Lynne Wong's PhD thesis

Lomauro et al. (1985) found that with 163 food materials including fruits, vegetables,
spices and starchy foods, the three-parameter GAB equation fits better for most of these
food materials than some two-parameter equations.
The BET model (Brunauer et al., 1938) represents a fundamental milestone in the
interpretation of multilayer sorption isotherms, particularly for type II and III adsorption.
It is the most popular isotherm but it is valid only from 10 – 50% relative humidity
(Labuza, 1968). Many research workers modified the BET equation to give a good fit up
to 90% relative humidity (Dincer & Esin, 1996).
The semi-empirical Halsey equation (Halsey, 1948) has also demonstrated its suitability to
describe the experimental isotherms of foods, and is representative of 69 different products
(Bosquet et al., 1978).
The Smith model (Smith, 1947) was found good for describing the sorption isotherm of
biological materials such as starch and cellulose; and the Henderson model (Henderson,
1952) for cereal grain. Day and Nelson (1965) modified the Henderson equation to
describe the sorption behaviour of wheat up to 70% relative humidity. The Chung-Pfost
model was good for grain over the 20 – 90% relative humidity range.
In general, the GAB, BET, Halsey, Henderson and Oswin models had been applied with
success to high sugar-containing foods (Tsami et al., 1990, Maskan and Göğüs, 1997, and
Kaymak-Ertekin and Gedik, 2004).
In this study, 14 of the isotherm models listed in Table 5.1 (all except the last two) and the
modified Chung-Pfost and modified GAB models with a temperature term incorporated in
their respective original model (Table 5.2) have been applied to predict the EMC of the
fibre components derived from the sugar cane plant.
5.3.3 Choice of isotherm models
The usefulness of a sorption model depends on the predetermined objectives set by the
user. For instance, if the user is interested in predicting shelf life, a model with good
agreement to the experimental data as opposed to one which fits well with theoretical
considerations will be required. The simplicity of a model can also influence the choice,
since a model with a lower number of parameters will have an improved usability in that
computation times will be lower (Bosquet et al., 1978).
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