Carbon dot unravels accumulation of triterpenoid in Evolvulus alsinoides hairy roots culture by stimulating growth, redox reactions and ANN machine learning model prediction of metabolic stress response.

Evolvulus alsinoides, a therapeutically valuable shrub can provide consistent supply of secondary metabolites (SM) with pharmaceutical significance. Nonetheless, because of its short life cycle, fresh plant material for research and medicinal diagnostics is severely scarce throughout the year. The effects of exogenous carbon quantum dot (CD) application on metabolic profiles, machine learning (ML) prediction of metabolic stress response, and SM yields in hairy root cultures of E. alsinoides were investigated and quantified. The range of the particle size distribution of the CDs was between 3 and 7 nm. The CDs EPR signal and spin trapping experiments demonstrated the formation of O2-•spin-adducts at (g = 2.0023). Carbon dot treatment increased the levels of hydrogen peroxide and malondialdehyde concentrations as well as increased antioxidant enzyme activity. CD treatments (6 µg mL-1) significantly enhanced the accumulation of squalene and stigmasterol (7 and 5-fold respectively). The multilayer perceptron (MLP) algorithm demonstrated remarkable prediction accuracy (MSE value = 1.99E-03 and R2 = 0.99939) in both the training and testing sets for modelling. Based on the prediction, the maximum oxidative stress index and enzymatic activities were highest in the medium supplemented with 10 µg mL-1 CDs. The outcome of this study indicated that, for the first time, using CD could serve as a novel elicitor for the production of valuable SM. MLP may also be used as a forward-thinking tool to optimize and predict SM with high pharmaceutical significance. This study would be a touchstone for understanding the use of ML and luminescent nanomaterials in the production and commercialization of important SM.