PMID- 33473314
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240330
IS  - 2048-7177 (Print)
IS  - 2048-7177 (Electronic)
IS  - 2048-7177 (Linking)
VI  - 9
IP  - 1
DP  - 2021 Jan
TI  - Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying 
      in an infrared-assisted continuous hybrid solar dryer.
PG  - 532-543
LID - 10.1002/fsn3.2022 [doi]
AB  - Drying characteristics of stevia leaves were investigated in an infrared 
      (IR)-assisted continuous-flow hybrid solar dryer. Drying experiments were 
      conducted at the inlet air temperatures of 30, 40, and 50 degrees C, air inlet velocities 
      of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results 
      indicated that inlet air temperature and IR lamp input power had significant 
      effect on drying time (p < .05). A comparative study was performed among 
      mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System 
      (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia 
      leaves during the drying process. The ANN model was the most accurate MR 
      predictor with coefficient of determination (R(2)), root mean squared error 
      (RMSE), and chi-squared error (chi(2)) values of 0.9995, 0.0005, and 0.0056, 
      respectively, on test dataset. These values of the ANFIS model on test dataset 
      were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the 
      Midilli model was the best-fitted model to experimental MR values in most of the 
      drying conditions. It was concluded that artificial intelligence modeling is an 
      effective approach for accurate prediction of the drying kinetics of stevia 
      leaves in the continuous-flow IR-assisted hybrid solar dryer.
CI  - (c) 2020 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.
FAU - Bakhshipour, Adel
AU  - Bakhshipour A
AUID- ORCID: 0000-0002-0292-8713
AD  - Department of Agricultural Mechanization Engineering Faculty of Agricultural 
      Sciences University of Guilan Rasht Iran.
FAU - Zareiforoush, Hemad
AU  - Zareiforoush H
AD  - Department of Agricultural Mechanization Engineering Faculty of Agricultural 
      Sciences University of Guilan Rasht Iran.
FAU - Bagheri, Iraj
AU  - Bagheri I
AD  - Department of Agricultural Mechanization Engineering Faculty of Agricultural 
      Sciences University of Guilan Rasht Iran.
LA  - eng
PT  - Journal Article
DEP - 20201112
PL  - United States
TA  - Food Sci Nutr
JT  - Food science & nutrition
JID - 101605473
PMC - PMC7802544
OTO - NOTNLM
OT  - drying kinetics
OT  - infrared radiation
OT  - intelligent modeling
OT  - medicinal plant
OT  - solar energy
EDAT- 2021/01/22 06:00
MHDA- 2021/01/22 06:01
PMCR- 2020/11/12
CRDT- 2021/01/21 05:34
PHST- 2020/09/30 00:00 [received]
PHST- 2020/11/01 00:00 [revised]
PHST- 2020/11/02 00:00 [accepted]
PHST- 2021/01/21 05:34 [entrez]
PHST- 2021/01/22 06:00 [pubmed]
PHST- 2021/01/22 06:01 [medline]
PHST- 2020/11/12 00:00 [pmc-release]
AID - FSN32022 [pii]
AID - 10.1002/fsn3.2022 [doi]
PST - epublish
SO  - Food Sci Nutr. 2020 Nov 12;9(1):532-543. doi: 10.1002/fsn3.2022. eCollection 2021 
      Jan.