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Applied and Environmental Microbiology, September 2001, p. 4128-4136, Vol. 67, No. 9
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.9.4128-4136.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Effect of Challenge Temperature and Solute Type on Heat Tolerance
of Salmonella Serovars at Low Water Activity
K. L.
Mattick,1,*
F.
Jørgensen,1
P.
Wang,2
J.
Pound,3
M. H.
Vandeven,2
L. R.
Ward,4
J. D.
Legan,2
H. M.
Lappin-Scott,3 and
T. J.
Humphrey1
PHLS Food Microbiology Research Unit,
Heavitree, Exeter EX2 5AD,1
Environmental Microbiology Research Group, University of
Exeter, Exeter EX4 4PS,3 and Salmonella
Reference Unit, Laboratory of Enteric Pathogens, Central Public
Health Laboratory, London NW9 5HT,4 United
Kingdom, and Nabisco, Inc., East Hanover, New Jersey
07936-19442
Received 15 November 2000/Accepted 11 June 2001
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ABSTRACT |
Salmonella spp. are reported to have an increased
heat tolerance at low water activity (aw;
measured by relative vapor pressure [rvp]), achieved either by drying
or by incorporating solutes. Much of the published data, however, cover
only a narrow treatment range and have been analyzed by assuming
first-order death kinetics. In this study, the death of
Salmonella enterica serovar Typhimurium DT104 when exposed
to 54 combinations of temperature (55 to 80°C) and
aw (rvp 0.65 to 0.90, reduced using
glucose-fructose) was investigated. The Weibull model (LogS = 
btn) was used to describe microbial
inactivation, and surface response models were developed to predict
death rates for serovar Typhimurium at all points within the design
surface. The models were evaluated with data generated by using six
different Salmonella strains in place of serovar
Typhimurium DT104 strain 30, two different solutes in place of
glucose-fructose to reduce aw, or six low-aw foods artificially contaminated with Salmonella in place of
the sugar broths. The data demonstrate that, at
temperatures of 
70°C, Salmonella cells at low
aw were more heat tolerant than those at a higher
aw but below 65°C the reverse was true. The same
patterns were generated when sucrose (rvp 0.80 compared with 0.90) or
NaCl (0.75 compared with 0.90) was used to reduce aw, but
the extent of the protection afforded varied with solute type. The
predictions of thermal death rates in the low-aw foods were
usually fail-safe, but the few exceptions
highlight the importance of validating models with specific
foods that may have additional factors affecting survival.
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INTRODUCTION |
Salmonella
enterica is an international food-borne pathogen, which
regularly causes large outbreaks of food poisoning. Salmonellosis can
be fatal, in addition to the significant morbidity caused, and the cost
associated with infection can be very high. A recent study estimated
that each case of salmonellosis in the United Kingdom costs
approximately £600 ($1,000) on average, through costs to the health
sector, direct costs to patients, and lost employment (4,
61). In addition, there are important implications for the food
industry through recall of products and lost prestige and income
(64).
Most outbreaks of salmonellosis have resulted from the consumption of
contaminated meat, eggs, or dairy products, but some large
international outbreaks have been associated with foods that have a low
water activity (aw; measured by relative vapor pressure
[rvp]) (2, 20, 22, 37, 55). The presence of Salmonella cells in low-aw foods raises specific
issues for food safety. Outbreak investigations indicate that only a
very few Salmonella cells may be required to cause disease
when consumed in low-aw foods (22, 53). In
addition, it is widely believed that cells suspended at lower
aw during thermal inactivation are more heat tolerant than
those suspended at a higher aw (6, 14, 19, 21, 38,
58). This has clear implications for food processing when
heating is used to ensure the elimination of potential food pathogens
including Salmonella.
The Salmonella serotypes responsible for most human
infection in the United Kingdom and the United States are
Salmonella enterica serovar Enteritidis and S. enterica serovar Typhimurium (1; Public Health Laboratory Service
Communicable Disease Surveillance Centre for 1990 to 1999). Outbreaks
associated with low-aw foods, however, have been caused by
a number of other serovars, such as S. enterica serovar
Napoli, S. enterica serovar Agona, and S. enterica serovar Ealing, which were associated with chocolate, a
chip-type snack, and infant dried milk, respectively (2, 20, 22,
37, 53, 55). It is interesting that a large proportion of
low-aw foods are snack foods, which are a common feature of
the modern diet (15, 29). It is possible that increased
consumption of these food types could result in more frequent outbreaks
of salmonellosis in the future, unless appropriate management steps are taken.
