食品微生物生长预测模型研究新进展

为了更好的了解食品微生物学预测模型的基本内容, 探讨数学模型在预测微生物学中的作用, 达到提高食品卫生检测效率, 保证食品质量安全的目的, 本文以文献综述形式, 简要概述了预测微生物学一级、二级和三级模型的含义与内容。并在此基础上, 着重介绍了全球范围内已经成功推广使用的多种三级模型, 阐述了它们的研究背景、研究进展、使用方法, 分析了各种模型存在的优缺点, 可为实际应用中选择合适的模型提供参考。在比较使用不同种类模型后, 发现Baranyi & Roberts、响应面和ComBase模型在各级模型中具有更好的使用价值。     

预测微生物学的研究进展

简要介绍了预测微生物学模型的2个类型(品质预测模型和安全评估模型),特定腐败菌在微生物预测中的特殊作用,可追溯技术、温度综合函数和生物指示器等新技术在微生物预测中的应用,以及国外的预测模型库和国内的研究现状,展望了预测微生物学未来的发展趋势。     

欢迎订阅《微生物学通报》

《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组     

欢迎订阅《微生物学通报》

正《微生物学通报》创刊于1974年,月刊,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

欢迎订阅《微生物学通报》

正《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

欢迎订阅《微生物学通报》

<正>《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

欢迎订阅《微生物学通报》

<正>《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

欢迎订阅《微生物学通报》

《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组     

欢迎订阅《微生物学通报》

《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物     

欢迎订阅《微生物学通报》

《微生物学通报》创刊于1974年,是中国微生物学会和中国科学院微生物研究所主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究;农业微生物学研究;工业微生物学研究;医学微生物学研究;食品微生物学研究;环境微生物学研究;微生物功能基因组研究;微生物蛋     

A prototype model structure for mixed microbial populations in homogeneous food products

An important factor which has not been included in many models in the field of predictive microbiology is the influence of a background of microflora in a food product. It is however generally known that the growth of a microorganism as a pure culture can be substantially different from its growth in a mixed culture, due to microbial interactions. Because of the importance of these interactions and the lack of suitable modeling techniques in the field of predictive microbiology to describe them, the potential of models in other research fields-namely ecology-to deal with interactions is explored in previous work of the authors. However, a model structure for microbial growth in food products cannot simply be copied from those elaborated in ecology. The structure of a predictive growth model is indeed typical, primarily due to the explicit modeling of a lag phase. The current paper proposes a prototype model structure for growth of mixed microbial populations in homogeneous food products. The model is able to describe a lag phase and reduces to a classical predictive growth model in the special case of single-species growth.     

Indices for performance evaluation of predictive models in food microbiology

Two complementary measures are proposed as simple indices of the performance of models in predictive food microbiology. The indices assess the level of confidence one can have in the predictions of the model and whether the model displays any bias which could lead to 'fail-dangerous'predictions. The use of the indices is demonstrated using data collated from independent and published literature. This analysis supports previous reports that evaluation of predictive models by comparison to published microbial growth rate data may be inappropriate because of limitations in that data. The indices may fail to reveal some forms of systematic deviation between observed and predicted behaviour. It is concluded, however, that the indices provide an objective and readily interpreted summary of model performance and may serve as a first step towards the development of an objective and useful definition of the term 'validated model'in predictive food microbiology.     

Modelling the growth,survival and death of Listeria monocytogenes

In this paper, the predictive microbiology approach has been generalized to the study of growth, survival and death of Listeria monocytogenes. As this micro-organism is involved in food poisoning, its growth, survival and death were studied as functions of low temperatures, NaCl and phenol compounds, in a synthetic medium, by a factorially designed experiment. A significant inactivation of L. monocytogenes was obtained with 20 ppm of phenol and 4% (w/v) NaCl at temperatures from 4 to 12 degrees C. An empirical model is proposed to describe, in a single step, the biomass profile vs studied factors. Thereby, the influence of temperature, NaCl and phenol concentration on L. monocytogenes biomass quantity (0.5-8 log cfu ml(-1)) are presented as a function of storage duration. The comparisons of the proposed model with existing models (Gompertz for growth, vitalistic for survival and death) were performed. The use of a single equation allows the prediction of contamination levels in all experimental conditions without knowledge a priori. The model offers considerable prospects for its use in food microbiology.     

