Kabak Dilimlerinin Konvektif Kurutulması; Ön İşlemlerin Kurutma Karakteristikleri ve Renk Özellikleri Üzerine Etkisi, Yapay Sinir Ağı Modellemesi ve İnce Tabaka Modellemesinin Değerlendirilmesi

Abstract

This study focused on the impact of citric acid, hot water blanching, and ultrasound pretreatment on the drying of zucchini slices, color properties, and the comparison of artificial neural network (ANN) and thin-layer modeling. The pretreatments enhanced the drying rate and reduced drying time. Ultrasound pretreatment was observed as the most effective, with a reduction rate of the drying time as 40%. Besides, mass transfer and moisture diffusion phenomena were positively affected by pretreatments, depending on the increment of the drying rate. The highest mass transfer coefficient (hm), moisture diffusivity (D) by the Dincer and Dost model, and effective moisture diffusivity (Deff) by the Crank equation were obtained with ultrasound pretreatment. On the other hand, Midilli and Kucuk, Parabolic, and Page gave the best predictions among the thin-layer models. However, ANN modeling had a better performance than thin-layer modeling due to a higher determination coefficient (R2) and lower root mean square error (RMSE) values. Color properties of the zucchini slices were affected by drying processes. In general, the redness and yellowness of the zucchini slices increased; however, lightness did not show statistical significance. Additionally, citric acid pretreatment gave the lowest total color difference (∆E).