Chapter 14
Predicting the Growth of Microbial Pathogens in Food
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M a r k L. Tamplin Microbial Food Safety Research Unit, Eastern Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture, 600 East Mermaid Lane, Wyndmoor, P A 19038
Predictive microbiology has emerged as an important field of applied science that describes the growth, survival and inactivation of microbial pathogens through mathematical expressions. Predictive models are especially useful for estimating responses of pathogens to intrinsic and extrinsic environmental factors that have not been experimentally tested. They are widely used to design and implement Hazard Analysis and Critical Control Points food safety systems, including identifying Critical Control Points, associated Critical Limits, and potential remedial actions when process deviations occur. Models are most valuable when they have been validated for specific pathogen-food combinations, and accepted by regulatory agencies for making food safety decision. This chapter discusses key steps in the design, production, and validation of pathogen growth models.
U.S. government work. Published 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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206 Microbial pathogens can display three primary patterns of behavior in food: growth, an increase in viability; survival, no significant change in cell numbers; or death, a decrease in viability. The science of predictive microbiology is based on the premise that such microbial behavior can be describe by mathematical expressions and that it is reproducible for a specific set of environmental conditions. A n additional assumption is that changes in behavioral parameters form smooth surfaces, allowing for predictions over interpolative regions that have not been experimentally tested. Validated microbial models have proven to be valuable tools for risk managers in food companies and pubic health organizations, in that they reduce uncertainty about estimations of risk. Models are actively used to develop and implement Hazard Analysis & Critical Control Points (HACCP) food safety systems and to estimate human exposure in quantitative microbial risk assessment. As such, their value to risk managers continues to increase, and drives new research leading to the development of more accurate and robust models. This chapter provides the reader with practical discussions about the design, production, and validation of models for predicting the growth of microbial pathogens in food. For perspectives on thermal and non-thermal inactivation modeling, as well as general concepts and applications of predictive microbiology, the author recommends articles by Juneja (7), McKellar and X u (2), McMeekin et al. (3), and Ross and McMeekin (4).
Pathogen Growth in Food Bacterial pathogens typically display up to three different phases of behavior in food: lag, growth, and maximum population density (i.e., stationary phase). These phases can be defined by fitting "primary" curves to the kinetic data, and are commonly referred to as growth parameters. Lag phase duration (LPD) is normally expressed in units of hour or day; growth rate as the log of cell counts per hour or day; and maximum population density (MPD) as counts per gram or milliliter of the matrix. For a specific set of environmental conditions, repeated experimental testing shows that growth rate and maximum population density vary less than lag phase duration (LPD). As described in greater detail below, L P D is not only dictated by the innate properties of the cell, but also by the cell's previous "history," more specifically its physiological state before it has transferred to a new environment (5). 1
In some instances, a death phase may occur, however this chapter focuses on growth scenarios only.
In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.
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207 The change in a primary growth parameter as a result of environment condition is described by secondary models or response surfaces. These surfaces have interpolative regions which encompass the experimental range of independent variables, and where model predictions have greater accuracy. Predictions out of this region are termed extrapolations, and are inherently uncertain. The literature is replete with growth models based on the behavior of single bacterial cultures in defined microbiological media. Far fewer models have been developed for marketplace foods, and less so for non-sterile foods where pathogens and native flora compete for survival. As a result, our perspectives on bacterial behavior are skewed towards homogeneous environments where single microbial species grow at fast rates and to high densities. It can be argued that models based on these defined test systems allow one to more clearly isolate the effect(s) of individual variables. In addition, such models generally provide more liberal estimates of bacterial growth, referred to as "worst-case" predictions. However, it can also be argued that models produced from pure cultures in defined media do not adequately fulfill the needs of risk assessors and risk managers who are interested in knowing realistic levels to which humans are exposed. Also, such liberal growth estimates may lead to over-designing food processing operations that expend unnecessary capital to control pathogens, while also reducing the quality of foods.
Growth Rate Factors that influence bacterial growth rate in food include intrinsic factors such as nutrient level, p H , water activity, and acidulants. Extrinsic factors are commonly temperature, the type of gaseous atmosphere, and relative humidity. In addition, growth rate can be influenced by the presence of native microbial populations (e.g. spoilage organisms), especially when the latter are present at proportionally higher levels than the pathogen, and at refrigeration temperatures where spoilage organisms typically have higher growth rates. In general, there is a positive relationship between temperature and growth rate, and an inverse relationship between temperature and the generation or doubling time. In the field of predictive microbiology, growth rate is expressed as the change in cell number per unit time. In mathematical expressions, growth rate is normally expressed in natural logarithm (In) form, termed the "specific growth rate" (h" ). One can convert the logio form of growth rate to specific growth rate by multiplying the former by the In (10) or -2.303. Figure 1 depicts die growth of Listeria monocytogenes on a slice of sterile cured ham at 37°C (