Historically, the heat inactivation of populations of bacteria has been
described using first-order kinetics, i.e., D values (18, 36, 59). This makes the assumption that all bacterial cells within a population have the same heat sensitivity, but significant deviations from linearity have been reported elsewhere (5, 9, 26, 33, 40, 60). In such cases, curve fitting will
give more accurate descriptions of the data than will D
values because shoulders or tailing can be incorporated. The modeling of complete data sets and the ability to predict inactivation kinetics
for given combinations of factors provide an invaluable risk assessment
tool, for example, in the initial stages of a novel product formulation
or in process development.
Despite most data indicating that Salmonella cells are more
heat tolerant when in low-aw environments, there are
reports describing exceptions to the general rule. The heat tolerance
of serovar Typhimurium (65.5 to 75°C with aw reduced
using sugar [19]) and S. enterica serovar
Anatum (50 to 54°C with aw reduced using glycerol
[25]) increased at intermediate aws but
decreased at rvp's below 0.75. In addition, an increase in heat
tolerance might be observed only when specific carbohydrates are used
to reduce aw (47). Clearly, further research
is required in order to understand fully the heat tolerance of
Salmonella spp. at low aw. Thermal inactivation
models are published elsewhere for Salmonella (52 to 59°C and high aw [17] and 55 to 65°C
and 0 to 9% NaCl [8]) and for Escherichia
coli O157 (52 to 60°C and 8% NaCl [57], 54 to
68°C with 9 or 17% NaCl [11], and 55 to 65°C and 0 to 9% NaCl [8]). There is very little
available information, however, on the inactivation of these
pathogens at higher temperatures (65 to 80°C) in combination with
lower aw (rvp.0.65 to 0.90), particularly when the
aw is depressed using solutes other than NaCl. In
addition, much of the existing data cover only a narrow range of
treatments, were analyzed assuming first-order death kinetics, or were
not subsequently evaluated in foods.
In this study, data on the inactivation of serovar Typhimurium
definitive type 104 (DT104) at 54 combinations of aw (rvp
0.65 to 0.90, reduced using glucose-fructose) and temperature (55 to 80°C) and six further Salmonella strains at a subset of
these conditions were generated. Secondary models were developed for serovar Typhimurium DT104 strain 30 (62) such that
inactivation curves could be predicted by interpolation for conditions
not tested, and this was evaluated by using intermediate-moisture foods
and different solutes to reduce aw. This paper demonstrates that, while low aw is protective for Salmonella
at temperatures of >70°C, it promotes more rapid death at lower temperatures.
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MATERIALS AND METHODS |
Salmonella strains and preparation of cultures.
Salmonella serovar Typhimurium DT104 strain 30 (62) was selected for this study on the basis of its
relative tolerance of high temperature and low aw as
separate stresses (32, 44). Salmonella serovar
Typhimurium DT104 strain 16 (41), serovar Enteritidis
PT4 strain E (27), and S. enterica
serovar Senftenberg 775W (63) were selected on the basis
of their stress tolerance. Serovar Napoli (20), serovar
Agona (37), and S. enterica serovar Java
(3) were selected to represent a wide range of serovars and relevant low-aw sources. Bacterial strains were
recovered from storage at 20°C, and stationary-phase cultures were
prepared in tryptone soy broth (TSB; Oxoid Ltd., Basingstoke,
Hampshire, United Kingdom) as described previously (45).
Preparation of low-aw (high-sugar) broths.
