Revista Mexicana de Ingeniería Química, Vol. 22, No. 3 (2023), Alim2386

Optimizing foam mat drying process for cornelian cherry pulp using response surface methodology and artificial neural networks

M. Güldane



This study aimed to improve the foam mat drying (FMD) process of cornelian cherry pulp. Response surface methodology (RSM) and artificial neural networks (ANN) were employed to investigate and predict the impact of ultrasound treatment (10-30 min), whipping (5-15 min, and hot-air temperature (60-80 °C) on selected responses. The results showed that maximum redness value and total phenolic content, and minimum drying time were obtained when optimum process parameters, 10 min for sonication time, 15 min for whipping time, and 60 °C for drying temperature, were employed. Analysis of variance (ANOVA) results indicated that the most important parameter influencing the FMD process of cornelian cherry pulp was the drying temperature. Furthermore, the statistical comparison of the empirical and predictive results for each response showed that the ANN model has a better prediction capability than the RSM. The overall findings revealed that the ANN model can be successfully applied in predicting responses in FMD of valuable fruits.

Keywords: ANOVA, drying temperature, prediction, ultrasound, whipping.



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