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Applied and Environmental Microbiology, January 2001, p. 133-141, Vol. 67, No. 1
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.1.133-141.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Modeling of Combined Processing Steps for Reducing
Escherichia coli O157:H7 Populations in Apple
Cider
Heidi E.
Uljas,1
Donald W.
Schaffner,2
Siobain
Duffy,2
Lihui
Zhao,2 and
Steven C.
Ingham1,*
Department of Food Science, University of
Wisconsin
Madison, Madison, Wisconsin
53706-1565,1 and Food Risk Analysis
Initiative, Rutgers University, New Brunswick, New Jersey
08901-85202
Received 12 July 2000/Accepted 8 October 2000
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ABSTRACT |
Probabilistic models were used as a systematic approach to describe
the response of Escherichia coli O157:H7 populations to combinations of commonly used preservation methods in unpasteurized apple cider. Using a complete factorial experimental design, the effect
of pH (3.1 to 4.3), storage temperature and time (5 to 35°C for 0 to
6 h or 12 h), preservatives (0, 0.05, or 0.1% potassium sorbate or sodium benzoate), and freeze-thaw (F-T; 
20°C, 48 h and
4°C, 4 h) treatment combinations (a total of 1,600 treatments) on the probability of achieving a 5-log10-unit reduction in
a three-strain E. coli O157:H7 mixture in cider was
determined. Using logistic regression techniques, pH, temperature,
time, and concentration were modeled in separate segments of the data
set, resulting in prediction equations for: (i) no preservatives,
before F-T; (ii) no preservatives, after F-T; (iii) sorbate, before
F-T; (iv) sorbate, after F-T; (v) benzoate, before F-T; and (vi)
benzoate, after F-T. Statistical analysis revealed a highly significant (P < 0.0001) effect of all four variables, with cider
pH being the most important, followed by temperature and time, and
finally by preservative concentration. All models predicted 92 to 99% of the responses correctly. To ensure safety, use of the models is most
appropriate at a 0.9 probability level, where the percentage of false
positives, i.e., falsely predicting a 5-log10-unit
reduction, is the lowest (0 to 4.4%). The present study demonstrates
the applicability of logistic regression approaches to describing the
effectiveness of multiple treatment combinations in pathogen control in
cider making. The resulting models can serve as valuable tools in
designing safe apple cider processes.
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INTRODUCTION |
Fresh apple cider is a ready-to-eat
product which often receives no microbial inactivation steps in its
manufacturing. Although rare, Escherichia coli O157:H7 has
become the pathogen most frequently transmitted by cider (3, 8,
9). To improve the safety of cider, the U.S. Food and Drug
Administration (FDA) is currently evaluating comments on a proposed
Hazard Analysis Critical Control Point (HACCP) regulation which, if
approved, must be implemented in fresh fruit juice plants. In addition,
the proposal states that processors must adopt at least one treatment
that reduces the numbers of target pathogens by 5 log10
units (17). Today, the only FDA-approved method for
pathogen control is pasteurization, which is, however, viewed by many
consumers and cider producers as detrimental to the taste and texture
of cider and, for small cider producers, may not be financially
feasible (23). The need for other effective intervention
treatments has led to numerous research projects (6, 10, 16, 24,
36, 46).
E. coli O157:H7 cannot grow in apple juice, with a pH of
typically 3.3 to 4.1 (25), but it can survive for long
periods (28, 47). Low pH and the presence of naturally
present organic acids, such as malic and citric acids, or added organic
acid preservatives may cause sublethal cellular injury (37, 41,
42). The numbers of E. coli O157:H7 in cider may be
reduced by exposing injured cells to further stresses such as exposure
of the cider to a warm temperature or else freezing and thawing the
cider (43). There are several steps in cider processing at
which E. coli O157:H7 may be stressed. In a noncontinuous
process, commonly used by small cider processors, a sufficient amount
of cider must be first collected before proceeding to the next step.
Cider may, therefore, sit in the holding tank for a time ranging from a
few hours to overnight (44), a period during which cells
may become injured. Injury and cell death are enhanced as this storage
temperature increases (42, 43, 47). Many cider makers also
freeze their cider for later sale (44), which further
stresses cells and may result in cell death (34). Although
individual studies have demonstrated the potential lethality of these
steps, a systematic approach to describe microbial responses to such
treatments and their combinations is needed for use in developing
effective pathogen control strategies for cider.
