Combining artificial neural network and moth-flame optimization algorithm for optimization of ultrasound-assisted and microwave-assisted extraction parameters: Bark of Pinus brutia

IF 1.2 4区农林科学 Q3 MATERIALS SCIENCE, PAPER & WOOD

Maderas-ciencia Y Tecnologia Pub Date : 2022-04-01 DOI:10.4067/s0718-221x2022000100424

Ayşenur Gürgen, Başak Atilgan, S. Yıldız, O. Gönültaş, Sami Imamoğlu

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Abstract

In this study, the extraction parameters of Pinus brutia bark were optimized using a hybrid artificial in telligence technique. Firstly, the bark samples were extracted by ultrasound-assisted extraction and micro -wave-assisted extraction which are defined as ‘green’ extraction methods at different conditions. The selected extraction parameters for ultrasound-assisted extraction were 0:100; 20:80; 40:60; 80:20 (%) ethanol: water ratios; 40 ºC, 60 °C extraction temperatures and 5 min, 10 min, 15 min, 20 min extraction times and for mi -crowave-assisted extraction were 90, 180, 360, 600, 900 (W) microwave power, 0:100; 20:80; 40:60; 60:40; 80:20 (%) ethanol: water ratios. Then Stiasny number, condensed tannin content and reducing sugar content of all extracts were determined. Next, the prediction models were developed for each studied parameter using Artificial Neural Network. Finally, the extraction parameters were optimized using Moth-Flame Optimization Algorithm. After that optimization process, while the extraction time was the same (5 min), the ethanol: water ratio and extraction temperature values differed for the optimization of all studied assays of ultrasound-assisted extraction. Also, microwave power and ethanol: water ratio variables were found in different values for each assay of microwave-assisted extraction. The results showed that the Artificial Neural Network and Moth-Flame Optimization could be a novel and powerful hybrid approach to optimize the extraction parameters of Pinus brutia barks with saving time, cost, chemical and effort. Developing prediction model for UAE extraction parameters of Pinus brutia bark using ANN Developing prediction model for MAE extraction parameters of Pinus brutia bark using ANN Optimization of UAE extraction parameters of Pinus brutia bark using MFO algorithm Optimization of MAE extraction parameters of Pinus brutia bark using MFO algorithm

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结合人工神经网络和蛾焰优化算法优化超声和微波辅助提取参数:棕松皮

本研究采用混合人工智能技术对粗松皮的提取工艺进行优化。首先，在不同条件下，分别采用超声辅助提取和微波辅助提取两种“绿色”提取方法提取树皮样品。超声辅助提取的提取参数为0∶100;20:80;40:6 0;80:20(%)乙醇:水比;40℃、60℃提取温度下5 min、10 min、15 min、20 min提取次数和微波辅助提取分别为90、180、360、600、900 (W)微波功率，0:100;20:80;40:6 0;比例;80:20(%)乙醇:水的比例。然后测定各提取物的Stiasny数、缩合单宁含量和还原糖含量。其次，利用人工神经网络对各研究参数建立预测模型。最后，采用蛾焰优化算法对提取参数进行优化。优化后，在提取时间相同(5 min)的情况下，不同的乙醇:水比和提取温度对超声辅助提取的各项指标进行优化。此外，微波功率和乙醇:水比变量在微波辅助提取的每个实验中都有不同的值。结果表明，人工神经网络和飞蛾火焰优化是一种新颖而有效的混合方法，可以优化松皮的提取参数，节省时间、成本、化学成分和精力。基于神经网络的油松UAE提取参数预测模型基于神经网络的油松MAE提取参数预测模型基于MFO算法的油松UAE提取参数优化基于MFO算法的油松MAE提取参数优化

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Maderas-ciencia Y Tecnologia 工程技术-材料科学：纸与木材

CiteScore

2.60

自引率

13.30%

发文量

审稿时长

>12 weeks

期刊介绍： Maderas-Cienc Tecnol publishes inedits and original research articles in Spanish and English. The contributions for their publication should be unpublished and the journal is reserved all the rights of reproduction of the content of the same ones. All the articles are subjected to evaluation to the Publishing Committee or external consultants. At least two reviewers under double blind system. Previous acceptance of the Publishing Committee, summaries of thesis of Magíster and Doctorate are also published, technical opinions, revision of books and reports of congresses, related with the Science and the Technology of the Wood. The journal have not articles processing and submission charges.