文章基本信息

标题：Polyphenols Recovery from Thymus serpyllum Industrial Waste Using Microwave-Assisted Extraction–Comparative RSM and ANN Approach for Process Optimization
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作者：Živan Mrkonjić ; Dušan Rakić ; Aleksandar Takači 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2022
卷号：11
期号：9
DOI：10.3390/foods11091184
语种：English
出版社：MDPI Publishing
摘要：The aim of this study was to valorize Thymus serpyllum L. herbal dust, the particular fraction distinguished as industrial waste from filter-tea production. This work demonstrated comparable analysis considering model fitting, influence analysis and optimization of microwave-assisted extraction (MAE) of bioactive compounds from the aforementioned herbal dust using face-centered central composite experimental design within the response surface methodology (RSM), as well as artificial neural networks (ANN). In order to increase yield and amount of compounds of interest and minimize solvent, time and energy consumption, the ethanol concentration (45, 60 and 75%), extraction time (5, 12.5 and 20 min), liquid–solid ratio (10, 20 and 30 mL/g) and irradiation power (400, 600 and 800 W) were used as independent variables. Total extraction yield (Y), total phenols yield (TP), as well as antioxidant activity parameters obtained by DPPH and ABTS assays, were selected as responses. It could be concluded that the MAE technique is an efficient approach for the extraction of biologically active compounds from T. serpyllum herbal dust, which represents a high-value source of natural antioxidants with great potential for further use in various forms within different branches of industry.
关键词：enwild thymeMAEantioxidant activitymulti-response optimizationartificial neural networkby-productLamiaceae