Crescimento de Salmonella Enteritidis e Listeria Monocytogenes em

Transcrição

Growth of Salmonella Enteriditidis and
Listeria Monocytogenes in Melon Pulp:
Predictive Modelling and Evaluation of
Model Performance
Crescimento de Salmonella Enteritidis e
Listeria Monocytogenes em Polpa de Melão:
Modelamento Preditivo e Avaliação do
Desempenho de Modelos
AUTORES
AUTHORS
Maria Fernanda Pontes Penteado
Moretzsohn de CASTRO
Instituto de Tecnologia de Alimentos (ITAL)
Grupo Especial de Engenharia e Pós-Colheita (GEPC)
Caixa Postal: 139
Av. Brasil 2880, Jd. Brasil
CEP 13070-178
Campinas/SP - Brasil
e-mail: [email protected]
Flávio Luis SCHMIDT
Faculdade de Engenharia de Alimentos (FEA)
Departamento de Tecnologia de Alimentos
Ana Lucia PENTEADO
Empresa Brasileira de Pesquisa Agropecuária
Centro Nacional de Pesquisa de Tecnologia Agroindustrial de
Alimentos (CTAA)
SUMMARY
The growth of Salmonella Enteritidis and Listeria monocytogenes in melon pulp
was verified at 10, 20 and 30 °C, and the observed growth parameters (generation time,
lag phase duration and exponential growth rate) for these microorganisms compared.
The experimental data were modelled using the Gompertz function and the DMFit
programme and the kinetic values obtained were compared with those obtained from
the observed growth and with the kinetic values for the growth of Salmonella spp.
and L. monocytogenes as predicted by the U.S. Department of Agriculture’s Pathogen
Modelling Program version 5.1. The results confirmed that low temperature is not a barrier
to the growth of Listeria and S. Enteritidis; it can retard growth but not inhibit it. Tukey’s
Paired Comparison Procedure, which compared the predicted and observed values for
GT, LPD and EGR, showed that the values predicted by PMP were generally those that
statistically differed most from the values observed, especially for the LPD values at 10 °C.
The use of PMP in this situation could be critical, as the predictions were fail dangerous The
DMFit and Gompertz values differed statistically from the observed ones in a few cases,
but these cases were not critical since they predicted on the safe side. It was concluded
that the Gompertz function, DMFit and PMP programmes can generally be used with a
certain confidence, with the exception of PMP at low temperatures, which, in some cases
can fail on the dangerous side.
RESUMO
PALAVRAS-CHAVE
KEY WORDS
Predictive microbiology; Salmonella Enteritidis; Listeria
monocytogenes; Melon pulp; Modelling; Validation.
Microbiologia preditiva; Salmonella Enteritidis; Listeria
monocytogenes; Polpa de melão; Modelamento; Validação.
O crescimento de Salmonella Enteritidis e Listeria monocytogenes na polpa de
melão foi verificado a 10, 20 e 30 °C e os parâmetros de crescimento observados (tempo
de geração, duração da fase lag (latência) e a taxa de crescimento exponencial para
estes microrganismos foram comparados. Os dados experimentais foram modelados
utilizando-se a função de Gompertz e o programa DMFit, e os valores cinéticos foram
comparados com os valores cinéticos obtidos para Salmonella spp. e L. monocytogenes
preditos pelo U.S. Department of Agriculture’s Pathogen Modelling Program (PMP)
versão 5.1. Os resultados confirmaram que baixa temperatura não é uma barreira para
o crescimento de L. monocytogenes e S. Enteritidis, pois ela pode retardar, mas não
inibir o crescimento desses microrganismos. O procedimento de comparação pareada
de Tukey, que compara os valores preditos e os observados para tempo de geração,
duração da fase lag e taxa de crescimento exponencial indicaram que, de um modo
geral, os valores preditos pelo PMP foram aqueles que mais diferiram estatisticamente
dos observados. Especialmente para os valores de duração da fase lag a 10 °C, o uso do
PMP, nessa situação, poderia ser crítico, uma vez que as predições falham perigosamente.
