Estimation of Aspergillus flavus Growth under the Influence of Different Formulation Factors by Means of Kinetic, Probabilistic, and Survival Models

Article (PDF Available) · December 2016with 127 Reads
DOI: 10.1016/j.profoo.2016.02.093
Cite this publication
Abstract
A Box-Behnken design was conducted to determine the effect of casein concentration (0, 5, or 10%), corn oil (0, 3, or 6%), aw (0.900, 0.945, or 0.990), pH (3.5, 5.0, or 6.5), concentration of cinnamon essential oil (CEO: 0, 200, or 400 ppm), and incubation temperature (15, 25, or 35 °C) on the growth of A. flavus during 50 days of incubation. Potato dextrose agars were adjusted to the different levels of tested factors and poured into Petri dishes, once solidified were inoculated with mold spores and incubated at studied temperatures. Mold response was modeled using Gompertz and quadratic polynomial equations. The obtained polynomial regression model (allowed the significant (p<0.05) for linear, quadratic, and interaction effects for the Gompertz equation coefficients’ parameters to be identified) adequately described (R2>0.97) mold growth. Additionally, in order to describe growth/not-growth boundary, collected data after 50 days of incubation were classified according to the observed response as 1 (growth) or 0 (not growth), then a binary logistic regression was used to model growth interface. Mold growth probability strongly depend on casein, oil, temperature, and aw, as well as variations of pH and CEO concentration, being lower for those systems with higher content of CEO (>180 ppm). Furthermore, survival analysis using failure time was utilized to estimate the time at which mold growth began. The time to fail was directly related to the temperature and CEO concentration; for systems formulated with more than 200 ppm of CEO, time to fail was >30 days for low protein and fat contents. The three tested approaches to describe A. flavus response, adequately predicted growth rate and lag time, or growth probability, or the time in which growth will occur. The use and selection of any of these approaches will depend on the intended application.
Procedia Food Science 7 ( 2016 ) 85 88
2211-601X © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Department of Food Science, Faculty of Food Engineering, University of Campinas.
doi: 10.1016/j.profoo.2016.02.093
Available online at www.sciencedirect.com
ScienceDirect
9th International Conference on Predictive Modelling in Food
Estimation of Aspergillus flavus growth under the influence of
different formulation factors by means of kinetic, probabilistic, and
survival models
C.E. Kosegarten, E. Mani-López, E. Palou, A. López-Malo*, and N. Ramírez-Corona
Departamento de Ingeniería Química, Alimentos y Ambiental, Universidad de las Américas Puebla, Ex hacienda Sta. Catarina Mártir,
San Andrés Cholula, Puebla 72810. México
Abstract
A Box-Behnken design was conducted to determine the effect of casein concentration (0, 5, or 10%), corn oil (0, 3, or 6%), aw
(0.900, 0.945, or 0.990), pH (3.5, 5.0, or 6.5), concentration of cinnamon essential oil (CEO: 0, 200, or 400 ppm), and incubation
temperature (15, 25, or 35°C) on the growth of A. flavus during 50 days of incubation. Potato dextrose agars were adjusted to the
different levels of tested factors and poured into Petri dishes, once solidified were inoculated with mold spores and incubated at
studied temperatures. Mold response was modeled using Gompertz and quadratic polynomial equations. The obtained
polynomial regression model (allowed the significant (p<0.05) for linear, quadratic, and interaction effects for the Gompertz
equation coefficients' parameters to be identified) adequately described (R2>0.97) mold growth. Additionally, in order to describe
growth/not-growth boundary, collected data after 50 days of incubation were classified according to the observed response as 1
(growth) or 0 (not growth), then a binary logistic regression was used to model growth interface. Mold growth probability
strongly depend on casein, oil, temperature, and aw, as well as variations of pH and CEO concentration, being lower for those
systems with higher content of CEO (>180 ppm). Furthermore, survival analysis using failure time was utilized to estimate the
time at which mold growth began. The time to fail was directly related to the temperature and CEO concentration; for systems
formulated with more than 200 ppm of CEO, time to fail was >30 days for low protein and fat contents. The three tested
approaches to describe A. flavus response, adequately predicted growth rate and lag time, or growth probability, or the time in
which growth will occur. The use and selection of any of these approaches will depend on the intended application.
© 2015 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of Department of Food Science, Faculty of Food Engineering, University of Campinas.
