在线热裂解/气相色谱-质谱分析3-吡啶甲酸茴香酯的热裂解产物

采用在线热裂解/气相色谱-质谱(Py/GC-MS)联用技术对3-吡啶甲酸茴香酯进行热裂解分析。通过酰氯化和酯化反应合成了新型目标化合物3-吡啶甲酸茴香酯,其分子式为C14H14NO3。目标化合物的结构经核磁氢谱(1H NMR)、核磁碳谱(13C NMR)、红外光谱(IR)和高分辨质谱(HRMS)进行确证,并通过热重-微热重-差示扫描量热(TG-DTG-DSC)分析方法对目标化合物的热稳定性进行分析。在空气氛围中,将目标化合物分别于300,600,900℃下进行热裂解,并通过气相色谱-质谱法对其挥发性热裂解产物进行定性和半定量分析。研究显示:目标化合物共经历了两次失重过程。第一次失重在129.9~158.9℃之间,失重2.3%;第二次失重在158.9~274.9℃之间,失重达90.1%,230.1℃时失重率最大。热裂解共产生44种产物,包括具有香味特征的大茴香醛、对甲基苯酚、松油醇、D-香茅醇、大茴香醚和茴香醚等化合物。其中裂解温度对裂解产物的种类和相对含量具有明显影响。300℃时α-二去氢菖蒲烯的相对含量最高,600,900℃时,相对含量最高的分别是大茴香醛和对甲基苯酚。苯甲醛、茴香醚和大茴香醚的相对含量随着温度的升高呈先增加后降低趋势;而对甲基苯酚含量则随着温度的升高而增加。根据主要裂解产物及其相对含量的变化,对目标化合物的裂解机理进行了初步探讨。     

烟草中β-胡萝卜素的热裂解产物的研究

为了研究烟草中β-胡萝卜素的高温裂解产物对卷烟抽吸品质的影响,利用热裂解气相色谱/质谱联用仪在不同裂解氛围(空气、氮气中含10%O2及N2)和不同温度(300,600和900 ℃)下对β-胡萝卜素进行裂解,裂解产物用固相微萃取装置进行吸附,然后将吸附到的裂解产物用气相色谱/质谱联用仪(GC/MS)进行分析。结果表明,β-胡萝卜素在不同裂解条件下主要的裂解产物是甲苯、对二甲苯、1,2,3,4-四氢-1,1,6-三甲基萘和2,7-二甲基萘等化合物,另外还生成异佛尔酮、β-环柠檬醛、β-紫罗兰酮、二氢猕猴桃内酯等香味化合物,这些物质随裂解温度和裂解氛围的不同其含量有所差异。     

麻浆卷烟纸热裂解产物的气相色谱/质谱分析

采用热失重(TG)和裂解气相色谱/质谱法(PyGC/MS)研究了麻浆卷烟纸的热裂解行为.在He气气氛围中,将麻浆卷烟纸分别在400、500、600、700、800和900℃下进行热裂解,并以GC/MS对其裂解产物进行定性和半定量分析.结果表明,不同的裂解温度直接影响生成产物的类型和相对含量.麻浆卷烟纸可裂解出1-甲基-1,3-环戊二烯、2-甲基呋喃、2,3-二氢香豆酮、苯和甲苯等156种产物.低温下,裂解产物主要为烯类、呋喃类和酮类化合物;随着裂解温度的增加,烯、酮类的含量下降,苯及其衍生物和稠环芳烃的含量逐渐增加.可通过降低卷烟燃烧温度来降低卷烟纸裂解产生的有害成分含量.如果单纯考虑麻浆卷烟纸的影响,卷烟的最佳燃烧温度应控制在500℃左右.     

活性白土催化松香反应产物的GC-MS分析

把活性白土上松香催化反应产物分离为酸性物和中性物,采用气相色谱-质谱联用技术(GC-MS)分别对各部分化学组成进行定性定量分析.结果表明,活性白土上松香主要进行脱氢、脱羧及裂解反应,其产物中的酸性物共分离出2个峰,主要成分是脱氢枞酸,含量为99.61%;中性物共分离出35个峰,初步鉴定出其中33个组分,大部分为甾族类化合物和环烯烃类化合物,主要成分是5α-雄甾烷、1,4-二甲基-7-(1-甲基乙烯基)-1,2,3,5,6,7,8,8a-八氢化-1S-(1α)-蓝烃、松香酸甲酯和(-)-石竹烯,含量分别为18.75%、13.00%、12.53%和11.74%.     

