrapolation of predictive models at the growth limits.     

Predictive Microbiology Coupled with Gas (O2/CO2) Transfer in Food/Packaging Systems: How to Develop an Efficient Decision Support Tool for Food Packaging Dimensioning

Coupling gas transfer with predictive microbiology is essential to rationally design modified atmosphere packaging (MAP) strategies to ensure and guarantee food safety. Nowadays, these strategies are generally empirically built and over?sized since packaging material with high barrier properties is often chosen by default even if such a high level of protection is not systematically required. Protection strategies could be improved using rational sizing based on quantitative analysis and mathematical modeling of mass transfer. This paper aims at reviewing the current knowledge available for developing such a tool and the further research needed. First there is a special focus on oxygen (O₂) and carbon dioxide (CO₂) solubility and diffusivity parameters, which are absolutely indispensable to accurately model mass transfer in MAP systems. Next, the current knowledge of the effect of O₂/CO₂ on the growth of microorganisms is explored with an emphasis on predictive microbiology. The last part points out the main bottlenecks and further research needed to be carried out in order to develop an efficient MAP modeling tool for food safety coupling O₂/CO₂ transfer and predictive microbiology.     

Application of predictive modelling techniques in industry: from food design up to risk assessment

In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.     

预测微生物学数学建模的方法构建

预测微生物学是运用微生物学、工程数学以及统计学进行数学建模,利用所建模型预测和描述处在特定食品环境下微生物的生长和死亡。预测微生物学的核心在于建立完善的数学模型。预测微生物学数学模型被分为三级:初级模型、二级模型和三级模型。初级模型描述微生物数量变化与时间的关系;二级模型描述初级模型中的参数与环境参数之间的关系;三级模型也称为专家系统,是在初级模型和二级模型的基础上,通过计算机编程制作出的友好软件,它使得非专业人士同样可以获得预测微生物学的相关信息和指导。本文介绍了预测微生物学模型的局限以及分类,并对建模方法进行了讨论。     

Development of a predictive model describing the growth of Yersinia enterocolitica in Camembert‐type cheese

Behaviour of Yersinia enterocolitica in mould‐ripened Camembert‐type cheese during storage at temperature range 3–15 °C was evaluated and mathematically described. The Baranyi and Gompertz models were adjusted to the results of the study to calculate the growth rate (GR) and lag time (LT) for Y. enterocolitica at each temperature. Goodness of fit was assessed by calculating the Akaike information criterion (AIC) and mean square error (MSE). Square root models were constructed which described the relations between GR, LT and applied storage temperature. The secondary models were mathematically validated based on the results generated by ComBase Predictor. Moreover, generated models were validated using external, independent data from ComBase database. Based on this, it was found that the square root models of Ratkowsky constructed on GR that were determined based on the Baranyi and Roberts model most accurately described the behaviour of Y. enterocolitica in Camembert‐type cheese during storage under refrigerated conditions.     

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.     

微生物预测模型研究及其在肉品工业中的应用

预测微生物的数学模型可以对食品中微生物的生长、残存和死亡进行数量化预测.简述了预测微生物的数学模型研究食品微生物行为的理由.介绍了微生物预测模型的研究概况及其在肉类工业中的应用情况.对目前存在的问题和未来的发展进行了分析和总结.     

Adding new factors to predictive models: the effect on the risk of extrapolation

Two aspects of the addition of new environmental factors to predictive microbiology models are discussed. The concept of risk of extrapolation is introduced to characterize the probability that a prediction is outside the model interpolation region. For an empirical model, the interpolation region is defined by the data which are used to estimate the parameters of the model. It is shown that the risk of extrapolation can become unexpectedly high during extending a model to describe the effect of newer factors, if the extension is supported by insufficient data. A convenient method of extending predictive models is also presented to accommodate observations on the effect of additional environmental factors. New factors can be added to a basic common model, in such a way that the basic model will be a special case of the extended models. Conditions under which this approach is useful are discussed. Published data are used to illustrate both points.     

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.     

预测微生物学在乳品安全领域的研究进展

乳品中含有丰富的营养物质,易被有害微生物污染,进而影响消费者身体健康。不同乳品由于自身属性、加工条件和所处环境的不同,被微生物污染的状况也不一样。预测微生物学可以根据微生物在乳品储藏、运输和加工技术条件下的生长存活情况,通过建立模型,判断其动态变化趋势,从而帮助科研人员和生产者有效评估和控制乳品安全,也为加工工艺改进提供信息。本文介绍了预测微生物学模型的分类及乳品安全方面常用的预测微生物学模型和数据测定方法,阐述了预测微生物学在控制乳品微生物风险中的应用,并针对预测微生物学在乳品安全领域应用中存在的问题进行了探讨,展望了其未来发展方向,旨在为保障乳品安全提供参考。     

预测微生物学的研究进展

预测微生物学是基于微生物的数量对于环境的响应是可以重现的 ,通过有关环境因素的信息就可以从过去的观测中预测目前食品中微生物的数量。预测微生物学的研究对食品微生物学具有重要应用价值。国际上对此产生浓厚的兴趣 ,欧盟自 1989年将其列入相关的研究计划并持续进行。文章中回顾了预测微生物学的发展历程 ,介绍了目前的发展状况、主要研究内容、方法、用途以及今后的发展方向。尽管许多食品体系具有复杂性 ,但预测模型能够简化问题 ,从而做出有用的预测分析     

基于食品安全性的预测微生物学研究模式

预测微生物学是依据各种食品微生物在不同加工、贮藏和流通条件下的基础信息库,预测食品中微生物数量的动态变化,并对食品的安全性做出快速判断的一门学科研究新领域。预测微生物学的核心在于建立完善的数学模型。介绍了预测微生物学的研究模式以及其与不同学科的结合方式,并聚焦到将成为预测微生物学未来发展源动力的神经元网络技术。     

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。