msiСовременная наука и инновацииModern Science and Innovations2307-910XNorth-Caucasus Federal University10.37493/2307-910X.2025.4.6msi-1783Research ArticleТЕХНОЛОГИЯ ПРОДОВОЛЬСТВЕННЫХ ПРОДУКТОВTECHNOLOGY OF FOOD PRODUCTSМатематическое моделирование и машинное обучение для оптимизации состава глубоких эвтектических растворителей при извлечении коллагена из костного остатка птицыMathematical Modeling and Machine Learning for Optimizing the Composition of Deep Eutectic Solvents in the Extraction of Collagen from Poultry Bone Residueshttps://orcid.org/0009-0009-4052-3379ОдиловаЗ. А.OdilovaZ. A.

Зайнаб Арзикуловна Одилова – аспирант кафедры технологии производства и переработки сельскохозяйственной продукции.

12, пер. Зоотехнический, Ставрополь, 355035

Zainab A. Odilova – PhD student at the Department of Technology of Production and Processing of Agricultural Products.

12 Zootekhnicheskiy Lane, Stavropol, 355035

zaynabodilova663@gmail.comhttps://orcid.org/0000-0001-9894-180XШлыковС. Н.ShlykovS. N.

Шлыков Сергей Николаевич – доктор биологических наук, доцент, заведующий кафедрой производства и переработки сельскохозяйственной продукции Ставропольского государственного аграрного университета, Scopus ID: 56362496400, Researcher ID: E-2567-2017.

12, пер. Зоотехнический, Ставрополь, 355035

Sergei N. Shlykov – Doctor of Biological Sciences, Associate Professor, Head of the Department of Production and Processing of Agricultural Products, Scopus ID: 56362496400, Researcher ID: E-2567-2017

12 Zootekhnicheskiy Lane, Stavropol, 355035

shlykovsn@gmail.comhttps://orcid.org/0000-0001-7352-636XОмаровР. С.OmarovR. S.

Омаров Руслан Сафербегович – кандидат технических наук, доцент, доцент кафедры производства и переработки сельскохозяйственной продукции Ставропольского государственного аграрного университета, Scopus ID: 56362626000, Researcher ID: N-8286-2016.

12, пер. Зоотехнический, Ставрополь, 355035

Ruslan S. Omarov – Candidate of Technical Sciences, Associate Professor at the Department of Production and Processing of Agricultural Products, Scopus ID: 56362626000, Researcher ID: N-8286-2016.

12 Zootekhnicheskiy Lane, Stavropol, 355035

doooctor@yandex.ruСтавропольский государственный аграрный университетРоссияStavropol State Agrarian UniversityRussian Federation202526012026046372Copyright © Одилова З.А., Шлыков С.Н., Омаров Р.С., 20262026Одилова З.А., Шлыков С.Н., Омаров Р.С.Odilova Z.A., Shlykov S.N., Omarov R.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://msi.elpub.ru/jour/article/view/1783Введение

Введение. В работе рассматривается применение глубоких эвтектических растворителей (DES) для повышения эффективности экстракции коллагена из животного сырья. Актуальность обусловлена необходимостью замены традиционных органических экстрагентов более безопасными и регулируемыми средами, обеспечивающими стабильность белковых структур и снижение экологической нагрузки.

Цель

Цель. Цель исследования — выявить оптимальные составы DES, способные обеспечивать высокий выход коллагена при низких температурах и мягких технологических условиях, а также определить дескрипторы, влияющие на экстракционную способность.

Материалы и методы

Материалы и методы. Анализ выполнен на основе рассчитанных физико-химических дескрипторов компонентов DES и моделирования выходов коллагена с использованием нейросетевой модели MLP. Рассмотрено 120 комбинаций HBA и HBD, охватывающих металлосодержащие и натуральные системы. Данные нормализованы и использованы для построения сравнительной таблицы дескрипторов и прогностических характеристик.

Результаты и обсуждение

Результаты и обсуждение. Выявлено, что DES, содержащие Lewis-кислоты Zn²⁺ и Sn²⁺, характеризуются пониженной полярностью и умеренной вязкостью, что обеспечивает максимальный прогнозируемый выход коллагена. Натуральные NADES демонстрируют несколько меньшую эффективность, но обладают преимуществами пищевой безопасности. Построенный бар-чарт визуализирует превосходство металлосодержащих систем по сравнению с классическим контрольным DES.

