Smart estimation of protective antioxidant enzymes' activity in savory (Satureja rechingeri L.) under drought stress and soil amendments
Journal
BMC Plant Biology
Date Issued
January 6, 2025
DOI
10.1186/s12870-024-06044-x
Abstract
Savory (Satureja rechingeri L.) is one of Iran's most important medicinal plants, having low irrigation needs, and thus is considered one of the most valuable plants for cultivation in arid and semi-arid regions, especially under drought conditions. The current research was carried out to develop a genetic algorithm-based artificial neural network (ΑΝΝ) model able of simulating the levels of antioxidants in savory when using soil amendments [biochar (BC) and superabsorbent (SA)] under drought. Data under different watering schemes and different levels of soil amendments showed that both BC and SA have mitigating effects over drought stress by optimizing enzymatic and non-enzymatic antioxidant traits (POD, CTA, and APX enzymes). Specifically, using biochar and superabsorbent led to improved homeostasis under water deficit as reflected by lower MDA levels. An ANN model with a 3-10-6 topology was found to be the best model to predict polyphenols (PHE), proline (PRO), peroxidase (POX), catalase (CAT), ascorbate peroxidase (APX) levels, and indicator of oxidative stress malondialdehyde (MDA). The model's efficiency was established using the R-value as the statistical parameter, and simulated GA-ANN data were highly correlated with experimental findings. Across enzymatic antioxidants, APX had the best model fit, having an R-value of 0.9733. On the other hand, POX had a lower predictive correlation (R = 0.8737), indicating a lower capacity of the ANN system in forecasting this parameter. On the other hand, MDA (R = 0.9690) had an elevated assimilation performance over PHE (R = 0.9604) and PRO (R = 0.9245) levels. The current study shows the potential of the ANN model in predicting the content of enzymatic and non-enzymatic antioxidants in savory plants under drought stress as a non-invasive, low-cost experimental alternative.