新型潜香类化合物N-(2,3-吡嗪二甲酰基)-丙氨酸甲酯热裂解气相色谱-质谱分析

采用在线热裂解-气相色谱-质谱联用技术(Py-GC-MS)分析了潜香吡嗪类化合物N-(2,3-吡嗪二甲酰基)-丙氨酸甲酯的热裂解行为。首先通过对2,3-吡嗪二羧酸酰胺化反应合成了新型目标化合物N-(2,3-吡嗪二甲酰基)-丙氨酸甲酯,其结构经X射线单晶衍射(XRD),1H NMR,13C NMR,IR和HR-MS证实,然后在空气氛围中,对目标化合物分别在300,600,900℃下进行热裂解,并通过GC-MS对其挥发性热裂解产物进行定性和半定量分析。结果表明:1裂解形成了包括具有香味特征的吡嗪类、大茴香醇和大茴香醛在内的裂解产物共48种。不同温度下挥发性热裂解产物的类型和相对含量不同,300℃和600℃时相对含量最高的均为乙酸,而900℃时相对含量最高的为吡嗪,且与300℃和600℃裂解条件相比所形成的吡嗪类衍生物种类较多,相对含量较高。2在具有香味特征的裂解产物中,大茴香醇的相对含量随温度升高而升高,而大茴香醛和苯甲醛的相对含量则呈现随着温度升高而降低的趋势。基于目标化合物的热裂解产物的定性及定量变化情况,初步探讨了该物质可能的裂解机理。     

异戊二烯-甲基丙烯酸甲酯共聚物的裂解色谱分析

]本文用裂解色谱(PGC)、裂解-色谱质谱(PY-GC/MS)和裂解质谱(PY/MS)等方法对异戊二烯-甲基丙烯酸甲酯共聚物进行了裂解研究,分离鉴定了主要裂解产物,检测出了特征裂解产物——杂二聚体和杂三聚体,以及异戊二烯的三聚体。考察了某些主要裂解产物同无规共聚物的组分含量和裂解温度之间的变化关系。建立了用裂解色谱鉴别不同共聚物和均聚混合物以及分析无规共聚物组分含量的方法。定量方法的精确度为相对标准偏差小于2％。对用均聚混合物作标样的定量可能性进行了探讨。     

杂多酸催化裂解松香产物中挥发油成分分析

杂多酸（ＨＰＡ）催化松香裂解的产物为油状物,本文利用气相色谱／质谱（ＧＣ／ＭＳ）联用技术对该产物的挥发组分进行了分析,共分离出１７７个峰,通过与数据库标准图谱对照鉴定出绝大部分组分。其中有２８种组分含量较高,占挥发成分总含量的５１．５２％,主要为松香裂解产生的Ｃ６～Ｃ２０的烯烃、芳香烃及酮类。     

裂解气相色谱-质谱法研究壬酸香草酰胺的热裂解

采用在线热裂解/气相色谱-质谱联用技术(Py/GC-MS)对壬酸香草酰胺(PAVA)的热裂解行为进行了研究,在氦气氛围中考察了不同裂解温度和裂解时间对PAVA裂解的影响,通过GC-MS对裂解产物进行定性和半定量分析。结果表明,随着裂解温度的升高,PAVA裂解率快速提高,裂解产物也进一步增多,当裂解温度达到700℃以上时,可裂解出壬酰胺、2-甲氧基-4-甲基苯酚、1-己烯、壬基腈、壬醛等14种产物。同一温度下随着裂解时间的延长,PAVA的裂解率逐步升高,裂解产物发生了进一步的裂解。根据热裂解产物及主要裂解产物的含量变化,初步推断了PAVA的裂解规律。     

PGC-MS鉴别乙烯-辛烯共聚物

乙烯-辛烯共聚物与聚乙烯性能、价格差别很大,但无鉴别二者的检验方法。我们采用裂解气相色谱一质谱(PGC-MS)方法研究这两种聚合物的热裂解产物的组成,发现两者裂解产物的成分相同,均为不同碳数的烷烃、烯烃化合物;但含量有区别,主要区别在辛烯。乙烯-辛烯共聚物裂解产物中辛烯(C8)的相对含量明显高于聚乙烯,C8峰可作为鉴别乙烯-辛烯共聚物的特征峰,据此建立了鉴别两种聚合物的方法,并在进出口商品检验中得到很好的应用。     

