OPTIMIZATION OF DRYING KINETICS OF PANEER WITH LOW PRESSURE SUPERHEATED STEAM USING AN ARTIFICIAL NEURAL NETWORK

Paneer is an important dairy product and is used to prepare different culinary dishes. It is highly perishable in nature at ambient conditions and its shelf life is very low. Moreover at high temperature, product develops a sour smell. bitter taste and also being sparsely covered with moulds. Therefore, here is need to develop some preservation technique to enhance the shelf life, Drying can be one of the methods to increase shelf life of paneer. Due to its numerous advantages such as less energy wastage, high heat transfer coefficient, no oxidative changes during drying, no resistance to mass transfer at the surface, higher porosity, Low pressure superheated steam drying (LPSSD) is used as a hybrid drying to dry food products. The neural network model consisted was developed of an input, a hidden and an output layer. The input layer has two nodes, which corresponded to two processing conditions or independent variables: Time of drying and weight changed with corresponding time. The output layer consisted of three neurons or dependent variables, representing the moisture content (%db), drying rate (dm/dt) and moisture ratio (MR). The correlation coefficients were greater than 0.98 in all cases. For all combined data set with superheated steam, the R-2 were found 0.9975, 0.9934 and 0.9983 for moisture content, drying rate and moisture ratio. Whereas For all combined data set without superheated steam, the R-2 were found 0.9991, 0.9846and 0.9991 for moisture content, drying rate and moisture ratio.