ArticleUltrasonics sonochemistry2024
ANN modeling and RSM optimization of ultrasound-assisted extraction of protodioscin-rich extracts from Tribulus terrestris L.
Article in Ultrasonics sonochemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Insect-Derived Anti-Aging Bioresources: Current Status, Bottlenecks, and Future Directions.Insects · 2026Review
- Ultrasonic-assisted extraction, purification, identification, and bioactivity evaluation of phlorotannins from Sargassum fusiforme: A comparative study of RSM and SVM-GA.Ultrasonics sonochemistry · 2026Article
- Article
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4 authors.
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Abstract
Tribulus terrestris L. is a herb renowned for its abundance of saponins, flavonoids, and alkaloids, which are utilized in treating various health conditions. Protodioscin functions by enhancing the conversion of testosterone into potent dihydrotestosterone, stimulating an increase in libido, red blood cell production from the bone marrow, and muscle development. Contemporary ultrasound-assisted-extraction (UAE) process employing green extraction solvents was selected to design the required separation system. The experimental plan was developed based on the independent operating variables - extraction time, operating temperature and solvent system composition in order to determine the influence of defined parameters and their interactions on the extraction yield and the presence of protodioscin. The 3D-RSM approach was introduced to determine the optimal values of studied independent variables in the area of maximal extraction yield. UAE process performed at optimal operating conditions generated maximal extraction yield (31 %, w/w) and protodioscin content of 5.9 mg/g dry plant matrix. Experimental data was used to develop an ANN for the defined extraction system using the operating variables values as the input matrix and observed yield as target matrix. Successfully designed and trained ANN generated high correlation (r = 0.9992) between observed data and predictive model' outputs, and MSE value of 0.29107.
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