Multi-objective optimization of ultrasound-assisted extraction for enhanced bioactive recovery from bee bread

Creative Commons License

Akhan M., AÇIKGÖZOĞLU E., Simsek M. A., Yikmis S., GÜRFİDAN R., Aksu F., ...Daha Fazla

FRONTIERS IN NUTRITION, cilt.13, 2026 (SCI-Expanded, Scopus)

Özet

This study aimed to optimize ultrasound-assisted extraction conditions for improving the recovery of phenolic compounds and antiradical activity from bee bread (perga). A three-factor Box-Behnken design comprising ultrasound amplitude (30-70%), extraction time (8-22 min), and ethanol concentration (20-80%, v/v) was combined with Gaussian Process surrogate modeling and a single-cycle GP-based multi-objective Bayesian optimization (GP-MOBO) candidate-selection strategy. Predictive uncertainty and Pareto-based analysis were used to identify a balanced extraction condition, which was subsequently validated experimentally and compared with an NUS and conventional extraction treatments. The selected ultrasound-assisted extraction condition (UAEopt; 56.5% amplitude, 19.0 min, and 56.8% ethanol) yielded a total phenolic content of 17.32 mg GAE/g, compared with 11.76 mg GAE/g in the matched NUS (p < 0.01). Antiradical activity also increased from 3.215 mg TEAC/g in NUS to 4.17 mg TEAC/g in UAEopt (p < 0.05). HPLC-DAD analysis showed that UAEopt produced the highest total concentration of the quantified phenolic compounds (44.19 +/- 1.23 mu g/g), with gallic acid as the predominant compound. LC-MS analysis further demonstrated that UAEopt yielded the highest total free amino acid concentration among the evaluated extraction conditions (23,409.32 +/- 766.26 mu g/g), with glutamic acid, aspartic acid, and proline as the major components. These findings demonstrate that Gaussian Process-guided multi-objective optimization can support the selection of effective ultrasound-assisted extraction conditions and improve the recovery of multiple bioactive fractions from bee bread.