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Identification strategy of Fructus Gardeniae and its adulterant based on UHPLC/Q-orbitrap-MS and UHPLC-QTRAP-MS/MS combined with PLS regression model

化学 栀子花 掺假者 轨道轨道 色谱法 化学计量学 三级四极质谱仪 偏最小二乘回归 质谱法 四极离子阱 氯雷他定 传统医学 选择性反应监测 串联质谱法 离子阱 数学 统计 药理学 医学 替代医学 病理
作者
Xue Zhang,Lei Wang,Rongrong Li,Liming Wang,Zhifei Fu,Feng He,Erwei Liu,Lifeng Han
出处
期刊:Talanta [Elsevier]
卷期号:267: 125136-125136 被引量:22
标识
DOI:10.1016/j.talanta.2023.125136
摘要

Fructus Gardeniae (FG) is the desiccative and ripe fruits of Gardenia jasminoides Ellis in the Rubiaceae family, which is a commonly used in traditional Chinese medicine (TCM) for clearing away heat, detoxification, relieving restlessness, and eliminating blood stasis. At the same time, it has also been announced as the first batch of TCM with homology of medicine and food. Fructus Gardeniae Grandiflorae (FGG), the fruit of Gardenia jasminoides Ellis var. grandiflora Nakai (Rubiaceae), is a common counterfeit herbal medicine of FG, which still appears in the TCM market, and causes a certain degree of confusion. In order to effectively distinguish FG and its adulterant, the compounds in these two species were thoroughly characterized firstly by ultrahigh-performance liquid chromatography/quadrupole-orbitrap mass spectrometry (UHPLC/Q-Orbitrap MS). Furthermore, a pseudo-targeted metabonomics method with 60 targeted ion pairs was established based on UHPLC-triple quadrupole-linear ion trap mass spectrometry (UHPLC-QTRAP-MS) for discrimination. Multivariate statistical analysis showed that FG and FGG were clustered obviously, and 13 significantly differential markers were screened out by variable importance for projection (VIP) > 1 and p < 0.05 for the construction of the partial least squares (PLS) regression prediction model. The validation of the model proved that its prediction ability was quite satisfactory. Moreover, based on the absolute quantitative analysis of these 13 characteristics, the quality control standards of FG and FFG were established. In summary, an integral method of pseudo-targeted metabonomics combined with chemometrics analysis and a PLS regression model was proposed to provide an effective identification strategy for discrimination FG and FGG.
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