Exploitation of Kiwi Juice Pomace for the Recovery of Natural Antioxidants through Microwave-Assisted Extraction
Exploitation of Kiwi Juice Pomace for the Recovery of Natural Antioxidants through Microwave-Assisted Extraction
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DOI：
10.3390/agriculture10100435
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发表时间：
2020-10-01
影响因子：
3.6
通讯作者：
Iadecola, Rosamaria
中科院分区：
文献类型：
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作者：
Carbone, Katya;Amoriello, Tiziana;Iadecola, Rosamaria
采用微波辅助萃取技术提取猕猴桃果渣中的抗氧化成分，研究了温度、萃取时间、溶剂组成和固液比对总酚含量的影响。采用三水平部分因子设计、响应面法和期望度优化相结合、前馈多层感知器人工神经网络结合反向传播算法，对KP中总多酚的绿色提取工艺进行优化。数据通过ANOVA进行分析，并使用回归方法拟合为二阶多项式方程。结果表明，T对总多酚提取率的影响最大，其次是R和C，而提取时间E对总多酚提取率的线性影响不显著。基于所有反应的单独和组合，找出了最佳条件（T：75 ℃; E：15分钟; C：50%乙醇：水; R：1：15），并且在这些条件下，所获得的提取物显示出高生物活性化合物含量和高抗氧化潜力，指出了这种副产物如何能够成为具有高附加值的化合物的廉价来源。一个非常好的协议之间观察到的实验和计算的提取收率，从而支持使用这些模型来定量描述从KP天然抗氧化剂的回收。最后，ANN模型表现出更准确的预测和更好的泛化能力比RSM模型（R-2：0.90和0.99，RSM和ANN，分别）。
In a completely green approach to the exploitation of kiwi juice pomace (KP), a microwaved-assisted extraction (MAE) process was performed to extract antioxidant compounds present in KP, evaluating the influence of four independent process variables (temperature (T), extraction time (E), solvent composition (C), and solid-to-solvent ratio (R)) on the response of total phenolic content (TPC). The optimal conditions for the green extraction of total polyphenols from KP were obtained using a three-level fractional factorial design under response surface methodology (RSM) coupled with desirability optimization, and a feed-forward multilayered perceptron artificial neural network (ANN) with a back-propagation algorithm. Data were analyzed by ANOVA and fitted to a second-order polynomial equation using the regression method. Results showed that T was the most influential factor, followed by R and C, whereas the extraction time (E) was not shown to have a significant linear effect on the extraction yield of total polyphenols (TPs). The optimal conditions based on both individual and combinations of all responses were found out (T: 75 degrees C; E: 15 min; C: 50% ethanol:water; R: 1:15), and under these conditions the obtained extract showed both a high bioactive compound content and a high antioxidant potential, pointing out how this by-product could become an inexpensive source of compounds with high added value. A very good agreement was observed between experimental and calculated extraction yields, thus supporting the use of these models to quantitatively describe the recovery of natural antioxidants from KP. Finally, the ANN model exhibited more accurate prediction and better generalization capabilities than the RSM model (R-2: 0.90 and 0.99, for RSM and ANN, respectively).