Convective Drying of Apples: Kinetic Study, Evaluation of Mass Transfer Properties and Data Analysis using Artificial Neural Networks

摘要

In the present work, the effect of drying was evaluated on some chemical and physical properties of apples, and the functions were modelled using feedforward artificial neural networks. The drying kinetics and the mass transfer properties were also studied. The results indicated that acidity and sugars were significantly reduced by drying. Regarding colour lightness decreases, whereas redness and yellowness increased. As for texture, the dried samples were softer and less cohesive as compared to the fresh ones. Mass diffusivity increased with temperature, from 4.4-10~(-10) m2/s at 30°C to 1.4x10?9 m2/s at 60°C, and so did the mass transfer coefficient, increasing from 3.7In the present work, the effect of drying was evaluated on some chemical and physical properties of apples, and the functions were modelled using feedforward artificial neural networks. The drying kinetics and the mass transfer properties were also studied. The results indicated that acidity and sugars were significantly reduced by drying. Regarding colour lightness decreases, whereas redness and yellowness increased. As for texture, the dried samples were softer and less cohesive as compared to the fresh ones. Mass diffusivity increased with temperature, from 4.4x10~(-10) m2/s at 30°C to 1.4x10~(-9) m2/s at 60°C, and so did the mass transfer coefficient, increasing from 3.7x10~(-10) m/s at 30°C to 7.4x10~(9) m/s at 60°C. As to the activation energy, it was found to be 34 kJ/mol. Neural network modelling showed that all properties can be correctly predicted by feed-forward neural networks. The analysis of the networks’ behaviours input layer weight values also shows which properties are more affected by dehydration or more dependent on variety. Keywords: activation energy, colour, convective drying, diffusion coefficient, dried apple, mass transfer coefficient, neural network modelling, texture10~(-10) m/s at 30°C to 7.4x10?9 m/s at 60°C. As to the activation energy, it was found to be 34 kJ/mol. Neural network modelling showed that all properties can be correctly predicted by feed-forward neural networks. The analysis of the networks’ behaviours input layer weight values also shows which properties are more affected by dehydration or more dependent on variety.