目的 建立不同生长年限实生芍药Paeonia lactiflora指纹图谱，同时测定6种差异标志物成分含量及15种农艺性状，筛选实生芍药与生长年限关联的关键化学成分和主要农艺性状特征属性，以了解不同生长年限实生芍药的生长发育规律。方法 5组实生芍药材料，每组随机取样10株，共建立50株不同生长年限的实生芍药HPLC指纹图谱，并测定没食子酸、氧化芍药苷、芍药内酯苷、芍药苷、五没食子酰葡萄糖、苯甲酰芍药苷6种差异标志物含量；同时测定15种农艺性状（株高、冠幅、分枝数、叶片数、叶面积、地上鲜质量、开花数、主根长、主根粗、主根数、须根长、须根数、地下鲜质量、主根鲜质量、须根鲜质量）。利用层次聚类分析（hierarchical cluster analysis，HCA）、主成分分析（principal component analysis，PCA）、正交偏最小二乘法-判别分析（orthogonal partial least squares-discriminant analysis，OPLS-DA）和Spearman相关性分析，筛选与生长年限关联的关键化学成分和主要农艺性状。结果 50株实生芍药共有27个共有峰，其中S0、S1、S2、S3、S4分别标定出10、10、13、16、19个特征峰；通过PCA和OPLS-DA分析，筛选出7个差异性化学成分标志物，其变量重要性投影（variable importance in projection，VIP）值依次为峰12（芍药苷）＞峰10（芍药内酯苷）＞峰24＞峰3（没食子酸）＞峰23＞峰19（五没食子酰葡萄糖）＞峰22（VIP＞1）。含量测定结果表明，芍药苷、氧化芍药苷、五没食子酰葡萄糖含量随生长年限呈显著增加趋势；芍药内酯苷含量先降低后升高，苯甲酰芍药苷缓慢升高但差异不显著，趋于稳定；没食子酸含量在第1年显著增加，之后均趋于稳定；其中VIP值为芍药苷＞芍药内酯苷（VIP＞1）。实生芍药的农艺性状分析表明，PCA和OPLS-DA筛选出3个差异标志性农艺性状，其VIP值依次为须根长＞开花数＞主根粗（VIP＞1）。结论 随着生长年限的增加，实生芍药根部药材的共有峰数量增多，相似度提高，化学组成的稳定性和一致性增强；农艺性状方面，地上部分体积增大，地下部分根系生长、分枝、扩展，芍药总生物量快速积累。芍药苷可作为区分不同生长年限实生芍药的关键差异性化学成分标志物；须根长、开花数、主根粗可作为区分不同生长年限实生芍药的重要农艺性状标志物。

Objective To establish fingerprint profiles of Paeonia lactiflora in different growth ages and measure six differential marker compounds content and 15 agronomic traits. To screen the key chemical components and main agronomic characteristics of P. lactiflora related to the growth ages in order to understand the growth and development law of P. lactiflora in different growth ages. Methods For five groups of P. lactiflora (10 plants per group), HPLC fingerprint profiles were created for 50 plants of different growth durations. The content of six differential marker compounds (gallic acid, oxypaeoniflorin, albiflorin, paeoniflorin, pentagalloylglucose and benzoylpaeoniflorin) and 15 agronomic traits (plant height, crown width, number of branches, number of leaves, leaf area, above ground fresh quality, number of flowers, length of taproot, thickness of taproot, number of taproot, length of fibril, number of fibril, underground fresh quality, fresh quality of taproot, fresh quality of fibril) were measured. Hierarchical cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA) and Spearman correlation analysis were used to identify key chemical components and major agronomic traits associated with growth duration. Results Among the 50 P. lactiflora, 27 common peaks were identified. S0, S1, S2, S3 and S4 had 10, 10, 13, 16 and 19 characteristic peaks, respectively. PCA and OPLS-DA identified seven differential chemical markers, and their variable importance in projection (VIP) values were peak 12 (paeoniflorin) > peak 10 (albiflorin) > peak 24 > peak 3 (gallic acid) > peak 23 > peak 19 (pentagalloylglucose) > peak 22 (VIP > 1). The results of content determination showed that the content of paeoniflorin, oxypaeoniflorin and pentagalloylglucose significantly increased with growth duration. Albiflorin decreased initially then increased, benzoylpaeoniflorin increased slowly but not significantly and tended to be stable, and gallic acid increased significantly in the first year then stabilized. The VIP value was paeoniflorin > albiflorin (VIP > 1). The analysis of agronomic traits of P. lactiflora showed that PCA and OPLS-DA selected three differential signature agronomic traits, and their VIP values were length of fibril > number of flowers > thickness of taproot (VIP > 1). Conclusion With increased growth duration, P. lactiflora show more common peaks, increased similarity, and increased stability and consistency of chemical composition. . In terms of agronomic traits, the volume of the above-ground part increases, the roots of the underground part grow, branch and expand, and the total biomass accumulated rapidly. Paeoniflorin can be used as a key differential chemical marker to distinguish P. lactiflora in different growth durations, while length of fibril, number of flowers, thickness of taproot are important agronomic markers to distinguish P. lactiflora in different growth durations.

R286.2

四川省“十四五”农作物及畜禽育种攻关项目（2021YFYZ0012）；四川省科技计划资助（2023YFQ0112）；四川省财政自主创新专项项目（2022ZZCX077）；国家现代农业产业技术体系建设专项项目（CARS-21）