Em poucas situações os valores do DMFit e Gompertz diferiram estatisticamente daqueles
observados e, nesses casos, não foi crítico, uma vez que predisseram pelo lado seguro.
Concluiu-se que a função de Gompertz e os programas DMFit e PMP, de um modo geral,
podem ser usados com alguma confiança, com exceção do PMP para temperaturas baixas,
que em alguns casos pode falhar perigosamente.
Autor Correspondente
Corresponding Author
Braz. J. Food Technol., Campinas, v. 10, n. 3, p. 176-182, jul./set. 2007
176
Recebido/Received: 16/04/2007. Aprovado/Approved: 03/10/2007.
CASTRO, M. F. P. P. M. et al.
1. INTRODUCTION
Predictive microbiology is a tool that uses mathematical
models to estimate or predict the responses of microorganisms
under environmental conditions of interest (QUINTAVALLA and
PAROLARI, 1993; WHITING and BUCHANAN, 1994). Amongst other
uses, predictive microbiology can be of great value in quantitative
microbial risk assessment by predicting the growth parameters
for pathogens of interest under different conditions in a specific
substrate, where little or no information is available. However, the
extrapolation of predicted data to real-world scenarios may not
provide completely accurate predictions. Consequently the validation of predictive models is necessary in order to use the models
with confidence, including the ability to understand where key
inaccuracies in the predication may exist (McELROY et al., 2000).
In recent years, fresh produce has been detected as the vehicle
of transmission in several food-borne outbreaks (De ROEVER, 1999).
The potential for the microbial contamination of fruits and vegetables
is high due to the wide variety of contamination conditions to which
the produce is exposed during growth, harvest and distribution
(MADEN, 1992). Melon is a highly popular fruit in Brazil. It is a low acid
fruit with an average pH above 4.5 and is often served sliced in food
establishments either as fresh pieces or in mixtures for salad bars and
deli counters, or as a pulp for drinking as a juice. Salmonella spp. and
Listeria monocytogenes can survive and grow in this fruit (PENTEADO
and LEITÃO, 2004a, b; GOLDEN et al. 1993). There is little experimental
information available regarding the growth of these pathogens in
fruits, making it difficult to use predictive microbiological models such
as the U.S. Department of Agriculture’s Pathogen Modelling Program
(PMP, R. Whiting U.S. Department of Agriculture-Agricultural Research
Service, Philadelphia, Pa). So this study was carried out (1) to compare
the laboratory-based growth of Salmonella Enteritidis and Listeria
monocytogenes in melon pulp, (2) to model the observed growth
data using the Gompertz and DMFit functions, and (3) to compare
the observed, Gompertz-derived and DMFit-derived kinetic values to
those predicted by the PMP version 7.0 (USDA, 2005).
2. MATERIAL AND METHODS
2.1 Fruit
Ripe, damage-free melons (Cucumis melo L. cv.) valenciano
amarelo were obtained from supermarkets in Campinas, State of
São Paulo, Brazil.
2.2 Bacterial cultures
A strain of Salmonella Enteritidis (SE) and another of Listeria
monocytogenes Scott A (serotype 4b), from the culture collection
of the “Laboratório de Higiene e Legislação, FEA-UNICAMP”, Brazil
were used in this study. The S. Enteritidis culture was maintained in
tryptone soy agar slants (TSA; Oxoid, Oxoid Ltd. Basingstoke, Hampshire, England) and the L. monocytogenes in tryptone soy agar slants
containing 0.6% yeast extract (TSA-YE); (TSA, Oxoid Ltd Basingstoke,
Hampshire, England; YE, Difco) at 5 °C. The identities of both strains
were previously confirmed by biochemical and serological tests.
