卷烟主流烟气中游离烟碱的测定及分布分析

烟碱是烟草中的特有成分,对烟草和卷烟的感官品质具有较重要的影响[1]。烟碱为易挥发、碱性较强的吡啶类生物碱,属于弱二级碱,通常以游离态、单质子态和双质子态等3种形态存在[2]。烟碱易与有机酸结合成盐,以质子态的形式存在,而非质子化的游离态烟碱含量较少,随烟草自身碱性的增强,非质子化的游离态烟碱含量增加[3]。反映吸烟生理强度的烟气劲头与烟气中游离烟碱的含量有关,含量越高,劲头越大,进而对卷烟的感官品质产     

基于超高效液相色谱串联质谱法的化学计量学分析不同产区烤烟烟叶中非挥发性有机酸的含量差异北大核心CSCD

非挥发性有机酸成分对烤烟的感官特征有显著影响,为研究不同产区烤烟烟叶中非挥发性有机酸含量差异及与感官特征之间的联系,采用超高效液相色谱串联质谱法(UHPLC-MS/MS)对黑龙江、四川、湖南、湖北、云南等5个不同产区烤烟样品中非挥发性有机酸成分进行了检测。通过正交偏最小二乘判别分析(OPLS-DA)模型、变量投影重要性(VIP)值、单因素方差分析(ANOVA)、京都基因和基因组数据库(KEGG)富集通路分析,得出了不同产区间非挥发性有机酸差异,并确定了特征差异组分的代谢通路。结果表明:共检测到69种非挥发性有机酸,其中多元酸23种,游离脂肪酸21种,其他有机酸25种。基于非挥发性有机酸构建的OPLS-DA模型对不同产区烟叶识别效果较理想,模型参数R^(2)Y和Q^(2)分别为0.931和0.595。通过模型的VIP值和ANOVA筛选出26种特征差异非挥发性有机酸,并通过KEGG富集分析得到16条与上述26种组分关联的代谢通路。研究发现黑龙江牡丹江产区烟叶中特征差异非挥发性有机酸含量与其他产区差异最大,其次为湖南衡阳产区,四川凉山、云南楚雄、湖北恩施产区烟叶中特征差异非挥发性有机酸组间差异较小,均与所属烟区烤烟感官特征相符。     

在线裂解冷阱捕集-气相色谱-质谱法测定芳酸果浸膏热裂解产物

芳酸果又名滇刺枣、酸枣等,鼠李科属,是一种典型的热带旱生植物,具有较高的营养和药用价值。芳酸果浸膏具有酸香、酿甜香韵特征,添加在卷烟或烟草薄片中具有降低刺激,改善余味,增加甜润,使烟香柔和绵长的作用,目前在卷烟产品加工中广泛使用。文献[1]报道了对芳酸果浸膏挥发性成分的研究,并进行了卷烟加香试验,此外也有文献对芳酸     

气相色谱-质谱/选择离子监测法分析烟叶中的香味成分

烟草中的致香成分主导着烟草品质的优劣,而烟草的感官香气质量取决于其内在的香味物质组成及其之间复杂的相互作用,因此,准确测定烟草中香味物质的差异对配方设计、加香加料和稳定卷烟产品质量有重要的实际意义.     

气相色谱-质谱法结合智能调香辅助软件对未知香精定性及香味分析

本文采用气相色谱-质谱(GC-MS)和"烟用香精成分剖析及计算机智能调香系统软件"相结合的方法,对成分未知的香精X-2进行成分剖析,然后进行香味香韵分析,最终确定了香精的主要成分和主体香韵组成,共鉴别出23种单体原料、3种浸膏,香精的主体香韵为坚果香韵、膏香韵、甜香韵和花香韵,与调香师的品香结果一致,为今后仿香和创香提供了理论依据。     