新型甲酰基吡咯衍生物的合成及其热裂解-气相色谱/质谱分析

本文以D-(+)-氨基葡萄糖盐酸盐为原料,通过环合、氧化和烷基化等系列反应,合成了新型化合物N-戊基-2-甲基-3-乙酰基-5-吡咯甲醛,并利用红外(IR)光谱、核磁共振(~1H NMR、~(13)C NMR)和高分辨质谱(HR-MS)等波谱技术对其结构进行了确证。采用正交试验优化了合成条件,通过热重-微分热重-差示扫描量热法(TG-DTG-DSC)研究了该化合物热失重变化,采用热裂解-气相色谱/质谱(Py-GC/MS)联用技术分析了该化合物的热裂解产物。结果表明化合物Ⅳ为目标化合物N-戊基-2-甲基-3-乙酰基-5-吡咯甲醛。目标化合物的最优合成条件为:反应温度60℃、反应时间24h、摩尔比1.0∶1.1,产率可达81%;目标化合物在136.3~290.0℃间有一个剧烈的失重过程,失重率达到98.58%;目标化合物在300、600、900℃裂解条件下共裂解出41种化合物,其中有吡嗪、2,5-二甲基吡嗪、戊醛等多种香味成分。初步探讨了目标化合物可能的热裂解机理。     

单叶蔓荆子挥发油成分的GC/MS分析

采用气相色谱／质谱（ＧＣ／ＭＳ）联用技术对山东泰山产单叶蔓荆子挥发油的化学成分进行了分析,分离出３０多个峰,确认了其中２８种成分,占总离子流的９５％以上,并对挥发油的主要化学成分Δ３－蒈烯,用气相色谱／傅里叶红外光谱（ＧＣ／ＦＴＩＲ）法进行了验证。     

气相色谱-质谱联用分析白兰叶油成分

采用气相色谱－质谱联用技术对苏州产白兰叶油的化学成分进行了分析,分离出３３个峰。鉴定了其中２７种成分,占总峰面积的９７％,并对油中的主要化学成分芳樟醇用气相色谱－傅立叶红外光谱法进行了验证。     

南方马尾松松针挥发油成分的气相色谱/质谱分析

采用水蒸了蒸馏方法从中国南方新鲜马尾松松针中提取出挥发油,利用气相色谱／质谱（ＧＣ／ＭＳ）联用技术对其化学成分进行了分析,共分离出９１个峰,各组分经数据库标准图谱核对,得到分子结构。其中３６种化合物的含量较高,占总离子流的８３．０５％,主要是单萜和倍半萜,以及少量萜烯的醇、酯类化合物。     

Application of comprehensive multidimensional gas chromatography combined with time-of-flight mass spectrometry (GC x GC-TOFMS) for high resolution analysis of hop essential oil

The selection and quality of hops is a major determinant in beer flavour. Brewers acknowledge that distinctive characteristics of different hop varieties can be traced to the composition of their essential oils. The difficulty in characterising complex mixtures such as hop oil using 1-D chromatography is that many compounds co-elute. With the introduction of comprehensive multidimensional capillary gas chromatography (GC x GC), there is a tremendous improvement in the separation power or peak capacity. Recent work using GC x GC with flame ionisation detection has suggested that there may be over 1,000 compounds in hop oil. This work describes the use of GC x GC combined with TOFMS detection (Leco Pegasus 4D instrument) to analyse Target hop oil. The TOFMS spectral acquisition rate of 60 Hz provided sufficient spectra per peak (2-D peak base width of 0.1-0.2 s) for identification (119 components were identified with 45 previously unreported compounds). When analysing results, an advantage of GC x GC coupled to TOFMS is that 2-D chromatograms can be viewed for individual masses that are characteristic of particular functional groups. This allows the analyst to view the various homologous series of compounds although in certain cases coelution may still be present as shown by the esters with mass 75.     

