Initial experiments to assess the effects of acetic acid, NaCl and sucrose concentrations, and pH,
were conducted with an E. coli O157 strain, SERL 2, in 81 broth formulations simulating the
aqueous phase of cold-fill sauces, dressings and mayonnaises (Chapman et al., 2006: Results
chapter 1). The effects of four factors at each of three levels were assessed; acetic acid (0.7, 1.4, and 2.1% [wt/wt]), NaCl (1, 3, and 8% [wt/wt]), sucrose (10, 20, and 30% [wt/wt]), and pH (3.2, 3.5 and 4.0). Broth models were sampled at pre-determined time intervals for up to 120 h, with a maximum of 16 time points. The factorial matrix was selected to parallel that employed by Jenkins
et al. (2000) in their development of a boundary model for growth of the major spoilage
microorganism Z. bailii in cold-filled acid products. The boundary model of Jenkins et al. (2000)
was produced as a contemporary alternative to the CIMSCEE Code, especially in response to consumer demands for milder tasting (i.e. less acidic) cold-filled products.
In these initial, and subsequent, studies E. coli inactivation was typically characterised by a biphasic response, with a sharp decline in numbers following an oftentimes prolonged lag phase
(Chapman et al., 2006: Results chapter 1). Inactivation thus was modelled using the log logistic
equation. When all other factors were held constant, the time to 3-log10 reduction (t3D) of E. coli
O157 SERL 2 generally increased with increasing pH and with decreasing acetic acid
concentrations, as expected from the literature (see Section 2.3.2). However the t3D was non-
monotonic, i.e. initially decreasing, but then increasing, in response to increasing NaCl
concentrations dependent on pH / acid concentration (Chapman et al., 2006: Results chapter 1),
consistent with the observations of others (e.g. McKellar et al., 2002). Increasing sucrose
concentration also appeared protective under some conditions, and a non-monotonic trend in the relative kill to increasing osmolarity was observed, regardless of whether NaCl or sucrose
dominated in the formulation. However multiplication of the lag time prior to inactivation (tc in the
log logistic model) by the rate of inactivation (k1 in the log logistic model) did not yield a constant
product, suggesting some differences in the specific effects of the formulation parameters
(Chapman et al., 2006: Results chapter 1).
As a probability-of-survival modelling exercise, the experimental design of McKellar et al. (2002)
did not permit observation of inactivation kinetics. Nor is the phenomenon of biphasic pathogen inactivation addressed by CIMSCEE (1992). The work of Tuynenberg Muys (1971), on which the CIMSCEE Code is based, assessed inactivation at only three time points (0, 48 and 72 h). From these data log linear inactivation was assumed. It is likely that the sample times chosen by Tuynenberg Muys (1971) were simply insufficient to capture the detail of the biphasic inactivation curve. However it is also possible that the pre-adaptation of pathogens to acidic conditions, achieved using culture in the presence of 1% glucose, resulted in increased acid tolerance compared with the non-adapted cultures used by Tuynenberg Muys (1971). Buchanan and
Edelson (1996) also observed relatively long lag times prior to acid inactivation for E. coli pre-
cultured in 1% glucose, compared with those of non-adapted cultures.
The results of initial experiments with E. coli O157:H7 SERL 2 were confirmed and extended in
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conserved among different strains of acid resistant E. coli and S. enterica (Chapman and Ross,
2009: Results chapter 2). The responses of five other strains of acid-resistant (non-pathogenic) E.
coli strains and four S. enterica strains to increasing NaCl concentrations in the presence of acetic
and hydrochloric acids were examined. As previously observed for E. coli O157 SERL 2, both tc
and k1 were non-monotonic with respect to increasing osmolarity in the presence of acetic acid; tc
was selected as the primary measure by which to compare inactivation under different conditions,
since generally a larger number of data points were collected for this inactivation phase. For E.