Keywords: Aspergillus flavus; survival estimation; kinetic models; probabilistic models
* Corresponding author. Tel.: +52 (222) 2292126; fax: +52 (222) 2292729.
E-mail address: aurelio.lopezm@udlap.mx
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Department of Food Science, Faculty of Food Engineering, University of Campinas.
86 C.E. Kosegarten et al. / Procedia Food Science 7 ( 2016 ) 85 – 88
1. Introduction
Mathematical modeling tools, together with experimental data, are utilized for estimating microbial responses in
order to define processing and storage conditions for processed foods. Today, predictive microbiology considers
kinetic models that allow predicting microbial growth in a wide range of conditions. These models, however, are not
able to predict information under conditions that result in no growth1. With the aim of developing predictive models
that describe the growth/no-growth boundary, some probabilistic models have been evaluated as useful tools for
defining the combination of factors to prevent the growth of microorganisms2, 3. Furthermore, time-to-fail models
have been used to estimate the time at which microbial growth occurs4. Molds are toxicological and spoilage
microorganisms that may produce mycotoxins; particularly, Aspergillus species have the ability to grow in a wide
range of environmental conditions and foods. Mold growth in food products depends on several factors such as
product composition, pH, aw, temperature, composition of the atmosphere, presence and concentration of
preservatives, and storage time. Since aw and temperature are the most important factors for Aspergillus responses,
several approaches take into account such factors during the estimation of spoilage (failure) time5, 6. However, most
available models ignore factors such as food composition and structure, as well as potential microbial interactions
and the presence of antifungal agents7. In this work, the response of A. flavus in a food model system under different
conditions was obtained. Then, a probabilistic model that considers the combinations of studied factors (aw, pH, fat,
protein, cinnamon essential oil (CEO), and incubation temperature) was developed to predict the growth boundary
for A. flavus. The performance of the growth/no-growth and time-to-fail models are also presented, comparing the
obtained predictions with those obtained through traditional growth kinetics using the Gompertz equation.
2 Materials and Methods
2.1 Experimental design and inoculation procedure
A Box-Behnken design was used to evaluate the effect of different factors on A. flavus lag time and radial growth.
The studied variables were incubation temperature (15, 25, 35°C), casein concentration (0, 5, 10%), corn oil
concentration (0, 3, 6%), aw (0.900, 0.945, 0.990), CEO (0, 200, 400 ppm), and pH (3.5, 5.0, 6.5). Every
combination was evaluated by triplicate. For each experiment, model systems were prepared with a sucrose solution
to adjust aw, casein (Sigma Chemical Co., Steinheim, Germany), corn oil (Mazola, Monterrey, Mexico) and potato
dextrose agar (3.9 g /100 g solution). The pH was adjusted with 0.1 N HCl or NaOH solutions (Merck, Darmstadt,
Germany) as appropriate. In systems containing corn oil, Tween 20 at 2% (w/w) (Chemical Meyer, Tláhuac, Mex)
was added as emulsifier. Systems were poured into Petri dishes and depending on the experimental design, tested
CEO (Aromatic Chemicals Potosinos SA de CV, San Luis Potosi, Mex) was added. A. flavus (ATCC 18672),
obtained from the Food Microbiology Laboratory at the University of the Américas Puebla, was grown in PDA
(Becton Dickinson de Mexico SA de CV, Cuautitlan, Edo. Mex) at 25°C during 7 days. The culture surface was
washed and spores were recovered to obtain a suspension of §106 spores/mL. Petri dishes were inoculated with 5 μL
of the spore suspension and incubated at selected temperatures in sealed containers (avoiding anoxic conditions).
Mold growth was daily monitored and the diameter of the colonies was measured for 50 days.
2.2 Modeling of growth curves.
The mold growth curves were modeled by the Gompertz equation, Eq. (1), by fitting the model parameters using
non-linear regression.
ܮ݋݃
ቁൌ
ܣ
݁ݔ݌െܾെܿݐ
ሻሻ where ߣ ൌ ሺܾ െ ͳሻȀܿ and ߤൌ
ܣ
ሺܾ െ ͳሻȀߣ
( 1)
where: ȝ is the maximum growth rate (1/h), A is the maximum growth, Ȝ is the phase of adaptation (h), D is the
colony diameter (mm) at time t (h), D0 is the initial diameter of the colony (mm), and a, b, c are Gompertz’s eq.
parameters. In order to determine the parameters dependence on the evaluated factors (temperature, aw, % protein, %
fat, pH, CEO concentration (ppm)), a response surface design was utilized for obtaining the coefficients of a
polynomial model for the significant (p<0.05) variables and interactions.