Заключение

Заключение. Установлено, что сочетание структурных дескрипторов и машинного обучения позволяет достоверно прогнозировать эффективность DES и минимизировать объём лабораторных испытаний. Металлосодержащие системы являются наиболее перспективными для последующей экспериментальной верификации.

Introduction

Introduction. This study examines the use of deep eutectic solvents (DESs) to enhance the efficiency of collagen extraction from animal-derived raw materials. The relevance of the work is driven by the need to replace traditional organic extractants with safer and more controllable media that ensure protein structural stability while reducing environmental impact.

Goal

Goal. The aim of the research is to identify optimal DES compositions capable of providing high collagen yield at low temperatures and under mild technological conditions, as well as to determine the descriptors influencing extraction performance.

Materials and methods

Materials and methods. The analysis is based on calculated physicochemical descriptors of DES components and modeling of collagen yields using a multilayer perceptron (MLP) neural network. A total of 120 combinations of hydrogen bond acceptors (HBAs) and hydrogen bond donors (HBDs), including metal-containing and natural systems, were evaluated. The data were normalized and used to construct a comparative table of descriptors and predictive characteristics.

Results and discussion

Results and discussion. DESs containing Lewis acids Zn²⁺ and Sn²⁺ were found to exhibit reduced polarity and moderate viscosity, which together provide the highest predicted collagen yields. Natural NADES showed slightly lower efficiency but offered advantages in terms of food safety. The constructed bar chart visualizes the superiority of metal-containing systems compared to a classical control DES.

Conclusion

Conclusion. It has been established that the combination of structural descriptors and machine learning enables reliable prediction of DES efficiency and minimizes the volume of laboratory testing. Metal-containing systems are the most promising candidates for subsequent experimental verification.

