基于组效关系的新疆薰衣草精油生物活性成分的辨识和质量控制研究

Lavender essential oil is widely used in the food and pharmaceuticals industries because of its strong antibacterial and antioxidant activity. At present, the chemical components of lavender essential oil with marked biological activity can not been identified because of the complexity of the chemical composition of the essential oil. In addition, the relationship between chromatographic fingerprint and bioactivity of lavender essential oil has not been studied so far. Firstly, the chemical fingerprint will be established of Xinjiang lavender essential oil using gas chromatography-mass spectrometry (GC/MS) in this project. Secondly, the antimicrobial and antioxidant activities of lavender essential oil will be evaluated. Lastly, researching the composition-activity relationship of lavender oil according to chemical fingerprint data and bioactivity data from experiments, the bioactive ingredients in antibacterial and antioxidant can be discovered and the activity prediction model of lavender oil can be constructed by chemometrics methods. These methods include partial least squares (PLS), particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN). The successful implementation of this project will provide the scientific basis for the identification of active ingredients and quality control of Xinjiang lavender essential oil.

薰衣草精油较强的抑菌和抗氧化能力使其在医药、食品中得到广泛应用。但由于薰衣草精油化学成分的复杂性，目前仍无法明确对精油生物活性起主要作用的化学组成；同时，用于质量评价的化学指纹图谱技术无法反映薰衣草精油的生物活性，缺乏对精油内在质量控制的研究。本课题首先采用气相色谱/质谱联用(GC/MS)技术研究薰衣草精油的化学成分，建立化学指纹图谱。其次，对薰衣草精油进行抑菌和抗氧化活性评价，建立精油的生物活性数据库。最后，采用偏最小二乘法(PLS)、粒子群优化算法(PSO)、遗传算法(GA)和人工神经网络(ANN)等化学计量学方法研究薰衣草精油的组效关系，揭示精油中的抑菌和抗氧化活性成分，并建立抑菌和抗氧化活性的预测模型，对薰衣草精油进行生物活性预测，评价精油质量的好坏。本项目的成功实施将对辨识新疆薰衣草精油中的生物活性成分及质量控制提供科学依据。

薰衣草精油较强的抑菌和抗氧化能力使其在医药、食品中得到广泛应用。薰衣草精油的化学成分极为复杂，本课题的完成成功揭示了薰衣草精油的生物活性成分、建立了精油的质量控制体系及高效的精油提取方法。首先采用GC-MS技术研究不同品种薰衣草精油的化学成分，以GC-MS谱图结合保留指数(RI)对复杂未知物进行定性分析，建立化学指纹图谱。其次，对薰衣草精油进行抑菌（金黄色葡萄球菌、枯草芽孢杆菌和大肠杆菌）和抗氧化活性（DPPH）评价。以半抑制浓度IC50和半数清除浓度EC50为评价指标，分别建立精油的抑菌和抗氧化活性数据库。最后，在获取薰衣草精油GC-MS指纹图谱及抑菌和抗氧化活性数据的基础上，采用主成分分析（PCA）、偏最小二乘法(PLS)和人工神经网络(ANN)等化学计量学方法研究薰衣草精油的组效关系，成功揭示出精油中的抑菌和抗氧化活性成分；同时，采用化学计量学方法对不同品种的薰衣草精油数据进行分析，筛选出不同品种的特征变量组分与差异标志物；采用Box-Behnken design（BBD）响应面法优化微波辅助水蒸气蒸馏（MAHD）提取薰衣草精油的提取工艺参数；以多响应值为优化指标，采用人工神经网络（ANN）建立微波辅助提取薰衣草精油提取工艺的预测模型；建立了Fe3O4磁性粒子结合微波辅助快速分析薰衣草干花中的挥发性成分的方法。本课题的顺利完成为薰衣草精油的深度开发利用及质量控制提供了科学依据。