Advances in Microbial Food Safety - ACS Publications - American

Chapter 17

Computational Tools in Predictive Microbiology

Downloaded by PENNSYLVANIA STATE UNIV on July 6, 2012 | http://pubs.acs.org Publication Date: April 6, 2006 | doi: 10.1021/bk-2006-0931.ch017

József B a r a n y i Institute of Food Research, Norwich N R 4 7UA, United Kingdom

Food microbiology has been applying mathematical concepts and computational techniques at an increasing rate. One reason for this is the growing demand to analyze vast amounts of microbiology data, whose quality has greatly improved due to better and better measuring systems. Another reason is the progress in developing mathematical, computational means to process those data. The application of powerful computational tools has had a key role in the evolution of predictive food microbiology. ComBase, the No.1 database of bacterial responses to food environments introduced in this paper, is an example how the computational and mathematical tools have strengthened each other during the last two decades.

Introduction Hardly more than a few decades ago, food microbiology was still a descriptive science. With the advent of powerful and easily available computers, a new discipline, a quantitative approach to describe the microbial ecology of food, started to take shape. The name "predictive food microbiology" has been universally accepted for this branch of microbiology, though it could have been more aptly called "quantitative microbial ecology of food". The first book on the subject was the monograph of McMeekin et al. (4) that established the quantitative nature of predictive microbiology, by introducing mathematical 252

© 2006 American Chemical Society

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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models to describe the effect of food environment on the microbial response. The most recent book in the field (J) put even more emphasis on using mathematical models. The evolution of predictive microbiology into a more exact science is well illustrated by the increasing number of food microbiology papers using mathematical modelling techniques (Figure 1).

Figure 1. Number ofpapers with keywords containing food microbiology* and 'modelling*. Source: Food Science and Technology Abstracts. A basic assumption of predictive microbiology is that, in a constant environment, the relative (or: specific) growth/death rate of a homogeneous microbial population is constant with time (7). In other words, the percentage increase/decrease of the cell population in unit time is constant. This is a simple, logical and understandable model, similar to those commonly used in physical and chemical sciences for processes such as dissipation, diffusion, etc, when the force that causes the change of a certain quantity is constant with time. The problem is that this idealistic scenario is disturbed by several intra- and extracellular factors. Examples for these are the physiological state of the cells, the heterogeneity of real-life microbes, the dynamically changing environment and the interactions between cells, competing populations and the environment. Still, because of the consistency of the specific growth rate of microbes in a given environment, this has remained the most important parameter to quantify the microbial response. Since the necessary direct measurements are difficult, especially at low cell concentrations, M A N Y data can substitute for their lack of A C C U R A C Y . The increasing amount of data, however, needs databases and computational tools. ComBase fwww.combase.ee) is such a database, a repository of measurements on microbial growth and survival in various environments. It is freely available via the Internet, and has become an invaluable source for academia, industry and regulatory officers (2).


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The ComBase Story When the U K Ministry of Agriculture Fisheries and Food initiated, in 1988, a coordinated program on growth and death of bacterial pathogens it became evident that relevant data should be collected and analyzed in a computerized and standardized way. The collected data became the basis of the first validated predictive models on the growth and survival of food-borne microbes, commercialized in a P C package called Food MicroModel. The task of supporting these developments was taken over, when established, by the U K Food Standards Agency (FSA). The F S A , in 2003, released all the data behind the Food MicroModel and funded the development of a program called Growth Predictor, by the Institute of Food Research. The program is freely available today at (www.ifr.ac.uk/Safetv/GrowthPredictor). It is the result o f a remodelling effort on all the available growth data (mainly on bacterial pathogens), utilizing the scientific developments of the 1990s. Parallel to these events in the U K , the U S counterpart of Food MicroModel, called P M P (Pathogen Modelling Program: www.arserrc.gov/mfs/pahogen.htm) was developed at the Eastern Regional Research Center of the U S D A Agricultural Research Service. Soon, the coordinators of these research centers and funding agencies on the two sides of the Atlantic recognized that a common, joint, database and unified model would be beneficial for everybody. This is how ComBase, the Combined Database of microbial responses to food environments (see www.combase.ee) started its life. It is now an internet-based, publicly and freely available database, for research and training/education purposes, for food microbiologists, manufacturers, risk assessors and legislative officers. The original Food MicroModel and P M P datasets have been supplemented with additional data submitted by supporting institutes, universities and companies; as well as by data compiled from the scientific literature. Under the funding of the European Union, many E U institutions are also adding their data to ComBase. As written by McMeekin (5), "... ComBase can be a watershed in the evolution of predictive modelling and its widespread applications". Table I summarizes the most important organisms and the respective number of ComBase records storing information on their kinetic responses to food environments. One record represents a specific combination of environmental factors to which a microbial response was recorded. The response can be either a measured viable count growth/survival curve (the majority of the records are like this) or a measured / estimated specific growth rate characteristic to the bacteria and the combination of environmental factors. Among those factors, the temperature is always mandatory to be recorded, however, the p H and water activity are not; it depends on i f they were reported at all. Besides, several other factors are recorded, depending on how detailed information is available on the measurement. The lack of compatibility between microbiological data measured by different people has always been hindering the computational, numerical processing o f those data. ComBase is an example to present pooled data in a



