Application of elements of microbiological risk assessment in the food industry via a tiered approach

Food safety control is a matter for concern for all parts of the food supply chain, including governments that develop food safety policy, food industries that must control potential hazards, and consumers who need to keep to the intended use of the food. In the future, food safety policy may be set using the framework of risk analysis, part of which is the development of (inter)national microbiological risk assessment (MRA) studies. MRA studies increase our understanding of the impact of risk management interventions and of the relationships among subsequent parts of food supply chains with regard to the safety of the food when it reaches the consumer. Application of aspects of MRA in the development of new food concepts has potential benefits for the food industry. A tiered approach to applying MRA can best realize these benefits. The tiered MRA approach involves calculation of microbial fate for a product and process design on the basis of experimental data (e.g., monitoring data on prevalence) and predictive microbiological models. Calculations on new product formulations and novel processing technologies provide improved understanding of microbial fate beyond currently known boundaries, which enables identification of new opportunities in process design. The outcome of the tiered approach focuses on developing benchmarks of potential consumer exposure to hazards associated with new products by comparison with exposure associated with products that are already on the market and have a safe history of use. The tiered prototype is a tool to be used by experienced microbiologists as a basis for advice to product developers and can help to make safety assurance for new food concepts transparent to food inspection services.     

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.     

食品微生物风险分级研究进展

通过开展食品中微生物危害的风险分级研究,筛选高危食品和微生物危害进行重点监管是国际上控制食品微生物危害的通行策略,但是目前尚无国际通用的食品微生物风险分级模型。本研究对国际上已经开展的食品微生物风险分级研究进行回顾,指出了存在的问题,并提出了基于风险监测的定量风险分级模型的构想。     

Use of Modeling to Enhance the Microbiological Safety of the Food System

ABSTRACT: The use of modeling techniques for safety and risk prediction in the food supply is strongly supported by the incidence of foodborne outbreaks worldwide. In microbiological food safety, important parameters include incidence, microbial growth, microbial inactivation or survival, and dose response of the host. The goals of the modeler should include (1) identifying possible inputs and outputs for a particular concern and (2) choosing factors for integration into a larger, overall model. A case study of ground beef from the farm to the consumer reviews multiple points of concern where relevant parameters for Escherichia coli O157:H7 can be modeled. The prevalence and concentration of E. coli O157:H7 in cattle have been confirmed and studies have explored various feeding regimens and hide‐cleaning methods for controlling the pathogen prior to slaughter. There is significant impact of incidence and cross‐contamination during slaughter operations. Intervention methods such as washing and heat treatments and proper carcass handling may reduce the contamination level. Microbial growth in meat products can be extensive, given the possibility of temperature abuse during transportation and storage. Dose–response models for susceptible consumers provide a safety perspective for quantitative microbial models. Growth prediction and lethality models, available online, are valuable tools for both researchers and processors. The goal of the processor should be the “highest reduction possible” (to meet adequate safety requirements) prior to the preparation of raw product. The researcher must continually question the use of a model and the availability of data to validate any conclusions.     

A user-friendly general-purpose predictive software package for food safety

Computer-aided engineering tools can help speed up food product, process and equipment design by making it easier to check “what if” scenarios, much as such tools have improved productivity in other industries. In particular, food safety is a critical area where such predictive tools can have great impact. A realistic, integrated and comprehensive software has been developed that can simulate a food process and its safety by combining a fundamental, physics-based model of the process with the kinetics of microbiological and chemical changes during processing to provide needed information at any time and at any location in the food during processing. Compositions for a large number of foods are integrated into the software, and therefore, composition-based prediction of thermophysical properties, needed for the model, can be obtained. Microbiological and chemical kinetic databases that are also built-in can cover many practical situations, based on the grouping of foods. An intuitive graphical user interface has been built with those in the food sector in mind.     

