predictive micro Flashcards
defn
integrates traditional models, information systems and technology to estimate behaviour of microorganisms under certain conditions
concept of P.M
MO can react reproducibly to environmental conditions.
If environment ca be measured then MO growth, survival and death can be predicted.
Goal of PM
develop mathematical equations that describe the behaviour of MO under different environmental factors
how does this aid the food industry
allows for the estimation of shelf-life of foods and gives insights into how environment changes can affect spoilage
How are predicitve models are made
environmental conditions
temperature,pH, water activity
Gather data on the growth characteristics of the specific microorganism of interest under varying conditions
Conduct controlled experiments, Use mathematical equations to describe the relationship between microbial growth and environmental factors.
Primary models
(describing microbial growth under constant conditions)
secondary models
(incorporating the effects of changing conditions).
modelling programs
combase, PMP, Sym’previus
Applications of PM
HACCP, Risk Assessment, Microbial shelf life studies, product R&D
Microbial Shelf life studies
prediction of growth of specific food pathogens
R&D
effect of altering product composition on food safety and spoilage
temperature function integration
consequences of temperature in the cold chain for safety and spoilage
PM and product innovation
assessing speed of microbial proliferation, growth limits, or inactivation rate associated with particular food formulations and/or process conditions in order to develop new products and processes, reformulate existing products, determine storage conditions and shelf-life.
PM and operation support
supporting food safety decisions that need to be made when implementing or running a food manufacturing operation such as setting CCPs in HACCP.
incident support
estimating the impact on consumer safety or product quality in the case of problems with products on the market
limitations
environment is not always known
usually predict faster growth than observed so this makes them fail-safe.
cannot be extrapolated outside ranges
models derived in static conditions may not be applucable to fluctuating conditions
