Future trends 641 New thinking in QMRA

It has recently been suggested that the QMRA approach for the global control of microbiological hazards be fine-tuned, and two additional definitions have been introduced. One is the Appropriate Level of Protection (ALOP), which is a level of protection of human health established for a specific food-borne pathogen and this must be applied to the co-product stream or to the product that contains the co-product. An additional definition is the Food Safety Objective (FSO) which is the maximum frequency of the hazard in a food at the time of consumption that provides the ALOP (Havelaar, 2004). These definitions are complex and must be applied only in the context of the QMRA approach which, as described above, is a global approach to microbiological risk management (see Fig. 6.1).

144 Handbook of waste management and co-product recovery 6.4.2 Predictive modelling

Over the last decade a major advance in understanding the interaction between the chemistry and physics local to the growth or survival of microorganisms is that demonstrated by predictive microbiology. This approach builds models that predict the growth or inactivation of micro-organisms given a constant local environment.

Inactivation models

These are the oldest models (established around the 1920s) and are the models on which the canning industry is based. The original models were developed for thermal inactivation, which is described above, but models relating to non-thermal inactivation such as the effects of water activity, pH, organic acids, ozone and ultraviolet light have been developed.

Growth boundary models

These are otherwise known as habitat domain or growth domain models and they define the chemical environment within a system where microorganisms are capable of growing. Equally, of course such models define the chemical environment where organisms are not capable of growing. These types of model, therefore, enable us to adjust the chemistry of a co-product or co-product stream to that which is outside the habitat domain of specific micro-organisms, and hence at a chemical composition that will impose microbiological safety or stability in respect of specific microorganisms (Fig. 6.5).

Low Sodium chloride High

Fig. 6.5 A schematic representation of habitat domain modelling showing that chemical domains can be identified where micro-organisms might be able to grow, or where the chemistry of co-products, co-product streams or end-products can be poised to ensure microbiological stability.

Low Sodium chloride High

Fig. 6.5 A schematic representation of habitat domain modelling showing that chemical domains can be identified where micro-organisms might be able to grow, or where the chemistry of co-products, co-product streams or end-products can be poised to ensure microbiological stability.

The importance of microbiological risk management 145 Growth rate models

Where the growth of micro-organisms might be tolerated in food the growth rate can be modelled. The standard approach is to develop a primary growth model which is the relationship between the number of bacteria and time. The primary model is then used to extract parameters such as lag time or specific growth rate. These are then built into secondary growth models where the effect of environmental conditions (such as temperature, pH and sodium chloride concentration) on those growth parameters can be modelled. A wide range of types of secondary model exists such as the Arrhenius model, square root models, polynomial models, cardinal models or artificial neural networks. Essentially, growth models function on the basis that organisms can be controlled by three major factors. These are temperature, pH and water activity. Most of the models that have been developed and exist in software rely upon definition of these three conditions within a system; the potential for growth of organisms in relation to these factors can then be predicted. Such predictive models are available as software and include the Pathogen Modelling Programme (www.arserrc.gov/ mfs/PMP6_CurMod.htm) and Growth Predictor (www.ifr.ac.uk/safety/ growthpredictor/).

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