Example of simulation under
fluctuating temperatures
Decimal Separators selection
The metric system of some countries (Greece, Italy etc.) includes the comma (,) as a
decimal separator whereas some other countries (English-speaking) use the comma
to separate sequence of three digits (before the decimals). The metric system of
these countries includes the period (.) as decimal separator.
The decimal separator can be adapted manually to the metric system of each country
by following the instructions below (Microsoft Excel 2007):
Office Button/ Excel Options/Advanced/ Unclick the System separators box:
In TextBox: Decimal Separator, the user can change comma (,) with period (.).
In TextBox: Thousands Separator, the user can change period (.) with comma (,).
By pressing OK command button, the decimal separator changes automatically.
1. Initial page
1. Define initial
contamination
level
2. Initial contamination level
2. Select level and
press “OK”
3. Select model
(Microorg.-food)
4. Select model
and press “Set
data to Launch
area”
3. Model selection
5. Press Return
6. Import data (time-T) from
loggers (XL files from your
PC). Select file and press
open
Some delay may occur!!!
4a. Simulation under dynamic T conditions
Introducing data from loggers
7. See a preview of the
selected profile and
approve it by pressing
“Proceed”
4b. Simulation under dynamic T conditions
Preview of profile
9. Press return for
9. main
Pressmenu
return for
the
the main menu
8. Click for
graphical
simulation of the
model
4c. Simulation under dynamic T conditions
Graphical representation of the model vs T
Example of simulation under constant temperature(s)
1. Two options:
1a. Prediction for one temperature
1b. Comparative predictions for up to 4 temperatures
1a. Steps 1-3 are common for dynamic and constant temperatures
6. Enter hours and
temperature of incubation
and press “OK”
4a. Simulation under constant temperature
Entering hours and temperature
9. Press return for
the main menu
8. Click for
graphical
simulation of the
model
4b. Simulation under constant temperature
Graphical representation of the model vs T
1b. Steps 1-3 are common for dynamic and constant temperatures
6b. For each temperature
press “Go” and after all 4
temperatures press
“Continue”
Some delay may occur!!!
6a. Enter hours and multiple
temperatures of incubation
and press “OK”
4a. Simulation under constant temperature
Entering hours and multiple temperatures
8. Press return for
the main menu
4b. Simulation under constant temperature
Graphical representation of the model vs T
LITERATURE FOR FURTHER EXPLOITATION
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Bovill, R. A., Bew, J., Baranyi, J., 2001. Measurements and predictions of growth for
Listeria monocytogenes and Salmonella during fluctuating temperature II. Rapidly
changing temperatures. International Journal of Food Microbiology 67, 131–137.
Dalgaard, P., Buch, P., Silberg, S., 2002. Seafood Spoilage Predictor—development
and distribution of a product specific application software, International Journal of
Food Microbiology 73, 343– 349.
Julie E. Jones,J. E., Walker, S. J., 1993. Advances in modeling microbial growth,
Journal of Industrial Microbiology 12, 200-205.
Le Marc, Y., Plowman, J., Aldus, C.F., Munoz-Cuevas, M., Baranyi, J., Peck, M.W.
2008. Modelling the growth of Clostridium perfringens during the cooling of bulk meat.
International Journal of Food Microbiology 128, 41-50.
Mataragas, M., Drosinos, E. H., Vaidanis, A., Metaxopoulos, I., 2006. Development of
a predictive model for spoilage of cooked cured meat products and its validation
under constant and dynamic temperature storage conditions. Journal of Food
Science, 71 (6), 157-167.
Microsoft, 2010. Visual Basic Developer Center. http://msdn.microsoft.com/enus/vbasic/default.aspx (retrieved August, 2010).
McMeekin, T.A., 2007. Predictive microbiology: Quantitative science delivering
quantifiable benefits to the meat industry and other food industries. Meat Science 77,
17-27.
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Oscar, T.P., 2002. Development and validation of a tertiary simulation
model for predicting the potential growth of Salmonella typhimurium on
cooked chicken. International Journal of Food Microbiology 76, 177– 190.
Pin, C., Avendaño-Perez, G., Cosciani-Cunico, E., Gómez, N., Gounadaki,
A.S., Nychas, G.-J., Skandamis, P., Barker, G. 2010. Modelling Salmonella
concentration throughout the pork supply chain by considering growth and
survival in fluctuating conditions of temperature, pH and aw, International
Journal of Food Microbiology (in press).
Rosso, L., Lobry, J.R., Bajard, S., Flandrois, J.P. 1995. Convenient model
to describe the combined effects of temperature and pH on microbial
growth. Applied and Environmental Microbiology 61, 610-616.
Shepherd, R. (Ed.), 2004. Excel VBA Macro Programming, McGraw-Hill
Education – Europe.
Van Impe, J., Nicolai, B.M., Martens, T., De Baerdemaeker, J., Vandewalle,
J., 1992. Dynamic Mathematical Model To Predict Microbial Growth and
Inactivation during Food Processing, Applied and Environmental
Microbiology 58 (9), 2901-2909.
Zwietering, M.H., de Koos, J.T., Hasenack, B.E., de Wit, J.C., van’t Riet, K.
1991. Modeling of bacterial growth as a function of temperature. Applied
and Environmental Microbiology 57, 1094-1101.