目的 利用熵权法结合灰色关联法-TOPSIS法建立三七Panax notoginseng质量评价模型，评价不同产地三七及其炮制品质量。方法 以水分、醇浸出物、皂苷含量、多糖得率、多糖总糖含量、糖蛋白含量及糖醛酸含量作为检测指标，以熵权法计算权重，结合灰色关联法与TOPSIS法对各项指标进行统计分析，建立三七质量评价模型，综合考察红河、文山和曲靖3个产地共15个批次的三七及其炮制品的质量。结果 检测结果表明不同产地不同批次的三七各指标水平大不相同；将三七炮制前后各项指标进行对比，发现蒸制后水分降低，浸出物含量增加，皂苷减少，多糖含量增加；15批三七及炮制品质量排序结果TOPSIS与灰色关联分析基本一致。结论 基于熵权法的灰色关联法-TOPSIS法建立的质量评价模型方便有效，可用于三七质量评价。

Objective To establish a quality evaluation model of Notoginseng Radix et Rhizoma using the entropy weight method combined with the gray correlation method and the TOPSIS method for evaluating the quality of Notoginseng Radix et Rhizoma and its processed products from different origins. Methods The water, alcohol extract, saponin content, polysaccharide yield, total polysaccharide content, glycoprotein content and uronic acid content were chosen as detection indexes for Notoginseng Radix et Rhizoma, the weight was calculated by entropy weight method, and the indexes were statistically analyzed by grey correlation method and TOPSIS method. Through statistical analysis, a quality evaluation model of Notoginseng Radix et Rhizoma was established to comprehensively inspect the quality of 15 batches of Notoginseng Radix et Rhizoma from three production areas, including Honghe, Wenshan and Qujing, and its processed products. Results The results showed that the indexes of Notoginseng Radix et Rhizoma from different regions and batches were quite different. Comparing the changes before and after processing, it was found that the moisture content decreased, the extract content increased, the saponin content decreased, and the polysaccharide content increased. Fifteen batches of Notoginseng Radix et Rhizoma and processed products quality ranking results TOPSIS were in good consistency with the grey relational analysis. Conclusion The quality evaluation model was established by the combined application of gray correlation method and TOPSIS method based on entropy weight method, it was simple and effective, which can be used for the quality evaluation of Notoginseng Radix et Rhizoma.

R286

国家重点研发计划项目（2018YFC1707100）