Multifactor kinetic models have been developed to describe the growth,
survival, and inactivation of E. coli O157:H7 in foods or
synthetic media (1, 4, 7, 40). Single endpoint modeling is
used to predict a single phenomenon, e.g., lag-phase duration, the time
required to reach a predetermined population density, toxin production,
or the probability of these events occurring (27). Such
models have been referred to as the first attempts to predict the risk
associated with foods (38).
Many investigators have found turbidimetric methods to be convenient
and cost-efficient for generating large data sets in model development
(11, 18, 26, 31, 33). These methods can simplify the
detection of growth, resulting in binary results (1 = growth,
0 = no growth) for which logistic regression techniques are
appropriate. This technique was successfully used in describing the
growth limits (the growth-no growth interface) of E. coli as
a function of temperature, pH, lactic acid concentration, and water
activity (31, 33). Models based on similar techniques have
also been used to predict the most probable number of Listeria monocytogenes (5), the probability of growth and
toxigenesis of Clostridium botulinum (14, 19),
and the occurrence of Campylobacter spp. in water
(39).
Our earlier work demonstrated the potential for combinations of
commonly used preservation methods to reduce E. coli O157:H7 populations by at least 5-log10 units in apple cider
(43). The present study extends this work to develop a
model using logistic regression techniques to relate the probability of
a 5-log10-unit reduction of E. coli O157:H7 to
cider pH, post-pressing storage temperature and time, type and
concentration of organic acid preservatives, and use of a freeze-thaw
(F-T) treatment. This model may be used to design novel processes
wherein common steps are used to achieve the 5-log10-unit
reduction in E. coli O157:H7 numbers in unpasteurized fresh cider.
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MATERIALS AND METHODS |
Apple cider.
Unpasteurized, preservative-free apple cider
was obtained from three different local cider plants (ciders A, B, and
C) and kept frozen (20°C) until use. Participating cider mills were about 200, 20, and 35 km, respectively, from Madison, Wis. For each
cider pH, the titratable acidity (expressed as percent malic acid) and
the degree Brix (percent soluble solids; Temperature-Compensated Hand-Held Refractometer; Leica, Inc., Buffalo, N.Y.) were determined. For cider A, the pH was 3.5, the titratable acidity was 0.32, and the
Brix value was 12.4°. For cider B, the respective values were 3.7, 0.35, and 11.0°, and for cider C the respective values were 3.5, 0.33, and 10.0°.
For each cider, 56 different combinations of treatment variables were
applied (see experimental design section, below). First, pH adjustments
from 3.1 to 6.5 were done in 200-ml portions by adding 1, 3, or 6 N HCl
or NaOH. Next, cider was further divided into seven 25-ml volumes to
which 2.5 and 5% (wt/vol) filter-sterilized stock solutions of
potassium sorbate (K-Sorbate; Sigma) or sodium benzoate (Na-Benzoate;
Aldrich Chemical Company, Inc., Milwaukee, Wis.) were added to attain
final concentrations of 0.05 or 0.1% (wt/vol). In addition, cider
samples with equal volumes of added sterilized distilled water were
used as controls. Finally, cider was dispensed (5 ml) in 10-ml
polystyrene tubes with flanged plugs (Fisher Scientific, Itasca, Ill.).
For sterilization, tubes were shipped overnight in dry ice to the
Linear Accelerator Facility at Iowa State University, Ames. Tubes were
exposed to electron beam radiation (first load, 29.5 kGy [range, 28.5 to 31.4 kGy]; second load, 29.4 kGy [range, 27.9 to 30.6 kGy]) the
same day and returned in dry ice to our laboratory by the following
day. Upon arrival tubes were placed in a freezer (20°C) until use. There were no cells detected (0 CFU/ml) in five control tubes of
irradiated cider as tested by spread plating on Trypticase soy agar
(TSA; BBL Becton Dickinson and Company, Cockeysville, Md.) and
incubation for 48 h at 35°C.
