Abstract

Drying is among the beneficial food preservation strategies and this method ensures food products last before they reach consumers. The most used drying method is direct drying under the sun. However, in this method, the negative effects of the external environment damage food products. Recently, solar drying systems have been the main subject of much research as they have been protecting food from the negative effects of the external environment. In this study, a solar drying system (SD), which have a drying chamber with different structure, was used for drying mushroom. At the same time, mushroom slices were dried under open sun (OSD) for observing the performance of drying system. Drying rate (DR) and moisture ratio (MR) values were determined from the experiments. In addition, the MR values obtained from the experiments were estimated by 6 different mathematical models and 6 different machine learning algorithms. According to the results of the experiments, the drying time of the mushroom slices using SD was approximately 12.4 hours less than the drying time under open sun. The best convergence in the results gathered from the mathematical models is Sripinyowanich and Noomhorn and Hii et al. models for SD and OSD, respectively. The best estimation for MR values was realized by the Multilayer Perception algorithm for both drying methods.

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