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”

5. Press Return

3. Model selection

6. Import data (time-T) from loggers (XL files from your PC). Select file and press openSome 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 conditionsPreview of profile

8. Click for graphical simulation of the model

4c. Simulation under dynamic T conditionsGraphical representation of the model vs T

9. Press return for the main menu9. Press return for

the main menu

Example of simulation under constant temperature(s)

1. Two options:1a. Prediction for one temperature

1b. Comparative predictions for up to 4 temperatures

6. Enter hours and temperature of incubation and press “OK”

4a. Simulation under constant temperature Entering hours and temperature

1a. Steps 1-3 are common for dynamic and constant temperatures

8. Click for graphical simulation of the model

4b. Simulation under constant temperature Graphical representation of the model vs T

9. Press return for the main menu

6a. Enter hours and multiple temperatures of incubation and press “OK”

4a. Simulation under constant temperature Entering hours and multiple temperatures

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!!!

4b. Simulation under constant temperature Graphical representation of the model vs T

8. Press return for the main menu

LITERATURE FOR FURTHER EXPLOITATION

• 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/en-us/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.

• 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.