Cultures and inoculum preparation.
A mixture of three
E. coli O157:H7 strains (ATCC 43895, C7927, and
USDA-FSIS-380-94) was used as an inoculum. The characteristics of these
strains and their maintenance have been previously described (43). All of these E. coli O157:H7 strains are
commonly used for challenge studies. For inoculating the cider, a
three-strain mixture was prepared from stationary-phase cultures (18 h,
35°C) as previously described (43). The inoculum was
prepared separately for each cider and trial because, due to time
constraints, only one cider sample could be treated daily. The final
concentration of cells in saline (0.85% [wt/vol]) was ca. 9.0 log10 CFU/ml.
Experimental design.
In a complete factorial design, all
combinations of the following variables were tested: pH (3.1, 3.3, 3.5, 3.7, 3.9, 4.1, and 4.3), temperature (5, 15, and 25°C for 0, 2, 4, 6, and 12 h and 35°C for 0, 2, 4, and 6 h), preservatives (0, 0.05, or 0.1% sodium benzoate or potassium sorbate), and F-T treatment
(20°C, 48 h and 4°C, 4 h) or no F-T treatment. A total of
ca. 1,600 treatments were tested in three different ciders. For one of
the three ciders duplicate testing was done; triplicate testing was
done for the other two ciders. All tests were independent.
Treatments of cider.
Apple cider samples (5 ml) with or
without preservatives were tempered to 5, 15, 25, and 35°C;
inoculated at 7.0 log10 CFU/ml (50 µl of the inoculum);
and kept at 5, 15, and 25°C for 12 h and at 35°C for 6 h.
During this storage, 1.0-ml samples were taken from the 5, 15, and
25°C ciders at 0, 2, 4, 6, and 12 h and from the 35°C ciders
at 0, 2, 4, and 6 h and placed in sterile 1.5-ml microcentrifuge
tubes (Fisher). Of each sample, 30 µl (three 10-µl samples) was
immediately used to determine the presence of viable cells, and the
rest was placed in a 20°C freezer for 48 h. After 48 h of
freezing, apple cider samples were thawed at 4°C for 4 h and
then analyzed for the presence of viable cells as described below.
Determination of 5-log10-unit reduction.
Our
approach was to determine the combinations of treatments that resulted
in a 5-log10-unit reduction in the cell population. This
reduction was determined by a turbidimetric method in microtiter plates
(96-Well Cell Culture Cluster; Corning Costar, Corning, N.Y.)
containing 240 µl of Trypticase soy broth (TSB) each. Inoculated cider (107 CFU/ml) was first exposed to appropriate
treatment(s), after which a 10-µl sample was removed from the cider
and transferred to a well containing TSB. If a 5-log10-unit
reduction in cell numbers occurred during the treatment, this 10-µl
sample would contain <1 CFU and result in no growth in TSB upon the
subsequent incubation. If, however, the treatment did not lead to at
least 5-log10-unit reduction, >1 CFU would be transferred
to TSB, resulting in turbid TSB after incubation. Three replicate wells
were used for each sample point. In each 96-well plate, 12 wells were
left uninoculated and used as negative controls. Wells were examined after 48 h of incubation at 35°C for visible turbidity
(<5-log10-unit reduction) or the absence of turbidity
(
5-log10-unit reduction). The number of turbid wells (0 to 3) for each sample point was recorded. Confirmation tests to
eliminate the possibility of any bacteriostatic effects in the well,
i.e., cell survival but no growth in the well, were carried out by
plating the contents of the first clear wells on TSA as described
earlier. An approximate probability for the 5-log10-unit
reduction was calculated by using the relative frequency concept of
probability (30): P(5-log10-unit reduction) 
ne/n, where
ne is the number of observed
5-log10-unit reductions and n is the total
number of observations.
Yeast, mold, and aerobic plate counts.
The effects of
selected treatment combinations on cider's natural (potential
spoilage) flora were determined. Nonirradiated fresh cider, with the pH
adjusted to 3.3, 3.7, and 4.1 and with no additives, 0.1% sorbate, or
0.1% benzoate added was incubated at 5 or 25°C for 12 h or at
35°C for 6 h. Samples were taken at the beginning and the end of
storage for cell enumerations. Yeasts and molds were enumerated on
Dichloran Rose Bengal Chloramphenicol (Oxoid/Unipath, Ltd.,
Basingstoke, Hampshire, England) as described before (43).
