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。     

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     

预测微生物学的研究进展及其在食品中的应用

预测微生物学是食品微生物学中的一个亚学科。本文阐述了预测模型的分类及修改后主要模型的方程式;预测微生物学的研究内容,包括预测模型的改进,环境因子(温度、pH、水分活度等)对微生物(以致病菌和腐败菌为主)生长的影响以及在食品货架期预测、食品安全质量管理等方面的应用等。提出目前研究中存在的问题,并展望该领域今后的研究方向。     

预测微生物模型在水产品中的应用

水产品在捕获后的微生物存活状况十分复杂,如果消费者在水产品中微生物状况未知的情况下食用了水产品,就可能会发生食物中毒。预测食品微生物学是食品微生物学的关键领域,也是食品安全控制的重要学科组成,能够帮助食品专家和从业人员有效评估和控制食品的安全状况。水产品中病原微生物生长模型的建立在水产品的食用安全性方面能够起到重要作用,微生物预测模型能够分析和预测水产品中微生物随时间的变化,以及不同温度、不同环境条件下微生物的存活情况,为水产品的生产加工方式、储存条件及安全状况提供参考。     

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。     

预测食品微生物学在肉品安全领域的应用

预测食品微生物学是一门对食品微生物进行数量化预测的新兴学科,可以在不进行传统微生物检测的条件下快速对食品的安全性和货架期进行预测。本文简述了预测食品微生物学的概念,模型分类及建模步骤,重点介绍了预测食品微生物学在肉品安全中的定量微生物风险评估、危害分析和关键控制点以及产品货架期等领域的应用,并对其未来发展进行了展望。     

预测微生物学与食品质量安全

预测食品微生物学利用数学模型定量地分析不同环境条件下食品中病原菌和腐败菌的生长和存活，对食品的货架期作出合理的预测。该文简要介绍了预测模型的3个水平及其在预测货架期中的应用，并展望了预测微生物学未来的发展趋势。     

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。     

预测微生物学在高压二氧化碳杀菌动力学的应用进展

高压二氧化碳(HPCD)技术是目前越来越受关注的非热杀菌技术之一。目前有关HPCD杀菌预测模型的研究报道较少。利用食品预测微生物学方法和理论,通过研究各个因素对HPCD杀菌过程的影响,建立预测模型和回归方程,可以更好的理解HPCD杀菌动力学及机理,提高其杀菌效果,确保食品安全。本文就食品预测微生物学在HPCD杀菌动力学研究中的应用进行综述分析,并提出未来可能的研究方向。     

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。     

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 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.     

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.     

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.     

Debating the biological reality of modelling preservation

Predictive food microbiology is a rapidly developing science and has made great advances. The aim is to debate a number of issues in modelling preservation: (1) inoculum and prehistory effects on lag times and process susceptibility; (2) mechanistic vs. empirical modelling; and (3) concluding remarks (the Species concept, methodology and biovariability). Increasing the awareness in these issues may bridge the gap between the complex reality in food microbial physiology and the application potential of predictive models. The challenge of bringing integrated preservation or risk analysis further and developing ways to truly model and link biological susceptibility distributions from raw ingredients via process survival to outgrowth probabilities in the final product remains.