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利用非靶向色谱指纹图谱、数据融合和化学计量学评估 的地理来源。

Assessing Geographical Origin of Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics.

机构信息

Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China.

The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresouces in China and Southeast Asia, Yunnan University, Kunming 650091, China.

出版信息

Molecules. 2019 Jul 14;24(14):2562. doi: 10.3390/molecules24142562.

Abstract

Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies ("low-level" and "mid-level"). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of and developed a reliable and accurate identification method for that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

摘要

Franchet 因其苦味而闻名,是中国治疗慢性肝炎的传统药物,也是制药行业的重要原料。本研究采用高效液相色谱法(HPLC),结合二极管阵列检测器(DAD)和化学计量学,研究了不同产地的化学成分的地理变化,并根据其生长纬度对药材进行分类。记录并分析了来自四个不同纬度地区的 280 个人和 840 个根茎、茎和叶样本的色谱指纹图谱,以追踪药材的地理来源。首先,使用反相液相色谱法生成地下和地上部分的 HPLC 指纹图谱。在初步数据探索之后,将两种有监督的模式识别技术,随机森林(RF)和正交偏最小二乘判别分析(OPLS-DA),分别应用于根茎、茎和叶的三个 HPLC 指纹数据集。此外,分别处理和合并地上和地下部分的指纹数据集,同时使用两种数据融合策略(“低水平”和“中水平”)。结果表明,基于 OPLS-DA 的分类模型比 RF 模型更有效。使用低水平数据融合方法构建的分类模型显示出相当好的识别和预测能力(准确性高于 99%,灵敏度、特异性、马修斯相关系数和效率范围为 0.95 至 1.00)。低水平数据融合策略与 OPLS-DA 相结合可以提供最佳的区分结果。总之,本研究探讨了 的植物化学成分的纬度变化,并基于非靶向 HPLC 指纹图谱、数据融合和化学计量学,为不同纬度生长的 开发了一种可靠且准确的鉴定方法。该研究结果对中药材的鉴定和质量控制具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31fd/6680800/10d546d51955/molecules-24-02562-g001.jpg

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