87
C.E. Kosegarten et al. / Procedia Food Science 7 ( 2016 ) 85 – 88
2.3 Probabilistic Model and time-to-fail model.
After the incubation time (50 d), mold responses were classified as one for those cases where growth was detected
and zero for no-growth. The probabilistic model based on logistic regression is described in Eq. (2).
ୣ୶୮σȕ
ଵାୣ୶୮σȕ and l ሺሻ ൌ ሺሻ ൌ  ୮ሺ୶ሻ
ଵି୮ሺ୶ሻ(2)
where g(x) follows a polynomial model. The Hosmer-Lemeshow and Pearson tests were used for the binary logistic
regression. Finally, based on the time at which the mold growth starts, a polynomial model to describe the time-to-
fail was also fitted. The variables for this model should be normalized before the regression (standard value = value
- average / standard deviation). Statistical analyzes were performed at the 95% confidence level, using the Minitab
16 (Minitab Inc., State College, PA) software.
3. Results
The Gompertz equation adequately described the growth of A. flavus for different systems (R2 0.967-0.991). Fig. 1
shows the fit between observed and predicted values for three systems incubated at 25°C. For temperatures of 15
and 35°C similar fits were obtained. The observed growth rates are in the range of reported values for this mold8.
The highest growth was observed in the system without CEO, while in systems containing 200 ppm of CEO an
important reduction was observed. The effect of pH was also important; at pH 3.5 a lower growth at the same
conditions of temperature and concentration of CEO was observed than at pH of 6.5. The highest growth rate was
observed at 35°C, being significantly slower at 15°C, where only two systems presented growth. Regarding fat and
protein concentration, greater antifungal effects are observed for the protein content than for fat concentration.
Furthermore, in systems containing protein and/or fat, increasing growth rate was observed even in presence of
CEO, since the affinity among these components resulting in a reduction in the availability of CEO as antimicrobial
agent9. Obtained results indicate that at low values of aw the adaptation phase is larger, particularly at low
temperatures, showing a clear interaction between both factors. It was observed that the effect of the concentration
of CEO on Ȝ strongly depends on fat and protein composition of the system, particularly at low aw. For illustration
purposes, an example of model fitness and parameters dependence is shown in Fig. 1
a)
b)
c)
Fig 1. a) Growth curves of A. flavus at 25°C (lines represents Gompertz eq. fits); b) Growth rate (μ) at protein (10%), fat (0%), pH (5), CEO (200
ppm); c) Lag phase (Ȝ) at protein (10%), fat (5%), pH (5), CEO (200 ppm)
In order to estimate the probability of growth for the different combinations of factors, the binary logistic
regression was performed using the growth data of every studied system after 50 days of incubation. Figure 2a
shows the probability of growth for variations of pH and CEO concentration, while other factors held constant. It is
important to point out that at CEO concentrations higher than 180 ppm the probability of growth is low for systems
that not contain protein and/or fat at 25°C and aw of 0.945. Increments in pH may produce increases in growth
probability, although at higher CEO concentrations, above 200 ppm, no growth was observed.
0.00
0.02
0.04
0.06
Wateractivity
μ
T(°C)
0
100
200
300
Wateractivit y
ʄ
T(°C)
88 C.E. Kosegarten et al. / Procedia Food Science 7 ( 2016 ) 85 – 88
a)
b)
Fig. 2. a) Probability of growth A. flavus growth at T (25°C), Protein (4%), Fat (2%), aw 0.99; b) time to initiate A. flavus growth for a system
containing protein (5%), fat (0%), aw (0.99) and pH (3.5).
Regarding the time-to-fail model, the time to initiate growth is greater as the temperature is lowered and the
concentration of CEO increases (Fig. 2b). For constant values of aw (0.99), protein content (5%) and fat
concentration (4%), time-to-growth decreases for any CEO concentration and temperature, resulting in favorable
conditions for the development of tested microorganism. In systems that showed growth even with 200 ppm of CEO
the time-to-fail was more than 30 days at any studied temperature.
4. Conclusions
Temperature, aw, and CEO concentration are the studied factors that most affect A. flavus growth. It was observed
that for protein and fat concentration, as well as for pH, there is a range of possible combinations that may induce
growth. The amount of CEO necessary for growth inhibition strongly depends on such values. Primary and
secondary models adequately fitted to the experimental data. Probabilistic and time-to-fail models constitute another
way to determine the appropriate conditions for processing and accurately predict the probability and/or the time at
which A. flavus growth occurs, depending on the combination and level of the studied factors.
