全二维气相色谱-飞行时间质谱法测定烟草的中性化学成分

建立了采用全二维气相色谱-飞行时间质谱(GC×GC-TOFMS)分析烟草的中性化学成分的方法。以DB-Petro(50 m×200 μm×0.5 μm) 为第一维色谱柱,DB-1701(2.3 m×100 μm×0.1 μm)为第二维色谱柱;调制周期为8 s;柱头压力为550 kPa;采用程序升温方式,初始温度分别为80 ℃和85 ℃。采用所建立的方法对不同部位的烟叶、不同品种烟草中的25种中性香味成分含量进行了测定和对比。结果表明：云南楚雄产云烟85的中性香味成分(不包括新植二烯)的总量以中部叶最高,其次是上部叶,下部叶最少;国内外不同品种的烤烟中中性香味成分的含量高低顺序为：巴西烤烟最高,其次是津巴布韦烤烟、云烟85、中烟101、NC89、K326;4类烟草中中性香味成分含量最高的是香料烟,其次是白肋烟、烤烟、马里兰烟。     

薄荷卷烟中香味成分的全二维气相色谱/飞行时间质谱分析

采用全二维气相色谱/飞行时间质谱法(GC×GC/TOFMS),以较长的非极性柱DB-5MS(30m×0.25mm×0.25μm)作为第一维柱,较短的中等极性柱DB-17MS(2m×0.1mm×0.1μm)作为第二维柱,利用固相微萃取法作为香味成分的萃取方法,对薄荷型ESSE卷烟的核心香味成分进行了定性分析,TOFMS谱图库检索结合全二维特有的包含结构信息的二维谱图,通过族分离和结构谱图鉴定,共鉴定了187种挥发性成分,其中对香气有贡献的成分118种.     

卷烟主流烟气粒相物中中性化学成分的全二维气相色谱飞行时间质谱(GC×GC-TOF MS)谱图特征识别

建立了全二维气相色谱-飞行时间质谱(GC×GC-TOF MS)分析卷烟主流烟气中中性化学成分的方法。以较长的弱极性柱HP-5MS(50 m×0.2 mm i.d.×0.33μm)作为第一维柱,较短的薄液膜中等极性柱DB-17MS(1.7 m×0.1 mm i.d.×0.1μm)作为第二维柱,对优质烟叶单料卷烟烟气的中性成分进行定性分析,经过人工纠错等分析初步鉴定出匹配度大于700的1 464种成分,重点讨论了中性香味羰基化合物全二维点阵的谱图特征,为烟气和复杂体系的深入研究提供了方法学基础。     

Characteristics Identification of Neutral Constituents in Cigarette Particulate Matter by GC × GC -TOF MS

建立了全二维气相色谱-飞行时间质谱( GC×GC - TOF MS)分析卷烟主流烟气中中性化学成分的方法.以较长的弱极性柱HP-5 MS(50 m×0.2 mm i.d.×0.33 μm)作为第一维柱,较短的薄液膜中等极性柱DB-17MS(1.7 m×0.1 mm i.d.×0.1 μm)作为第二维柱,对优质烟叶单料卷烟烟气的中性成分进行定性分析,经过人工纠错等分析初步鉴定出匹配度大于700的1 464种成分,重点讨论了中性香味羰基化合物全二维点阵的谱图特征,为烟气和复杂体系的深入研究提供了方法学基础.     

