Volume 19, Issue 131 (2022)
FSCT 2022, 19(131): 173-186 |
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Hassanloofard Z, Gharekhani M, Zandi M, Roufegarinejad L, Ganjloo A. Kinetic modeling and artificial neural network approach for the modelling of Ohmic-assisted extraction of Falcaria vulgaris extract. FSCT 2022; 19 (131) :173-186
URL: http://fsct.modares.ac.ir/article-7-64180-en.html
URL: http://fsct.modares.ac.ir/article-7-64180-en.html
1- Ph.D. student, Department Food Science and Technology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
2- Assistant professor, Department Food Science and Technology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
3- Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan 4537-38791 Iran ,zandi@znu.ac.ir
4- Assistant professor, Department of Food Science and Technology, Tabriz branch, Islamic Azad University, Tabriz, Iran.
5- دانشیارگروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزي، دانشگاه زنجان، زنجان، ایران
2- Assistant professor, Department Food Science and Technology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
3- Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan 4537-38791 Iran ,
4- Assistant professor, Department of Food Science and Technology, Tabriz branch, Islamic Azad University, Tabriz, Iran.
5- دانشیارگروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزي، دانشگاه زنجان، زنجان، ایران
Abstract: (1383 Views)
Analysis of the extraction modelling for natural compounds is essential for industrial application. In the present paper, ohmic-assisted extraction was investigated for the extraction of Falcaria vulgaris extract. This study was performed in order to express the effect of some pecific parameters (as: voltage gradiant, ethanol to water ratio, extraction time and temperature) on the extraction yield and total phenolic content (TPC). Kinetics models (first-order, second's-order and pelleg models) and artificial neural network were used for modeling ohmic-assisted extraction process. Kinetic study plays a very important role in evaluating the extraction process because it allows estimation of the cost-effectiveness of the process in saving time, money and energy. The results showed that the second-order and plleg's kinetic models respectively, were successfully predicted the value of the total phenol content of the extract and extraction yield in all experiments. The correlation coefficient between experimentally obtained extraction yield and total phenolic content and values predicted by artificial neural network (4-16-2) were 0.995 for training, 0.963 for validation, and 0.979 for testing, indicating the good predictive ability of the model. The artificial neural network model had a higher prediction efficiency than the kinetic models. Artificial neural network can reliably model the process with better predictive and estimation capabilities than the kinetic’s models.
Article Type: Original Research |
Subject:
Extraction of effective compounds
Received: 2022/09/13 | Accepted: 2022/12/12 | Published: 2022/12/31
Received: 2022/09/13 | Accepted: 2022/12/12 | Published: 2022/12/31
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