Model Performance
use in the experiment, the inoculae of S. Enteritidis were transferred to TSA and those of L. monocytogenes to TSA-YE after three
consecutive 24 h intervals. Cells were collected from the respective
media and transferred to 5 mL saline solution (0.85% NaCl ) to
adjust the suspension to a concentration of 2 x 108.mL–1 according
to the MacFarland turbidity scale using the Densimat equipment
(bioMerieux). The bacterial suspensions were serially diluted (1:10)
in 0.1% peptone water, and 1mL aliquots of each dilution pour
plated in TSA agar and TSA-YE, respectively, for S. Enteritidis e
L. monocytogenes, followed by incubation at 35 °C for 24 h to
determine the viable cell concentration. A dilution concentration
of 104 CFU.mL–1 of each bacterium was used to inoculate the fruit
pulp.
2.4 Sample pulp preparation
After being washed and scrubbed the external surfaces of
the fruit were cotton scrubbed with an alcoholic iodine solution (2%)
(FDA, BACTERIOLOGICAL ANALYTICAL MANUAL 1995), and allowed
to air dry inside a laminar airflow cabinet (VLFS-12, VECO). Defined
areas (100 cm2) of the fruit skin, and the inner fruit pulp without
seeds, were aseptically removed with sterilised spoons and the fruit
portions transferred to a sterilised shaker. After mixing, 50 g portions
of the pulp were carefully removed with a spoon and transferred
to sterilised conical flasks (250 mL). Before the inoculation tests, the
pulp was checked for sterility and then frozen.
2.5 Pulp inoculation and enumeration
of S. Enteritidis and L. monocytogenes
For each bacteria, triplicate test portions (50 g) of homogenized pulp were inoculated with 1 mL (104 CFU) suspension and
incubated for 0, 24, 48, 72, 96, 120 and 144 h at 10 °C; 0, 12, 18,
24, 36, 42 and 48 h at 20 °C; and 0, 2, 4, 6, 8, 10, 12 and 24 h, at
30 °C. For sampling, 1 mL portions of fruit pulp were collected, serially diluted (1:10) in peptone water (0.1%) and pour plate dispersed
in TSA-YE for L. monocytogenes (45 °C) and TSA for S. Enteritidis.
The plates were incubated at 35 °C for 24 h and then counted using
a colony counter (Phoenix, CP 600), the results being expressed in
CFU.g-1. Uninoculated pulp controls were also prepared to assure
the absence of any background microflora before and after the
incubation time.
2.6 Chemical and physical-chemical analyses
The pH of the uninoculated fruit pulp was determined
using a calibrated pH meter (model B374, Micronal). The pH was
not monitored during the incubation period.
Brix was determined using a Carl Zeiss (Jena) refractometer,
model 32-G 110d.
Titratable acidity was determined using the method
described by Anon (1997).
Sugars (total and reducing) were analysed as described by
Lara et al. (1976).
2.7 Growth parameters
2.3 Inoculum preparation
S. Enteritidis and L. monocytogenes were cultured, respectively, in TSA and TSA-YE slants at 35 °C. Immediately before their
Parameters for comparison with the laboratory-based
growth of Salmonella Enteritidis and Listeria monocytogenes in
melon pulp, were obtained as follows:
177
Model Performance
2.7.1 Generation time (GT)
2.7.2 Exponential growth rate (EGR)
The EGR value was the slope of the line obtained in the
calculation of GT as described in item 2.7.1
Log [population (CFU.g–1)]
The generation time (GT) was calculated from the slope of
the line obtained in the semi-logarithmic plot of exponential growth,
using the mean of three repetitions for each temperature and time.
The following equation was used: GT = 0.301/slope as described
by Madigan et al. (1997).
