School of Pharmacy, Xi'an Jiaotong University, Xi'an, 710061, China.
National Institutes for Food and Drug Control, No. 2 Tiantan Xili, Dongcheng District, Beijing, 100050, China.
Environ Sci Pollut Res Int. 2020 Dec;27(36):45018-45030. doi: 10.1007/s11356-020-10309-x. Epub 2020 Aug 9.
Traceability offers significant information about the quality and safety of Chinese Angelica, a medicine and food homologous substance. In this study, a systematic four-step strategy, including sample collection, specific metal element fingerprinting, multivariate statistical analysis, and benefit-risk assessment, was developed for the first time to identify Chinese Angelica based on geographical origins. Fifteen metals in fifty-six Chinese Angelica samples originated from three provinces were analyzed. The multivariate statistical analysis model established, involving hierarchical cluster analysis (HCA), principal component analysis (PCA), and self-organizing map clustering analysis was able to identify the origins of samples. Furthermore, benefit-risk assessment models were created by combinational calculation of chemical daily intake (CDI), hazard index (HI), and cancer risk (CR) levels to evaluate the potential risks of Chinese Angelica using as traditional Chinese medicine (TCM) and food, respectively. Our systematic strategy was well convinced to accurately and effectively differentiate Chinese Angelica based on geographical origins.
追溯性提供了关于中药当归质量和安全性的重要信息，当归是一种药食同源物质。在这项研究中，首次开发了一种系统的四步策略，包括样品采集、特定金属元素指纹图谱、多元统计分析和效益风险评估，以基于地理起源来识别当归。对来自三个省份的 56 个当归样本中的 15 种金属进行了分析。建立的多元统计分析模型，包括层次聚类分析（HCA）、主成分分析（PCA）和自组织映射聚类分析，能够识别样品的来源。此外，通过组合计算化学日摄入量（CDI）、危害指数（HI）和癌症风险（CR）水平，创建了效益风险评估模型，分别评估当归作为中药和食品的潜在风险。我们的系统策略被证明可以准确有效地基于地理起源来区分当归。
