Application of artificial neural network to moisture ratio anticipation and investigation of sense and nutrition properties of tomato during dried

In this research, thin-layer drying of tomato slices was simulated via a laboratory scale hot air dryer. The drying process was carried out at two different temperatures (60 and 70 oC). Drying data were fitted to 8 drying kinetic models. The goodness of fit was determined by means of R2, RMSE and χ2, it was concluded that Logarithmic model represented the best performance. Also, physicochemical properties of tomato slice such as shrinkage, acidity, pH and color were determined during drying. The result indicated that drying air temperature had a significantly affect on the color change of tomato slices. Furthermore, in this research, tomato slice favorite is evaluated by sensory evaluation. In this test sense qualities as color, aroma, flavor, appearance and chew ability (tissue brittleness) are considered. The results revealed that air temperature had a significant effect on the appearance (P<0.01). The results showed that perceptron neural network with logsig activation function as a goodness activation function can be estimated moisture ratio with R2 value 0.996.