Acknowledgements
We acknowledge financial support from the National Council for Science and Technology of Mexico
(CONACyT) and Universidad de las Americas Puebla (UDLAP). Author Kosegarten acknowledges financial
support for his Ph.D. studies in Food Science from CONACyT and UDLAP.
References
1. McKellar RC, Lu X. A probability of growth model for Escherichia coli O157:H7 as a function of temperature, pH, acetic acid and salt. J
Food Protect 2001; 64:1922-1928.
2. Masana MO, Baranyi J. Growth/no growth interface of Brochothrix thermosfacta as a function of pH and water activity. Food Microbiol 2000;
17:485-493.
3. Polese P, De la Torre M, Spaziani M, Stecchini ML. A simplified approach for modelling the bacterial growth/ no growth boundary. Food
Microbiol 2011; 28:384-391.
4. Gómez-Ramírez C, Sosa-Morales ME, Palou E, López-Malo A. Aspergillus niger time to growth in dried tomatoes. Int J Food Microbiol
2013; 164: 23-25.
5. García D, Ramos AJ, Sanchis V, Marín S. Modelling the effect of temperature and water activity in the growth boundaries of Aspergillus
ochraceus and Aspergillus parasiticus. Food Microbiol 2011; 28:406-417.
6. Gougouli M, Koutsoumanis KP. Modelling growth of Penicillium expansum and Aspergillus niger at constant and fluctuating temperature
conditions. Int J Food Microbiol 2010; 140:254-262.
7. Gougouli M, Kalantzi K, Beletsiotis E, Koutsoumanis P. Development and application of predictive models for fungal growth as tools to
improve quality control in yogurt production. Food Microbiol 2011; 28:1453-1462.
8. Samapundo S, Devlieghere F, Geeraerd AH, De Meulenaer B, Van Impe JF, Debevere J. Modelling of the individual and combined effects of
water activity and temperature on the radial growth of Aspergillus flavus and A. parasiticus on corn. Food Microbiol; 2007:24:517-529.
9. Mellon JE, Dowd MK, Beltz SB. Effects of temperature and medium composition on inhibitory activities of gossypol-related compounds
against aflatoxigenic fungi. J App Microbiol 2013;115:179-186.
0.0
0.2
0.4
0.6
0.8
1.0
3.5
4.0
4.4
4.9
5.3
5.7
CEO(ppm)
Growth
Probability
pH
0
20
40
60
80
100
120
140
15 20 25 30 35
CEO
(ppm)
Tem p e r at u r e (°C)
<5d
>30d
30d
20d
10d
5d
This research hasn't been cited in any other publications.
  • Article
    A growth/no growth boundary model based on interpolated probabilities is presented in this paper. The boundary is defined as the loci where the probability of growth for a sample is equal to its chance of no growth, P (growth)=P (no growth). Brochothrix thermosphacta was used as a test micro-organism to collect data near the boundary of growth/no growth, in a pH/NaCl grid. Results showed a dramatic change in the probability of growth at the boundary with minor changes of pH or NaCl, and that the location of the boundary was affected by the inoculum level. A quadratic polynomial was used to describe the boundary of the growth region (interpolated P=0·5). The importance of growth/no growth boundary models as limits for the application of kinetic models is discussed.
  • Article
    To investigate the effects of temperature and medium composition on growth/aflatoxin inhibitory activities of terpenoids gossypol, gossypolone and apogossypolone against Aspergillus flavus and A. parasiticus. The compounds were tested at a concentration of 100 μg ml−1 in a Czapek Dox (Czapek) agar medium at 25, 31 and 37°C. Increased incubation temperature marginally increased growth inhibition caused by these compounds, but reduced the aflatoxin inhibition effected by gossypol. Gossypolone and apogossypolone retained good aflatoxin inhibitory activity against A. flavus and A. parasiticus at higher incubation temperatures. However, increased temperature also significantly reduced aflatoxin production in control cultures. The effects of the terpenoids on fungal growth and aflatoxin production against the same fungi were also determined in Czapek, Czapek with a protein/amino acid addendum and yeast extract sucrose (YES) media. Growth of these fungi in the protein-supplemented Czapek medium or in the YES medium greatly reduced the growth inhibition effects of the terpenoids. Apogossypolone displayed strong anti-aflatoxigenic activity in the Czapek medium, but this activity was significantly reduced in the protein-amended Czapek and YES media. Gossypol, which displayed little to no aflatoxin inhibitory activity in the Czapek medium, did yield significant anti-aflatoxigenic activity in the YES medium. Incubation temperature and media composition are important parameters involved in the regulation of aflatoxin production in A. flavus and A. parasiticus. These parameters also affect the potency of growth and aflatoxin inhibitory activities of these gossypol-related compounds against aflatoxigenic fungi. Studies utilizing gossypol-related compounds as inhibitory agents of biological activities should be interpreted with caution due to compound interaction with multiple components of the test system, especially serum proteins.
