基于质子转移反应-飞行时间质谱快速鉴别不同产地闽北水仙茶

采用质子转移反应-飞行时间质谱仪(PTR-TOF-MS), 构建了3个产地(武夷山、建阳、建瓯)113个闽北水仙茶样品香气的化学指纹图谱, 对所得的闽北水仙茶香气指纹图谱进行主成分分析(PCA), 获得了不同产地闽北水仙茶样品的质谱信息特征, 然后采用软独立建模分类法(SIMCA)、K最邻近结点算法(KNN)、偏最小二乘判别分析法(PLS-DA)对闽北水仙茶的质谱信息进行了模式识别.结果表明, PTR-TOF-MS结合分类识别模式能有效区分不同产地的闽北水仙茶.PCA 提取了3个主成分, 累计贡献率为84.66%;3个识别模型的校正集判别正确率分别为89.38%、100.00%和100.00%, 预测集的判别正确率分别为83.18%、 96.46%和95.57%.基于此成功建立了不同产地的闽北水仙茶识别模型.本方法无需样品预处理、分析速度快、灵敏度高、对茶叶无损伤, 为茶叶产地溯源提供了新方法.     

全二维气相色谱-飞行时间质谱结合聚类分析与Fisher判别分析对铁观音品质等级的评价研究

采用丙酮超声萃取铁观音样品,以全二维气相色谱-飞行时间质谱分析丙酮提取物。经筛选,在24份不同产季与等级的铁观音中获得68种共有组分,并结合质谱数据库、保留指数与结构谱图等进行了初步鉴定。在此基础上以基于Ward法的聚类分析将所有样品分为3个类别,获得了与感官审评基本相似的结果。通过逐步判别获得5种对分类结果有显著影响的组分,以此为变量通过Fisher判别法建立了4个判别函数,其对样品等级分类的结果与感官审评结果的符合率达到95.8%。证实了通过分析茶叶生化成分进行品质评判的可能。     

化学计量学结合气质联用法对4种食用动物油判别模型的构建

通过测定4种不同类型动物油(鸡油、牛油、羊油和猪油)的挥发性成分,从动物油挥发性风味物质的角度,探讨了对不同类型动物油进行判别和分类的可能性。采用热脱附/气相色谱-质谱联用法(TD/GCMS)对4种动物油的挥发性成分进行准确定性以及相对含量测定,利用SPSS 22.0统计软件对4种动物油的挥发性化合物进行主成分分析、聚类分析以及判别分析。依据挥发性化合物的风味阈值、特性以及相对含量水平,进行变量与指标损益之后应用主成分分析,在4种动物油的53种挥发性成分中筛选出19种主要风味物质,并将其相对含量进行降维得到3个主成分,应用主成分分析结果依次进行聚类分析和判别分析,建立了动物油的3个典则判别函数模型。对鸡油、牛油、羊油和猪油的挥发性成分在训练模型内进行判别,模型验证结果的正确率为100%。     

龙井茶特征香气成分分析及种类判别

采用顶空固相微萃取(HS-SPME)和气相色谱-质谱(GC-MS)联用技术分析了39份龙井茶叶中的挥发性成分,结合质谱数据库与保留指数,从24份西湖核心区龙井茶中获得了200余种挥发性化合物,这些挥发性成分中主要含有醇类、酯类、酮类、杂环类等化合物,经筛选得到26种共有组分.并采用主成分分析法(PCA)对24份西湖核心区龙井茶和15份疑似龙井茶进行了有效区分.该方法适用于茶叶的品质鉴定、真假识别、产地溯源等方面的研究,也为其他产品的挥发性成分分析提供参考.     

顶空-气相色谱-质谱联用分析桂花和叶中挥发性成分

采用谱库检索结合准确质量测定、保留指数、串联质谱技术的多维定性分析策略鉴定化合物, 能够提高定性分析的效率和准确性. 运用顶空-气相色谱-四极质谱、顶空-气相色谱-飞行时间质谱以及顶空-气相色谱-串联质谱联用技术对桂花样品进行了分析检测, 并采用多维定性分析思路对检出的挥发性成分进行了鉴定. 结果共确认出47种挥发性成分, 其中单萜类和倍半萜类化合物为主要组分. 该定性分析策略准确可靠, 可以广泛应用于复杂样品挥发性成分的定性分析中.     