The aerobic plate count (APC) was determined using Plate Count Agar
(Difco, Detroit, Mich.) supplemented with 0.01% (wt/vol) cycloheximide
(Sigma) for the inhibition of yeast and mold growth. All results were
expressed as the log10 CFU per milliliter of cider. A
Student t test (22) was used to test for
significant (P < 0.05) differences in mean
(n = 3) cell concentrations between 0 and 6 h or
between 0 and 12 h samples for each type of cider.
Data analysis and construction of models.
Seven predictor
variables were included in the data set: cider type, F-T treatment,
storage temperature, storage time, preservative concentration,
preservative type, and cider pH. Among them were three class variables:
cider type, F-T treatment, and preservative type. The other four
variables were continuous: cider pH, storage temperature, storage time,
and preservative concentration. To determine the grouping of data, the
three class variables were analyzed by using SAS PROC analysis of
variance. Before the analysis, each class variable was given numerical
codings (cider type, three levels, values 1, 2, and 3; F-T treatment,
two levels, values 0 and 1; and preservative type, three levels, values
0, 1, and 2).
The response variable in this research has two possible values: (i)
achieving a 5-log
10-unit reduction or (ii) not achieving
a
5-log
10-unit reduction. Logistic regression is the most
commonly
used technique to model such a binary response. Logistic
regression
uses a logit transformation of the binary response variable
so
that the transformed response variable extends from
to
just
as the predictor variables do (
29). A response
surface model
describing the probability of achieving a
5-log
10-unit reduction
by different combinations of
treatments was developed using PROC
LOGISTIC (
35). All of
the fitted models were of the following
form: logit(
P) = ln(
P/[1
P]) =
a +
b1(
T) +
b2(
t) +
b3(
C) +
b4(pH),
where
P is
the overall predicted probability of achieving a
5-log
10-unit
reduction,
T is storage
temperature,
t is the storage time,
C is the
preservative concentration, pH is the cider pH,
a is the
intercept, and
b1 to
b4
are the corresponding parameter estimates.
Models with quadratic
[i.e.,
b5(
T2)] and interaction
[i.e.,
bq(
Tt)] terms were evaluated
and were
not found to offer any significant benefit compared to the
simple
models presented
here.
An equivalent equation,
where
e is the Naperian base, was solved to generate
all response surface plots at a given probability level for each
parameter
as a function of two independent variables, while the other
variables
were held
constant.
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RESULTS |
Effects of experiment variables on achieving a
5-log10-unit reduction in cider.
Figure
1 illustrates the effects of pH, storage
temperature, and storage time on achieving a 5-log10-unit
reduction in the numbers of E. coli O157:H7 populations in
cider. At each storage temperature tested, the lower the cider pH, the
shorter the storage time at which a 5-log10-unit reduction
was achieved. Also, the higher the storage temperature at any given pH,
the shorter the storage time at which the desired reduction in cell
numbers was achieved. Figure 1A further shows that, with increased
cider pH, higher temperatures were required to obtain a
5-log10-unit reduction. For example, at pH 3.1 a
5-log10-unit reduction was achieved at a 5°C storage
temperature, but at pH 3.5 and 3.9 this reduction was only achieved at
temperatures of at least 25 and 35°C, respectively. The same trend
was observed when sorbate (Fig. 1B) or benzoate (Fig. 1A) was added,
although benzoate had a greater antibacterial activity.
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FIG. 1.
Effects of storage time, storage temperature, and cider
pH on achieving a 5-log10-unit reduction in E. coli O157:H7 populations in apple cider containing 0.1% benzoate
(A) and 0.1% sorbate (B) after F-T treatment. The height of a given
bar indicates the time, for a given temperature and pH, at which the
observed probability of achieving a 5-log10-unit reduction
was at least 0.5. A bar with no height indicates 0 h, and no bar
present indicates >12 h (5, 15, and 25°C) or >6 h (35°C) for a
given temperature and pH at which the observed probability of achieving
a 5-log10-unit reduction was at least 0.5.
