RT Journal Article
T1 Combining medicinal plant in vitro culture with machine learning technologies for maximizing the production of phenolic compounds
A1 García Pérez, Pascual
A1 Lozano Milo, Eva
A1 Landín Pérez, Mariana
A1 Gallego Veigas, Pedro Pablo
K1 2417.19 Fisiología Vegetal
K1 3302 Tecnología Bioquímica
K1 1203.04 Inteligencia Artificial
AB 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.
PB Antioxidants
SN 20763921
YR 2020
FD 2020-03-04
LK http://hdl.handle.net/11093/1762
UL http://hdl.handle.net/11093/1762
LA eng
NO Antioxidants, 9(3): 210 (2020)
NO Xunta de Galicia | Ref. ED431D 2017/18
DS Investigo
RD 11-dic-2024