The convective drying of apples from two different cultivars, Golden Delicious and Granny Smith, in a range of temperatures from 30 to 60 • C is studied. Some chemical and physical properties were determined fresh after drying: moisture, acidity and sugar content, color, and textural attributes, in order to evaluate the effect of drying and drying temperature on these properties. Furthermore, the drying kinetics were studied in terms of thin layer models and also by means of the Fick's equation of diffusion, and some mass transfer properties were evaluated, such as effective moisture diffusivity and convective mass transfer coefficient. It was concluded that drying decreased both acidity and sugar content for both apple cultivars. Color was significantly affected by drying, resulting in high values of the total color difference, between 19.43 and 25.04. Drying turned the products less hard and less cohesive. Furthermore, it was found that mass diffusivity increased with temperature following an Arrhenius type function, where D e 0 was 5.4621 × 10 −4 and 1.8401 × 10 −4 m 2 /s, and E was 35.3 and 32.8 kJ/mol, respectively, for cultivars Golden Delicious and Granny Smith.

In the present work, the effect of drying was evaluated on some chemical and physical properties of apples, and the functions were modelled using feed-forward 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 1.4×10−9 m2/s at 60°C, and so did the mass transfer coefficient, increasing from 3.7×10−10 m/s at 30°C to 7.4×10−9 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.

No abstract