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Type: Conference paper
Title: Application of a Modified Davey-Linear Arrhenius Model for High Pressure (< 600 MPa)-Cold Pasteurisation of Vacuum Packed Foods
Author: Phua, S.
Davey, K.
Daughtry, B.
Citation: Proceedings from Chemeca 2005, 2005 / Hardin, M. (ed./s), pp.CD ROM 1-CD ROM 6
Part of: Proceedings from Chemeca 2005
Publisher: The Institution of Engineers Australia
Publisher Place: CD ROM
Issue Date: 2005
Conference Name: CHEMECA (33rd : 2005 : Brisbane, Australia)
Editor: Hardin, M.
Statement of
Samuel Phua, K R Davey, Ben Daughtry
Abstract: High Pressure-Cold Pasteurisation (HP-CP) (< 600 MPa, < 35 oC) of microbial contaminants in vacuum packed foods is an alternative to widely used thermal processing. Present operating conditions however have evolved empirically. An overestimate of treatment will mean that there are actually more microbial survivors than expected. This is potentially a serious risk to public health. The synthesis and validation of an adequate mathematical process model can be justified therefore by an increased reliability of operation and is a necessary first step in the optimisation of HP-CP technology. Here, a modified form of the additive Davey-Linear Arrhenius (D-LA) model for the inactivation rate coefficient of microbial contaminants, together with a first-order chemical reaction rate equation, is assessed against five independent HP-CP survivor data for a range of foods including: fruit juices, milk and buffer solutions contaminated with a range of micro organisms including: Listeria monocytogenes, Escherichia coli and Yersinia enterocolitica. Applied to HP-CP the modified model form has three principal terms: holding pressure (P), holding time (t) and t2. The criteria for goodness of fit include: accuracy of prediction and ease of use together with an appraisal of residuals. The D-LA model gave an overall mean percent variance of 87.5 %V over the five independent data sets for HP-CP processing. Results highlight the model form is practically suited to characterizing shouldering and tailing in a wide range of independent survivor data. The model is amenable to process optimisation and, if carefully used, can reduce the number of challenge tests for validation of product safety.
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