10
5
4
3
2
100
150
200
Time (h)
PMP predicted growth
DMFit modeled growth (r2 = 1.00)
Observed growth
Gompertz modeled growth (r2 = 0.99)
50
10
9
20 °C
8
7
6
5
4
3
2
1
0
0
10
20
30
Time (h)
40
50
60
Observed growth
3. RESULTS AND DISCUSSION
As shown in Figures 1 and 2, the results confirmed that low
temperature was not a barrier to the growth of Listeria and S. Enteritidis; it can retard growth but not inhibit it. Although the growth
rate was reduced at 10 °C, both microorganisms were able to survive
and grow in melon pulp stored at 10, 20 and 30 °C. The present
results confirmed the observations of Ukuku and Fett (2002) who
verified that L. monocytogenes could survive but not grow during
15 days of storage in cantaloupe at 4 °C, while growth was evident
6
0
The growth parameters were compared using Tukey’s
paired comparison procedure (BOX et al., 1978).
The procedure applied for the aseptic removal of the fruit
pulp was adequate. All the analyses of uninoculated samples
performed initially and during the incubation period revealed the
absence of Listeria, Salmonellae and of any other endogenous
micro flora in the internal tissues of the fruits. Samish et al. (1963)
mentioned that in healthy fruits the bacterial flora is assumed to be
limited to the surface, while the inner tissue remains sterile.
7
0
2.9 Statistical analyses
The mean of three repetitions for each experiment plus the
standard deviation for the following melon characteristics were:
acidity (%) 1.99 ± 0.27; total sugar (%) 7.76 ± 1.40; reducing sugar
(%) 4.63 ± 0.51; pH 5.87 ± 0.13 and Brix (°Brix) 10.25 ± 2.33. The
carbohydrate contents of the fruit pulp showed it was an adequate
substrate for the growth of both of the microorganisms under
study. In addition the average pH values could not be considered
inhibitory for either L. monocytogenes or S. Enteritidis, and thus
the fruit pulp composition was not a barrier to the growth of these
microorganisms.
10 °C
8
1
2.8 Growth modelling and evaluation of performance
Data for growth modelling were compiled using Excel
spreadsheets (Microsoft, Redmond, WA). The Statistica Program
was used to fit the observed growth data to the Gompertz function. Kinetic values, including the exponential growth rate (EGR),
generation time (GT) and lag-phase duration (LPD) were calculated
from the Gompertz parameters generated (WHITING, 1995). The
curves were also fitted to the Microsoft Excel Add-In “DMFit.xls”,
downloadable from http://www.ifr.ac.uk/safety/DMFit/, based on
the model of Baranyi and Roberts (1994). An evaluation of the
performance of the models was accomplished by comparing the
coefficient of determination values (R2), the use of a “fail-safe/faildangerous” graphical method and calculations of the bias and
accuracy factors (ROSS, 1996).
9
10
9
8
7
6
5
4
3
2
1
0
30 °C
0
5
10
15
Time (h)
20
25
30
DMFit modeled growth (r2 = 099)
Observed growth
FIGURE 1. Observed, Gompertz-modelled, DMFit and PMPpredicted growth curves for Salmonella Enteritidis in melon pulp
held at 10, 20 and 30 °C.
after 4 h of storage at 8 and 20 °C. These observations emphasise
the importance of strict low temperature control and good manufacturing practices to avoid contamination and growth.
For both microorganisms there was an increase of about six
log cycles after 24, 48 and 168 h, respectively, at 30, 20 and 10 °C
(Figures 1 and 2). Leverentz et al. (2001) observed an increase of
178
was provided about the characteristics of the cantaloupe in this
work (mainly pH).
10
9
Table 1 shows the growth parameters observed for S.
Enteritidis and L. monocytogenes in the melon pulp.
10 °C
8
7
6
5
4
3
2
1
0
0
50
100
Time (h)
150
200
Observed growth
9
20 °C
8
7
6
5
4
3
2
1
0
0
30
40
50
60
Time (h)
DMFit modeld growth (r2 = 1.00)
Observed growth
10
9
8
7
6
5
4
3
2
1
0
10
There was no significant difference in the growth parameters
observed at 10 and 20 and 30 °C amongst the microorganisms
under study with the exception of LPD at 30 °C, when Salmonella
Enteritidis showed a two-hour shorter lag phase. Golden et al.
