FRCFT, School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
Ultrason Sonochem. 2014 Mar;21(2):706-15. doi: 10.1016/j.ultsonch.2013.09.005. Epub 2013 Sep 13.
The ultrasound-assisted extraction process of phenolics including anthocyanins from wine lees was modeled and optimized in this research. An ultrasound bath system with the frequency of 40 kHz was used and the acoustic energy density during extraction was identified to 48 W/L. The effects of extraction time, extraction temperature, solvent-to-solid ratio and the solvent composition on the extraction yields of total phenolics and total anthocyanins were taken into account. The extraction process was simulated and optimized by means of artificial neural network (ANN) and genetic algorithm (GA). The constructed ANN models were accurate to predict the extraction yields of both total phenolics and total anthocyanins according to the statistical analysis. Meanwhile, the input space of the ANN models was optimized by GA, so as to maximize the extraction yields. Under the optimal conditions, the experimental yields of total phenolics and total anthocyanins were 58.76 and 6.69 mg/g, respectively, which agreed with the predicted values. Furthermore, more amounts of total phenolics and total anthocyanins were extracted by ultrasound at the optimal conditions than by conventional maceration. On the other hand, the stability of phenolics in the liquid extracts obtained from ultrasound-assisted extraction during storage was evaluated. After 30-day storage, the total phenolic contents in extracts stored at 4 °C and 20 °C decreased by 12.5% and 12.1%, respectively. Moreover, anthocyanins were more stable at 4 °C while tartaric esters and flavonols exhibited a better stability at 20 °C. Overall, the loss of phenolics during storage found in this study could be acceptable.
本研究对从葡萄酒渣中提取酚类物质（包括花色苷）的超声辅助提取工艺进行了建模和优化。采用频率为 40 kHz 的超声浴系统，提取过程中的声能密度确定为 48 W/L。考察了提取时间、提取温度、溶剂固比和溶剂组成对总酚和总花色苷提取率的影响。采用人工神经网络（ANN）和遗传算法（GA）对提取过程进行模拟和优化。根据统计分析，构建的 ANN 模型能够准确预测总酚和总花色苷的提取率。同时，通过 GA 对 ANN 模型的输入空间进行了优化，以最大化提取率。在最优条件下，总酚和总花色苷的实验提取率分别为 58.76 和 6.69 mg/g，与预测值相符。此外，与常规浸渍法相比，在最优条件下超声提取可提取出更多的总酚和总花色苷。另一方面，评估了超声辅助提取获得的液体提取物中酚类物质在储存过程中的稳定性。在 30 天的储存期后，在 4℃和 20℃下储存的提取物中总酚含量分别下降了 12.5%和 12.1%。此外，花色苷在 4℃下更稳定，而酒石酸酯和黄酮醇在 20℃下稳定性更好。总体而言，本研究中发现的酚类物质在储存过程中的损失是可以接受的。