Low-aw TSB (pH 6.5; Oxoid Ltd.) was prepared as described
previously (45) using AnalaR grades of glucose and
fructose (in equal proportion) and sucrose or NaCl (BDH, Poole,
Dorset, United Kingdom) as the humectants. The aw of the
broth was adjusted by adding TSB (rvp 0.99: pH 6.5) to give rvp values
between 0.65 and 0.90 and measured at 25°C using an
Aqualab CX-3T (Labcell, Basingstoke, Hampshire, United Kingdom) water
activity meter with an accuracy of ±0.003. Note that values for rvp
and aw are identical in very dilute solutions, but the term
aw is defined in terms of ideal equilibrium solutions, and
the high-solute broths used in this study are too concentrated to
approach ideal behavior.
Increased survival of S. enterica serovar Anatum during heat
challenge in TSB compared with that in phosphate buffer has been reported elsewhere (47); therefore, TSB was used in order
to produce as fail-safe a model as possible. There was no significant change in pH during heat challenge (data not shown).
Measurement of death rates at high temperature.
One hundred
fifty microliters of a stationary-phase Salmonella culture
was inoculated into 15 ml of low-aw TSB, giving an initial
cell density of approximately 107 CFU ml
1. To
allow Salmonella cells to adapt to the low-aw
environment (as would occur in a low-aw food ingredient
prior to heat processing), cells were held at 21°C for 1 h prior
to heat challenge. On some occasions, control cells were heat
challenged immediately, in order to establish the possible effect of
the 1-h pretreatment. Cultures were heat challenged as described
previously (45) using a submerged heating coil
(12). Strain 30 was challenged at 55, 60, 65, 70, 72, 74, 76, 78, and 80°C and rvp's of 0.65, 0.70, 0.75, 0.80, 0.85, and 0.90 (54 combinations), whereas the other Salmonella strains were
challenged at 60, 65, and 72°C and rvp's of 0.65, 0.80, and 0.90 (9 combinations). Dilutions were made in maximal recovery diluent (Oxoid),
and viable counts were performed using the method of Miles and Misra
(46) with plating onto blood agar and incubation for
48 h at 37°C to ensure optimal recovery of injured cells that
might have had prolonged lag periods (30).
To investigate the effect of solute used to reduce a
w,
strain 30 was heat challenged at 55, 60, and 74°C at low
a
w achieved
using NaCl and sucrose. The lowest achievable
a
w prior to saturation
was rvp 0.75 for NaCl and rvp 0.80 for sucrose. These values were
compared with inactivation at rvp 0.90 achieved using each solute.
There was no 1-h pretreatment prior to heat
challenge, since the
resulting degree of survival and injury could vary
considerably
with solute (
44).
Evaluation of the models in foods.
The models were evaluated
in six low- or intermediate-moisture foods (coconut cake, pecorino
cheese, pepperoni sausage, strawberry jam, dried apricots, and peanut
butter). These experiments were performed in New Jersey, using foods
purchased in a local retail store, whereas data for the broth model
were generated in the United Kingdom.
Each food was homogenized by blending. The rvp was measured using an
earlier model (CX-2) of the meter described above (Decagon
Devices
Inc., Pullman, Wash.). The pH was measured three times
using a pH meter
(Corning Inc., Corning, N.Y.), and a mean value
was calculated. No
attempt was made to adjust the natural pH of
the food. Aliquots (2 g)
of the food were added to each of 10
bags which were inoculated
directly into the base with 20 µl of
a stationary-phase culture of
DT104 strain 30, giving an initial
cell density of approximately
10
7 CFU ml
1. The food was mixed, spread
thinly along the bottom of the bag,
and allowed to stand for 1 h
at 21°C. One bag was removed to a
water bath at 21°C as a time zero
sample, and the remainder were
weighted and suspended in a preheated
water bath, such that the
sample was >15 cm below the water level. The
required challenge
temperature was achieved in less than 5 s. Bags
were removed at
predetermined time intervals into the 21°C water
bath. Tenfold
dilutions of the foods were made by adding 18 ml of
diluent to
the bags, and further 10-fold dilutions were made in a
microtiter
plate, as described above. Dilutions were plated onto blood
agar
and xylose lysine desoxycholate agar, with plate incubation for
48 h at 37 °C. There was minimal background flora observed from
any of the foods on blood agar following incubation, and
Salmonella bacteria were enumerated from these plates, since
selective xylose
lysine desoxycholate agar did not adequately recover
injured
cells.
