Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel
Abstract The peel of pomegranate (Punica granatum) is rich in bioactive compounds, specifically phenolic compounds and tannin compounds. However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of traditional methods. Microwave-assisted extra...
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Nature Portfolio
2025-06-01
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| Online Access: | https://doi.org/10.1038/s41598-025-04798-4 |
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| author | Fatemeh Mobasheri Mostafa Khajeh Mansour Ghaffari-Moghaddam Jamshid Piri Mousa Bohlooli |
| author_facet | Fatemeh Mobasheri Mostafa Khajeh Mansour Ghaffari-Moghaddam Jamshid Piri Mousa Bohlooli |
| author_sort | Fatemeh Mobasheri |
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| description | Abstract The peel of pomegranate (Punica granatum) is rich in bioactive compounds, specifically phenolic compounds and tannin compounds. However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of traditional methods. Microwave-assisted extraction (MAE) has shown promise, but optimizing it for maximum efficiency and yield remains a challenge. In this work, a microwave-assisted extraction improved using machine learning approaches was used to extract tannins and phenolic compounds from pomegranate peel. The experimental design consisted of four independent variables: microwave power (100–300 W), extraction time (10–40 min), temperature (35–50 °C), and food-to-solvent ratio (0.25–0.5 g/10 mL). The evaluated response variables were total phenolic (mg GAE/g), total tannin (mg CE/g), and antioxidant activity (DPPH scavenging activity). Thirty experiments were conducted using the microwave extraction system. Two machine learning models, LSBoost with Random Forest (LSBoost/RF) and LSBoost with K-Nearest Neighbors Neural Network (LSBoost/KNN-NN), were developed and compared for predicting extraction outcomes. The LSBoost/RF model demonstrated superior performance, achieving correlation coefficients (R²) of 0.9998, 0.9018, and 0.9269 for total phenolic, total tannin, and DPPH %, respectively. Feature importance analysis revealed microwave power as the most influential parameter, particularly for tannin content and antioxidant potency. The findings indicate that the combination of microwave-assisted extraction with machine learning provides an effective and accurate approach for the extraction and prediction of phenolic and tannin compounds in natural sources. |
| format | Article |
| id | doaj-art-f4e2b0caf92e4b82ac699a84aaadd7a4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f4e2b0caf92e4b82ac699a84aaadd7a42025-08-20T03:26:42ZengNature PortfolioScientific Reports2045-23222025-06-0115111410.1038/s41598-025-04798-4Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peelFatemeh Mobasheri0Mostafa Khajeh1Mansour Ghaffari-Moghaddam2Jamshid Piri3Mousa Bohlooli4Department of Chemistry, Faculty of Science, University of ZabolDepartment of Chemistry, Faculty of Science, University of ZabolDepartment of Chemistry, Faculty of Science, University of ZabolAdvanced Materials & Manufacturing Laboratory, University of ZabolDepartment of Cell & Molecular Sciences, Kharazmi UniversityAbstract The peel of pomegranate (Punica granatum) is rich in bioactive compounds, specifically phenolic compounds and tannin compounds. However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of traditional methods. Microwave-assisted extraction (MAE) has shown promise, but optimizing it for maximum efficiency and yield remains a challenge. In this work, a microwave-assisted extraction improved using machine learning approaches was used to extract tannins and phenolic compounds from pomegranate peel. The experimental design consisted of four independent variables: microwave power (100–300 W), extraction time (10–40 min), temperature (35–50 °C), and food-to-solvent ratio (0.25–0.5 g/10 mL). The evaluated response variables were total phenolic (mg GAE/g), total tannin (mg CE/g), and antioxidant activity (DPPH scavenging activity). Thirty experiments were conducted using the microwave extraction system. Two machine learning models, LSBoost with Random Forest (LSBoost/RF) and LSBoost with K-Nearest Neighbors Neural Network (LSBoost/KNN-NN), were developed and compared for predicting extraction outcomes. The LSBoost/RF model demonstrated superior performance, achieving correlation coefficients (R²) of 0.9998, 0.9018, and 0.9269 for total phenolic, total tannin, and DPPH %, respectively. Feature importance analysis revealed microwave power as the most influential parameter, particularly for tannin content and antioxidant potency. The findings indicate that the combination of microwave-assisted extraction with machine learning provides an effective and accurate approach for the extraction and prediction of phenolic and tannin compounds in natural sources.https://doi.org/10.1038/s41598-025-04798-4Microwave-assisted extractionPhenolic compoundsTanninsMachine learningAntioxidant activity |
| spellingShingle | Fatemeh Mobasheri Mostafa Khajeh Mansour Ghaffari-Moghaddam Jamshid Piri Mousa Bohlooli Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel Scientific Reports Microwave-assisted extraction Phenolic compounds Tannins Machine learning Antioxidant activity |
| title | Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel |
| title_full | Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel |
| title_fullStr | Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel |
| title_full_unstemmed | Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel |
| title_short | Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel |
| title_sort | machine learning optimization of microwave assisted extraction of phenolics and tannins from pomegranate peel |
| topic | Microwave-assisted extraction Phenolic compounds Tannins Machine learning Antioxidant activity |
| url | https://doi.org/10.1038/s41598-025-04798-4 |
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