Predictive modeling of mixed microbial populations in food products: Evaluation of two-species models

Predictive microbiology is an emerging research domain in which biological and mathematical knowledge is combined to develop models for the prediction of microbial proliferation in foods. To provide accurate predictions, mode ls must incorporate essential factors controlling microbial growth. Current models often take into account environmental conditions such as temperatur e, pH and water activity. One factor which has not been included in many mo dels is the influence of a background microflora, which brings along microb ial interactions. The present research explores the potential of autonomous continuous-time/two-species models to describe mixed population growth in foods. A set of four basic requirements, which a model should satisfy to be of use for this particular application, is specified. Further, a number of models originating from research fields outside predictive microbiology, b ut all dealing with interacting species, are evaluated with respect to the formulated model requirements by means of both graphical and analytical tec hniques. The analysis reveals that of the investigated models, the classica l Lotka-Volterra model for two species in competition and several extension s of this model fulfill three of the four requirements. However, none of th e models is in agreement with all requirements. Moreover, from the analytic al approach, it is clear that the development of a model satisfying all req uirements, within a framework of two autonomous differential equations, is not straightforward. Therefore, a novel prototype model structure, extendin g the Lotka-Volterra model with two differential equations describing two a dditional state variables, is proposed to describe mixed microbial populati ons in foods. (C) 2000 Academic Press.