毛细管色谱直接进样法测定白酒中高碳脂肪酸乙酯的研究

介绍了采用ＦＦＡＰ键合毛细管柱直接进样测定白酒中５种高碳脂肪酸乙酯的方法，操作简捷，定量准确，检测限低达０４ｍｇ／Ｌ。用该法测定了各种香型近９０个白酒样品。改变色谱条件后，在一次直接进样分析中除能测定高碳脂肪酸乙酯外，而且还能对白酒中醇、酯、醛、酮以及有机酸等５２种香味组分进行定量测定，结果重现性良好。     

毛细管柱直接进样法测定白酒香味组分的研究

直接注入白酒以准确测定微量香味组分含量是近年来研究白酒香味的首选技术,围绕所选的３种柱型,对色谱分离条件、气相色谱／质谱（ＧＣ／ＭＳ）定性及采用３个内标物的定量方法作了较详细的探讨,列出了可供定量的６８个组分名称,并对３种毛细管柱的应用特性作了比较。     

全二维气相色谱/飞行时间质谱用于烟用香精化学组分的分析

采用全二维气相色谱/飞行时间质谱(GC×GC/TOFMS),以较长的极性柱DB-WAX(60 m×0.25 mm×0.25μm)作为第一柱,较短的中等极性柱DB-1701(3 m×0.1 mm,0.4μm)作为第二柱,对一种国产香烟中常用的烟用香精进行了分析,共鉴定出相似度和反相似度分别大于800和900的化合物有84种(在相同条件下,GC/MS鉴定出的组分数仅为21种)。在与GC×GC/TOFMS相同的分析条件下,利用多内标定量法,用GC-GC/氢火焰离子化检测器(FID)进一步对这些组分进行了准确定量。实验结果很好地解释了该香精的致香机理。     

专用型国产气相仪及DNP填充柱测定白酒中的主要香味组分

 采用微机控制的国产气相仪和自制的DNP填充柱测定了白酒中的主要香味组分。通过对各种操作参数的优化 ,确立了等温及程序升温时合适的条件。该方法除可测定十余种主要醇、酯、醛组分外 ,还将分析对象扩大至丙酮、异戊醛、2 戊酮、2 戊醇、3 羟基 2 丁酮等组分 ,从而使采用这一技术可准确测定的组分增至 1 8种。在选定的条件下 ,仪器具有良好的稳定性 ,各香味组分定量结果的重复性好。并对科技部组织研制的这一专用气相仪的适用性作了研讨。     

气相色谱法测定车用汽油中含氧化合物和苯胺类化合物

利用中心切割技术和双毛细管色谱柱系统,采用两次进样的方式,建立了气相色谱测定车用汽油中含氧化合物和苯胺类化合物的分析方法。第一次进样分析,组分首先进入非极性DB-1色谱柱(30 m×0.32 mm×1.0μm),按沸点由低到高的顺序分离,通过电磁阀切换将沸点小于2-己酮的组分切割至强极性GS-OxyPLOT色谱柱(10 m×0.53 mm×10μm)或CP-Lowox色谱柱(10 m×0.53 mm×10μm),其余重烃组分通过阻尼柱进入FID检测器。在GS-OxyPLOT或CP-Lowox色谱柱上,烃类组分与含氧化合物分离并进入检测器检测,消除了大量的烃类组分对含氧化合物测定的影响。第二次进样分析,设定电磁阀切换时间为间-甲基苯胺从非极性色谱柱流出的时间,苯胺类化合物在GS-OxyPLOT或CP-Lowox色谱柱上与烃类和含氧化合物分离并进入检测器检测。以乙二醇二甲基醚为内标化合物进行内标法定量。实现了在一套系统上同时测定车用汽油中添加的甲基叔丁基醚(MTBE)、甲醇、甲缩醛、乙酸仲丁酯、乙酸乙酯、苯胺、邻/间/对-甲基苯胺和N-甲基苯胺的含量,各组分的检测范围为0.01%~10%(质量分数),回收率为86.0%~102.6%。该法可以为车用汽油的质量控制提供有效的检测手段。     

二维气相色谱/质谱分离分析中国苦水玫瑰精油中的复杂天然产物

运用二维气相色谱/质谱联用技术对天然植物萃取精油-中国苦水玫瑰精油中难分离的复杂成分进行了分离分析,同时对色谱共流出组分的切割方式和切割时间进行了研究探讨。玫瑰精油组分首先在预毛细管柱J＆W Scientific DB-1701（60m×250μm×0.5μm）上得到初步分离,通过FID信号确定了目标组分的切割时间段（...     

