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.     

论中国食品安全风险监测和评估工作的形势和任务

从食品安全风险监测和评估的基本概念开始,系统地介绍了我国食品安全风险监测和评估的法律法规、发展现状、近2年的工作目标以及下一步的具体工作。我国的食品安全风险监测和评估工作在保障食品安全和确保食品食用安全性方面发挥了重要作用,但仍存在许多不足。进一步提高我国食品安全风险监测和评估体系的国际认可度,建立与国际接轨的食品安全风险监测和评估体系已迫在眉睫。     

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.     

从检验检测环节解读《食品安全法》——访中国分析测试协会资深专家 蒋士强教授

<正> 随着《食品安全法》的颁布与实施,贯穿于《食品安全法》所确定的食品安全风险检测、安全标准的制定和实施以及监管等环节的的检验检测工作也受到了社会各界的高度关注。中国的检验检测环节将受到哪些影响,如何为《食品安全法》的实施提供强有力的技术支撑和保障?近期,记者带着这些问题采访了中国分析测试协会资深专家蒋士强教授。     

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 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.     

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.     

Bioremediation: a novel approach to food waste management

Bioremediation is a general concept that includes all those processes and actions that take place in order to biotransform an environment, already altered by contaminants, to its original status. Although the processes that can be used in order to achieve the desirable results vary, they still have the same principles; the use of microorganisms or their enzymes, that are either indigenous and are stimulated by the addition of nutrients or optimization of conditions, or are seeded into the soil. There are several advantages of the implementation of such methods but mainly they have to do with the lack of interference with the ecology of the ecosystem. This article presents general bioremediation principles and techniques along with representative examples of their use both in the laboratory and industry and the ways that they work and give results in the five main areas of the food industry where bioremediation is applicable. Although the application of bioremediation to the food industry is not new, developments in microbiology and genetic engineering have given a valuable instrument to scientists to deal with contaminants in the environment. Pesticides, herbicides, insecticides, cleaning chemicals and chemicals used in the food chain are among the new contaminants which have entered the biogeochemical cycles. Bioremediating methods transform the contaminants into substances that can be absorbed and used by the autotrophic organisms with no toxic effect on them.     

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.