Ponticelli Maria, Carlucci Vittorio, Mecca Marisabel, Todaro Luigi, Milella Luigi, Russo Daniela
Department of Science, University of Basilicata, Via dell'Ateneo Lucano 10, 85100 Potenza, Italy.
Department of Biochemical Pharmacology & Drug Design, Institute of Molecular Biology "Roumen Tsanev", Bulgarian Academy of Sciences (BAS), Acad. G. Bonchev Str., bl. 21, 1113 Sofia, Bulgaria.
Antioxidants (Basel). 2024 Sep 14;13(9):1115. doi: 10.3390/antiox13091115.
From a circular bio-economy perspective, biomass valorization requires the implementation of increasingly efficient extraction techniques to ensure the environmental and economic sustainability of biorefining processes. This research focuses on optimizing the specialized metabolite extraction of Turkey oak chips from L. by applying a 3 levels Full Factorial Design (FFD). The goal is to obtain an extract with the highest antioxidant activity [evaluated by 1,1-diphenyl-2-picryl hydrazyl (DPPH) scavenging activity and ferric reducing antioxidant power (FRAP) assays] and specialized metabolites content [measured as total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC), and hydrolysable tannins content (THC)]. With this objective, three different variables were investigated and compared: temperature (20 °C, 50 °C, 80 °C), solvents EtOH/HO (0%, 20%, 40%), and time (3 h, 6 h, 24 h), resulting in 27 different extracts. Following the FFD analysis, the optimal extractive conditions were determined to be 80 °C, 40% EtOH/HO, and 19.8 h. Finally, the prediction ability of FFD was compared with that of artificial neural network (ANN) for DPPH scavenging activity, FRAP, and TPC data based on the coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that ANN predictions were more precise than FFD ones; however, both methods were useful in optimizing the extraction process as they returned comparable optimized extraction parameters.
从循环生物经济的角度来看，生物质增值需要采用效率越来越高的提取技术，以确保生物精炼过程的环境和经济可持续性。本研究聚焦于通过应用三水平全因子设计（FFD）来优化土耳其栎木屑中特定代谢产物的提取。目标是获得具有最高抗氧化活性的提取物[通过1,1-二苯基-2-苦基肼（DPPH）清除活性和铁还原抗氧化能力（FRAP）测定来评估]以及特定代谢产物含量[以总酚含量（TPC）、总黄酮含量（TFC）、缩合单宁含量（CTC）和水解单宁含量（THC）来衡量]。为实现这一目标，研究并比较了三个不同变量：温度（20℃、50℃、80℃）、乙醇/水溶剂（0%、20%、40%）和时间（3小时、6小时、24小时），从而得到27种不同的提取物。通过FFD分析，确定最佳提取条件为80℃、40%乙醇/水和19.8小时。最后，基于决定系数（R）、平均绝对误差（MAE）和均方根误差（RMSE），将FFD对DPPH清除活性、FRAP和TPC数据的预测能力与人工神经网络（ANN）的预测能力进行了比较。结果表明，ANN的预测比FFD的更精确；然而，两种方法在优化提取过程中都很有用，因为它们得出了相当的优化提取参数。