Lewis-кислотымолекулярные дескрипторыMLP-модельHBA/HBD-системыNADESэкстракция коллагенаDeep eutectic solvents.Natural deep eutectic solventsMolecular descriptorsMachine learningMLP modelCollagen extractionReferencesFrancisco M., van den Bruinhorst A., Kroon M.C. New natural and renewable low transition temperature mixtures (LTTMs): screening as solvents for lignocellulosic biomass processing. Green Chem. 2012. № 14(8). С. 2153–2157.Francisco M., van den Bruinhorst A., Kroon M.C. New natural and renewable low transition temperature mixtures (LTTMs): screening as solvents for lignocellulosic biomass processing. Green Chem. 2012; 14(8): 2153-2157. https://doi.org/10.1039/c2gc35028dAbbott A.P., Capper G., Davies D.L., Rasheed R.K., Tambyrajah V. Novel solvent properties of choline chloride/urea mixtures. Chem. Commun. 2003. № 1. С. 70–71.Abbott A.P., Capper G., Davies D.L., Rasheed R.K., Tambyrajah V. Novel solvent properties of choline chloride/urea mixtures. Chem. Commun. 2003; 1: 70-71. https://doi.org/10.1039/b210714gKarimi A. et al. Extraction of canola protein via natural deep eutectic solvents compared to alkaline treatments: Isolate characteristics and protein structural and functional properties. Food Hydrocoll. 2024. № 152. С. 109922.Karimi A. et al. Extraction of canola protein via natural deep eutectic solvents compared to alkaline treatments: Isolate characteristics and protein structural and functional properties. Food Hydrocoll. 2024; 152: 109922. https://doi.org/10.1016/j.foodhyd.2024.109922Du L. et al. Extraction of protein from sesame meal: Impact of deep eutectic solvents on protein structure and functionality. LWT. 2023. № 187. С. 115345.Du L. et al. Extraction of protein from sesame meal: Impact of deep eutectic solvents on protein structure and functionality. LWT 2023; 187: 115345. https://doi.org/10.1016/j.lwt.2023.115345Bai C., Wei Q., Ren X. Selective extraction of collagen peptides with high purity from cod skins by deep eutectic solvents. ACS Sustain. Chem. Eng. 2017. № 5(6). С. 5476–5484.Bai C., Wei Q., Ren X. Selective extraction of collagen peptides with high purity from cod skins by deep eutectic solvents. ACS Sustain. Chem. Eng. 2017; 5(6): 5476-5484. https://doi.org/10.1021/acssuschemeng.7b00316Tan Y.T. et al. Deep eutectic solvent as an innovative solvent for collagen extraction from marine by-products. J. Food Process. Preserv. 2023. № 47(10). С. e17123.Tan Y.T. et al. Deep eutectic solvent as an innovative solvent for collagen extraction from marine by-products. J. Food Process. Preserv. 2023; 47(10): e17123. https://doi.org/10.1111/jfpp.17123Bhowmik P. et al. Impact of a Novel Two-Phase Natural Deep Eutectic Solvent-Assisted Extraction on the Structural, Functional, and Flavor Properties of Hemp Protein Isolates. Plants. 2025. № 14(2). С. 274.Bhowmik P. et al. Impact of a Novel Two-Phase Natural Deep Eutectic Solvent-Assisted Extraction on the Structural, Functional, and Flavor Properties of Hemp Protein Isolates. Plants 2025; 14(2): 274. https://doi.org/10.3390/plants14020274Kudłak B. et al. Application of deep eutectic solvents in protein extraction and purification. Front. Chem. 2022. № 10. С. 912411.Kudłak B. et al. Application of deep eutectic solvents in protein extraction and purification. Front. Chem. 2022; 10: 912411. https://doi.org/10.3389/fchem.2022.912411Abdipour M., Younessi-Hmazekhanlu M., Ramazani S.H.R., Omidi A. Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.). Ind. Crops Prod. 2019. № 127. С. 185–194.Abdipour M., Younessi-Hmazekhanlu M., Ramazani S.H.R., Omidi A. Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.). Ind. Crops Prod. 2019; 127: 185-194. https://doi.org/10.1016/j.indcrop.2018.10.050Niazian M., Sadat-Noori S.A., Abdipour M. Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models. Ind. Crops Prod. 2018. № 117. С. 224–234.Niazian M., Sadat-Noori S.A., Abdipour M. Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models. Ind. Crops Prod. 2018; 117: 224-234. https://doi.org/10.1016/j.indcrop.2018.03.024Chang C.C., Song J., Tey B.T., Ramanan R.N. Bioinformatics approaches for improved recombinant protein production in Escherichia coli: Protein solubility prediction. Brief. Bioinform. 2014. № 15(6). С. 1047–1059.Chang C.C., Song J., Tey B.T., Ramanan R.N. Bioinformatics approaches for improved recombinant protein production in Escherichia coli: Protein solubility prediction. Brief. Bioinform. 