快速毛细管气相色谱分析白酒中的香味成分

本文建立了快速毛细管气相色谱法,在10 min内分离了白酒中的香味成分,并对其中的醇、醛、酸、酯进行定性分析,对主要呈味成分己酸乙酯进行定量分析,方法重复性好,回收率较高。与常规毛细管色谱法相比,快速毛细管气相色谱法有效地缩短了分析时间,提高了白酒的分析效率。     

顶空固相微萃取-气相色谱-质谱法分离鉴定金华火腿的挥发性风味物质

采用顶空固相微萃取法(HS-SPME)制备样品,利用气相色谱-质谱法（GC-MS）分离鉴定了金华火腿的挥发性风味物质。实验中筛选了固相微萃取纤维头,优化了固相微萃取的操作条件。用75 μm碳分子筛/聚二甲基硅氧烷（CAR-PDMS）纤维头,于60 ℃下对金华火腿样品顶空吸附40 min,于250　℃下解吸2 min,采用GC-MS对解吸物进行分离鉴定。金华火腿样品的分析结果表明,其挥发性风味物质中含量较高的是醛、酸和酮类化合物,还有一些含硫或杂环化合物。     

Characterization of Aromatic Liquor by Gas Chromatography and Principal Component Analysis

The differentiation of aromas of Chinese liquor is important for their unique flavors. In this work, aromas of Chinese liquor were characterized by gas chromatography and chemometrics. Ten representative aroma compounds, including three alcohols, four esters, two organic acids, and acetal in 16 Chinese liquor were determined by gas chromatography with flame ionization detection. The relationship between these compounds and six classic aromas was investigated using principal component analysis and k-means clustering. The cumulative contribution of the first three principal components reached up to 84.607%, which effectively differentiated the liquors. The variables with the highest loading absolute value were acetal and ethyl acetate for principal component 1, ethyl butanoate and ethyl hexanoate for principal component 2, and the hexanoic acid and ethyl butanoate for principal component 3. The aromas of the liquors were characterized by k-means clustering with the first three principal component scores, indicating that the acetal, ethyl acetate, ethyl butanoate, ethyl hexanoate, and hexanoic acid are important for the aroma of Chinese liquors. This work demonstrated that the gas chromatography with chemometrics is effective for the characterization of aromatic liquor.     

基于液体阵列味觉仿生传感器鉴别白酒香型的新方法

通过模拟哺乳动物的味觉系统, 建立了交叉响应的液体阵列传感器, 为鉴别白酒香型提供了新方法. 选用7种染料和1种卟啉化合物作为传感单元, 构建液体阵列传感器, 集合8个传感单元的光谱响应信号构成分析物的指纹图谱, 达到识别的目的. 使用96孔板酶标仪采集响应数据, 结合主成分分析(PCA)、分层聚类分析(HCA)和判别分析(LDA)等模式识别方法进行数据处理, 对9种具有代表性的不同香型白酒样品进行了鉴别分析. PCA结果表明, 该方法对于白酒的检测主要基于酒体微量成分, 其中酸类物质对识别的贡献最大(贡献率达54.3%), 芳香类物质贡献率为18.6%; 同时, 仅用63.4%的数据信息量即可对白酒香型进行区分. HCA结果表明, 平行样均正确归类, 各白酒之间的相似程度在聚类图上得到体现. LDA结果表明, 该阵列对于9种白酒样品香型识别的准确率达到100%.     