(1993) also reported the ability of Salmonella ssp. to grow inside
honeydew melons. The fruits were inoculated with a pool of five
species of Salmonella (S. Anatum, S. Chester, S. Havana, S. Poona and
S. Senftenberg) and the generation time detected at 23 °C was 1.1 h.
The result obtained in the present work was in relative agreement
with this published data, since at 20 °C the GT was 1.54 h.
Table 2 shows the growth parameters obtained from the
observed values, Gompertz function, DMFit and PMP programs.
In general there was less than half an hour’s difference
between the observed GT values and the Gompertz modelled,
DMFit and PMP predicted GT values for both bacteria at 20 and
30 °C; the observed GT values always being higher than those
modelled by either Gompertz or by the PMP and DMFit programs,
with the exception of the GT at 20 °C for DMFit. On the other
hand at 10 °C the observed GT value (7.17 h) for S. Enteritidis
was about 6 h shorter than the PMP-predicted GT value (13.3 h),
whilst for Listeria monocytogenes it was 1.5 h longer than the
PMP-predicted value.
10
Model Performance
20
30 °C
In general there was little difference (equal or inferior to
2 h) between the observed LPD values and those obtained from
the modelled function or predicted by PMP, with the exception of
L. monocytogenes at 20 °C and both microorganisms at 10 °C in
relation to the values obtained with DMFit and PMP. At 10 °C the
observed LPD value was 31 h and 14.5 h shorter than the LPD
predicted by PMP for Salmonella and L. monocytogenes, respectively.
In general the data modelled by DMFit showed the safest values
with the exception of Salmonella Enteritidis at 20 °C. In these cases,
the LPD values obtained using PMP were much higher than the
values observed, representing a great risk for those making use of
this program in this situation. For Salmonella at 30 °C, none of the
predicted or modelled values were safe.
With respect to EGR, the observed values were, in general,
equal or inferior to those obtained from Gompertz and predicted
by PMP. So for this parameter, the Gompertz function and PMP
programme made safe predictions.
0
5
10
15
Time (h)
20
25
30
Observed growth
FIGURE 2. Observed, Gompertz-modelled, DMFit and PMPpredicted growth curves for Listeria monocytogenes in melon pulp
held at 10, 20 and 30 °C.
about 2 log units in cut honeydew melon slices inoculated with S.
Enteritidis at 10 °C, and of up to 5 log units at 20 °C after 168 h
incubation. Ukuku and Fett (2002) observed that populations of L.
monocytogenes in fresh cut cantaloupe pieces stored at 8 or 20 °C
increased by 1 log unit by the end of storage. These results are in
disagreement with those of the present study, but no information
The Tukey’s paired comparison procedure, which compared
the predicted and observed values for GT, LPD and EGR showed that,
in general, the values predicted by PMP were the ones showing the
greatest statistical difference from the observed values at the 5%
level, especially at 10 and 20 °C. The use of PMP in these situations
could be critical, since the predictions were in the “fail dangerous”
zone. In a few cases the DMFit and Gompertz values differed statistically from the observed ones, but in these cases it was not critical,
since they predicted on the safe side.
Figures 1 and 2 show the observed, Gompertz-modelled,
DMFit and PMP predicted growth curves for S. Enteritidis and L.
monocytogenes in melon pulp. Duh and Schaffner (1993) used the r2
values as an indication of the reliability of the models when applied
to foods. In the present study the values for the determination coefficients (r2) of the Gompertz and DMFit functions were fitted to both
179
of the data sets and also used to compare these equations. As can
be seen, all the determination coefficient values were either equal
or higher for the DMFit program than for the Gompertz function,
showing that the first had a better fit to the observed data.
The predicted and observed values were also compared using
a graphical method. Figures 3 and 4 show the “fail safe” and “fail
dangerous” plots for GT, LPD and EGR for both microorganisms.