气相色谱-质谱法测定姜黄挥发油化学成分

采用水蒸气蒸馏法提取姜黄挥发油,用气相色谱－质谱联用技术分离和鉴定姜黄挥发油的化学成分。经计算机ＮＢＳ谱库检索,发现姜黄挥发油中至少有１５个峰,鉴定出α－姜黄烯、α－姜烯、桉叶油素和球姜酮等１５种组分;另外还有１－（３－环戊基醛）－２,４－二甲基苯、β－倍半水芹烯、大根香叶酮、大根香叶烷、顺双环［３,３,１］酮－２－烯－９－醇等成分。姜黄挥发油中主要成分为α－姜黄烯。     

Discovery of mass spectral characteristics and automatic identification of wax esters from gas chromatography mass spectrometry data

The mass spectral characteristics of wax esters were systemically summarized and interpreted through data mining of their standard mass spectra taken from NIST standard mass spectral library. Combining with the rules of retention indices described in the previous study, an automatic system was subsequently developed to identify the structural information for wax esters from GC/MS data. After tested and illustrated by both simulated and real GC/MS data, the results indicate that this system could identify wax esters except the polyunsaturated ones and the mass spectral characteristics are useful and effective information for identification of wax esters.     

Tracking juniper berry content in oils and distillates by spectral deconvolution of gas chromatography/mass spectrometry data

The complex nature of botanicals and essential oils makes it difficult to identify all of the constituents by gas chromatography/mass spectrometry (GC/MS) alone. In this paper, automated sequential, multidimensional gas chromatography/mass spectrometry (GC-GC/MS) was used to obtain a matrix-specific, retention time/mass spectrometry library of 190 juniper berry oil compounds. GC/MS analysis on stationary phases with different polarities confirmed the identities of each compound when spectral deconvolution software was used to analyze the oil. Also analyzed were distillates of juniper berry and its oil as well as gin from four different manufacturers. Findings showed the chemical content of juniper berry can be traced from starting material to final product and can be used to authenticate and differentiate brands.     

Phytochemical analysis of the essential oil from botanically certified oleogum resin of Boswellia sacra (Omani Luban)

The yield of hydrodistillation of a botanically certified Oleogum Resin of Boswellia sacra essential oil (5.5%); and its chemical constituents were determined. The GC/MS technique was used for the analysis of the oil. Several oil components were identified based upon comparison of their mass spectral data with those of reference compounds published in literature or stored in a computer library. The oil was characterized by the high content of the monoterpenes (34) which constituted 97.3% in which E-beta-ocimene and limonene were the major constituents. The remaining 2.7% was accounted for the sesquiterpenes (16) in which the E-caryophyllene was the major constituent. The analysis proved the complete absence of the diterpenes.     

Search criteria and rules for comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry analysis of airborne particulate matter

Direct thermal desorption-gas chromatography-time-of-flight mass spectrometry (DTD-GC-TOFMS) and comprehensive two-dimensional (2D) gas chromatography-time-of-flight mass spectrometry (GC x GC-TOFMS) was applied for characterisation of semi-volatile organic compounds (SVOC) in fine particulate matter (PM), with a diameter of up to 2.5 microm (PM2.5), from ambient air in Augsburg, Germany. DTD-GC-TOFMS measurements on the SVOC in PM2.5 are done on a daily basis (time series over several years). The data will be used in an epidemiological study questioning the influence of SVOC in PM2.5 on ambient aerosol related health effects. The outcome of the first measurements periods is that the organic inventory in the ambient aerosol can undergo drastic fluctuations, e.g. due to meteorological influences or specific emission sources. This includes also the large fraction of chromatographically not resolved peaks (unresolved carbonaceous matter (UCM)). The UCM fraction contains about 70% of the SVOC mass in PM2.5. GC x GC-TOFMS is a suited technique to study the nature of the yet unidentified compounds forming the UCM. The considerably increased chromatographic resolution in GC x GC allows separation of many UCM compounds while the TOFMS supplies mass spectral data of all separated compounds. However, the data sets are getting enormously complex. In a typical PM2.5 sample from Augsburg more than 15,000 peaks can be detected. Thus, it is important to classify the observed GC x GC peaks by rational means. A classification procedure based on GC x GC retention times and the fragmentation patterns is suggested. With a preliminary classification procedure it is already possible to group compounds with some certainty into substance classes. After some further development, this approach can be used for classifying GC x GC data, e.g. for environmental and epidemiological studies.