抄録
Storage temperature for fruits is usually maintained constant at low level. In recent years, however, there has been much interest in heat stress application that reduces the quality loss of fruit during storage. In this study, dynamic optimization of heat stress application for reducing the water loss and respiration in fruit during storage was investigated using intelligent approaches. Over a temperature range from 15 to 40℃, 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 set points for temperature that minimized the rate of water loss (or respiration) was searched for through simulation of the identified model using genetic algorithms. A double heat stress application, such as Topt={40, 15, 40, 15, 15, 15℃}, was obtained as an optimal value under the range of 15 ≤T≤40℃. The length of one step is 24 hours. With a single heat stress application, the temperature first rises to the highest level (40℃), which is maintained over a period of 24h, and then suddenly drops to the lowest level (15℃). This operation repeats two times in the double heat stress application. 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 the optimal combination of high temperature (heat stress) and low temperature allows to maintaining freshness and improving the quality of fruit during storage.