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The effects of preservative type and concentration, pH, and F-T
treatment on achieving a 5-log
10-unit reduction are shown
in Fig.
2. The storage time required to
achieve a 5-log
10-unit
reduction decreased when the
preservative concentration was increased.
Furthermore, at increased
preservative concentrations the pH of
cider at which a
5-log
10-unit reduction was achieved increased.
For example,
in the presence of 0, 0.05, or 0.1% benzoate after
F-T treatment the
maximum pHs where desired cell reduction was
observed were <3.1, 3.5, and 3.9, respectively (Fig.
2). The F-T
treatment always further
increased the pH and decreased the storage
time where a
5-log
10-unit reduction was achieved. Of the organic
acid
preservatives used, benzoate always had a greater effect
compared to
sorbate. The presence of either preservative was necessary
in nearly
all treatment combinations to achieve the desired
5-log
10-unit
reduction. Achieving a
5-log
10-unit reduction was most probable
when cider
contained 0.05 or 0.1% sorbate or benzoate, had pH
of 3.1 to 3.9, was
stored at 25°C for up to 12 h or at 35°C for
up to 6 h,
and went through F-T treatment (Fig.
1 and
2). These
results
demonstrate the potential of the described treatment combinations
to
decrease the numbers of
E. coli O157:H7 populations by
5-log
10 units in apple cider.
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FIG. 2.
Effects of storage time, preservative concentration, and
pH on achieving a 5-log10-unit reduction in E. coli O157:H7 populations in apple cider at 35°C before and after
an F-T treatment. The height of a given bar indicates the time, for a
given preservative concentration and pH, at which the observed
probability of achieving a 5-log10-unit reduction was at
least 0.5. A bar with no height indicates 0 h, and no bar present
indicates >6 h for a given preservative concentration and pH at which
the observed probability of achieving a 5-log10-unit
reduction was at least 0.5.
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Yeasts, molds, and APC as affected by selected treatment
combinations.
The effect of selected treatment combinations on
yeast and mold populations and APC in cider is presented in Table
1. Most of the treatments tested
significantly (P < 0.05) decreased or had no
significant effect on the yeast and mold populations and APC in cider.
A significant (P < 0.05) increase in yeast and mold counts, although less than 1-log10 unit, was observed only
when no preservatives were present and storage lasted 12 h at
25°C. The APC increased significantly in most ciders tested when no preservatives were present during 12 h of storage at 25°C or
when no preservatives were present during 6 h of storage at
35°C. While decreases in yeasts and molds and in APC were less than
those for E. coli O157:H7 in corresponding experiments, the
same general trends were observed. Significant decreases were only
observed when an organic acid preservative, particularly sodium
benzoate, was added. Population decreases were greater with decreasing
cider pH and with increasing storage temperature.
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TABLE 1.
Effect of storing apple cider with or without 0.1%
sorbate or benzoate for 6 or 12 h at 5, 25, or 35°C on yeast
and mold counts and on APC
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Modeling.
The data set contained 36,480 cases of observations,
2,294 of which had response of "a 5-log10-unit reduction
achieved" and 34,186 of which had a response of "a
5-log10-unit reduction not achieved."
Statistical analysis of the three class variables (preservative type,
F-T treatment, and cider type) revealed their high significance
(Table
2). Although it had a significant effect,
cider type was
not used in further data classification because the
model was
meant to represent the average of three different ciders.
Thus,
the final grouping of the data was based on preservative type
and
F-T treatment.
The four continuous variables were modeled in separate segments of the
data set, segments based on preservative type and before
or after F-T
treatment. The models were for the following data
groups: (i) 0%
preservatives, before F-T treatment; (ii) 0% preservatives,
after F-T
treatment; (iii) benzoate, before F-T treatment; (iv)
benzoate, after
F-T treatment; (v) sorbate, before F-T treatment;
and (vi) sorbate,
after F-T treatment. Summaries of parameter
estimates for fitted
response surface equations (models 1 to 6)
are presented in Table
3. Three-dimensional surface response
plots were developed to graphically illustrate the effects of
treatment
combinations on the probability of achieving a 5-log
10-unit
reduction (Fig.