  • Article
    Individual and combined effects of aw and incorporation of selected concentrations of Mexican oregano essential oil on the time to growth (TTG) of Aspergillus niger intentionally inoculated into dried tomatoes were studied during storage at 25°C for 100days. For aw 0.96, 1000ppm of Mexican oregano essential oil inhibited A. niger growth during 100days, whereas 500ppm were sufficient at aw 0.91 and 250ppm for tomatoes with aw 0.78. A. niger growth was evident at different incubation times depending on tested tomato aw and concentration of essential oil; these data were utilized to model TTG. Regression analysis revealed good agreement between experimental and predicted data with a correlation coefficient higher than 0.98. Analysis of mold growth data through TTG models makes possible to include observations detected as no growth and can be utilized to predict mold time to growth for specific preservation factor combinations or to select preservation factor levels for an expected shelf-life based on A. niger growth.
  • Article
    The effect of storage temperature (0-40 °C) and inoculum size (10¹-10⁵ spores) on the mycelium growth kinetics of 12 fungal species on yogurt were monitored. A cardinal model with inflection (CMI) was used to describe the effect of temperature on the growth rate (μ) and the lag time (λ) of each isolate. Significant differences on the temperature dependence of the mycelium growth between the tested species were observed. Depending on the strain, the estimated minimum, optimum and maximum temperature parameters for μ (T(min), T(opt), T(max)) ranged from -7.6 to 9.6, 19.5 to 37.8 and 29.8 to 46.9 °C, respectively. Only λ was found to be affected by the inoculum size and a linear relation between Ln (λ) and Log (inoculum size) was revealed. The inoculum level did not influence the values of T(min), T(opt) and T(max) for λ. Based on the above observations, the combined effect of inoculum size and temperature on λ was modeled using a modified CMI. The parameter λ(opt) (λ at optimum conditions) was expressed as a function of the inoculum size. Validation studies showed a good performance of the developed models. The application scheme of the models for improving fungi control in yogurt productions is discussed.
  • Article
    The aim of this work was to model the growth of Aspergillus parasiticus and Aspergillus ochraceus, both mycotoxin producers, near to the growth/no growth boundaries and validate those models in sterile maize grain, peanuts and coffee beans. Malt extract agar was adjusted to six different water activities: 0.93, 0.91, 0.89, 0.87, 0.85 and 0.80. Plates were incubated at 10, 15, 20, 25, 30, 37 and 42 °C. For each of the 42 conditions, 10 Petri dishes were inoculated. Both kinetic and probability models were applied to colony growth data. The results of the present study indicate that the developed probability modelling approach could be satisfactorily employed to quantify the combined effect of temperature and water activity on the growth responses of A. ochraceus and A. parasiticus. However, validation of kinetic results led to poor goodness of prediction. In this study, the validation samples were placed near to the expected boundaries of the models in order to test them under the worst situation. Probability of growth prediction under extreme growth conditions was somewhat compromised, but it can be considered acceptable.
  • Article
    A simplified growth/no growth (G/NG) model, conceptually derived from the Gamma model and making direct and explicit use of growth limits of bacteria through a normalization constant (η), was proposed. The η value, which quantifies the product of the cardinal optimal distances for growth probability, is a species-independent constant. This is of importance when experimental data is missing or insufficient. The simplified G/NG model was developed including the effect of temperature, pH and water activity, and was expanded incorporating the preservative effects. As a practical application, the model was investigated for its ability to describe published data. The successful validation of the simplified G/NG model is discussed in regard to its potential applicability as a first estimate method for the development of safe food products.
