茶花和新鲜茶叶是茶叶的生物制品,但是茶花在茶叶生产过程中通常被视为废物,造成茶花资源的显著浪费。目前,茶花中的化学成分分析侧重于氨基酸和茶多酚等单一类型的化学成分,关于同时分析茶花中多种化学成分的报道很少。研究人员对茶花中化学成分的种类和数量并不完全清楚,这阻碍了茶花的深入开发和有效利用。在这项研究中,采用超高效液相色谱-高分辨质谱(UPLC-HRMS)检测茶花的化学成分。该技术与氮规则滤波(NRF)的集成滤波策略(IFS)相结合,质量缺陷过滤(MDF),和诊断碎片离子过滤(DFIF),用于筛选目标化学成分的特征质谱。此外,用保留时间的信息注释了茶花的化学成分,MS碎片,和MS/MS片段化。所有定性化学成分分为六类,共137种化学成分,包括3种生物碱,38类黄酮,31酚酸及其衍生物,37儿茶素及其衍生物,18个氨基酸,和其他10个组件。使用内标法对所有定性化学成分进行定量。定量结果表明,茶花中6种化学成分的含量依次为:氨基酸、9371.42μg/g;儿茶素及其衍生物,9068.43μg/g;酚酸及其衍生物,8696.92μg/g;生物碱,4392.52μg/g;类黄酮,1192.88μg/g;和其他,139.94μg/g。质量控制样品用于评估仪器的稳定性和测试数据的可重复性。九种具有代表性的高化学成分,中等,选择了低含量的茶叶,用结果的相对标准偏差(RSD)来评价数据的重复性。这九种化学成分选自氨基酸,生物碱,黄酮类化合物,酚酸及其衍生物,儿茶素及其衍生物,和其他组件,反应强度不同。9种化学成分的相对标准偏差在2.11%~12.17%之间。以上结果证明了仪器的良好稳定性和测试数据的良好重复性。以绿原酸组分(CGAs)和糖基化槲皮素组分(GQs)为代表组分,说明了IFS提取目标化合物的全过程。CGA包括一类特殊的酯,其通过以奎尼酸为母体结构的肉桂酸衍生物的酯化形成。最常见的肉桂酸衍生物是对香豆酸,咖啡酸,和阿魏酸.一方面,根据上述信息和奎尼酸酯化的不同位置和程度,CGA在结构上被分类为单取代的CGA(Mono-CGA),二取代CGA(Di-CGA),和三取代CGA(三-CGA),并设置了三个不同的质量缺陷过滤窗口。因此,根据氮规则从3537质谱中选择751种可能的目标组分。另一方面,通过进一步筛选m/z191.0551离子作为CGA的诊断碎片离子,得到22种符合氮规则的目标组分。将整体分析数据与上述质量缺陷过滤和诊断碎片离子过滤筛选结果相结合,根据MS信息选择并表征9个目标CGA。这项研究揭示了茶花中积累的化学成分的类型和数量,从而为茶花的深入开发和有效利用提供有价值的信息和数据参考。
Tea flowers and fresh tea leaves are biological products of tea, but tea flower is often regarded as waste during tea production, resulting in notable waste of tea flower resources. At present, analysis of the chemical components in tea flowers focuses on single types of chemical components such as amino acids and tea polyphenols, and there are only a few reports on the simultaneous analysis of numerous chemical components in tea flowers. Researchers are not completely clear about the types and amounts of the chemical components in tea flowers; this has hindered the in-depth development and effective utilization of tea flowers. In this study, ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) was used to detect the chemical constituents of tea flowers. This technique was combined with the integrated filtering strategy (IFS) of nitrogen rule filtering (NRF), mass defect filtering (MDF), and diagnostic fragment ion filtering (DFIF) for screening the characteristic mass spectra of the target chemical components. Furthermore, the chemical constituents of tea flowers were annotated with information about the retention time, MS fragmentation, and MS/MS fragmentation. All the qualitative chemical components were divided into six categories with a total of 137 chemical constituents, including 3 alkaloids, 38 flavonoids, 31 phenolic acids and their derivatives, 37 catechins and their derivatives, 18 amino acids, and 10 other components. The internal standard method was used to quantify all the qualitative chemical components. The quantitative results showed that the amounts of the six kinds of chemical components in tea flowers were as follows: amino acids, 9371.42 μg/g; catechins and their derivatives, 9068.43 μg/g; phenolic acids and their derivatives, 8696.92 μg/g; alkaloids, 4392.52 μg/g; flavonoids, 1192.88 μg/g; and others, 139.94 μg/g. Quality control samples were used to evaluate the stability of the instrument and the repeatability of the tested data. Nine representative chemical components with high, medium, and low contents in tea were selected, and the relative standard deviation (RSD) of the results was used to evaluate the repeatability of the data. These nine chemical constituents are selected from amino acids, alkaloids, flavonoids, phenolic acids and their derivatives, catechins and their derivatives, and other components, and the response intensities were different. The relative standard deviations of the nine chemical components were in the range of 2.11% to 12.17%. The above results demonstrated the good stability of the instrument and excellent repeatability of the test data. Chlorogenic acid components (CGAs) and glycosylated quercetin components (GQs) were used as two representative components to explain the entire process of extracting the target compounds by IFS. CGAs comprise a class of special esters formed by the esterification of cinnamic acid derivatives with quinic acid as the parent structure. The most common cinnamic acid derivatives are p-coumaric acid, caffeic acid, and ferulic acid. On the one hand, according to the above information and the different positions and degree of quinic acid esterification, the CGAs were structurally classified as monosubstituted CGAs (Mono-CGAs), disubstituted CGAs (Di-CGAs), and trisubstituted CGAs (Tri-CGAs), and three different mass defect filtering windows were set. Therefore, 751 possible target components were selected from 3537 mass spectra in accordance with the nitrogen rule. On the other hand, 22 target components in accordance with the nitrogen rule were obtained by further screening the m/z 191.0551 ion as the diagnostic fragment ion of the CGAs. Combining the overall analytical data with the above mass defect filtering and diagnostic fragment ion filtering screening results, nine target CGAs were selected and characterized based on the MS information. This study reveals the types and amounts of the chemical components accumulated in tea flowers, thus providing valuable information and serving as data reference for the in-depth development and effective utilization of tea flowers.