Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment
Ebrahim Taghinezhad , Antoni Szumny , Mohammad Kaveh , Vali Rasooli Sharabiani , Anil Kumar , Naoto Shimizu
AbstractThe effect of hybrid infrared-convective (IRC), microwave (MIC) and infrared-convectivemicrowave (IRCM) drying methods on thermodynamic (drying kinetics, effective moisture diffusivity coefficient (Deff), specific energy consumption (SEC)) and quality (head rice yield (HRY), color value and lightness) characteristics of parboiled rice samples were investigated in this study. Experimental data were fitted into empirical drying models to explain moisture ratio (MR) variations during drying. The Artificial Neural Network (ANN) method was applied to predict MR. The IRCM method provided shorter drying time (reduce percentage = 71%) than IRC (41%) and microwave (69%) methods. The Deff of MIC drying (6.85 × 10−11–4.32 × 10−10 m2 /s) was found to be more than the observed in IRC (1.32 × 10−10–1.87 × 10−10 m2 /s) and IRCM methods (1.58 × 10−11–2.31 × 10−11 m2 /s). SEC decreased during drying. Microwave drying had the lowest SEC (0.457 MJ/kg) compared to other drying methods (with mean 28 MJ/kg). Aghbashlo’s model was found to be the best for MR prediction. According to the ANN results, the highest determination coefficient (R 2 ) values for MR prediction in IRC, IRCM and MIC drying methods were 0.9993, 0.9995 and 0.9990, respectively. The HRY (from 60.2 to 74.07%) and the color value (from 18.08 to 19.63) increased with the drying process severity, thereby decreasing the lightness (from 57.74 to 62.17). The results of this research can be recommended for the selection of the best dryer for parboiled paddy. Best drying conditions in the study is related to the lowest dryer SEC and sample color value and the highest HRY and sample lightness.
|Journal series||Foods, ISSN 2304-8158, (N/A 70 pkt)|
|Publication size in sheets||0.8|
|Keywords in English||parboiled paddy; thermodynamic; quality; Artificial Neural Network; mathematical modeling|
|License||Journal (articles only); published final; ; with publication|
|Score||= 70.0, 16-04-2020, ArticleFromJournal|
|Publication indicators||= 0; = 0; : 2018 = 3.011 (2)|
|Citation count*||1 (2020-07-05)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.