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An improved experimental and regression methodology for sorption isotherms
Author(s) -
Quirijns Elisabeth J,
van Boxtel Anton JB,
van Loon Wilko KP,
van Straten Gerrit
Publication year - 2004
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.1773
Subject(s) - sorption , regression analysis , water activity , chemistry , experimental data , thermodynamics , linear regression , regression , range (aeronautics) , data point , biological system , mathematics , statistics , adsorption , materials science , water content , organic chemistry , physics , engineering , geotechnical engineering , composite material , biology
Sorption isotherms of corn and starch cylinders with immobilised catalase are experimentally determined at different temperatures for use in drying models in optimal control studies. This application of the sorption isotherm requires an accurate prediction of the sorption data at different temperatures for the low water activity range. The GAB equation is used for the prediction of the sorption isotherms. Two major problems are encountered by employing standard procedures, ie prediction of sorption at a w < 0.11 and sensitivity of the GAB parameters to the applied data range. An improved methodology is developed, consisting of extending the standard experimental procedure with additional data points in the low water activity range and changing the criterion in the regression procedure in the sum of squares, which is weighed by the variance of the experimental data. The new methodology leads to accurate, consistent and physically relevant parameters of the GAB equation, which are independent of the applied data range in the regression analysis and which result in accurate predictions of the sorption behaviour at low water activity. The sorption data at different temperatures at low water activity can be predicted in the best way with parameters obtained after direct regression based on weighed SSQ. Copyright © 2004 Society of Chemical Industry