Statistical analysis, curve fitting, and data modeling.
A
minimum of three replicate trials for each combination of
temperature, rvp, serovar, and solute was performed. The data were
analyzed in Excel. The raw data (CFU milliliter1) were
converted into the log10 of the surviving fraction of
bacteria (LogS) at a given time, t.
(i) Curve fitting.
Curves were fitted using the Weibull
model, LogS = btn, where LogS is the
log10 of survival ratio at time t, and
b and n are the scale and shape parameters,
respectively (49). The Weibull model can describe linear
inactivations or curves with shoulders or tailing and is mathematically
simple. Values for b and n were derived for each
inactivation curve in the Solver function of Excel, aiming to minimize
the sum of the squares of LogS (observed) minus LogS (predicted).
Curves were fitted by instructing Solver to minimize the residual sum
of squares by iteratively changing b and n. The
convergence criterion was set to 1 × 109, and the
nonnegative option was selected. Default settings for other
options were used. Note that Solver will occasionally fail to converge
and an error message will occur to this effect. This can be overcome by
substituting some small positive value (e.g., 0.000001) for the first
(i.e., zero) time point. The derived values for b and
n were used to predict the time required to obtain a 3-log10 or 5-log10 reduction in cell concentration.
When the death curves dropped below the detection threshold of the
experiment (20 CFU ml
1), a value of 5 was used for the
first censored observation only,
with further censored data being
excluded. This value was chosen
since there was no significant
difference between the fitted
b and
n values when
using censored observations of 5 or 20 (data
not shown) and because the
square root of the threshold value
(in this case close to 5) has been
used previously (Keith Jewell,
personal
communication).
To validate the use of Excel Solver for curve fitting, 10 individual
data sets were fitted using both Solver and the nonlinear
regression
module of Statistica (a statistical analysis package;
Statsoft, Tulsa,
Okla.), and these gave reasonable agreement (
P < 0.05).
(ii) Response surface modeling.
To simplify the numerical
computation and make the regression coefficients more comparable, the
experimental variables rvp and temperature were first normalized as
follows:
where rvpn and tn are normalized rvp and normalized
temperature,
respectively.
Inspection of frequency plots of
b and
n (data
not shown) showed that the distribution of
n was
approximately normal but that
of
b was highly skewed. A high
degree of skewness indicates that
the variable should be transformed
before regression analysis,
in order to improve the goodness of fit. A
Box-Cox transformation
was used, in this case a power transformation
such that
btrans =
b0.3.
Multiple regression analysis was performed using SAS software (SAS
Institute, Cary, N.C.) to estimate the response of
btrans and
n to rvpn and tn. All main
effects, interactions, and quadratic
terms were included, and a table
of regression coefficient estimates
and
P values was
prepared (Table
1). The relative
importance
of each factor can be judged by the
P value, with
factors with
small
P values being most influential and
predictive of the response
variable.
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RESULTS |
Inactivation of DT104 strain 30 at high temperature and low
aw (achieved using glucose-fructose).
The
semilogarithmic inactivation curves were nonlinear but could be
described by the Weibull model. The inactivation curve for each set of
conditions can be derived by substituting the values for b
and n from Tables 2 and
3 into the Weibull equation. Values for
n were usually <1, indicating that the curvature was concave upwards, with distinct tailing during inactivation (Table 2).
Values for b also showed clear trends (Table 3). For any given aw, b increased from 55 to 80°C. That
is, the slope of the curve became steeper, indicating that there was
faster death at the higher temperatures (as expected). At 55 and
60°C, b tended to be higher at the lower aw
tested, indicating that cells died faster when exposed to the dual
stress of temperature and low aw. At temperatures of
70°C, however, the b value was greater at rvp 0.90 than
at rvp 0.65, and this was very pronounced at high temperature. At
80°C, for example, the b value was 7.11 at rvp 0.90 whereas it was 3.63 at rvp 0.65 (Table 3). This indicates that, at high
temperature, low aw protected the cell against thermal death (Fig. 1) with a longer time to
obtain a 3-log10 reduction (Fig.