2014; 15(6): 1047-1059. https://doi.org/10.1093/bib/bbt076Rawi R., O’Connell J., Mall R. et al. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Bioinformatics. 2018. № 34(15). С. 2605–2613.Rawi R., O’Connell J., Mall R. et al. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Bioinformatics 2018; 34(15): 2605-2613. https://doi.org/10.1093/bioinformatics/bty166Zhou P. Protein engineering in the deep learning era. mLife. 2024. № 3(4). С. 308–325.Zhou P. Protein engineering in the deep learnin era. mLife 2024; 3(4): 308-325. https://doi.org/10.1016/j.mlife.2024.03.004Wang Y., Zhang L., Li J. et al. Modeling and optimization of collagen hydrolysis process using multilayer perceptron. LWT. 2023. № 175. С. 114489.Wang Y., Zhang L., Li J. et al. Modeling and optimization of collagen hydrolysis process using multilayer perceptron. LWT 2023; 175: 114489. https://doi.org/10.1016/j.lwt.2023.114489Chen X., Zhou Y., Wang J. et al. Deep learning prediction of collagen peptide solubility using physicochemical descriptors. J. Food Eng. 2024. № 345. С. 111423.Chen X., Zhou Y., Wang J. et al. Deep learning prediction of collagen peptide solubility using physicochemical descriptors. J. Food Eng. 2024; 345: 111423. https://doi.org/10.1016/j.jfoodeng.2023.111423Lundberg S.M., Lee S.-I. A unified approach to interpreting model predictions. Proc. Adv. Neural Inf. Process. Syst. 2017. № 30. С. 4765–4774.Lundberg S.M., Lee S.-I. A unified approach to interpreting model predictions. Proc. Adv. Neural Inf. Process. Syst. 2017; 30: 4765-4774. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.htmlRawi R., Mall R., Kloczkowski A. et al. Deeper profiles and cascaded recurrent and convolutional neural networks for state-of-the-art protein secondary structure prediction. Sci. Rep. 2019. № 9. С. 12374.Rawi R., Mall R., Kloczkowski A. et al. Deeper profiles and cascaded recurrent and convolutional neural networks for state-of-the-art protein secondary structure prediction. Sci. Rep. 2019; 9: 12374. https://doi.org/10.1038/s41598-019-48786-3Wang H., Li Y., Zhang Z. Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map. Bioinformatics. 2021. № 37(5). С. 640–647.Wang H., Li Y., Zhang Z. Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map. Bioinformatics 2021; 37(5): 640-647. https://doi.org/10.1093/bioinformatics/btaa720Pappu S.M.J., Gummadi S.N. Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor. Biochem. Eng. J. 2017. № 120. С. 136–145.Pappu S.M.J., Gummadi S.N. Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor. Biochem. Eng. J. 2017; 120: 136–145. https://doi.org/10.1016/j.bej.2017.04.013Goodfellow I., Pouget-Abadie J., Mirza M. et al. Generative adversarial nets. Proc. Adv. Neural Inf. Process. Syst. 2014. № 27. С. 2672–2680.Goodfellow I., Pouget-Abadie J., Mirza M. et al. Generative adversarial nets. Proc. Adv. Neural Inf. Process. Syst. 2014; 27: 2672-2680. https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.htmlAlanazi W., Meng D., Pollastri G. DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction. Front. Bioinform. 2025. № 5. С. 1607402.Alanazi W., Meng D., Pollastri G. DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction. Front. Bioinform. 2025; 5: 1607402. https://doi.org/10.3389/fbioe.2025.1607402Scarselli F., Gori M., Tsoi A.C. et al. The graph neural network model. IEEE Trans. Neural Netw. 2009. № 20(1). С. 61–80.Scarselli F., Gori M., Tsoi A.C. et al. The graph neural network model. IEEE Trans. Neural Netw. 2009; 20(1): 61-80. https://doi.org/10.1109/TNN.2008.2005605Lee S., Lee M., Gyak K.-W. et al. Novel solubility prediction models: molecular fingerprints and physicochemical features vs graph convolutional neural networks. ACS Omega. 2022. № 7(14). С. 12268–12277.Lee S., Lee M., Gyak K.-W. et al. Novel solubility prediction models: molecular fingerprints and physicochemical features vs graph convolutional neural networks. ACS Omega 2022; 7(14): 12268- 12277. https://doi.org/10.1021/acsomega.2c01039Ahmad W., Tayara H., Shim H.J., Chong K.T. SolPredictor: predicting solubility with residual gated graph neural network. Int. J. Mol. Sci. 2024. № 25(2). С. 715.Ahmad W., Tayara H., Shim H.J., Chong K.T. SolPredictor: predicting solubility with residual gated graph neural network. Int. J. Mol. Sci. 2024; 25(2): 715. https://doi.org/10.3390/ijms25020715Tan Y.T., Khoo C.H., Chong K.L. et al. Graph neural networks for biodiesel yield prediction in lignocellulosic biorefineries. Renew. Energy. 