  • Article
    The growth of Penicillium expansum and Aspergillus niger, isolated from yogurt production environment, was investigated on malt extract agar with pH=4.2 and a(w)=0.997, simulating yogurt, at isothermal conditions ranging from -1.3 to 35 degrees C and from 5 to 42.3 degrees C, respectively. The growth rate (mu) and (apparent) lag time (lambda) of the mycelium growth were modelled as a function of temperature using a Cardinal Model with Inflection (CMI). The results showed that the CMI can describe successfully the effect of temperature on fungal growth within the entire biokinetic range for both isolates. The estimated values of the CMI for mu were T(min)=-5.74 degrees C, T(max)=30.97 degrees C, T(opt)=22.08 degrees C and mu(opt)=0.221 mm/h for P. expansum and T(min)=10.13 degrees C, T(max)=43.13 degrees C, T(opt)=31.44 degrees C, and mu(opt)=0.840 mm/h for A. niger. The cardinal values for lambda were very close to the respective values for mu indicating similar temperature dependence of the growth rate and the lag time of the mycelium growth. The developed models were further validated under fluctuating temperature conditions using various dynamic temperature scenarios. The time-temperature conditions studied included single temperature shifts before or after the end of the lag time and continuous periodic temperature fluctuations. The prediction of growth at changing temperature was based on the assumption that after a temperature shift the growth rate is adopted instantaneously to the new temperature, while the lag time was predicted using a cumulative lag approach. The results showed that when the temperature shifts occurred before the end of the lag, they did not cause any significant additional lag and the observed total lag was very close to the cumulative lag predicted by the model. In experiments with temperature shifts after the end of the lag time, accurate predictions were obtained when the temperature profile included temperatures which were inside the region of growth, showing that the assumption that mu is adopted instantaneously to the current temperature is concrete. In contrast, for scenarios with temperatures close or outside the growth region the models overestimated growth, indicating that fungi were stressed by this type of temperature shifts. The present study provides useful data for understanding the behavior of P. expansum and A. niger at dynamic temperature conditions while the developed models can be used as effective tools in assessing the risk of fungal spoilage and predicting shelf life of foods.
  • Article
    Data accumulated on the growth of Escherichia coli O157:H7 in tryptic soy broth (TSB) were used to develop a logistic regression model describing the growth-no growth interface as a function of temperature, pH, salt, sucrose, and acetic acid. A fractional factorial design with five factors was used at the following levels: temperature (10 to 30 degrees C), acetic acid (0 to 4%), salt (0.5 to 16.5%), sucrose (0 to 8%), and pH (3.5 to 6.0). A total of 1,820 treatment combinations were used to create the model, which correctly predicted 1,802 (99%) of the points, with 10 false positives and 8 false negatives. Concordance was 99.9%, discordance was 0.1%, and the maximum rescaled R2 value was 0.927. Acetic acid was the factor having the most influence on the growth-no growth interface; addition of as little as 0.5% resulted in an increase in the observed minimum pH for growth from 4.0 to 5.5. Increasing the salt concentration also had a significant effect on the interface; at all acetic acid concentrations, increasing salt increased the minimum temperature at which growth was observed. Using two literature data sets (26 conditions), the logistic model failed to predict growth in only one case. The results of this study suggest that the logistic regression model can be used to make conservative predictions of the growth-no growth interface of E. coli O157:H7.
  • Article
    A full factorial design of five temperatures (16, 22, 25, 30 and 37 degrees C) and seven a(w) values between 0.801 and 0.982 was used to investigate the growth of the two major aflatoxin producing Aspergillus isolates on corn. The colony growth rates (g, mmd(-1)) and lag phases (lambda, d) were estimated by fitting a flexible primary growth model. Subsequently, secondary models relating g or lambda to a(w) or temperature or a(w) and temperature combined, were developed and validated by using independently collected data. The Gibson and linear Arrhenius-Davey model describing the individual effects of a(w) or temperature on g or lambda proved an adequate predictor of either growth parameter. Based on the validation criteria, a quadratic polynomial function proved to be more suitable than a Gaussian function or extended Davey model for describing the combined effect of a(w) and temperature on g or lambda. Both isolates studied had optimum growth temperatures of approximately 30 degrees C. No growth was observed for both isolates at a(w) 0.801, growth only occurring at 25 and 30 degrees C at a(w) 0.822. Significant interaction between a(w) and temperature on g and lambda was observed for both isolates. The developed models can be applied in the preservation of corn and the development of models that incorporate other factors important to mould growth on corn.