2). The resulting models for b
and n were
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These equations can be used to derive
b and
n for any given conditions of (normalized) temperature and
rvp in the model range.
Three-dimensional representations of these
models are given in
Fig.
3. For both
models, normal probability plots of residuals
were inspected and no
significant deviations from normality were
observed (data not shown).
The models were used to predict
b and
n values
for all conditions in the experimental design matrix.
The predicted
values of
b and
n were substituted into the
Weibull
equation and used to predict survival curves for all
treatments.
Observed values were compared with these predicted
survival curves
and compared favorably, and a plot of observed
and predicted time
to obtain a 3-log
10 reduction in cell
concentration showed good
agreement (Fig.
4).
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TABLE 2.
Mean values of n (representing curvature in
the Weibull model) derived from experimental data observations for the
inactivation of serovar Typhimurium DT104 at 54 combinations of
aw and temperature
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TABLE 3.
Mean values of b (representing rate of
inactivation in the Weibull model) derived from experimental data
observations for serovar Typhimurium DT104 at 54 combinations of
aw and temperature
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FIG. 1.
Graph of the log surviving serovar Typhimurium DT104 at
55°C (squares), 70°C (circles), and 80°C (triangles) and rvp 0.65 (open symbols) or 0.90 (closed symbols) plotted against log time in
minutes, demonstrating the effect of the two water activities on the
heat tolerance at three temperatures. The dashed line represents the
initial CFU of Salmonella milliliter1.
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FIG. 2.
Graph of the log10 time to obtain a
3-log10 reduction in the concentration of serovar
Typhimurium DT104 for each aw (rvp 0.65 [closed circle],
0.70 [closed square], 0.75 [closed triangle], 0.80 [open circle],
0.85 [open square], and 0.90 [open triangle]) against the challenge
temperature, demonstrating that the protective effect of low water
activity is apparent only at temperatures of 70°C.
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FIG. 3.
Three-dimensional representation of the
b-aw-temperature relationship (top) and the
n-aw-temperature relationship (bottom),
demonstrating the effect of aw on the survival of high
temperature by serovar Typhimurium DT104.
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FIG. 4.
Plot of observed (from experimental data) and predicted
(using the models generated using the experimental data) time to obtain
a 3-log10 reduction in cell concentration.
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Incubation at low a
w (achieved using glucose-fructose) for
1 h at 21°C had no effect on the thermal death of serovar
Typhimurium
DT104 strain 30 (data not shown), and therefore data for
the effect
of solute type can be compared
directly.
Heat tolerance of different Salmonella strains at
low aw (achieved using glucose-fructose).
The
inactivation curve for each set of conditions can be derived by
substituting the values for b and n from Tables
4 and 5
into the Weibull equation. With the other Salmonella strains tested, the time to obtain a 3-log10 reduction at 60°C
was always lower at rvp 0.65 than at rvp 0.90 but at 72°C the
opposite was observed. No clear trends in heat tolerance at 65°C were
observed, presumably because this is the approximate temperature at
which the reversal in effect of low aw occurs. For example,
the slowest death was at rvp 0.65 for serovar Typhimurium strain 16;
rvp 0.80 for serovar Typhimurium strain 30, serovar Agona, serovar
Java, and serovar Senftenberg 775W; and rvp 0.90 for serovar
Enteritidis strain E.
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TABLE 4.
Values for n in the equation LogS = b × tn, when used to
describe inactivation curves for Salmonella serovars
exposed to high temperature and low
awa
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TABLE 5.
Values for b in the equation LogS = b × tn, when used to
describe inactivation curves for Salmonella serovars
exposed to high temperature and low aw
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Overall, serovar Senftenberg 775W, serovar Java, and serovar Agona were
the least heat-tolerant isolates for the a
w range
tested.
Of the
Salmonella strains from outbreaks, only serovar
Napoli showed heat tolerance similar to that of serovar Typhimurium
and
serovar Enteritidis.
