ArticleScientific reports2026
Artificial neural networks (ANN) and response surface methodology (RSM) for optimizing antioxidant and α-amylase inhibitory compound extraction from Ballota limbata.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Ballota limbata (syn. Otostegia limbata), a medicinal plant with various ethno-therapeutic uses, was investigated for the recovery of its bioactive compounds using sustainable extraction strategies. This study compared microwave-assisted extraction (MA) and conventional heat-assisted extraction (HA), employing 70% hydro-ethanol as the solvent and optimizing both techniques via response surface methodology (RSM) based on a 3-factor-3-level Box-Behnken design and Artificial Neural Network (ANN), comprising three input variables and one hidden layer. The response factors included total phenolic content (TPC), total flavonoid content (TFC), DPPH radical scavenging activity (RSA), and α-amylase inhibitory activity (AAI). MA under optimized conditions (power 220 W, time 20 s, solvent-to-solid ratio (SSR) 40 mL/g) yielded TPC, TFC, RSA, and AAI as 3.27 mg gallic acid equivalents/g (GAE/g) dry weight (DW), 2.43 mg rutin equivalents/g (RE/g) DW, 2.16 mg ascorbic acid equivalents/g (AAE/g) DW, and 60.99%, respectively; while HA under optimized conditions (temperature 57 °C, time 150 min, SSR 40 mL/g) produced 3.85 mg GAE/g DW TPC, 2.40 mg RE/g DW TFC, 2.50 mg AAE/g DW RSA, and 86.22% AAI. Statistical modeling showed that MA and HA both were reliable, with low p-values, strong fit statistics, and small differences between predicted and observed values. The yield of bioactives obtained from HA was higher than MAE, but MAE was more efficient in terms of time, which aligns directly with green chemistry principles. Both ANN and RSM performed well; however, ANN had superior predictive accuracy with higher R
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