谱图检索结合二级质谱定性分析烟草中挥发性成分

采用同时蒸馏萃取法提取烟叶中的挥发性成分,利用气相色谱-质谱联用仪(GC-MS)分离测定,通过谱库检索和匹配度定性结合色谱保留指数方法鉴定烟叶中挥发性成分,并引入离子阱二级质谱对谱库检索匹配度差距小、含量低,背景干扰大的物质准确定性.采用谱图检索结合二级质谱定性共鉴定144个化合物,其中104个化合物在烟草挥发性成分的文献中已有报道,报道中有9个化合物是通过二级质谱定性,其余40个化合物还未见报道.结果表明,离子阱二级质谱定性的引入提高了对未知化合物定性的准确性和可靠性,适合于烟叶这类复杂植物体系的化学组分研究.     

气相色谱-质谱指纹图谱法研究普洱茶醇溶物挥发性成分

以普洱茶7542为参考样本, 乙醇为溶剂, 超声提取制备普洱茶醇溶物, 建立了普洱茶7542醇溶物气相色谱-质谱(GC-MS)指纹图谱, 同时对其指纹图谱进行相似度计算和主成分分析.试验结果表明, 普洱茶醇溶物的最佳提取方法:采用50 mL 95%乙醇超声提取30 min, 顶空进样最佳条件为振荡箱温度110℃, 振荡时间20 min.通过对9个不同年限、不同批次的普洱茶7542系列样品醇溶物挥发性成分GC-MS指纹图谱进行相似度分析, 测定样品醇溶物挥发性成分图谱与对照图谱之间的相似度在0.706~0.906之间, 说明不同年限、不同批次的9个普洱茶7542醇溶物挥发性成分和参照样本相比发生了改变, 但变化较小, 但其共有成分峰面积百分含量存在差异.通过对9个不同年限、不同批次普洱茶7542醇溶物挥发性成分共有成分进行主成分分析, 提取了2个成分, 即2-羟甲基-2-甲基-吡咯烷-1-甲醛和N-丁基苯磺酰胺, 其特征值大于1, 累计方差贡献率达90.2%, 能较好的代表 9个不同年限、不同批次普洱茶7542的全部信息, 可以用主成分分析来反映样品的全部信息.因此可通过测定9个不同年限、不同批次普洱茶7542醇溶物中2-羟甲基-2-甲基-吡咯烷-1-甲醛和N-丁基苯磺酰胺的含量差异评价普洱茶7542的质量.     

利用气相色谱/质谱-化学计量学分辨法分析川芎挥发性成分

采用加速溶剂萃取法提取挥发性成分,气相色谱-质谱(GC-MS)联用分析,并借助化学计量学分辨方法,对川芎的挥发性成分进行了定性定量分析。通过采用子窗口因子分析法对重叠色谱峰进行分辨,从而获得每一组分的纯色谱和质谱。依靠每一组分纯质谱在NIST质谱库进行相似性检索而定性分析,用总体积积分法进行定量分析。在川芎挥发油中共分辨出67个色谱峰,通过质谱库检索得到其中56个组分的定性定量结果,占总含量的87.67%。该方法增加了可鉴定化合物的数目,提高了定性的准确度,能用于川芎的质量控制。     

顶空气相色谱质谱法分析避蚊贴中挥发性成分

采用顶空-气相色谱-质谱联用技术分析鉴定了4种避蚊贴中的挥发性物质.样品经90℃、45 min顶空后,采用GC-MS进行定性分析.用面积归一化法得出各组分的相对含量.结果表明:经NIST质谱数据库检索和文献对照,样品Ⅰ、Ⅱ、Ⅲ和Ⅳ分别检出并确定了33、39、39和24种成分.样品Ⅰ、Ⅱ、Ⅲ和Ⅳ主要成分分别为桉树醇(相对...     

