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Predicting Bacterial Spoilage of Meat Products.

Betts, Gail. (2004) Predicting Bacterial Spoilage of Meat Products. Doctoral thesis, University of Surrey (United Kingdom)..

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Abstract

Spoilage of meat products can be caused by either microbial growth, chemical changes such as rancidity or sensory changes such as loss of texture, colour or flavour. The ability to predict how long it will take before such changes become unacceptable is important to the food industry as it enables them to assign a realistic shelf-life to the food product. Predictive microbiological models are being increasingly used to predict the growth of spoilage organisms and pathogens. The specific aim of this study was to develop a robust model for meat spoilage organisms, which could be used to estimate the likely shelf-life based on microbial numbers and sensory loss. A predictive model was successfully produced using a mixed cocktail of sixteen organisms comprising the major spoilage genera on meats. The model encompassed a wide range of environmental conditions (pH, temperature, nitrite and salt) including many at the boundaries for growth where microbial growth is highly variable. The variability in microbial growth response at these boundaries was measured, which increases the confidence in the reliability of model predictions at extreme conditions. A model has not previously been developed for such a large mix of microbial genera over such a range of conditions. The performance of the complex model was as good as may be expected from a single species model and it was able to account for the range of microbial interactions observed in the typical microflora observed in meat products. Furthermore, this work demonstrated that it is possible to predict the likely sensory failure of a cooked meat product based on levels of meat spoilage organisms present. The use of a mixed microbial group to predict the growth and likely sensory failure of food products over a wide range of conditions is a significant contribution to the development of future models for the food industry.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors : Betts, Gail.
Date : 2004
Additional Information : Thesis (Ph.D.)--University of Surrey (United Kingdom), 2004.
Depositing User : EPrints Services
Date Deposited : 30 Apr 2019 08:08
Last Modified : 20 Aug 2019 15:33
URI: http://epubs.surrey.ac.uk/id/eprint/851493

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