Predictive food microbiology, a well-established subdiscipline of food microbiology used for nearly 100 years, is reemerging. Its progress and impact on food safety practices and hazard analysis and critical control point (HACCP) systems will require the cooperation of industry, academia, and regulatory agencies.
Explore this issueApril/May 2008
About a century before Svante Arrhenius and Jan Belehrádek—the early fathers of predictive microbiology—were considering the best mathematical approach to quantifying microbial behavior, the industry that spurred their continuing debate was born. Early in the 1800s, Nicholas Appert had discovered that food heated in sealed containers would not spoil during extended storage. His discovery earned Appert a large cash award and allowed for the feeding of Napoleon’s vast armies. Appert didn’t understand why food spoiled, and the causes of spoilage remained unknown until the discoveries of Louis Pasteur some 50 years later.
Predictive microbiology considers such factors as bacterial heat resistance, the heat-transfer properties of food, and time/ temperature history. Ever since the important scientific and technological discoveries of Appert and others, the canning—or more appropriately, the thermo-stabilization—industry has employed predictive microbiology to ensure the quality and safety of its products. Today, nearly 100 years after Arrhenius and Belehrádek, we have a well-established form of thermal processing for low-acid foods (pH>4.6) that is accepted by regulatory authorities.
The 12-D process, as it is often called, demonstrates the benefits of predictive microbiology in action. This temperature-specific process is based on assumptions of first-order microbial inactivation kinetics and a decimal reduction time (D-value). It is intended to achieve a 12-D or 12 log10 reduction of the most heat-stable microorganism capable of causing human illness (usually Clostridium botulinum spores) or spoilage of the product under normal storage conditions. For example, if the time (D-value) in minutes at 250°F (121°C) for the inactivation of C. botulinum spores is 0.2 (1-D or 1 log10 reduction), then the 12-D (12 log10 reduction) equivalent would be 2.4 minutes.
Because initial spore levels cannot be adequately determined for each container of food, the 12-D process offers a degree of overkill that reduces potential risk to an acceptable level. For example, if we assume a can of food initially contains 1,000 (103) C. botulinum spores, a 12-D process will result in a 109-fold risk reduction, resulting in a one-in-a-billion chance of a can containing a surviving C. botulinum spore. This practice has been business-as-usual for several decades now and, during this time, the low-acid canned food industry has achieved an enviable safety record.
Fast forward to the late twentieth century. The shift in predictive microbiology is toward modeling the growth and survival of microorganisms rather than inactivating them. The mathematical methodology used to describe these biochemical processes has also evolved, and many methods, some of which are quite complex, have been described. In 1993, R.C. Whiting and R.L. Buchanan proposed the further classification of these mathematical models as primary, secondary, or tertiary; this serves as the framework for understanding the basic structure of the predictive microbiology software packages available today.
Primary models describe microbial response (e.g., lag phase duration, growth rate, inactivation rate) to a specific condition or conditions over time. Secondary models describe microbial response over a range of primary model conditions, while tertiary models are an assembly of primary and/or secondary models into an end-user software package. Some examples of primary models include the Gompertz function, the Baranyi model, the Buchanan three-phase linear model, McKellar’s heterogeneous population model, and Natick’s quasi-chemical model. These methods follow similar approaches; for example, most predictive microbiology tools used in the food industry are kinetic rather than probabilistic and empirical (or semi-mechanistic) rather than completely mechanistic. Many excellent reviews on predictive microbiology are available.