食品微生物危险性评估

食品中来自微生物性危害的危险性密切关系到人类的健康。国际食品法典委员会确定的微生物危险性评估框架包括四个主要步骤：危害的确定、危害特征的描述、暴露评估和危险性特征的描述。这些步骤构成了评估食用可能污染致病菌或，和微生物毒素的食品而对人产生不良健康后果及其发生概率的系统过程。本结合这一领域的研究进展，对微生物危害危险性评估框架的各个部分及其相关因素作了综述性介绍。     

我国食品微生物定量风险评估的研究进展

近10余年来,我国政府重视食品安全并逐渐加强风险分析体系的构建和实施。根据历年来国家卫生与计生委的统计数据,表明由致病微生物导致的食物中毒发病率一直高于其他危害。食源性致病菌引起的食品安全风险是全球性问题,发展中国家面临的情况更为严峻,因此加强我国微生物定量风险评估以减少与发达国家的差距,从国家层面上势在必行。特别是2009年我国颁布实施《食品安全法》和2011年成立国家食品安全风险评估中心（ChinaNational Center for Food Safety Risk Assessment,CFSA）以来,已有不少针对国内具体食品致病菌情况开展的食品微生物定量风险评估（quantitative microbial risk assessment,QMRA）研究。本文对2000年至今我国已开展的QMRA研究,包括涉及的食品、致病菌、微生物预测模型、剂量-效应模型等进行详细综述。同时指出我国开展QMRA面临的技术性难题及解决方法,并对采用QMRA结果用于构建危害分析与关键控制点（Hazard Analysis and CriticalControl Point,HACCP）和食品安全目标（ food safety objective,FSO）以及执行目标（performance objective,PO）的应用前景进行探讨。建议加强风险评估与风险管理的互动交流、进一步完善风险监测、微生物限量制定和国际合作,在完善实施指南的基础上针对我国具体国情开展更多具有科学性和系统性的QMRA研究。     

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。     

Modeling the growth of Listeria monocytogenes in pasteurized white asparagus

Growth of Listeria monocytogenes in pasteurized white asparagus was monitored at different storage temperatures (4, 10, 20, and 30 degrees C). Among the main microbial kinetic parameters, growth rate (mu) per hour was calculated at each temperature using the Baranyi-Roberts model. L. monocytogenes was able to grow at all temperatures, although at 4 degrees C only a slight increment of the microbial population was observed (approximately 1 log CFU/g) after 300 h of storage. Subsequently, two different secondary modeling approaches were proposed to study the relationship between mu and storage temperature: the Arrhenius and Ratkowsky models. Although both models properly described the data observed, smaller values of root mean square error (RMSE) and standard error of prediction (SEP) were obtained with the Ratkowsky model, providing a better goodness of fit (Ratkowsky model: RMSE = 0.010, SEP = 21.23%; Arrhenius model: RMSE = 0.026, SEP = 54.37%). The maximum population density (MPD) was calculated at each temperature studied. A clear dependence between MPD and temperature was found; lower temperatures produced lower values of MPD. This finding confirmed the Jameson effect, indicating that multiple hurdles in the food-processing chain plus lower temperatures reduced L. monocytogenes growth. Predicting the growth of L. monocytogenes along the food chain will help to reduce microbial risks associated with consumption of pasteurized white asparagus.     

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.     

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。     

食品加工用水微生物检测的重要性

食品加工业是用水型工业之一,水质的优劣对食品安全至关重要,对加工用水进行安全卫生控制是生产过程控制的重要环节。微生物污染是食品加工用水最主要的安全隐患之一,我国食品加工用水微生物污染风险高、概率大,每年因食品加工用水微生物污染导致的产品质量问题不在少数,给企业造成了巨大的经济损失。食品加工企业对加工用水的质量控制必不可少,尤其是加工用水的微生物检测能力会影响最终产品的安全性。企业应打破只重视原料、过程产品和最终产品微生物检测的老思路,注意监控加工用水的微生物污染情况。参加水质微生物能力验证能够加强企业实验室对生产加工用水的自我检测能力,确保检测结果的准确性和认可性,对提高产品质量和品质有促进作用。     

基于微生物和量纲模型对茶叶货架期的研究

茶叶在贮藏和销售过程中易受到理化及微生物的影响,造成茶叶保质期短,严重阻碍茶业发展。为更好地探索茶叶微生物生长模型对茶叶货架期的影响,该文对微生物生长学一级、二级模型的含义与内容进行概述,并剖析茶叶品质的影响因素,主要从微生物和物理角度来对茶叶货架期的评价,归纳和建立茶叶货架期的预测模型。以提高茶叶卫生检测效率,保证茶叶质量安全,延长茶叶保质期。     

