A method for reducing the energy consumption
during freeze drying has been proposed. Water activity variation with time has been explored for button mushroom
(Agaricus bisporus L.). The effect of primary and secondary
drying temperatures on water activity was found significant
(p < 0.05) as compared to sample thickness and pressure. The
economics of the process showed that an energy reduction up
to 34.9% could be achieved if the final water activity was
constrained at 0.6. Artificial neural network tool has been used
to develop a model for predicting the water activity precisely
for a given combination of time, initial moisture content, vacuum pressure, sample thickness, and primary and secondary
drying temperatures. The model-predicted values were found
to be in good agreement (R = 0.97) with the experimental data.
The model developed is expected to extend its aid in energy
reduction for freeze drying of other food products.

• Artificial Neural Network Modeling of Water Activity: a Low Energy Approach to Freeze Drying