With the exception of the PMP predicted value at 10 °C and
the DMFit predicted value at 20 °C for Salmonella, in general the GT
values for both microorganisms provided a margin of safety, since
all the GT points were below the line of equivalence, indicating
that in general all the observed GT values were longer than the
predicted ones.
The DMFit programme provided safer predictions for the
LPD values for Listeria monocytogenes than for Salmonella. Safe
values for LPD were only obtained for Salmonella at 10 °C with
DMFit and at 20 °C with PMP.
Model Performance
All the points for EGR were either on or above the line of
equivalence (with the exception of Salmonella at 20 °C for DMFit),
so the prediction can be considered safe in these cases.
Ross (1993) introduced the bias and accuracy factors for the
quantitative measurement of the plots shown in Figures 3 and 4.
The bias factor checks for systematic over or under prediction by the
model, and the accuracy (or precision) factor provides a measure of
the average difference between the observed and predicted values
(McMEEKIN and ROSS, 1996). Bias and accuracy factors of 1 indicate
perfect agreement between the observed and predicted values
(Mac ELROY et al., 2000), while, for example, factors of 1.1 and
0.9, respectively, indicate over prediction and under prediction at
an average of 10% (McMEEKIN and ROSS, 1996). As the calculation is an average of all the factors obtained at each temperature,
the result should be cautiously evaluated, because distortions can
occur at individual temperatures. Table 3 shows the results for the
bias and accuracy factors.
TABLE 1. Growth parameters1 observed for S. Enteritidis and L. monocytogenes in melon pulp stored at 10, 20 and 30 °C.
Microorganisms
S. Enteritidis
L. monocytogenes
S. Enteritidis
L. monocytogenes
S. Enteritidis
L. monocytogenes
Temperature (°C)
G.T (h)2
7.17a ± 0.20
7.16a ± 0.77
1.54a ± 0.14
1.74a ± 0.05
0.79a ± 0.30
0.86a ± 0.12
10
20
30
L.P.D. (h)2
24.00a ± 0
24.00a ± 0
8.00a ± 3
6.00a ± 0
2.00a ± 0
4.00b ± 0
E.G.R. [log(CFU.g–1.h–1]2
0.04a ± 0.00
0.04a ± 0.01
0.20a ± 0.02
0.17a ± 0.01
0.41a ± 0.16
0.38a ± 0.01
Results expressed as the mean of three repetitions plus the standard deviation for each parameter.
1
Results between microorganisms followed by different letters differ significantly at the 5% level.
2
TABLE 2. Observed, Gompertz function, DMFit and PMP growth parameters for S. Enteritidis and L. monocytogenes in melon pulp.
Growth parameters1
10 °C
20 °C
30 °C
GT (h)
Observed
Gompertz
DMFit
PMP
LPD (h)
Observed
Gompertz
DMFit
PMP
Observed
EGR
-1
Log [(CFU.g ) /h] Gompertz
DMFit
PMP
S.
L.
Enteritidis2
monocytogenes2
7.17 ± 0.20a.d
7.16 ± 0.77a
6.02 ± 0.00b, d
5.69 ± 0.58a
d
6.84 ± 0.00
6.84 ± 1.18a
c
13.30 ± 0.00
5.70 ± 0.00a
a
24.00 ± 0.00
24.00 ± 0.00a
a
24.51 ± 1.10
24.13 ± 1.82a
a
17.83 ± 5.19
16.62 ± 6.41a
55.00 ± 0.00b
38.50 ± 0.00b
0.04 ± 0.00a
0.04 ± 0.01a
c
0.05 ± 0.00
0.05 ± 0.01a
a, b, c
0.04 ± 0.00
0.05 ± 0.01a
a, b
0.04 ± 0.00
0.05 ± 0.00a
S.
L.
