Is Food Safety Compatible with Food Waste Prevention and Sustainability of the Food Chain?

In a context where the sustainability of food chains and food waste prevention are subjects of interest for public authorities and professionals, it is important to assess if these new objectives of food policy are compatible with food safety. The objective of this work was to develop a global model for a ready-to-eat meat product that provides three different outputs, i.e. energy consumption, percentage of spoiled products and exposure levels of Listeria monocytogenes. First a cold chain model was developed. The cold chain model was then coupled with (i) predictive microbiology models and (ii) energy consumption models for cold equipments. Various scenarios were tested for assessing the consequences of potential changes in cold chain equipment on safety, food waste and energy cost. This global approach could help policy makers in decision making.     

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.     

预测微生物学及其在食品科学中的应用

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,通过计算机及其配套软件,预测和描述处在特定的环境下微生物的生长和死亡规律。预测微生物学模型按Whiting和Buchanan的分类方法分为初级模型、次级模型和三级模型,文中按该分类方法分别对预测微生物学及其模型的发展进行了系统的介绍,并对预测微生物学在食品科学中的应用进行了综述,为在食品工业中更好的利用预测微生物学提供帮助。     

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.     

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.     

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.     

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     

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".     

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。     

微生物预测模型技术研究进展及其在食品工业中的应用

为了更好的了解微生物预测模型与食品微生物生长、残存、死亡的量化关系。本文简要的概述了微生物预测模型技术的基本内容和初级、二级和三级模型的形成与发展。并在此基础上,分别介绍了食品腐败微生物和食品致病微生物预测模型的研究进展及其在食品工业中的应用,并对目前存在的问题和研究方向进行分析,旨在为推广微生物预测模型技术,并将其应用在实际生产,提高食品工业加工技术水平。     

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.     

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。     

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.     

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。     

A Web-based Application Customized to Food Safety Requirements of Small-sized Enterprises

Today, European legislation considers predictive microbiology as a tool to define food safety. People in the food industry, including those in small-sized enterprises, even if they are unable to avail themselves of specific knowledge, are encouraged to use the same approach. To extend a bridge between both sides, a user-friendly, simplified, web-based application (Praedicere Possumus, PP) has been developed. Through this application, users have access to different modules, which apply a set of models, some of them already validated and considered reliable for determining the compliance of a food product with EU safety criteria¹. In particular, the PP applies the growth/no-growth boundary model², coupled with a three-phase linear growth model and thermal/non-thermal models. Two complementary functionalities, such as the fractional contribution of each inhibitory factor to growth probability (f) and the time evolution of the growth probability (P_t) have also been included³. The PP application is expected to assist users in defining processing and storage conditions to attain a desirable food safety level and to support food safety authorities in demonstrating compliance with legislation.     

Practical application of predictive microbiology software programs to HACCP plans

We studied how predictive microbiology models could practically be applied to HACCP plans with two predictive software programs that are currently available. The software programs were the Food Micromodel elaborated by the Ministry of Agriculture, Fisheries, and Food, U.K. and the Pathogen Modeling Program of Eastern Regional Research Center, U.S. Department of Agriculture. They successfully provided useful information on (i) the determination of Critical Control Points (CCPs), (ii) the estimation of critical limits at CCPs, (iii) the decision of abused products, (iv) the assessment of equivalence of HACCP plans, and further (v) the development of new products. With the information simulated by the software programs, HACCP teams could make scientific and objective decisions for developing their individual plans. It was also confirmed that microbiological process standards for food processing are indispensable for the application of the predictive programs to HACCP plans.     

Basis of predictive mycology

For over 20 years, predictive microbiology focused on food-pathogenic bacteria. Few studies concerned modelling fungal development. On one hand, most of food mycologists are not familiar with modelling techniques; on the other hand, people involved in modelling are developing tools dedicated to bacteria. Therefore, there is a tendency to extend the use of models that were developed for bacteria to moulds. However, some mould specificities should be taken into account. The use of specific models for predicting germination and growth of fungi was advocated previously []. This paper provides a short review of fungal modelling studies.