Carciochi Ramiro Ariel, Manrique Guillermo Daniel, Dimitrov Krasimir
Faculty of Engineering, Universidad Nacional del Centro de la Provincia de Buenos Aires, Av. del Valle 5737, 7400 Olavarría, Argentina.
ProBioGEM Laboratory, Polytech'Lille, Université Lille Nord de France, Avenue Paul Langevin, 59655 Villeneuve d'Ascq, France.
J Food Sci Technol. 2015 Jul;52(7):4396-404. doi: 10.1007/s13197-014-1514-4. Epub 2014 Aug 12.
The objective of this study was to optimize the extraction conditions of phenolic and flavonoids compounds from quinoa (Chenopodium quinoa) seeds using ultrasound assistance technology. A randomized central composite face-centered design was used to evaluate the effect of extraction temperature, ethanol concentration in the solvent, and ultrasound power on the total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity by response surface analysis. Predicted model equations were obtained to describe the experimental data regarding TPC, TFC and antioxidant activity, with significant variation in the linear, quadratic, and interaction effects of the independent variables. Regression analysis showed that more than 88 % of the variability was explained by the models. The best extraction conditions obtained by simultaneous maximization of the responses were: extraction temperature of 60 °C, 80 % ethanol as solvent and non-application of ultrasounds. Under the optimal conditions, the corresponding predicted response values were 103.6 mg GAE/100 g dry weight (dw), 25.0 mg quercetin equiv./100 g dw and 28.6 % DPPH radical scavenging, for TPC, TFC and antioxidant activity, respectively. The experimental values agreed with those predicted within a 95 % confidence level, indicating the suitability of the employed model. HPLC analysis of the obtained extracts confirmed the highest phenolic compound yield in the extract obtained under optimal extraction conditions. Considering the characteristics of the antioxidant-rich extracts obtained, they could be consider for potential application in the food industry, as nutraceutical and functional foods ingredient or well as replacement of synthetic antioxidants.
本研究的目的是利用超声辅助技术优化藜麦种子中酚类和黄酮类化合物的提取条件。采用随机中心复合面心设计,通过响应面分析评估提取温度、溶剂中的乙醇浓度和超声功率对总酚含量(TPC)、总黄酮含量(TFC)和抗氧化活性的影响。获得了预测模型方程来描述关于TPC、TFC和抗氧化活性的实验数据,自变量的线性、二次和交互效应存在显著差异。回归分析表明,模型解释了超过88%的变异性。通过同时最大化响应获得的最佳提取条件为:提取温度60℃,80%乙醇作为溶剂且不使用超声。在最佳条件下,TPC、TFC和抗氧化活性的相应预测响应值分别为103.6mg GAE/100g干重(dw)、25.0mg槲皮素当量/100g dw和28.6%的DPPH自由基清除率。实验值与在95%置信水平内预测的值一致,表明所采用模型的适用性。对所得提取物的HPLC分析证实,在最佳提取条件下获得的提取物中酚类化合物产量最高。考虑到所获得的富含抗氧化剂提取物的特性,它们可被考虑用于食品工业中的潜在应用,作为营养保健品和功能性食品成分以及替代合成抗氧化剂。