In this study, dynamic optimization of heat treatment for reducing the water loss in fruit during storage was
investigated using intelligent approaches. Over a temperature range from 15.C to 40.C, the control process was divided into
l steps (l = 6). The dynamic change in the rate of water loss as affected by temperature was first identified using neural
networks, and then the optimal combination of the l–step setpoints for temperature that minimized the rate of water loss was
searched for through simulation of the identified model using genetic algorithms. Two types of optimal values, a single
application of heat stress and a double application of heat stress, were obtained under the range of 15.C < T < 40.C. The
length of each step was 24 h. The former treatment is useful for short–term storage, and the latter is useful for comparatively
long–term storage. With the single heat treatment, the temperature first rises to the highest level (40.C), which is maintained
over a period of 24 h, and then suddenly drops to the lowest level (15.C). In particular, the sudden drop in temperature from
the highest level to the lowest level provided lower values of the rate of water loss than maintaining the temperature constantly
at the lowest level throughout the control process. These results suggest that application of heat stress to fruit is effective in
maintaining freshness of fruit during storage.