Enteritidis2 monocytogenes2
1.54 ± 0.14a
1.74 ± 0.05a, c
1.24 ± 0.18a
1.46 ± 0.11b, d
a
1.70 ± 0.28
1.68 ± 0.9c
b
1.20 ± 0.00
1.40 ± 0.00d
a
8.00 ± 3.46
6.00 ± 0.00a
a
9.61 ± 4.08
5.84 ± 0.32a
a
12.74 ± 0.00
4.43 ± 0.72b
6.00 ± 0.00a
10.20 ± 0.00c
0.20 ± 0.02a, b
0.17 ± 0.01a
a
0.25 ± 0.04
0.21 ± 0.02a, b
b
0.18 ± 0.03
0.18 ± 0.01a, b
a, b
0.20 ± 0.00
0.21 ± 0.00b
1
Results expressed as the mean of three repetitions for each parameter.
2
Results for the different models of the same microorganism followed by different letters (a, b, c or d) differ significantly at the 5% level.
S.
Enteritidis2
0.79 ± 0.30a
0.61 ± 0.20a
0.76 ± 0.36a
0.40 ± 0.00a
2.00 ± 0.00a
3.39 ± 0.68a
2.87 ± 0.00a
3.30 ± 0.00a
0.41 ± 0.16a
0.52 ± 0.17a
0.45 ± 0.21a
0.54 ± 0.00a
L.
monocytogenes2
0.86 ± 0.12a
0.73 ± 0.07a
0.83 ± 0.18a
0.60 ± 0.00a
4.00 ± 0.00a
3.23 ± 0.16a, c
2.29 ± 0.96b, c
4.00 ± 0.00a
0.38 ± 0.01a
0.41 ± 0.04a, c
0.37 ± 0.07a
0.51 ± 0.00b, c
TABLE 3. Bias and accuracy factors for the GT, LPD and EGR of Salmonella Enteritidis and Listeria monocytogenes in melon pulp.
Bacteria
Factors
Salmonella sp.
Listeria monocytogenes
Gompertz
Bias
0.81
Accuracy
1.24
Bias
0.84
Accuracy
1.20
GT
DMFit
1.01
1.06
0.97
1.03
PMP
0.90
1.70
0.77
1.30
Gompertz
1.28
1.31
0.92
1.09
180
LPD
DMFit
1.19
1.46
0.66
1.52
PMP
1.42
1.76
1.40
1.43
Gompertz
1.24
1.24
1.19
1.19
EGR
DMFit
1.01
1.08
1.04
1.04
PMP
1.11
1.12
1.26
1.27
Model Performance
1.2
1.2
1.0
1.0
"Fail dangerous"
Log (GT (h))
Log (GT (h))
0.6
0.4
0.2
0.0
–0.2
0.0
0.2
0.4
0.6
0.8
Log (observed GT (h))
1.0
"Fail safe"
0.2
0.4
0.6 0.8 1.0 1.2
Log (observed LPD (h))
1.4
1.6
1.8
"Fail safe"
–0.2
0.0
0.2
0.4
0.6
0.8
Log (observed GT (h))
2.0
1.8
"Fail dangerous"
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Log (observed LPD (h))
1.0
1.2
"Fail safe"
1.6
1.8
2.0
–0.2
–0.2
"Fail safe"
–0.4
Log (EGR[log(CFU.g–1)/h])
Log (EGR[log(CFU.g–1)/h])
0.2
–0.4
–0.4
1.2
"Fail dangerous"
0.0
0.4
–0.2
Log (LPD (h))
Log (LPD (h))
–0.4
–0.4
0.6
0.0
"Fail safe"
–0.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
"Fail dangerous"
0.8
0.8
–0.6
–0.8
–1.0
–1.2
"Fail dangerous"
–1.4
–1.4
–1.2
–1.0
–0.8
–0.6
–0.4
–0.2
–0.6
–0.8
–1.0
DMfit
PMP
"Fail dangerous"
–1.2
–1.4
–1.4
Log (observed EGR[log(CFU.g–1)/h])
Gompertz
"Fail safe"
–0.4
–1.2
–1.0
–0.8
–0.6
–0.4
Log (observed EGR[log(CFU.g–1)/h])
Gompertz
Line of equivalence
DMfit
PMP
–0.2
Line of equivalence
FIGURE 3. Graphical comparison of observed versus predicted
kinetic values for the growth of Salmonella sp. in melon pulp.