Comparison of Arrhenius-type and Bêlehrádek-type models for prediction of bacterial growth in foods

D.A. RATKOWSKY, T. ROSS, T.A. WCMEEKIN AND J. OLLEY. 1991. The development of Arrhenius-type ('Schoolfield') and Bêlehrádek-type (square root) models that describe microbial growth rates is briefly described. Both types of model have been advocated for use in predictive microbiology. On the basis of published data sets for the growth of bacteria, the consequences of mathematical transformation of data and the use of invalid stochastic assumptions upon model predictions are demonstrated. Mean square error is shown to be an inappropriate criterion by which to compare the performance of predictive models. The data show that bacterial growth responses such as generation time and lag time become more variable as their mean magnitude increases. The practical consequences of such variability for predictive microbiology are discussed.     

Developing and distributing user-friendly application software

Summary The adoption of new techniques in predictive microbiology by the food industry will ultimately be dependent on the development of user-friendly application software that makes it easy for non-research personnel to employ the mathematical models. Such applications should be an integral part of projects in predictive microbiology. Recommendations related to the architecture, speed, protection, testing, and distribution of application software are presented based on our experience in developing and distributing the Microbial Food Safety Pathogen Modeling Program.Reference to a brand or firm name does not constitute an endorsement by the US Department of Agriculture over others of a similar nature not mentioned.     

Development and validation of experimental protocols for use of cardinal models for prediction of microorganism growth in food products

An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (aw) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food- and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10 degrees C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20 degrees C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, Sym'Previus, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.     

Bělehrádek-type models

Summary Square-root (or Ratkowsky) models are a special case of Blehrádek's temperature rate-relationship first published in 1926 and widely used in several fields of biology. Blehrádek-type models also describe microbial growth, and have been extended for use in food microbiology by the inclusion of terms for water activity and pH. The parameters of the square root-type models are defined and their determination described. Favorable features of square root-type models include parsimony, parameter estimation properties, and ease of use. Square root-type models have been developed for a number of organisms of concern to the food industry and have also been adopted for use in a number of electronic devices used in predictive microbiology. Criticisms of square root-type models are also considered.Mention of brand or firm names does not constitute an endorsement by the US Department of Agriculture over others of a similar nature not mentioned.     

Mathematical Models for the Effects of pH, Temperature, and Sodium Chloride on the Growth of Bacillus stearothermophilus in Salty Carrots

Estimating the shelf life and safety of any food product is an important part of food product development. Predictive food microbiology reduces the time and expense associated with conventional challenge and shelf life testing. The purpose of this study was to characterize and model germination, outgrowth, and lag (GOL) time and the exponential growth rate (EGR) of Bacillus stearothermophilus in salty carrot medium (SCM) as a function of pH, temperature, and NaCl concentration. B. stearothermophilus is a spore-forming thermophilic organism associated with flat sour spoilage of canned foods. A split-split plot design was used to measure the effects and interactions of pH (5.5 to 7.0), temperature (45 to 60(deg)C), and NaCl (0 to 1%) on the growth kinetics of B. stearothermophilus in SCM. A total of 96 experiments were analyzed, with individual curve parameters determined by using the Gompertz equation. Quadratic polynomial models for GOL time and EGR of B. stearothermophilus in terms of temperature, pH, and NaCl were generated by response surface analysis. The r(sup2) values for the GOL time and EGR models were 0.917 and 0.916, respectively. These models provide an estimate of bacterial growth in response to combinations of the variables studied within the specified ranges. The models were used to predict GOL times and EGRs for additional experimental conditions. The accuracy of these predictions validated the model's predictive ability in SCM.