Salmonella serovar Enteritidis was
always
relatively heat sensitive at rvp 0.65 but was one of the most
heat-tolerant strains at rvp 0.90. For each inactivation with
n < 1, an increase in time to obtain a
3-log
10 reduction was usually
associated with an increase
in
n value, such that it was closer
to 1 (Table
8). In other words, increased heat
tolerance of
Salmonella was usually associated with more
linear death kinetics. For all
treatments, higher
n values
were usually associated with the more
heat-tolerant
Salmonella serovars (serovar Typhimurium, serovar
Enteritidis, and serovar Napoli).
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TABLE 8.
Effect of reduced water activity on the heat tolerance of
Salmonella strains, measured as the time in minutes to
obtain a 3-log10 reduction (standard error)
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Effect of solute type on heat tolerance of serovar Typhimurium
DT104 strain 30.
Use of sucrose and NaCl to reduce the
aw of the challenge broth revealed that the
temperature-dependent effects of low aw were still observed
when using these solutes in place of glucose-fructose but that the
extent of the effects varied with solute type (Table 6).
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TABLE 6.
Effect of inactivation of serovar Typhimurium DT104
strain 30 at low aw (achieved using sucrose and NaCl),
measured as the time in minutes to achieve a 3-log10
reduction in cell concentration (standard error)
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At 55 and 60°C, the presence of NaCl in the challenge broth with an
a
w close to saturation (rvp 0.75) was detrimental compared
with the effect at a higher a
w (rvp 0.90;
P = 0.03 and 0.05, respectively).
At 68°C, there was no difference in
death rate at the two a
ws
tested (
P = 0.79). At 70 and 72°C, the lower a
w gave marginal
protection (
P = 0.20 and 0.47, respectively), and at 74°C,
cell
death was too rapid for accurate measurements to be taken. At
55°C, a broth containing sucrose (rvp 0.80) was detrimental to
heat
tolerance compared with the effect of a higher a
w (rvp
0.90;
P = 0.04); at 60°C, there was no difference in
effect; and at
74°C, significant protection was observed
(
P = 0.003). Sucrose
was more protective at all
temperatures than was glucose-fructose,
which in turn was more
protective than NaCl (Table
6). The difference
in observed death rates
at 55°C and rvp 0.90 between use of sucrose
and use of NaCl to reduce
a
w was nearly 20-fold.
Evaluation of the thermal inactivation models using food
products.
With pecorino cheese, pepperoni sausage, strawberry jam,
and dried apricots, death occurred at the rate predicted by the models or higher at each temperature (Table 7),
indicating that the models gave conservative (fail-safe) predictions
for these foods. The discrepancies between the observed and predicted
times to a 3-log10 reduction are probably the result of
variations in pH, fat content, and other factors between the foods and
the sugar broths.
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TABLE 7.
Survival of serovar Typhimurium DT104 in sugar solutions
and in low aw foods at 55 to 74°C, expressed as the
time to obtain a 3-log10 decrease in cell concentration
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In coconut cake and peanut butter, however,
Salmonella
sometimes survived for longer than predicted (Table
7). The predicted
and observed times to achieve a 3-log
10 reduction were
<2-fold
different at each temperature in coconut cake but >100-fold
different
at 55°C in peanut butter. The peanut butter had an rvp that
was
significantly below the intended range of the model, and these
data
confirm that extrapolating a model far beyond its intended
range should be
avoided.
The a
w and pH of the six low- and intermediate-moisture
foods are given in Table
7. In strawberry jam, a 1- to
2-log
10 decrease
in cell concentration was observed during
the 1-h pretreatment
at 21°C (
P = 0.00001),
presumably due to the very low pH, but
in all other foods there was no
significant change in cell concentration
during
pretreatment.
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DISCUSSION |
Much of the thermal inactivation data generated in this study
showed significant tailing, particularly at the higher temperatures tested, and this is consistent with published data for other bacteria under similar conditions (60). Since linear descriptions
of cellular death would not accurately describe the data, curves were
fitted to the inactivation data using the Weibull model
(49). Other investigators have used the logistic
(13) and Gompertz (10, 43) models and other
models (52).
