Author
Listed:
- Igor Timofeev
(Engels Technological Institute, Branch of the Saratov State Technical University Named after Gagarin Yu.A., pl. Freedom, 17, 413100 Engels, Russia)
- Ekaterina Pleshakova
(Department of Data Analysis and Machine Learning, Federal State Budgetary Institution of Higher Education, Financial University under the Government of the Russian Federation, 4th Veshnyakovsky pr. 4, 111395 Moscow, Russia)
- Elena Dogadina
(Department of Data Analysis and Machine Learning, Federal State Budgetary Institution of Higher Education, Financial University under the Government of the Russian Federation, 4th Veshnyakovsky pr. 4, 111395 Moscow, Russia)
- Aleksey Osipov
(Department of Information Security, Federal State Budgetary Institution of Higher Education, Financial University under the Government of the Russian Federation, 4th Veshnyakovsky pr. 4, 111395 Moscow, Russia)
- Azret Kochkarov
(Department of Data Analysis and Machine Learning, Federal State Budgetary Institution of Higher Education, Financial University under the Government of the Russian Federation, 4th Veshnyakovsky pr. 4, 111395 Moscow, Russia
Research Centre of Biotechnology RAS, 119071 Moscow, Russia)
- Stefan Ignar
(Institute of Environmental Sciences, Warsaw University of Life Sciences, 02-787 Warsaw, Poland)
- Stanislav Suvorov
(Federal State Autonomous Educational Institution of Higher Education, Moscow Polytechnic University, 107023 Moscow, Russia)
- Sergey Gataullin
(Department of Data Analysis and Machine Learning, Federal State Budgetary Institution of Higher Education, Financial University under the Government of the Russian Federation, 4th Veshnyakovsky pr. 4, 111395 Moscow, Russia)
- Sergey Korchagin
(Department of Data Analysis and Machine Learning, Federal State Budgetary Institution of Higher Education, Financial University under the Government of the Russian Federation, 4th Veshnyakovsky pr. 4, 111395 Moscow, Russia)
Abstract
The development of mathematical models and efficient technologies for the processing of protein-containing dairy and vegetable raw materials and the production of food and feed concentrates with controlled functional properties is one of the most promising areas within the agricultural industry. In this work, the suitability of the electroflotation coagulation method for the combined extraction of vegetable and milk proteins was established by changing the electrolysis parameters and directed regulation of the isoelectric state of proteins. The research methodology is based on modern achievements of leading domestic and foreign researchers in the field of electrolysis of solutions and the creation of reagentless technologies for extracting proteins, as well as on the use of guest methods of physicochemical analysis, pH-metry, potentiometric and organoleptic analysis, methods of cyclic chronovoltammetry and currentless chronopotentiometry. The paper presents technological schemes for the extraction of vegetable and milk proteins, based on the combination of electroflotation and electrocoagulation processes. We carried out technological tests, which made it possible to determine the optimal conditions that ensure the highest yield of the product and its quality indicators. Ready-made isolates and concentrates of chickpea proteins and curd whey were obtained.
Suggested Citation
Igor Timofeev & Ekaterina Pleshakova & Elena Dogadina & Aleksey Osipov & Azret Kochkarov & Stefan Ignar & Stanislav Suvorov & Sergey Gataullin & Sergey Korchagin, 2022.
"Mathematical Models and Methods for Research and Optimization of Protein Extraction Processes from Chickpea and Curd Whey Solutions by Electroflotation Coagulation Method,"
Mathematics, MDPI, vol. 10(8), pages 1-27, April.
Handle:
RePEc:gam:jmathe:v:10:y:2022:i:8:p:1284-:d:792317
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