How can I be sure (other than the R-squared value) that the value I obtain for the GAB and BET constants are correct (especially the mono-layer moisture value)? what is the reference especially if the product has many ingredients such as a starch base (cereals) along with meat protein.
Dear Shefali,
independant of your R-squared value you can never be sure that the calculated constants are correct. Esp. for GAB this is curve fitting, and there is a physical/chemical explaination why one parameter is changing more rapidly than an other in a certain range of water activity. The concept (like mono layer moisture value) works more or less for sigmoid curves and pure substances, like lactose.
But even for these simple substances you can have spray dried samples that are partly amorphous and start to recrystallize during water adsorption. Therefore you have an overlapping of two sorption isotherms (amorphous and crystalline), which are partially identical (high water activities) and partly sufficiently different. This becomes even worse, when you have complex food stuffs.
In my opinion it is good to have concepts that explain why and how things happen (GAB, BET), but there is no true constant (like in physics), but there are good measurements and good curve fitting, that can be used for interpolations and to some extend for extrapolations..
Harald
1st of all non-linear regression analysis is performed for the data and then to find out model constants. The extent of fitting of models is evaluated and compared based on at least three statistical criteria namely coefficient of regression (R2), root mean square error (RMSE), and reduced-χ2. Generally, low values of the correlation coefficient, high values of RMSE and reduced-χ2, means the model is not able to explain the variation in the experimental data. It is also important that a single statistical parameter cannot be used to select the best model and the model must always be evaluated based on multiple statistical criteria.
I agree with Dr Ahmed....Chi-sqare and RMSE values can be used to evaluate the adequacy of model fit.
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