Fingerprint developing of coffee flavor by gas chromatography-mass spectrometry and combined chemometrics methods

In this paper, chromatographic fingerprint was firstly used for quality control of tobacco flavors. Based on gas chromatography-mass spectrometry (GC-MS) and combined chemometrics methods, a simple, reliable and reproducible method for developing chromatographic fingerprint of coffee flavor, one of tobacco flavors, was described. Six coffee flavor samples obtained from different locations were used to establish the fingerprint. The qualitative and quantitative analysis of coffee flavor sample from Shenzhen was completed with the help of subwindow factor analysis (SFA). Fifty-two components of 68 separated constituents in coffee flavor sample from Shenzhen, accounting for 88.42% of the total content, were identified and quantified. Then, spectral correlative chromatography (SCC) was used to extract the common peaks from other five studied coffee flavor samples. Thirty-eight components were found to exist in all six samples. Finally, the method validation of fingerprint analysis was performed based on the relative retention time and the relative peak area of common peaks, sample stability and similarity analysis. The similarities of six coffee flavor samples were more than 0.9104 and showed that samples from different locations were consistent to some extent. The developed chromatographic fingerprint was successfully used to differentiate coffee flavor from coco flavor and some little difference sample prepared with coffee flavor and coco flavor by both similarity comparison and principal component projection analysis. The developed method can be used for quality control of coffee flavor.     

Comparison of the volatile constituents of Artemisia capillaris from different locations by gas chromatography-mass spectrometry and projection method

The volatile chemical constituents of Artemisia capillaries (an important traditional Chinese medicine) were determined by gas chromatography-mass spectrometry (GC-MS) and sub-window factor analysis (SFA). Seventy-five components were separated and 43 of them were qualitatively and quantitatively determined, which represented about 89.03% of the total content. This profile was then used to identify and assess the consistency of the herb by using an orthogonal projection method. Four different sources of A. capillaries were analyzed and compared with each other. Among the components determined, there were 51 components coexisting in all samples although the relative peak areas of a few showed variations. It is the first time to apply orthogonal projection method to the comparison of different samples, and it reduces the burden of qualitative analysis as well as the subjectivity. The results showed a fair consistency in their GC-MS fingerprint. A. capillaris was distinguished from Artemisia sacrorum L., a possible substitute in traditional Chinese medicine by comparing the fingerprints with each other.     

Characterization of Aroma Compounds in Cooked Sorghum Using Comprehensive Two-Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry and Gas Chromatography-Olfactometry-Mass Spectrometry

Sorghum is the major raw material for the production of Chinese Baijiu (Chinese liquor) and has a great effect on the flavor of Baijiu. Volatiles in cooked glutinous and non-glutinous sorghum samples were extracted using solid-phase microextraction (SPME) and analyzed via comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) and gas chromatography-olfactometry/mass spectrometry (GC-O/MS). A total of 145 volatile compounds and 52 potent odorant compounds were identified from both sorghum types according to the retention index, MS, aroma, and standards. Based on their aroma features, the compounds were grouped into eight general categories, and the intensities of each aroma group were summed. Moreover, most of the compounds detected in the cooked sorghums were also detected in commercial Chinese Baijiu, indicating that the aroma compounds produced during the sorghum cooking process have a direct and significant influence on the final flavor quality of Baijiu.     

Classification of Liquor Using Near-Infrared Spectroscopy and Chemometrics

The authenticity of Chinese liquor concerns consumer health and economic issues. The traditional characterization methods are time-consuming and require experienced analysts. The use of near-infrared (NIR) spectroscopy and chemometrics to classify Chinese liquor samples was investigated using 128 liquors. The spectral region between 5340 cm^?1 and 7400 cm^?1 was found to be most informative. Principal component analysis was employed to characterize liquor and principal components were extracted as inputs of training classifiers. Several supervised pattern recognition methods including K-nearest neighbor, perceptron, and multiclass support vector machine were used as algorithms of constructing classifiers. The initial principal components and all spectral variables were used as the input of training models. In terms of the misclassification ratio, the support vector machine approach was the most accurate. The results indicated that near-infrared spectroscopy and chemometrics are an alternative to conventional methods for the characterization of liquor.