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Inactivation of E. coli in UCFM5. Modelling of Inactivation DataIn this section we attempt, as far as possible from the existing literature, to discern andquantify the patterns of E. coli inactivation in environments relevant to UCFM products andprocesses. To understand the role of factors affecting inactivation of E. coli in UCFM better,data from a variety of published and unpublished sources was collated and inactivation ratescalculated. In many cases the inactivation data was not ideal, e.g. few points, multiphasicinactivation rates, different strains, different methods, few points from which to estimateinactivation rates, etc. Consequently, the data are less than ideal. Data were drawn fromexperiments both in product and in various types of broth systems, and the change innumbers of surviving E. coli in time used to estimate inactivation rates.Where inactivation curves were genuinely multiphasic and there was no evidence ofenvironmental change, the rate of inactivation in the second, slower, stage was calculatedconsistent with a worst-case approach and recorded together with temperature, wateractivity, pH and any other details of the environment, where available. Some fermentationinactivation data was compiled and recorded separately. Data were plotted as a function oftemperature using the Arrhenius plot, and are presented in Figures 9a, b 10 overleaf.5.1 TemperatureWe have made much of our perception that temperature is the factor that most affects therate of E. coli inactivation in UCFM and that, by inference, time and temperature mostinfluence total inactivation. While there is variability in the rates of inactivation shown inFigures 9, it is apparent that there is a strong and uniform effect of temperature oninactivation in almost all the data sets shown. This is evident as a common slope of the linesdescribing the inactivation rates for each product/experimental system at differenttemperatures in the ‘normal physiological range’ of temperature for E. coli growth (~7.5 –49°C).The majority of the data derived from in-product studies seem to fall within a relatively narrowband of inactivation rates, with a variability of ~0.5(SD) ln units, equivalent to a factor of 1.5in the inactivation rate. To explore the strength of this apparent relationship, all data in theplot at temperatures in the ranges 15-16°C (n = 13), and in the range 25-26°C (n = 13) werecollated. A simple Arrhenius model was fitted to the data at these temperatures and is:ln(Inactivation rate [logCFU/hr])=33.387-11255*(1/Temperature[K])(1a)which can be rewritten:11 Inactivation rate (log CFU/hr)= e (33.387) / e 11255/Temperature [K]) (1b)The standard deviation of the ln(inactivation rates) at each of the temperatures ranges wasalso calculated 12 . The standard deviations were 0.57 and 0.47, respectively. As an example,assuming a standard deviation of 0.5 in the ln(inactivation rate) approximates to a 95%confidence interval of ± 270% of the estimated value, i.e. approximately 3-fold variability. TheRSQ 13 for Eqn. 1a is 66%, i.e. temperature alone accounts for 66% of the variability in data.From the data in Figure 9a can be seen that pH and water activity levels also affect the rateof inactivation. These influences are discussed separately (see Sections 5.2 and 5.3).10111213Figure 9b is the same as Figure 9a but excluding, for clarity, data not from studies in UCFM systems.In the more familiar terminology of thermal inactivation rates, Eqns. 1 indicate that the z-value for non-thermalinactivation for E. coli is ~17 - 18°C. z-values for thermal inactivation are typically < 10°C.This value will overestimate the true variance because it is based on a range of temperatures. Thus, the effect of thattemperature variation is also included in the estimate.RSQ: the square of the value of the Pearson product moment correlation co-efficient.Page 31 of 59