Evaluation of growtho growth modelling approaches on a non-abrupt growtho growth interface of Listeria monocytogenes

摘要

To minimise and estimate food poisoning risks, it is necessary that the growth ability of pathogenic microorganisms in food products can be predicted. Growtho growth models are being developed that predict the interface between environmental conditions that allow growth and conditions that do not. In this paper, the differences and limitations of existing growtho growth model types are illustrated for a case study describing the growth probability of Listeria monocytogenes under conditions typical for mayonnaise based salads. A data set embeds the effects of pH (5.0-6.0), water activity (0.960-0.990) and acetic acid percentage (0-0.8% (w/w)), narrow intervals between the factor levels and many replicates (usually 20). Thus information was gained not only about presence/absence of growth, but also about probability of growth. Among three existing groups of growtho growth models considered: (i) probabilistic models, (ii) a deterministic approach separating growtho growth/uncertainty regions, and (iii) deterministic approaches, only the probabilistic models fully describe the data, since they reflect the incremental increase in growth probability present in the experimental growtho growth interface. The second group of models represented by the Minimum Convex Polyhedron (MCP) method, can partially describe this incremental increase if an extension proposed by the authors is used. Models developed by the third method are not able to describe any increments in growth probability between 0 (no probability of growth) or 1 (certain growth). An MCP based on total acid data is not capable of describing non-convexity in the interface, in contrast to an MCP based on undissociated acid data..