三种丁香花精油的挥发性成分比较分析

为了解紫丁香、白丁香和红丁香花蕾精油样品挥发性成分的差异,采用气相色谱-质谱联用技术（GC-MS）分析了三种丁香花精油样品的挥发性成分,并对三者的挥发性成分进行了主成分分析和聚类分析。结果从紫丁香、白丁香和红丁香精油中分别解析出46、64和56种挥发性物质,包括烷烃类、酯类、醇类、酮类、烯烃类、酚类、有机酸等,其中共有的挥发性成分为9种。三种丁香花精油样品的挥发性成分比较分析显示,酯类是含量最高的挥发性成分,其次是烷烃类,并且这两类物质在不同品种的丁香花精油中含量不同。统计分析结果表明三种样品的挥发性成分之间没有明显差别。本研究为增加丁香花精油的应用范围提供了理论依据。     

全二维气相色谱-飞行时间质谱分析焦化柴油中饱和烃的分子组成

建立了全二维气相色谱-飞行时间质谱(GC×GC-TOF MS)分析柴油馏分中饱和烃的分子组成的方法。结合谱库检索、质谱图解析、沸点与分子结构关系和全二维谱图特征,定性(或归类)了焦化柴油饱和烃组分中1057个化合物单体,其中正构烷烃排列规律性最强,一环~三环环烷烃按照极性和沸点的差异呈瓦片状分布在其上方。另外,还准确区分了在一维气相色谱上共流出的正构烷基环己烷和正构烷基环戊烷,以及正构 α 单烯烃。根据质谱采集的总离子流色谱图,采用峰面积归一化法得到了饱和烃组分的碳数分布结果,并将该方法应用于研究不同类型柴油馏分饱和烃的分子组成特点。结果表明,催化裂化和焦化柴油馏分饱和烃组分的化合物类型和分布各不相同。分子组成分析能为油品加工工艺机理的研究提供方法支持。     

全二维气相色谱-飞行时间质谱对催化裂化汽油的定性与定量分析

采用全二维气相色谱-飞行时间质谱(GC×GC-TOF MS)对催化裂化汽油全馏分进行了定性与定量分析,建立了相应的分析方法.结果表明,汽油族组成中的烷烃、烯烃、环烷烃、芳烃在全二维点阵谱图中呈分区域、带状的分布特点.GC×GC-TOF MS根据催化裂化汽油组分内分子的沸点及极性差异对其进行两个维度分离,极大地避免了普通色谱法分析过程中沸点相似化合物共流的弊端,实现催化裂化汽油组分的精确分离和准确定性分析.通过引入响应因子,修正了不同性质的烃类在电离源上电离效率的差异,使得TOF对催化裂化汽油族组成的定量结果与普通气相色谱法的定量结果的相关性较好,且应用GC×GC-TOF MS方法获得了催化裂化汽油更为精确的族组成信息.GC×GC-TOF MS为催化裂化汽油精确表征提供了一种有效方法.     

顶空固相微萃取结合气相色谱-三重四极杆质谱法快速测定茶叶中11种酰胺类除草剂残留

利用顶空固相微萃取(HS-SPME)与气相色谱-三重四极杆质谱(GC-MS/MS)联用,建立了快速测定茶叶中11种酰胺类除草剂残留的检测方法。以全发酵红茶为基质,对影响萃取性能的因素(如萃取涂层种类、无机盐种类、水用量、盐用量、萃取温度和萃取时间)进行了优化。在最优条件下,选取绿茶、乌龙茶、红茶和普洱茶4种茶叶基质对方法学进行考察。结果表明,11种酰胺类农药在1~1 000μg/kg含量范围内线性关系良好,相关系数(r2)为0.992 5~0.999 9,定量下限为1~10μg/kg。11种农药在红茶、绿茶、乌龙茶和黑茶基质中3个添加水平下的平均回收率分别为70.3%~119.1%、85.2%~118.7%和74.6%~113.3%,相对标准偏差(RSD)均不大于17.4%。该方法操作简单、快速、灵敏度高、重现性好,可满足不同种类茶叶基质中11种酰胺类除草剂农药残留的检测要求。     

