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首页> 美国卫生研究院文献>Antioxidants >Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds
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Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds

机译：将药用植物体外培养与机器学习技术相结合以最大程度地生产酚类化合物

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摘要

We combined machine learning and plant in vitro culture methodologies as a novel approach for unraveling the phytochemical potential of unexploited medicinal plants. In order to induce phenolic compound biosynthesis, the in vitro culture of three different species of under nutritional stress was established. To optimize phenolic extraction, four solvents with different MeOH proportions were used, and total phenolic content (TPC), flavonoid content (FC) and radical-scavenging activity (RSA) were determined. All results were subjected to data modeling with the application of artificial neural networks to provide insight into the significant factors that influence such multifactorial processes. Our findings suggest that aerial parts accumulate a higher proportion of phenolic compounds and flavonoids in comparison to roots. TPC was increased under ammonium concentrations below 15 mM, and their extraction was maximum when using solvents with intermediate methanol proportions (55–85%). The same behavior was reported for RSA, and, conversely, FC was independent of culture media composition, and their extraction was enhanced using solvents with high methanol proportions (>85%). These findings confer a wide perspective about the relationship between abiotic stress and secondary metabolism and could serve as the starting point for the optimization of bioactive compound production at a biotechnological scale.
机译：我们将机器学习与植物体外培养方法相结合，作为一种新方法，用于挖掘未开发药用植物的植物化学潜力。为了诱导酚类化合物的生物合成，建立了在营养胁迫下三种不同物种的体外培养。为了优化酚醛萃取，使用了四种具有不同MeOH比例的溶剂，并确定了总酚含量（TPC），类黄酮含量（FC）和自由基清除活性（RSA）。使用人工神经网络对所有结果进行数据建模，以深入了解影响此类多因素过程的重要因素。我们的发现表明，与根部相比，地上部分积聚了更高比例的酚类化合物和类黄酮。在铵浓度低于15 mM的情况下，TPC会增加，当使用中等甲醇比例（55-85％）的溶剂时，TPC的提取量最大。据报道，RSA具有相同的行为，相反，FC不受培养基组成的影响，使用高甲醇比例（> 85％）的溶剂可提高其提取率。这些发现为非生物胁迫与次级代谢之间的关系提供了广阔的前景，并可作为在生物技术规模上优化生物活性化合物生产的起点。

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