2025. № 215. С. 119012.Tan Y.T., Khoo C.H., Chong K.L. et al. Graph neural networks for biodiesel yield prediction in lignocellulosic biorefineries. Renew. Energy 2025; 215: 119012. https://doi.org/10.1016/j.renene.2024.119012Rives A., Meier J., Sercu T. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. USA. 2021. № 118(15). С. e2016239118.Rives A., Meier J., Sercu T. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. USA 2021; 118(15): e2016239118. https://doi.org/10.1073/pnas.2016239118Boadu R., Wang Y., Li X. Deep learning methods for protein function prediction. PROTEOMICS. 2025. № 25(1–2). С. e2300471.Boadu R., Wang Y., Li X. Deep learning methods for protein function prediction. PROTEOMICS 2025; 25(1-2): e2300471. https://doi.org/10.1002/pmic.202300471Choi Y.H. et al. Are natural deep eutectic solvents the missing link in understanding cellular metabolism and physiology? Plant Physiol. 2011. № 156(4). С. 1701–1705.Choi Y.H. et al. Are natural deep eutectic solvents the missing link in understanding cellular metabolism and physiology? Plant Physiol. 2011; 156(4): 1701-1705. https://doi.org/10.1104/pp.111.179448Zhang T. et al. Effects of pH and molecular weight on emulsifying and foaming properties of collagen peptides from fish skin. J. Food Sci. Technol. 2024. № 61(4). С. 712–721.Zhang T. et al. Effects of pH and molecular weight on emulsifying and foaming properties of collagen peptides from fish skin. J. Food Sci. Technol. 2024; 61(4): 712-721. https://doi.org/10.1007/s13197-023-06648-9León-López A. et al. Hydrolyzed collagen – sources and applications. Molecules. 2019. № 24(22). С. 4031.León-López A. et al. Hydrolyzed collagen – sources and applications. Molecules 2019; 24(22): 4031. https://doi.org/10.3390/molecules24224031Chen X. et al. Surface activity and emulsifying properties of low molecular weight collagen peptides from marine fish skin. Food Chem. 2023. № 410. С. 135412.Chen X. et al. Surface activity and emulsifying properties of low molecular weight collagen peptides from marine fish skin. Food Chem. 2023; 410: 135412. https://doi.org/10.1016/j.foodchem.2022.135412Wang Y. et al. Effect of pH on the Structure, Functional Properties and Rheological Properties of Collagen from Greenfin Horse-Faced Filefish (Thamnaconus septentrionalis) Skin. Foods. 2024. № 13(3). С. 456.Wang Y. et al. Effect of pH on the Structure, Functional Properties and Rheological Properties of Collagen from Greenfin Horse-Faced Filefish (Thamnaconus septentrionalis) Skin. Foods 2024; 13(3): 456. https://doi.org/10.3390/foods13030456Santana L.B. et al. Emulsifying properties of collagen fibers: Effect of pH, protein concentration and homogenization pressure. Food Hydrocoll. 2011. № 25(4). С. 791–799.Santana L.B. et al. Emulsifying properties of collagen fibers: Effect of pH, protein concentration and homogenization pressure. Food Hydrocoll. 2011; 25(4): 791-799. https://doi.org/10.1016/j.foodhyd.2010.09.007Nikoo M. et al. Collagen and gelatin: Structure, properties, and applications in food industry. Int. J. Biol. Macromol. 2024. № 269. С. 131950.Nikoo M. et al. Collagen and gelatin: Structure, properties, and applications in food industry. Int. J. Biol. Macromol. 2024; 269: 131950. https://doi.org/10.1016/j.ijbiomac.2024.131950Bai C. et al. Selective extraction of collagen peptides with high purity from cod skins by deep eutectic solvents. ACS Sustain. Chem. Eng. 2017. № 5(6). С. 5476–5484.Bai C. et al. Selective extraction of collagen peptides with high purity from cod skins by deep eutectic solvents. ACS Sustain. Chem. Eng. 2017; 5(6): 5476-5484. https://doi.org/10.1021/acssuschemeng.7b00316Tan Y.T. et al. Deep eutectic solvent as an innovative solvent for collagen extraction from marine by-products. J. Food Process. Preserv. 2023. № 47(10). С. e17123.Tan Y.T. et al. Deep eutectic solvent as an innovative solvent for collagen extraction from marine by-products. J. Food Process. Preserv. 2023; 47(10): e17123. https://doi.org/10.1111/jfpp.17123Heliyon Editorial Board. Deep eutectic solvents: Preparation, properties, and food applications. Heliyon. 2024. № 10(7). С. e04815Heliyon Editorial Board. Deep eutectic solvents: Preparation, properties, and food applications. Heliyon 2024; 10(7): e04815. https://doi.org/10.1016/j.heliyon.2024.e04815

The authors declare that there are no conflicts of interest present.