Modeling moisture sorption isotherms in roasted green wheat using least square regression and neural-fuzzy techniques

Roasted green wheat at moisture content from 0.052 to 0.25 (decimal d.b.) and temperatures from 25 to 43 degrees C was used to model moisture sorption isotherms using conventional non-linear least square regression (NLR) and neural-fuzzy (NF) techniques. The results showed that neural-fuzzy techniques provided a better fit than conventional least square regression with: R-2=0.99 and 0.97, RMSE=0.01 and 0.0038, E%=1.01 and 5.9 and SSE=0.0008 and 0.009, for NF and NLR techniques, respectively. Differential enthalpy decreased from 477.9 kJ/kg at 0.052 (decimal d.b. mc) to 44.7 kJ/kg at 0.25 (decimal d.b. mc) and entropy decreased from 1.16 kJ/kg K at 0.052 (decimal d.b. mc) to 0.014 kJ/kg K at 0.25 (decimal d.b. mc). A linear plot between enthalpy and entropy showed that compensation exists. The isokinetic temperature T-beta was 376.13K which was larger than the harmonic mean temperature T-hm=307.31 K, showing that the water sorption was entropy-driven. The free energy change Delta G was positive (+38.42 kJ/kg) indicating a non-spontaneous water sorption process. (C) 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.