A polynomial function derived by multiple regression analysis was used
for the secondary inactivation models, in common with previous
researchers (13, 34), whereas others have used
Arrhenius-Eyring (51) and linear Arrhenius-Davey
(16) models. By modeling a response surface, reliable
predictions of thermal inactivation under conditions that have not been
tested but are within the range of the experimental matrix can be
generated by interpolation. The secondary models were produced with
data generated using serovar Typhimurium DT104 strain 30, and to
confirm the main observations of the models, six further strains of
Salmonella were analyzed at a subset of conditions. These
were serovar Typhimurium DT104 strain 16, serovar Enteritidis (reported
elsewhere to be relatively tolerant to low aw
[44]), serovar Senftenberg 775W (reported to be more
heat tolerant than other Salmonella strains at high aw [23, 40, 48]), and three outbreak strains
(serovars Napoli, Agona, and Java).
Existing inactivation data for Salmonella and related
organisms at high temperature and low aw were compared to
the data from this study. Gibson (19) presented
D values for heat tolerance (55 to 70°C) of serovar
Typhimurium and serovar Senftenberg at rvp 0.71 to 0.99 (reduced using
sucrose, adding glucose for rvp's of <0.85). Gibson's work gave
similar results for five comparable combinations of temperature and
rvp, but at rvp 0.90 and 60°C our data indicated approximately
fourfold-higher heat tolerance, using glucose-fructose in place of
sucrose. Sumner et al. (58) looked at sucrose solutions
(rvp 0.83 to 0.98) with 3-h osmotic equilibration prior to heat
challenge at 66 to 77°C. Results were comparable at 74°C and 0.90, but at rvp <0.90 or a temperature of <74°C our data indicated a
lower level of heat tolerance, using glucose-fructose in place of sucrose.
All Salmonella strains tested demonstrated that low
aw (rvp 0.65 compared with 0.90) was detrimental to
survival at 55 or 60°C, whereas at 70°C the lower aw
was always protective. The most heat-tolerant serovars over the range
of conditions tested were serovar Typhimurium DT104, serovar
Enteritidis PT4, and serovar Napoli. Strains isolated from outbreaks
associated with low-aw foods did not appear to be more heat
tolerant at low aw than did other strains. This indicates
that Salmonella strains from outbreaks associated with
low-aw foods may not have particular characteristics promoting their survival during heat processing and subsequent storage
in low-aw foods but that their characteristics may
instead relate to the contamination source. The temperature-dependent effect of low aw on heat tolerance was independent of the
solute type used to reduce aw, although the extent of
protection afforded did vary. Sucrose was generally more protective
than was glucose-fructose, which in turn resulted in significantly
lower thermal death rates than with NaCl at all temperatures tested.
Most published reports indicate that the heat tolerance of
Salmonella increases as the aw decreases
(14, 21, 38, 58), but a small number of reports indicate
that heat tolerance of Salmonella may increase or decrease
at low aw (6, 19, 25). We propose that the
temperature-dependent effects of low aw on the heat
tolerance of Salmonella reflect different targets for death
at low temperatures than at high temperatures. The high-temperature target(s) appears to be protected by low aw, perhaps
through improved stability of proteins, reduced mobility of water, or
the direct effects of solutes, whereas the lower-temperature target(s)
is clearly not protected by low aw. For example, the
high-temperature targets could be the ribosomes, since it is reported
that their heat stability is increased at low aw
(56). Air-dried cells, as well as those suspended at low
aw, exhibit increased tolerance (38);
therefore, general dehydration probably gives rise to the observed
increased heat tolerance at the higher temperatures tested. Gibson
stated that proteins and other macromolecules are more stable in
the dry state (19). Corry studied the turbidity of serovar
Typhimurium as a measure of the degree of plasmolysis, and this
correlated with the degree of protection afforded by the high
concentration of solutes during heating at 65°C
(14).
Published data indicate that serovar Senftenberg strain 775W
is significantly more heat tolerant than are most other
Salmonella strains at optimal aw (rvp 0.995) but
not at low aw (6, 19, 21). Our data confirm
that serovar Senftenberg strain 775W was relatively heat sensitive over
the rvp range 0.65 to 0.90. A disparity between the behavior of strain
775W and that of serovar Typhimurium was reported previously, with the
heat tolerance of 775W being virtually unaffected by reducing the
aw (rvp 0.94 to 0.997 [24]). These data
indicate that there is an unusual interaction between heat tolerance
and aw in this strain. They also show clearly that serovar
Senftenberg strain 775W is not an appropriate strain to estimate the
efficacy of thermal processes for low-aw foods.