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Figure 2. A query and output screen of the stand-alone version of the ComBasebrowser program. It shows that, at storage temperatures between 0 and 10°C, altogether 296 records were found on the microbial responses of Listeria, with pH between 5 and 7, and water activity between 0.8 and 1. The particular record displayed (record 276) shows a growth curve measured at 5°C, pH 6, and a = 0.986. The raw data (dots) can be compared with prediction (curve) generated by Growth Predictor.


256 Table I. Combine Records for K e y Organisms.


Organism Aeromonas hydrophila Aeromonas sobria Aeromonas caviae Bacillus cereus Bacillus licheniformis Bacillus subtilis Clostridium botulinum (prot.) Clostridium botulinum (non-prot.) Campylobacter Clostridium perfringens Escherichia coli Listeria monocytogenes/innocua Staphylococcus aureus Shigella flexneri salmonellae Yersinia enterocolitica Brochothrix thermosphacta lactic acid bacteria pseudomonads total flora Enterobacteriaceae yeast spp

Number of records in Combase 2269 576 432 2508 328 914 367 358 506 1031 3946 8017 1583 745 4302 2203 640 721 504 198 260 2203

standardized format and so to make the data available for everybody via an Internet database. As McMeekin et al. (6) remarks, the Internet has been playing a similar role in the spread and availability of information as the invention of printing by Gutenberg in the 16 century. Indeed, vast amounts of information have become easily available and accessible, via the Internet, for a vast number of users. ComBase is an example for the development in "e-science". According to John Bell, the Chief Executive Officer of the U K Food Standards Agency "ComBase is an example of the way that governments and the research community can successfully work together to help improve the safety of food products. The Food Standards Agency strongly supports this initiative, its widespread application and its use to reduce food borne disease." Although collaboration began as an academic exercise, having a single database of information and joint models offers huge benefits to assuring the safety of foods in international trade. th


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Acknowledgements The support of the U K Food Standards Agency (project no: FS 3113) and the European Commission, Quality of Life and Management of Living Resources Programme (QoL), Key Action 1 on Food, Nutrition and Health; Contract n°QLKl-CT-2002-30513 (e-ComBase) is thankfully acknowledged.


Reference 1. 2. 3. 4. 5. 6.

Baranyi J.; Roberts T. A . Int. J. Food Microbiol. 1994, 23, 277-294. Baranyi J.; Tamplin M. J. Food Prot. 2004, 67, 1967-1971 McKellar, R. C., L u , X., Eds. Modelling Microbial Responses in Foods. C R C Press: Boca Raton, F L , 2003. McMeekin, Τ. Α., Olley, J. N., Ross, T., Ratkowsky, D . A . Predictive Microbiology. John Wiley & Sons Ltd. Chichester, U K , 1993. McMeekin, T. A . In: Modelling Microbial Responses in Foods. McKellar, R.C., L u , X. Eds.; C R C Press: Boca Raton, F L , 2003. McMeekin, J., Baranyi J., Zwietering, M., Ross, T., Dalgard, P., Bowman, J., Kirk, M. Int. J. Food Microbiol. 2004. (in press)


Advances in Microbial Food Safety - ACS Publications - American

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