gropin

Name:

GroPIN

Company / Institution:

Agricultural University of Athens

Development Partners:

Contact person:

Panagiotis N. Skandamis, PhD
Assistant Professor

Link:

http://www.aua.gr/psomas/gropin

Designed for:

Teachers
Researchers
Students
Food business operators
All subscribers and partners

Software modules:

Databases
Growth module
Fitting tool for growth
Inactivation module
Fitting tool for inactivation
Growth / no growth interface
Risk assessment module

Media covered:

Acidified sauces
All foods (generic)
Ambient stable sauces
Beef (lean)
Beef carcass
Beef gravy
Beef meat
Bologna
Brain Heart Infusion agar
Brain Heart Infusion broth
Broths & Foods (ComBase data)
Cheese salad (pH 4.5)
Cold-filled acid dressings and sauces (mayonnaises, dressings, sauces, diluted vinegar, cider)
Cooked cured meat products
Cooked ham
Cooked meat model (in mBHI)
Cooked pork ham
Dairy
Deli salads (Fava-, Pepper-, Cheese salad)
Egg salad
Eggplant salad (homemade)
Fish: Red mullet, gilthead seabream, boque
Food products
Frankfurters
Fresh milk
Glucose-Mineral salts
Grape berry of "Red Globe" cultivars
Ground beef 75%
Ground meat (pork & beef)
Ground pork
Iceberg lettuce (fresh cut)
Intermediate Moisture Foods
LA-W
Lettuce salad
Liquid culture medium
Malt extract agar
Mayonnaise based salads
Meat
Meat (Cooked)
Melons
Milk (dried, dehydrated non fat)
modified BHI
Modified meat
MRS broth
Nitrogen limited media
nutrient agar surface
Nutrient broth
Ox muscle
Oxoid tryptone soya broth
Pasta salad
Pasteurized meat products
Pasteurized milk
Pectin-NH4Cl-MgSO4
Pig carcass
Pistachios
Pork (fresh)
Potato Dextrose Agar
Poultry
Poultry (Cooked)
Poultry (Raw)
Salmon fillets
Sausage (beef)
Seafood
Seafood salad
Sealed cultures
Sliced vacuum-packaged cooked cured meat product
Soft cheese (Mizithra)
Soy milk
Spicy cheese salad (pH 4.2)
Traditional custard
Tris-HCl
Tryptic Soy Agar Plus Yeast extract
Tryptic Soy Broth
Tryptone-Peptone-Yeast-C
TSB+G
Various
W-W
Yeast Nitrogen Broth

Microorganisms covered:

22 Pathogens
50 Spoilers

Growth / Inactivation Factors covered:

Temperature
pH
aw (water activity)
Salt
Lactic acid
Other orgnaic acids
CO2
Acetic acid
Ascorbic acid
Citric acid
Diacetate
Fructose
Gelatin
Irradiation dose
Na-Lactate
Sodium nitrite
nitrites
NO2
Oleuropein
Phenols
Salt+sugar
Water phase salt
Bean Oil
Benzoic acid
Citric acid
Disaccharide
Hexoses
NaCl
Na-Diacetate
Na-Lactate
Sodium nitrite
O2
Essential oil
Ultra High Pressure
Phenols
Sorbic acid
SoyBean Protein
Sodium pyrophosphate
Sucrose
Undissociated acetic acid
Undisociated Nitrous acid

Modeling approach:

Deterministic
Probabilistic, variability taken into account

Description:

An integrated tertiary model called GroPIN is developed in-house using Visual Basic for Applications. The application may serve as a user-friendly and highly transparent predictive modeling data base for kinetic (growth or inactivation) and probabilistic models. It also offers the flexibility of interactive options in selecting the graphical and numerical simulation of models. An unlimited number of mathematical models can be introduced into the database via equation editor, as compared with other applications, where only a limited number of equations are already embedded into the source code and are not (at least not easily) updatable or expandable.
The current version of GroPIN has a total of 490 published models for the behavior of 22 pathogens and 50 spoilage organisms, including spoilage and mycotoxigenic fungi, bacteria and yeasts in various foods of plant (e.g., fresh-cut salads, deli salads, berries, juices, etc.) or animal origin (meat and meat products, dairy products). The impact on microbial behavior of a variety of critical and commonly encountered intrinsic (preservatives, organic acids in total or undissociate/dissociate form, salt, aw, nitrates, etc.) and extrinsic (temperature, CO2, pressure, anaerobic conditions) factors is accounted for by the models registered in GroPIN up to date. The microbial responses modeled (i.e., dependent variables) include the maximum specific growth rate, the death rate, the lag phase duration, maximum population density, time to X-log reduction/growth, D-values and the probability of growth.
A search engine has been established for locating and selecting the model of interest. Then the user may select variables and assign values for each variable though list boxes or by direct typing.
The simulation of the selected model can be displayed as Response Surface-Contour Plot, Time to x log Response Surface-Contour Plot, growth or inactivation curve, as well as 2D growth/no growth (probabilistic) interface with potential illustration of up to 3 interfaces (i.e., three levels of the 3rd variable). The following model categories have been included: (1) Probabilistic models; (2) Growth models; (3) Inactivation-survival models, and; (4) Gamma (Cardinal) Models with interactions (“” term based on “” and “” functions, according to Augustin and Carlier., 2000; Le Marc et al. 2002). All kinetic models, including growth or inactivation, plus gamma models with interactions, can be simulated under both static and dynamic conditions. The 271 registered models include 231 growth models, 46 inactivation models, 64 probability of growth models and 18 gamma models with interaction terms. The user can use the available models as a basis for setting performance-, process- or product-criteria, as well as to evaluate the compliance of a product with microbiological criteria regulation. The integrated models constitute a special category which combines selected models from a series of 26 growth models, 14 inactivation models and 32 probability of growth models under dynamic conditions. The final graphical representation is the integral of the selected models.
Moreover, a series of computational algorithms that rely on repeated random sampling have been applied in order to estimate the risk in food products (Monte Carlo simulation).
The spirit of the software stems from similar initiatives, such as SymPrevius and COMBASE modeling toolbox. The major innovative features of this software in relation to the state-of-the art are the user-friendliness, the updatable character by the user, the simplicity and functionality (including interactive options) of outputs and the inclusion of all major predictive modeling classes.