3). The data set for
model 1 (no preservatives,
before F-T) cannot be modeled because all
observations had the
same response (no 5-log
10-unit
reduction was observed). The lowest
number of observations of
5-log
10-unit reductions compared to
data sets of the other
models was expected for this model, because
two important factors
contributing to cell death, preservatives
and F-T treatment, were not
included in these treatment combinations.
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TABLE 3.
Summary of logistic modelsa for
six different data subsets describing the probability of achieving a
5-log10-unit reduction in numbers of E. coli
O157:H7 in cider by applying various combinations of
inactivation treatmentsb
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FIG. 3.
Observed (points) and predicted (surfaces) probabilities
of achieving a 5-log10-unit reduction in E. coli
O157:H7 populations in apple cider as a function of cider pH and
storage temperature after 12 h storage at 5 to 25°C or 6 h
storage at 35°C. Model 1, 0% preservatives, before F-T treatment;
model 2, 0% preservatives, after F-T treatment; model 3, benzoate,
before F-T treatment; model 4, benzoate, after F-T treatment; model 5, sorbate, before F-T treatment; and model 6, sorbate, after F-T
treatment.
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Values of standardized coefficients (Table
3) were used to rank the
significance of the variables affecting
E. coli O157:H7
survival in cider. Of all continuous variables, pH stood out as
the
most important factor (largest absolute value for standardized
coefficient) determining the probability of the
5-log
10-unit reduction.
It ranked as the most important
factor in three of the models
and as the second (but relatively close
to the most important
factor) in two of the other models. In the
presence of sorbate,
the pH played a relatively more significant role
after F-T treatment
than before the treatment in achieving a
5-log
10-unit reduction.
The second most important variable
in three of the models was
temperature, followed closely by time. The
preservative concentration
was also always significant (
P < 0.001). However, increasing the
concentration of benzoate from
0.05 to 0.1% had a less significant
role relative to the other factors
in achieving a 5-log
10-unit
reduction than if the sorbate
concentration was increased the
same amount. The significance of the
benzoate concentration was
further decreased when F-T treatment was
applied. It should be
noted, however, that benzoate is clearly more
effective in attempts
to achieve a 5-log
10-unit reduction
than is sorbate (Fig.
1 and
2).
As expected, parameter estimates of the four continuous variables all
had the same sign (pH always negative and the others
always positive)
throughout the six models, indicating that their
effects were generally
the same no matter which preservative type
was considered and no matter
whether the effect was noted before
or after F-T treatment (Table
3).
Next, predicted values were compared with observed values to determine
the goodness of fit of the models. The observed binary
data responses
are either a 5-log
10-unit reduction (event) or
not a
5-log
10-unit reduction (nonevent). The predicted response
is a real number between 0 and 1. To compare observed and predicted
values, any predicted probability greater than 0.5 (i.e., there
is a
50% or greater chance that the event will occur) was categorized
as an
event; any predicted probability less than 0.5 was deemed
a nonevent.
As shown in Table
4, at a 0.5 probability
all five
models predicted more than 92% (range, 92 to 99%) of
responses
correctly. While a value of 0.5 gives the highest percent
correct
in prediction, a probability value of 0.9 is stricter in
predicting
events, which can offer safety where human pathogens are
involved.
Table
5 shows that an increase
in probability from 0.5 to 0.9
decreased the range of false positives
from 0 to 19.9% to 0 to
4.4%, respectively. Decreasing the
probability level to 0.1 had
the opposite effect, resulting in less
conservative, or more dangerous,
models (Table
5). Three-dimensional
plots of predicted and approximate
observed probabilities for models 2 to 6 are shown in Fig.
3.
Any prediction is considered safe when a
5-log
10-unit reduction
is predicted to occur at lower pH
values and higher temperatures
than what was actually observed. For
example, in pH 3.5 at 25°C
for 12 h (Fig.
3), model 4 predicted
a lower (safer) probability
than the observed probability, i.e., 0.60 and 0.75, respectively.
For the same conditions (Fig.
3), model 6 predicted a higher (less
safe) probability than the observed
probability, i.e., 0.91 and
0.46, respectively.