气相色谱-单光子电离飞行时间质谱的联用及在柴油组分表征中的应用

建立了一种气相色谱(GC)与单光子电离-飞行时间质谱(SPI-TOF MS)联用(GC/SPI-TOF MS)的分析方法。首先,设计了一种双层套管的传输管用于连接GC与SPI-TOF MS,实现了GC与单光子电离离子源的无缝连接。在此基础上,以n-十五烷标准品和苯/甲苯/二甲苯的标准气为对象,对电离源的重要电压参数进行了优化,得到了纯净的分子离子峰,实现了对各类有机物的快速和准确定性。最后,将该方法用于分析柴油中的挥发性与半挥发性有机物,获得了柴油组分的二维GC×SPI-TOF MS谱图。不需要复杂的谱图解析和数据处理,根据谱图中离子的质荷比(m/z)归纳了柴油的主要成分,包括脂肪烃、芳香烃和含量很低的苯并吡咯等含氮化合物;根据色谱的保留时间将柴油中的同分异构体区分开来。结果表明GC/SPI-TOF MS法是一种简单、有效的分析方法,非常适于柴油及复杂环境样品等的分析表征。     

Flash Evaporation and Headspace Solid-phase Microextraction for the Analysis of the Essential Oils in Traditional Chinese Medicine, Houttuynia Cordata Thunb

We have investigated the use of flash evaporation, headspace solid-phase microextraction (HS-SPME) and steam distillation (SD) as sample concentration and preparation techniques for the analysis of volatile constituents present in Houttuynia cordata Thunb. The samples were analyzed by gas chromatography (GC) and identified by mass spectrometry (MS). Comparison studies were performed. It was found that the results obtained between Headspace solid-phase microextraction HS-SPME and SD techniques were in good agreement, Seventy-nine compounds in Houlluynia cordata Thunh were identified by MS. In flash evaporation, thirty-nine compounds were identified. Discrimination in the response for many constituents studied was not observed, which can be clearly observed in SD and HS-SPME techniques. As a conclusion, HS-SPME is a powerful tool for determining the volatile constitutes present in the Houttuynia cordata.     

基于气相色谱-质谱联用与液相色谱-质谱联用的非靶向代谢组学用于3类茶叶中化学成分分析（英文）

茶叶中的化学成分是构成其风味特征的物质基础。本研究建立了基于气相色谱-质谱联用和液相色谱-质谱联用的非靶向代谢组学方法。完成预处理及分析条件优化后,使用标准样品考察方法的线性、回收率及重复性,结果表明方法整体稳定且结果可靠。将该方法用于绿茶、乌龙茶和红茶中化学成分的分析。通过超声辅助溶剂提取及气相色谱-质谱联用分析共获得1812个特征峰,而使用加热溶剂提取及液相色谱-质谱联用分析可获得2608个特征峰。结合保留规律及质谱数据库,共定性173种化合物(109种随后经标准样品验证),其中只有9种化合物在上述两类分析中被同时检出,表明方法互补性良好。3类茶叶数据的偏最小二乘判别分析结果表明,三者间存在显著差异。结合模型的变量重要因子(VIP)与非参数检验共筛选出90种化合物,其中包含儿茶素、氨基酸、糖、有机酸和黄酮苷类等众多与茶叶滋味密切相关的化学成分。     

NMR,RP-HPLC-PDA-ESI-MSn,and RP-HPLC-FD Characterization of Green and Oolong Teas (Camellia sinensis L.)

Untargeted (NMR) and targeted (RP-HPLC-PDA-ESI-MSⁿ, RP-HPLC-FD) analytical methodologies were used to determine the bioactive components of 19 tea samples, characterized by different production processes (common tea and GABA tea), degrees of fermentation (green and oolong teas), and harvesting season (autumn and spring). The combination of NMR data and a multivariate statistical approach led to a statistical model able to discriminate between GABA and non-GABA teas and green and oolong teas. Targeted analyses showed that green and GABA green teas had similar polyphenol and caffeine contents, but the GABA level was higher in GABA green teas than in regular green tea samples. GABA oolong teas showed lower contents of polyphenols, caffeine, and amino acids, and a higher content of GABA, in comparison with non-GABA oolong teas. In conclusion, the results of this study suggest that the healthy properties of teas, especially GABA teas, have to be evaluated via comprehensive metabolic profiling rather than only the GABA content.     