FIGURE 4. Graphical comparison of observed versus predicted
kinetic values for the growth of Listeria sp. in melon pulp.
For instance, for both microorganisms the bias factors for
GT obtained with Gompertz, DMFit and PMP were below 1 (with
the exception of DMFit with a bias factor of 1.01) indicating a safe
prediction for this parameter. For the accuracy factors all values for
GT were above 1 for both bacteria, indicating a percentage of variation between the predicted and observed data of 24% (Gompertz),
6% (DMFit) and 70% (PMP) in the case of Salmonella, and of 20%
(Gompertz), 3% (DMFit) and 30% (PMP) for L. monocytogenes.
(PMP), and for L. monocytogenes of 9% (Gompertz), 52% (DMFit)
and 43% (PMP).
The bias factor for LPD only showed safe values for Gompertz and DMFit for Listeria monocytogenes. For the accuracy factors,
all the values for LPD were above 1 indicating a percentage of
variation for Salmonella of 31% (Gompertz), 46% (DMFit) and 76%
Dalgaard and Jorgensen (1998) calculated accuracy factors
in seafood ranging from 1.4 to 4.0 for the growth rates of L.
monocytogenes, while in the present study the EGR values ranged
from 1.04 to1.27.
For the bias factor, all values for EGR were above 1 indicating
safe values for Listeria monocytogenes and Salmonella. For the accuracy factor, all values for EGR were above 1 indicating a percentage
of variation for Salmonella of 24% (Gompertz), 8% (DMFit) and 12%
(PMP), and for L. monocytogenes of 19% (Gompertz), 4% (DMFit)
and 27% (PMP).
181
Model Performance
The PMP has been validated for some food borne pathogens in various food systems (HUDSOM and MOTT, 1993; WALLS
and SCOTT, 1997; WALLS et al., 1996) but there are no studies
for S almonellae in any food substrate so far, and for Listeria
monocytogenes the few studies available focussed on other
foods rather than on fresh fruits. Validations of the PMP for L.
monocytogenes in baby food, ham salad, and pâté demonstrated good agreement between the model and observed values
(HUDSON and MOTT, 1993; WALLS and SCOTT, 1997). This fact
was not verified in all the situations of the present study, as, for
instance the lag phase duration at 10 °C (fail dangerous). Mc Elroy
et al. (2000) in a validation study for B. cereus spores in boiled rice
also observed that the model was “fail safe” for lag-phase duration
at 20 and 30 °C but not at 15 °C. These authors verified that as
the temperature decreased below 15 °C, the PMP may become fail
dangerous for LPD when evaluating the growth of psychotropic B.
cereus strains, and they suggested additional validation experiments
to better evaluate this phenomenon. In the present study we also
verified that PMP was fail dangerous for LPD for both microorganisms, as well as for GT for Salmonella.
LARA, A. B. W. H. et al., Métodos químicos e físicos para análise
de alimentos. In: REBOCHO, D. D. E. (Ed.). Normas analíticas
do Instituto Adolfo Lutz, v. 1, Instituto Adolfo Lutz, São Paulo,
p. 42-44, 1976.
It was concluded that in general the Gompertz, DMFit and
PMP models could be used with reasonable confidence, with the
exception of the PMP at low temperatures, when it can become fail
dangerous in some cases.
PENTEADO, A. L.; LEITÃO, M. F. F. Growth of Salmonella Enteritidis
in Melon, Watermelon and Papaya Pulp Stored at Different Times
and Temperatures. Food Control, London, v. 15, n. 5, p. 369-373,
2004a.
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182

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