Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage
This study investigated the diverse antioxidant and enzyme-inhibiting properties of 18 date palm cultivars, correlating these bioactivities with polyphenol profiles using biochemical methods and machine learning (ML). Maktoomi exhibited the highest phenolic content (759.42 mg GAE/100g), while Fard s...
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| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154325003904 |
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| author | Nashi K. Alqahtani Tareq M. Alnemr Rania Ismail Hosam M. Habib |
| author_facet | Nashi K. Alqahtani Tareq M. Alnemr Rania Ismail Hosam M. Habib |
| author_sort | Nashi K. Alqahtani |
| collection | DOAJ |
| description | This study investigated the diverse antioxidant and enzyme-inhibiting properties of 18 date palm cultivars, correlating these bioactivities with polyphenol profiles using biochemical methods and machine learning (ML). Maktoomi exhibited the highest phenolic content (759.42 mg GAE/100g), while Fard showed strong ferric-reducing antioxidant power (FRAP) activity (2456.13 mmol/100g). Significant enzyme inhibition variation was observed, Jabri (8.69 % AChE inhibition), Shikat alkahlas (21.06 % α-amylase inhibition), and Barhe (51.39 % tyrosinase inhibition). Maghool provided the highest protein protection (95–100 % BSA). These bioactivity data were integrated into an extreme gradient boosting (XGBoost) ML model to connect chemical features with experimental outcomes. The model demonstrated high predictive capability (R2 ∼ 0.9–0.95) for amylase, acetylcholine, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays, but lower values (R2 < 0.9) for more complex assays involving DNA or superoxide systems, indicating data quality limitations. This highlights targeted method improvements. These findings demonstrate that certain date components offer higher specific bioactivity, and the ML approach validates these methods, revealing benefits and limitations. Date extracts possess therapeutic potential, and the combined approach of experimental testing and ML mapping provides a framework for multi-parameter analysis of complex biological systems. However, further in-vivo validation is needed. |
| format | Article |
| id | doaj-art-3cb6f249ec434ff4ad9d4b090fe026cd |
| institution | Kabale University |
| issn | 2666-1543 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-3cb6f249ec434ff4ad9d4b090fe026cd2025-08-20T03:56:04ZengElsevierJournal of Agriculture and Food Research2666-15432025-08-012210201910.1016/j.jafr.2025.102019Machine learning prediction of 18 date palm polyphenol protection against biomolecular damageNashi K. Alqahtani0Tareq M. Alnemr1Rania Ismail2Hosam M. Habib3Department of Food and Nutrition Sciences, College of Agricultural and Food Sciences, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi Arabia; Date Palm Research Center of Excellence, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi ArabiaDepartment of Food and Nutrition Sciences, College of Agricultural and Food Sciences, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi ArabiaFaculty of Computer Science & Engineering, Alamein International University (AIU), Alameen City, 5060310, EgyptResearch & Innovation Hub, Alamein International University (AIU), Alameen City, 5060310, Egypt; Corresponding author.This study investigated the diverse antioxidant and enzyme-inhibiting properties of 18 date palm cultivars, correlating these bioactivities with polyphenol profiles using biochemical methods and machine learning (ML). Maktoomi exhibited the highest phenolic content (759.42 mg GAE/100g), while Fard showed strong ferric-reducing antioxidant power (FRAP) activity (2456.13 mmol/100g). Significant enzyme inhibition variation was observed, Jabri (8.69 % AChE inhibition), Shikat alkahlas (21.06 % α-amylase inhibition), and Barhe (51.39 % tyrosinase inhibition). Maghool provided the highest protein protection (95–100 % BSA). These bioactivity data were integrated into an extreme gradient boosting (XGBoost) ML model to connect chemical features with experimental outcomes. The model demonstrated high predictive capability (R2 ∼ 0.9–0.95) for amylase, acetylcholine, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays, but lower values (R2 < 0.9) for more complex assays involving DNA or superoxide systems, indicating data quality limitations. This highlights targeted method improvements. These findings demonstrate that certain date components offer higher specific bioactivity, and the ML approach validates these methods, revealing benefits and limitations. Date extracts possess therapeutic potential, and the combined approach of experimental testing and ML mapping provides a framework for multi-parameter analysis of complex biological systems. However, further in-vivo validation is needed.http://www.sciencedirect.com/science/article/pii/S2666154325003904Polyphenol profilingBioactive compoundsIn Silico screeningPredictive modelingOxidative stress mitigationNutraceutical development |
| spellingShingle | Nashi K. Alqahtani Tareq M. Alnemr Rania Ismail Hosam M. Habib Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage Journal of Agriculture and Food Research Polyphenol profiling Bioactive compounds In Silico screening Predictive modeling Oxidative stress mitigation Nutraceutical development |
| title | Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage |
| title_full | Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage |
| title_fullStr | Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage |
| title_full_unstemmed | Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage |
| title_short | Machine learning prediction of 18 date palm polyphenol protection against biomolecular damage |
| title_sort | machine learning prediction of 18 date palm polyphenol protection against biomolecular damage |
| topic | Polyphenol profiling Bioactive compounds In Silico screening Predictive modeling Oxidative stress mitigation Nutraceutical development |
| url | http://www.sciencedirect.com/science/article/pii/S2666154325003904 |
| work_keys_str_mv | AT nashikalqahtani machinelearningpredictionof18datepalmpolyphenolprotectionagainstbiomoleculardamage AT tareqmalnemr machinelearningpredictionof18datepalmpolyphenolprotectionagainstbiomoleculardamage AT raniaismail machinelearningpredictionof18datepalmpolyphenolprotectionagainstbiomoleculardamage AT hosammhabib machinelearningpredictionof18datepalmpolyphenolprotectionagainstbiomoleculardamage |