Harnessing the power of artificial neural networks methodology and multi-objective optimization for enhanced yield and bioactivity of plants polyphenolic compounds

IF 3.8 2区 农林科学 Q1 PLANT SCIENCES
Yousra Touami , Rafik Marir
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引用次数: 0

Abstract

The extraction of polyphenolic compounds from plants is crucial in the industrial production of functional nutraceuticals, but traditional methods often yield low and variable results. In this research, an innovative strategy for optimizing polyphenol extraction from two plants Cistus creticus L. and Ephedra alata subsp. alenda (Stapf) Trab., known for their rich composition in polyphenols and their bioactivities, using Ultrasound-Assisted Extraction in conjunction with artificial neural networks (ANNs) and multi-objective optimization is presented. ANNs were trained to model the intricate relationships among UAE parameters, including solvent concentration, temperature, and time, and the outcomes, encompassing polyphenol yield and bioactivity. Multi-objective optimization techniques were subsequently applied to identify extraction conditions that maximize both yield and bioactivity simultaneously. Results validate the accuracy of the ANNs model in predicting polyphenol yields and the significant enhancement in extraction efficiency and bioactivity achieved through multi-objective optimization. The extracts prepared in the optimal conditions have demonstrated superior antioxidant activities, compared to the non-optimized extracts, with the smallest values of IC50 of 242,378 µg/mL, and 146,736 µg/mL for the plants Ephedra alata subsp alenda (Stapf) Trab. and Cistus creticus L. respectively. This study introduces a promising approach for elevating the extraction of plant-derived polyphenols, augmenting their bioactivity with ANNs and multi-objective optimization. In light of the obtained results, it is recommended that further research explore the scalability and applicability of the presented innovative strategy in larger-scale industrial settings. Considering the demonstrated success in optimizing polyphenol extraction from Cistus creticus L. and Ephedra alata subsp. alenda (Stapf) Trab., extending the application of Ultrasound-Assisted Extraction, coupled with artificial neural networks (ANNs) and multi-objective optimization, to other plant species could offer valuable insights. Additionally, investigating the economic feasibility and environmental impact of implementing this strategy on an industrial scale would contribute to its practical viability.

Abstract Image

利用人工神经网络方法和多目标优化的力量提高植物多酚化合物的产量和生物活性
从植物中提取多酚化合物对于功能性营养保健品的工业化生产至关重要,但传统方法的提取率往往较低,且结果不一。在这项研究中,介绍了一种创新策略,即利用超声辅助萃取技术,结合人工神经网络(ANN)和多目标优化,从两种植物 Cistus creticus L. 和 Ephedra alata subsp.对人工神经网络进行了训练,以模拟超声辅助萃取参数(包括溶剂浓度、温度和时间)与结果(包括多酚产量和生物活性)之间的复杂关系。随后,应用多目标优化技术确定了同时使产量和生物活性最大化的提取条件。结果验证了 ANNs 模型在预测多酚产量方面的准确性,以及通过多目标优化显著提高的萃取效率和生物活性。与未优化的提取物相比,在优化条件下制备的提取物具有更高的抗氧化活性,其中麻黄(Ephedra alata subsp alenda (Stapf) Trab.)和肉苁蓉(Cistus creticus L.)的 IC50 最小值分别为 242378 微克/毫升和 146736 微克/毫升。这项研究介绍了一种很有前景的方法,即利用方差网络和多目标优化来提高植物多酚的提取率,增强其生物活性。鉴于所获得的结果,建议进一步研究探索所提出的创新策略在更大规模工业环境中的可扩展性和适用性。考虑到从 Cistus creticus L. 和 Ephedra alata subsp.此外,调查在工业规模上实施这一策略的经济可行性和环境影响将有助于提高其实际可行性。
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来源期刊
Journal of Applied Research on Medicinal and Aromatic Plants
Journal of Applied Research on Medicinal and Aromatic Plants Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
6.40
自引率
7.70%
发文量
80
审稿时长
41 days
期刊介绍: JARMAP is a peer reviewed and multidisciplinary communication platform, covering all aspects of the raw material supply chain of medicinal and aromatic plants. JARMAP aims to improve production of tailor made commodities by addressing the various requirements of manufacturers of herbal medicines, herbal teas, seasoning herbs, food and feed supplements and cosmetics. JARMAP covers research on genetic resources, breeding, wild-collection, domestication, propagation, cultivation, phytopathology and plant protection, mechanization, conservation, processing, quality assurance, analytics and economics. JARMAP publishes reviews, original research articles and short communications related to research.
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