基于一步法的金枪鱼生鱼片沙门氏菌生长数值模拟

食物中沙门氏菌的生长是公共健康的重大威胁之一。以生食金枪鱼为研究对象,构建生鱼片中沙门氏菌生长的预测模型。首先,考察恒定温度(8~35℃)条件下沙门氏菌在生鱼片中的生长特性,随机选取两次独立重复试验中一组生长数据,采用一步法同步构建初级模型(Huang模型、Baranyi模型)和二级模型(Huang Square-Root模型),并通过四阶龙格-库塔法联合最小二乘法估计模型参数;其次,选取另一组恒温条件下的独立重复试验数据及波动温度条件下的生长数据,对模型进行验证。结果表明:一步法适用于生鱼片中沙门氏菌的生长曲线分析,同步构建的Huang-HSR模型和Baranyi-HSR模型具有等同的拟合效果,基于Huang模型对迟滞期有着明确的定义,建议选择Huang-HSR模型;通过一步法估计的沙门氏菌的最低生长温度为6.91℃,最大生长浓度为9.15 lg(CFU/g);恒定温度和波动温度验证试验的RMSE分别为0.37 lg(CFU/g)和0.44 lg(CFU/g),其误差分别服从正太分布和拉普拉斯分布。本研究构建的预测模型可用于金枪鱼生鱼片中沙门氏菌的生长预测和风险评估。     

A Quasi-Chemical Kinetics Model for the Growth and Death of Staphylococcus aureus in Intermediate Moisture Bread

ABSTRACT: The "quasi-chemical" kinetics model accounts for all 4 phases of the microbial lifecycle based on a proposed series of chemical rate equations. The model fits continuous growth-death kinetics for Staphylococcus aureus in intermediate moisture bread in various conditions of water activity, pH, and temperature. Growth rates evaluated using the quasi-chemical model are compared with values obtained with the Gompertz model. Kinetics data obtained with the quasi-chemical model are integrated with a probabilistic approach to estimate the boundary between growth and no-growth conditions. Continuous modeling of microbial growth/death kinetics in actual foods advances predictive modeling that conventionally separates growth and death models.     

A risk modeling framework to evaluate the impacts of climate change and adaptation on food and water safety

Climate change may be a factor leading to increased risks of food- and waterborne illnesses from consumption of existing and emerging biological hazards. It is beneficial to develop integrated approaches to evaluate, and provide scientific assessments of, potential climate change adaptation measures to inform risk management related to climate and weather events. To this end, a risk modeling framework was created to facilitate estimations of the impact of weather and climate change on public health risks from biological hazards in food and water and to compare potential adaptation and risk mitigation strategies. The framework integrates knowledge synthesis methods, data storage and maintenance, and stochastic modeling. Risk assessment models were developed for food and water safety case studies for demonstrative purposes. Scenario analyses indicated that implementing intervention measures to adapt to changing climate impacts might mitigate future public health risks from pathogens to varying degrees. The framework brings a generic approach to allow for comparison of relative public health risks and potential adaptation strategies across hazards, exposure pathways, and regions to assist with preventive efforts and decision-making.     

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。     

A radial basis function neural network approach to determine the survival of Listeria monocytogenes in Katiki, a traditional Greek soft cheese

A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 10(6) CFU g(-1) and stored at 5, 10, 15, and 20 degrees C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5 degrees C, whereas with the neural network all fvalues were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17 degrees C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary models at both validation temperatures. The results of this work could increase the knowledge basis for the applicability of neural networks as an alternative tool in predictive microbiology.     

剂量反应分析在食品微生物风险评估中的应用

微生物风险评估(Microbiological risk assessment, MRA)是食品安全管理的重要工具之一,一直是国际食品安全研究的热点。微生物剂量-反应分析是进行食品微生物风险评估中的重要工具,描述了特定人群中特定病原体(或其毒素)暴露引起特定反应的概率。本文综述了食品微生物风险评估中剂量-反应分析的研究现状,阐述了剂量反应分析研究中存在的问题,并对其进一步需开展的工作进行了展望,以期为我国食品微生物风险评估和食品安全监管提供参考。