Identification and optimal control of tomato respiration through pre-storage heat treatment

Suitably applied pre-storage heat treatment (35 similar to 45 degrees C) can maintain better fruit quality by delaying the ripening process of fruit during storage. The respiration rate of treated fruits remains lower compared with non-treated fruits during subsequent storage. In this paper, the temperature was the control input, varying from 5 similar to 40 degrees C, and the quality changes of tomato fruit were indicated by fruit respiration rate, which was monitored as the output. The purpose was to minimize the respiration by the temperature (e.g. pre-storage heat treatment). An objective function was given by the reciprocal number of the average value of the respiration. The dynamic change in the respiration, as affected by temperature, was first identified using neural networks. The neural-network model showed good agreement with the observed responses of respiration. Optimal control of the respiration was then carried out, aiming to reduce the respiration as much as possible. For convenience, the control process was divided into 1-steps. The optimal I-step set points of temperature which maximized the objective function were obtained using genetic algorithms. Optimal values were obtained under several different constraints of the temperature.