Validation of the models was performed with six retail foods, selected
to represent a wide range of low- and intermediate-moisture food types.
In addition, some of the foods had particular properties that could
increase the heat tolerance of Salmonella. In other words,
the foods were selected to challenge the ability of the model to
produce fail-safe predictions of thermal inactivation. Pepperoni
sausage was used since it has been demonstrated that Salmonella strains attached to muscle tissue may be more
resistant to heat than strains that are not (28). Coconut
cake was chosen since desiccated coconut has previously been associated
with outbreaks of Salmonella (3, 7). Peanut
butter has a high fat content, which has been reported elsewhere to
protect microorganisms against high temperature (33, 42,
54), although other reports are less conclusive (35,
39).
Salmonella died more quickly in pecorino cheese, pepperoni
sausage, strawberry jam, and dried apricot than would be predicted by
the broth models. This indicates that predictions are fail-safe, as is
required in order to design safe processes. The more rapid death
observed is likely to be due partly to the additional stress of low pH
in some of the foods, compared to the model predictions based on data
generated at pH 6.5. Unfortunately there is currently no model
available to generate predictions for relevant combinations of high
temperature, low aw, and low pH. Predicted inactivations for coconut cake (55 and 65°C) and peanut butter (65 and 74°C), however, were more rapid than those actually observed in the foods. The
predicted and observed times to achieve a 3-log10 reduction differed by approximately twofold in coconut cake. With peanut butter
(rvp 0.50), however, death was far slower than predicted by the model,
and this highlights the dangers associated with extrapolating a
predictive model beyond its intended range. The peanut butter had a
relatively neutral pH and a very high fat content (53% [wt/wt]), and
these factors may account for the differences seen.
It is clearly important to evaluate laboratory-based models with real
foods, since the individual properties of foods will have a great
effect on the survival of microorganisms within foods. Other
researchers have demonstrated increased heat tolerance of microorganisms in foods compared with that predicted in broth models
(31, 50). In addition, the food validation studies reported here indicate that pH is an important factor influencing the
survival of Salmonella at low aw when exposed to
heat, and this requires further investigation.
In conclusion, the greater heat sensitivity at low aw (rvp
0.65 compared with 0.90) at the lower inactivation temperatures (55 and
60°C) could have implications for food process design, development of
new food formulations, and risk assessment. It is clearly important
that thermal processes for low-aw foods are designed using
thermal inactivation data generated in low-aw systems. Therefore, it is hoped that these data will make a positive
contribution to food safety for manufacturers of low-aw
foods whose process involves a heat treatment step.
| |
ACKNOWLEDGMENTS |
We gratefully acknowledge funding from Nabisco Inc. and the
Public Health Laboratory Service.
We also thank Martin Cole for his involvement in initiating the project
and Micha Peleg, Louise Slade, and Cindy Stewart for useful discussions.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: PHLS Food
Microbiology Research Unit, Church Lane, Heavitree, Exeter EX2 5AD,
United Kingdom. Phone: 44 (0) 1392 402966. Fax: 44 (0) 1392 412835. E-mail: kmattick{at}phls.org.uk.
| |
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Applied and Environmental Microbiology, September 2001, p. 4128-4136, Vol. 67, No. 9
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.9.4128-4136.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Top
Abstract
Introduction
![<AR><R><C>rvpn = [rvp − mean (rvp)]/standard deviation</C></R><R><C> = [rvp − 0.775]/0.094</C></R></AR>](/content/vol67/issue9/fulltext/4128/img001.gif)
![<AR><R><C><UP>tn </UP><UP>= </UP>[<UP>temperature − mean </UP>(<UP>temperature</UP>)]<UP>/standard deviation</UP></C></R><R><C><UP> = </UP>[<UP>temperature − 70.0</UP>]<UP>/8.441</UP></C></R></AR>](/content/vol67/issue9/fulltext/4128/img002.gif)
