目的 建立18个品种苦荞麦Fagopyrum tataricum指纹图谱，并以芦丁为内标物，采用一测多评法（quantitative analysis of multi-components by single-marker，QAMS）同时检测苦荞麦中葫芦巴碱、表儿茶素、山柰酚-3-O-芸香糖苷、槲皮素、山柰酚和大黄素含量，以期对不同产地苦荞麦进行质量控制。方法 采用超高效液相-三重四级杆液质联用技术（UPLC-QQQ-MS/MS），首次以一级全扫（Full-MS）和多反应监测（multiple reaction monitoring，MRM）2种采集模式相结合的方式建立UPLC-QQQ-MS/MS指纹图谱共有模式，采用夹角余弦法进行相似度评价。以ThermoFisher Accucore^TM C₁₈色谱柱（100 mm×3.0 mm，2.6 μm）；流动相为乙腈-0.1%甲酸水溶液，体积流量0.3 mL/min，柱温30 ℃，梯度洗脱。以芦丁作为内标物，建立苦荞中其他6种指标成分的相对校正因子（f_s/x），从而计算各待测成分的量，并将QAMS计算值与外标法（external standard method，ESM）实测值进行比较，以验证QAMS的准确性和方法适宜性。运用聚类分析、主成分分析、加权分析等化学计量学分析手段建立苦荞麦综合质量优劣评价方法。结果 建立了苦荞麦的UPLC-QQQ-MS/MS指纹图谱，在Full-MS扫描模式下标定了15个共有峰，各品种间相似度在0.399～0.973；同时以MRM扫描模式结合对照品对其中芦丁等7种指标成分进行了峰指认。以芦丁为内标物，与葫芦巴碱、表儿茶素、山柰酚-3-O-芸香糖苷、槲皮素、山柰酚和大黄素6种有效成分的建立的f_s/x重现性良好，分别为0.283、1.338、0.654、1.162、8.509、0.709。且采用QAMS测定的18个品种苦荞麦中7种指标成分含量与ESM的实测值之间无显著性差异（P＞0.05）。化学计量学分析结果表明，芦丁、山柰酚-3-O-芸香糖苷和山柰酚是影响苦荞麦质量的主要潜在标志物，质量排序相对靠前的苦荞麦产区主要集中在海拔高、光照强、昼夜温差大的中国西南地区，而指纹图谱相似度差异较明显的荞杂2号、综甜2号和通荞1号排在后3位。结论 所建立的指纹图谱结合QAMS简便可行，结果准确，化学计量学分析方法全面客观，可为苦荞麦品质评价和质量控制提供参考和依据。

Objective In order to control the overall quality of Kuqiaomai [Fagopyrum tataricum (L.) Gaertn.] from different producing areas, the fingerprint of 18 varieties of F. tataricum was established, and rutin was used as internal standard. At the same time, the contents of trigonelline, epicatechin, kauniferol-3-O-rutinoside, quercetin, kaempferol and emodin were determined by quantitative analysis of multi-components by single-marker (QAMS) method. Methods The UPLC-QQQ-MS/MS fingerprint common pattern was established by using UPLC-QQQ-MS/MS for the first time by combining two acquisition modes: Full-MS and multiple reaction monitoring (MRM). The similarity was evaluated by angle cosine method. The chromatography was performed on ThermoFisher Accucore^TM C₁₈ column (100 mm×3.0 mm, 2.6 μm). The mobile phase was acetonitrile-0.1% formic acid aqueous solution, the flow rate was 0.3 mL/min, the column temperature was 30 ℃, and the gradient elution was performed. With rutin as the internal standard, the relative correction factors (f_s/x) of other six index components in buckwheat were established to calculate the amount of each component to be measured, and the calculated values of QAMS were compared with the measured values of external standard method (ESM) to verify the accuracy of QAMS and the suitability of the method. Cluster analysis, principal component analysis and other stoichiometric methods were used to analyze the content differences of 18 varieties of F. tataricum. Results The UPLC-QQQ-MS/MS fingerprint of F. tataricum was established, and 15 common peaks were identified in Full-MS scanning mode, and the similarity between varieties ranged from 0.399 to 0.973. At the same time, the peak identification of seven index components such as rutin was carried out by MRM scanning mode combined with reference substances. The f_s/x reproducibility of rutin was good with trigonelline, epicatechin, kaempferol-3-o rutin, quercetin, kaempferol and emodin was 0.283, 1.338, 0.654, 1.162, 8.509 and 0.709, respectively. There was no significant difference between the content of seven indexes in 18 varieties of F. tataricum measured by QAMS and the measured value of ESM (P＞0.05). The results of stoichiometric analysis showed that: Rutin, kauniferol-3-O-rutinoside and kauniferol were the main potential markers affecting the quality of F. tataricum. The F. tataricum producing areas with relatively high quality ranking were mainly concentrated in southwest China where the altitude was high, the light was strong and the temperature difference between day and night was large, while Qiaoza-2, Zongtian-2 and Tongqiao-1 with obvious fingerprint similarity differences were ranked the last three. Conclusion The established fingerprint combined with QAMS is simple, feasible, accurate and the stoichiometric analysis method is comprehensive and objective, which can provide reference and basis for quality evaluation and quality control of F. tataricum.

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国家社会科学基金资助项目（CGA220304）；四川省科技创新苗子工程资助项目（MZGC20230055）；农业农村部杂粮加工重点实验室开放基金（2021CC003）；成都中医药大学“杏林学者”学科人才科研提升计划苗圃人才专项（MPRC2022017）