Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds

Antioxidants
Pascual García-PérezPedro Pablo Gallego

Abstract

We combined machine learning and plant in vitro culture methodologies as a novel approach for unraveling the phytochemical potential of unexploited medicinal plants. In order to induce phenolic compound biosynthesis, the in vitro culture of three different species of Bryophyllum under nutritional stress was established. To optimize phenolic extraction, four solvents with different MeOH proportions were used, and total phenolic content (TPC), flavonoid content (FC) and radical-scavenging activity (RSA) were determined. All results were subjected to data modeling with the application of artificial neural networks to provide insight into the significant factors that influence such multifactorial processes. Our findings suggest that aerial parts accumulate a higher proportion of phenolic compounds and flavonoids in comparison to roots. TPC was increased under ammonium concentrations below 15 mM, and their extraction was maximum when using solvents with intermediate methanol proportions (55-85%). The same behavior was reported for RSA, and, conversely, FC was independent of culture media composition, and their extraction was enhanced using solvents with high methanol proportions (>85%). These findings confer a wide perspective about...Continue Reading

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Citations

Sep 29, 2020·Applied Microbiology and Biotechnology·Mohsen Hesami, Andrew Maxwell Phineas Jones
Sep 30, 2020·Plants·Robin Lardon, Danny Geelen
Dec 18, 2020·Frontiers in Plant Science·Pascual García-PérezPedro Pablo Gallego
May 19, 2021·Progress in Biophysics and Molecular Biology·Harshita Singh, Navneeta Bharadvaja

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