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Citation

Thesis Title
Probability -of-growth modelling to optimize the use of hurdle technology to achieve microbiological stability of high moisture processed cheese

Read article at Massey Research Online:

Producing high moisture and ambient shelf-stable processed cheese is microbiologically challenging. Hurdle technology makes use of a combination of mild stress factors that can be more effective in inhibiting or inactivating the growth of micro-organism than individual stress factors. Ms Khani Pour employed hurdle technology to develop models that can predict the shelf stability of processed cheese. Effectiveness of selected preservatives (sodium chloride, potassium sorbate, nisin and lysozyme) on the growth of the target micro-organism (Clostridium sporogenes) during eight weeks storage was expressed as the probability of growth and was modelled as function of their concentrations as well as pHs and type of growth media (nutrient broth and processed cheese). The developed models enable the ability to predict shelf stability of high moisture processed cheese as function of concentration of the selected preservatives. This described general approach could be applied in the development of other high moisture, low acid foods.

Supervisors
Professor Steve Flint
Dr Owen McCarthy
Associate Professor Jon Palmer
Professor Matt Golding