coli, the inflection points in the non-monotonic response of tc were dependent on pH. Inflection
occurred between 2 and 4% at pH 4.0, 4 and 6% at pH 3.8, and 4 and 7% (wt/wt of water) at pH
3.6. These NaCl concentration ranges are approximately equal to 0.7 – 1.3, 1.3 – 2 and 1.3 – 2.3
OsM, respectively. Assuming isotonicity to be around 1% NaCl or 0.3 OsM (Record et al., 1998),
the inflection point in the inactivation response was under (initially) hypertonic conditions. Higher NaCl concentrations were more protective under lower pH conditions, where the concentration of
undissociated acetic acid would also be greater. In comparison with E. coli, S. enterica was
inactivated much more rapidly. Similar to E. coli, a non-monotonic response to increasing NaCl in
the tc was observed for S. enterica in the presence of acetic acid under some conditions; at pH
4.0 up to 1 to 4% NaCl was protective, and at pH 3.8 up to 1 to 2% NaCl delayed the onset of inactivation (Chapman and Ross, 2009: Results chapter 2).
The inactivation response of E. coli and S. enterica was confirmed to be largely independent of
osmolyte type (NaCl, sucrose) (Chapman and Ross, 2009: Results chapter 2). Mildly hypertonic conditions of 10% (wt/wt of water) sucrose with 0.5% NaCl (approximately 0.4 OsM), 3% NaCl alone (approximately 1 OsM), and 10% sucrose with 3% NaCl (approximately 1.2 OsM) were all found to be protective against inimical acetic acid concentrations. However, the non-monotonic nature of the inactivation response to increasing osmolarity was found to be dependent on acid type. In comparison with acetic acid, increasing osmolarity in the presence of hydrochloric acid more often resulted in a monotonic decrease in the average lag time prior to inactivation (Chapman and Ross, 2009: Results chapter 2).
Models based on data available in the published literature were developed as a means to
generate further evidence of osmolyte protection of E. coli and S. enterica against acetic acid
inactivation (Chapman et al., 2010: Results chapter 3). For a “typical” (median) Australian cold-
filled acetic acid-containing dressing or sauce an increase in the ln(inactivation rate) and the
ln(rate of 3-log10 inactivation) in response to increasing disaccharide (sucrose) concentration was
predicted for E. coli, but not S enterica. However, it was noted that very little data was available in
the published literature regarding the response of S. enterica to disaccharides in the presence of
acetic acid. In comparison with disaccharides, the concentration of NaCl accounted for much less
variability in the models, and was among the four most influential predictor variables for S.
enterica only. For S. enterica, the effect of increasing NaCl concentration was predicted to be protective, but again, the amount of data available in the published literature on which to construct the model was small, and no inactivation data for NaCl concentrations > 3% (wt/wt) was available
(Chapman et al., 2010: Results chapter 3).
For E. coli, increasing NaCl concentration in the presence of acetic acid was predicted to increase
both the ln(inactivation rate) and the ln(rate of 3-log10 inactivation) (Chapman et al., 2010: Results
chapter 3). Although this model prediction is apparently in contrast to the central observation of
this thesis, it must be remembered that even within the studies of Chapman et al. (2006) and
Chapman and Ross (2009) (Results chapters 1 and 2), increasing NaCl concentrations did
eventually reduce survival of E. coli in the presence of acetic acid. The disparity between the
observations of protection presented in this thesis and in the work of others (Casey and Condon,
2002; McKellar et al., 2002; Larson et al., 2003; Lee and Kang, 2009; Lee et al., 2010), and the
results of the modelling based on previously published (up to mid-2009) data, is most probably a result of the quality of the available published data on which the models were constructed, as
discussed in Chapman et al. (2010) (Results chapter 3).
Despite the recognised limitations in constructing models based on data from the published literature, the models clearly identified pH and 1/ absolute temperature to be the most influential
variables predicting survival in the presence of acetic acid (Chapman et al., 2010: Results chapter
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both E. coli and S. enterica. As a result, further studies (i.e. Chapman et al., in preparation:
Results chapter 4; Chapman and Ross, in preparation: Results chapter 5) included observations of the effects of storage temperature and pH on cell survival, to complement studies of the effect of osmolarity and time.
8.1.3 Comparison with CIMSCEE and relevance to contemporary manufacture of cold-