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TABLE 4.
Goodness of fit of models predicting the probability of
achieving a 5-log10-unit reduction in the numbers of
E. coli O157:H7 in cider as a result of applying various
combinations of inactivation treatments
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DISCUSSION |
Survival curves of bacteria that are subjected to combined stress
conditions are often complicated and multiphasic (32). In
some cases, it may be less important to predict the rates of inactivation in each step than to focus on the resulting end point. For
example, Whiting (45) determined the time for a
4-log10-unit reduction in numbers of L. monocytogenes in fermented sausage, a reduction that would
eliminate the numbers of a pathogen likely to be present in products
made under good manufacturing practices. Similarly, Ratkowsky and Ross
(33) and Presser et al. (31) focused on the
bacterial growth-no growth interface and adopted a probabilistic
approach using logistic regression to develop models defining this
interface. Considering the complexity of treatments applied in the
present work, determining the probability of desired reduction was thus
considered the most appropriate approach.
In the present study, data to be modeled were collected from three
different ciders. We considered this the minimum number of ciders upon
which to base our model, since the composition of apple ciders from
different sources can vary considerably (25). Expectedly,
such differences resulted in the cider type having a significant
(P < 0.0001) effect on achieving the
5-log10-unit reduction. Reasons for compositional
differences between ciders may include the type and the maturity of the
apples used to make cider (13). Furthermore, the number of
different apple varieties used in one batch of cider varies, usually
from 3 to 10 (44). These results highlight the importance
of testing several types of cider to validate intervention treatments.
Cider pH was found to be a significant factor affecting a
5-log10-unit reduction of E. coli O157:H7. Cider
pH affects the concentration of the undissociated (more bactericidal)
form of naturally present organic acids and added organic acid
preservatives. The concentration of the undissociated form increases
with decreasing pH. The important role of low pH in injuring the cell
becomes evident when sorbate was present (models 5 and 6). Such injury can well be taken advantage of by subjecting the cell to further stress, e.g., F-T treatment. Interestingly, while benzoate was clearly
more effective than sorbate in reducing cell populations, the
significance of the benzoate concentration was less than in the case of
sorbate, and the significance was further decreased when F-T treatment
was applied. This suggests that benzoate was already relatively
effective in injuring and killing cells before F-T treatment at the
0.05% concentration. It could be concluded that if a lower
preservative concentration is preferred for optimal organoleptic
properties, benzoate allows the user to reduce concentration with less
decrease in the overall effectiveness of the preservative, especially
if F-T treatment is applied.
At refrigeration temperatures, it may take several days
(47) or weeks (28) before the presence of
commonly used preservatives, such as benzoate or sorbate, result in a
5-log10-unit reduction in E. coli O157:H7
populations in cider. Similarly, in our study at low temperatures a
5-log10-unit reduction was seldom seen and there was only a
minimal decline in yeast and mold counts and in APC in the presence of
either preservative. However, holding the cider for a short time at a
warm temperature was essential in increasing the lethality of tested
preservatives against E. coli O157:H7 and other microflora.
When E. coli O157:H7 was eliminated, the numbers of yeasts
and molds and aerobic plate counts also declined, i.e., improving the
safety and shelflife of the cider. At the higher end of the typical pH
range of cider, however, the preservatives became less effective,
regardless of the holding temperature.
It is generally desirable to find models that predict the data as
closely as possible. When making predictions about food safety,
however, the possibility of predicting that a food is safe when the
food actually contains pathogens must be considered. If the model
predicted a 5-log10-unit reduction, but this level of
reduction did not occur in actuality, the model would be considered "fail-dangerous" for this condition. A model can be made more "fail-safe" by raising the probability level from 0.5 to 0.9 (Table 5). This means that a 5-log10-unit reduction would only be
predicted when the predicted probability value of the model is 0.9.
To lower the probability level to 0.1 would have the opposite effect, making a model more fail-dangerous.
The model's accuracy in predicting new datum points can be improved by
increasing the number of cider samples tested or by decreasing the
sampling intervals. Smaller sampling intervals would allow for a more
precise determination of the transition value for the factor studied.
