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

1
Plant Biology and Soil Science Department, Biology Faculty, University of Vigo, 36310 Vigo, Spain
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Pharmacology, Pharmacy and Pharmaceutical Technology Department, Faculty of Pharmacy, University of Santiago, E-15782 Santiago de Compostela, Spain
*
Author to whom correspondence should be addressed.
Antioxidants 2020, 9(3), 210; https://doi.org/10.3390/antiox9030210
Received: 17 February 2020 / Revised: 27 February 2020 / Accepted: 2 March 2020 / Published: 4 March 2020
(This article belongs to the Section Natural and Synthetic Antioxidants)
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 the relationship between abiotic stress and secondary metabolism and could serve as the starting point for the optimization of bioactive compound production at a biotechnological scale. View Full-Text
Keywords: antioxidants; artificial intelligence; biotechnology; fuzzy logic; Kalanchoe; phytochemistry; plant tissue culture; polyphenols; secondary metabolites antioxidants; artificial intelligence; biotechnology; fuzzy logic; Kalanchoe; phytochemistry; plant tissue culture; polyphenols; secondary metabolites
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MDPI and ACS Style

García-Pérez, P.; Lozano-Milo, E.; Landín, M.; Gallego, P.P. Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds. Antioxidants 2020, 9, 210. https://doi.org/10.3390/antiox9030210

AMA Style

García-Pérez P, Lozano-Milo E, Landín M, Gallego PP. Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds. Antioxidants. 2020; 9(3):210. https://doi.org/10.3390/antiox9030210

Chicago/Turabian Style

García-Pérez, Pascual, Eva Lozano-Milo, Mariana Landín, and Pedro P. Gallego. 2020. "Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds" Antioxidants 9, no. 3: 210. https://doi.org/10.3390/antiox9030210

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