Use of a Headspace Solid-Phase Microextraction-Based Methodology Followed by Gas Chromatography–Tandem Mass Spectrometry for Pesticide Multiresidue Determination in Teas

This study reports on the development of a fast and efficient method based on headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography–tandem mass spectrometry (GC–MS/MS) for simultaneous analysis of 128 volatile or semi-volatile pesticide residues belonging to nine classes of pesticides. The important factors related to HS-SPME performance were optimized; these factors include fiber types, water volume, ion strength, extraction temperature, and extraction time. The best extraction conditions include a PDMS/DVB fiber, and analytes were extracted at 90 °C for 60 min from 1 g of tea added to 5 mL of 0.2 g mL^?1 NaCl solution. The methodology was validated using tea samples spiked with pesticides at three concentration levels (10, 50, and 100 μg kg^?1). In green tea, oolong tea, black tea, and puer tea, 82.8, 88.3, 79.7, and 84.3% of the targeted pesticides meet recoveries ranging from 70 to 120% with a relative standard deviation of?≤?20%, respectively, when spiked at a level of 10 μg kg^?1. Limits of quantification in this method for most of the pesticides were 1 or 5 μg kg^?1, which are far below their maximum residue limits prescribed by EU. The optimized method was employed to analyze 30 commercial samples obtained from local markets; 17 pesticide residues were detected at concentrations of 2–452 μg kg^?1. Chlorpyrifos was the most detected pesticide in 80% of the samples, and the highest concentration of dicofol (452 μg kg^?1) was found in a puer tea. This is the first time to find that the optimized extraction temperature for pesticide residues is 90 °C, which is much higher than other reported HS-SPME extraction conditions in tea samples. This developed method could be used to screen over one hundred volatile or semi-volatile pesticide residues which belong to multiple classes in tea samples, and it is an accurate and reliable technique.     

Recognition of volatile compounds as markers in geographical discrimination of Spanish extra virgin olive oils by chemometric analysis of non-specific chromatography volatile profiles

Chromatographic profiles obtained by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography (GC) were processed as continuous and non-specific signals through multivariate analysis techniques in order to select and identify the most discriminate volatile marker compounds related to the geographical origin of extra virgin olive oils. The blind analysis of the chromatographic profiles was carried out on several steps including preliminary mathematical treatments, explorative analysis, feature selection and classification. The results obtained through the application of stepwise linear discriminant analysis (SLDA) method revealed a perfect discrimination between the different Spanish geographical regions considered (La Rioja, Andalusia and Catalonia). The assignment success rate was 100% in both classification and prediction by using cross validation procedure. In addition, it must be noted that the proposed strategy was able to verify the geographical origin of the samples involving only a reduced number of discriminate retention times selected by the stepwise procedure. This fact emphasizes the quality of the accurate results obtained and encourages the feasibility of similar procedures in olive oil quality and traceability studies. Finally, volatile compounds corresponding to the predictors retained were identified by gas chromatography-mass spectrometry (GC-MS) for a chemical interpretation of their importance in quality virgin olive oils.     

The use of high performance liquid chromatography-quadrupole time-of-flight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety

In this study, the potential of high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (HPLC–QTOFMS) for metabolomic profiling of red wine samples was examined. Fifty one wines representing three varieties (Cabernet Sauvignon, Merlot, and Pinot Noir) of various geographical origins were sourced from the European and US retail market. To find compounds detected in analyzed samples, an automated compound (feature) extraction algorithm was employed for processing background subtracted single MS data. Stepwise reduction of the data dimensionality was followed by principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) which were employed to explore the structure of the data and construct classification models. The validated PLS-DA model based on data recorded in positive ionization mode enabled correct classification of 96% of samples. Determination of molecular formula and tentative identification of marker compound was carried out using accurate mass measurement of full single MS spectra. Additional information was obtained by correlating the fragments obtained by MS/MS accurate mass spectra using the QTOF with collision induced dissociation (CID) of precursor ions.