For example, under one set of conditions, a 5-log10-unit
reduction might be achieved by holding at 35°C but not at 25°C.
Another experimental design (that includes additional observations at
30°C) would improve the accuracy of predictions, especially in the
temperature range of ca. 30°C.
To best resemble the practices used in small cider mills, several
adjustments were made to the previously reported procedure (43). All conditions were tested with three different
ciders, compared to only one in our previous study. The ciders used
were sterilized by ionizing irradiation instead of by heat to prevent precipitation of the pulp. The presence of pulp was desired because it
may provide extra protection for cells to survive applied treatments, as was previously observed (20). Heat sterilization may
also have chemically altered the sugars present. Sugars may protect the
survival of E. coli O157:H7 since a faster decline in cell numbers has been shown in diluted forms of apple cider and juice (21). In our previous work, we aimed to demonstrate the
inverse relation between the cider pH and the survival of E. coli O157:H7; therefore, all pH adjustments were done after
addition of organic acids. In the present study, preservatives were
added after pH adjustment, resembling the order used in real cider
making. The addition of benzoate and sorbate salts increased the pH of
the cider by ca. 0.2 pH unit. Compared to our previous study, an
increased pH due to the added organic acids or preservatives may
therefore have weakened the effectiveness of further treatments. One or several of these differences in the experimental procedure may have
contributed to the differences seen in the effectiveness of the treatments.
Our study has documented effectiveness of treatment combinations in
reducing the E. coli O157:H7 populations in apple cider and
how probabilistic modeling can be used to define these combinations for
production of safe cider. To understand the effects of these treatment
combinations in other types of juices and/or against other pathogens,
further experiments are warranted. However, the laboratory and modeling
techniques used could be applied to a variety of treatment combinations
in such experiments. Furthermore, if one prefers modeling the microbial
death rate instead of modeling the point of 5-log10-unit
reduction during treatment combinations in juice, then several
considerations should be taken into account. Different bacterial
strains in an inoculum mixture may respond differently to each stress,
with the numbers of the most sensitive strain declining the fastest.
Prediction becomes more complicated in the presence of two or more
stresses where the proportion of sensitive cells increases due to
increasing injury during each stress applied. The effect of
preservatives should be known, i.e., whether the preservative can
inhibit, injure, or inactivate the cells or whether the cells can grow
in its presence. The rate of freezing controls water loss, relocation,
and the size and shape of ice crystals formed (2), all of
which affect the chemical and physical structure of cells and the
degree of disruption of cellular constituents. Variation in the natural
microflora in unpasteurized juice would further complicate the model
development. Competing microbes have been shown to have a protective
effect during inactivation treatments when present in high numbers
(15), but during a long-term survival the opposite
occurred (47).
We report here a probabilistic modeling approach describing the effects
of treatment combinations in pathogen control in cider. In further
research, the described modeling strategy can be used to evaluate
additional treatments and their combinations. The present study was
conducted as full factorial design; however, information gathered in
the present study may be used in further studies to determine the
combinations that could be left out of the design, and effort could be
concentrated on combinations near the point of 5-log10-unit
reduction, as in a partial factorial design (12).
Suggestions to improve the applicability and the prediction capability
of the model would include testing more types of ciders, testing
additional pathogens, and testing the addition of naturally occurring
organic acids (malic and citric), ascorbic acid, and/or their
combinations. Once fully evaluated, such a model would provide a good
first step for making decisions in HACCP plan development in cider
mills and/or for further development of the described processes. The
use of multiple antimicrobial treatments has provided an effective way
to increase the safety of many food products, and the present study
also demonstrates its potential for unpasteurized apple cider.
| |
ACKNOWLEDGMENTS |
This research was funded by a grant from the University
of WisconsiMadison College of Agricultural and Life Sciences' Hatch Fund.
We thank John B. Luchansky for providing bacterial cultures.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Food Science, University of WisconsinMadison, 1605 Linden Dr.,
Madison, WI 53706. Phone: (608) 265-4801. Fax: (608) 262-6872. E-mail: scingham{at}facstaff.wisc.edu.
| |
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Applied and Environmental Microbiology, January 2001, p. 133-141, Vol. 67, No. 1
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.1.133-141.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
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