气相色谱-质谱法分析卷烟烟丝和“九曲红梅”红茶样品在加热不燃烧条件下释放出的挥发物及用掺入红茶的烟丝制卷烟的初步探讨

将"九曲红梅"红茶(以下简称红茶)与卷烟烟丝样品分别经过粉碎并通过0.177mm网筛,制成分析用样品。称取0.2mg样品,在裂解炉中,温度为200℃的条件下,在氦气氛围中,样品挥发性成分瞬间气化,由载气带入气相色谱-质谱仪器系统,经分离和测定,得到了两种样品在此条件下所释放出成分。烟丝样品在此加热不燃烧条件下,释放的物质主要有人为添加的发烟剂丙三醇(59.34%),以及其本身的组分烟碱(20.34%)、3-丁炔-1-醇(4.32%)、5-羟甲基糠醛(3.23%)和2,3-二氢-3,5-二羟基-6-甲基-4(H)-吡喃-4-酮(2.12%)等。红茶样品中释放出的主要成分有咖啡因(86.23%)以及16种低含量特征组分。将此红茶样品按质量百分比为5%,10%,30%的比例加入卷烟烟丝中,并测定其在加热不燃烧的条件下释放物质量的变化,并结合感官评价。结果发现:混入不同比例红茶的烟丝,加热后释放出的气体中咖啡因的含量按茶叶的配比量的增加依次为0.71%,1.96%,7.23%;感官评价表明红茶加入卷烟烟丝中,提升了卷烟烟气的优雅感,强化了卷烟的果香、甜香、花香等香韵,提升了香气量和香气丰富性,细腻柔和烟气,改善余味,增强回甜感。当茶叶的加入量为10%时,在余味和口感上有较好的效果,具有全面提升感官品质的功效,整体抽吸舒适性和协调性较好,香气的质和量适中。     

离子色谱法及聚类分析研究主流烟气中的有机酸

建立了超声辅助萃取-离子色谱法同时测定卷烟主流烟气中主要有机酸的方法.采用20 mL 30 mmol/L J NaOH溶液在50℃下对捕集了卷烟主流烟气的剑桥滤片超声萃取50 min,AS11-HC分离柱、Ion AG11-HC保护柱、ASRS-ULTRAⅡ抑制器,KOH溶液为淋洗液,用电导检测器同时测定了卷烟主流烟气中的乳酸、甲酸、乙酸、丙酸、苹果酸和草酸的含量.方法的线性范围1.0～400 mg/L,相关系数为0.9990～0.9995,相对标准偏差1.4%～4.8%,检出限0.03～0.09 mg/L,回收率为92%～104%.采用此方法测定了19种不同规格卷烟样品主流烟气中6种有机酸的含量,并且以6种有机酸为指标对样品进行了聚类分析.结果表明,6种有机酸的含量分布特征可反映不同等级卷烟产品的特性,可用于不用等级卷烟产品的比较和分类.     

基于内源性致香物质和化学计量学的烟草感官评价研究

采用主成分分析法结合遗传算法和神经网络,建立了基于烟草内源性致香物质的感官质量评价预测模型。利用气相色谱-质谱(GC-MS)技术对超临界萃取-分子蒸馏所得烟草精油中的内源性致香组分进行定性定量分析,汇总各类致香指标后,对其进行主成分分析;以提取所得5个主成分的得分作为输入变量,感官评吸分数作为输出变量,分别使用标准BP神经网络和遗传算法(GA)优化的BP神经网络建立预测模型。对比实验结果表明,GA优化后的模型预测效果更优,其预测值与实验值间的相关系数为0.96,预测均方根误差为1.81,说明GA-BP模型具有更好的拟合能力和预测能力,该模型能有效地预测烟草精油的感官品质。     

复杂香精配方的化学组成解析及香韵识别

结合气相色谱-质谱(GC-MS)分析结果和"烟用香精香料智能辅助调香系统"对复杂香精配方的化学组成进行解析,共解析出14种单体、2种精油。对精油类香料采用混合物加标法判断其是否存在于香精中,同时结合香原料的香韵分析进一步确定复杂香精的配方,香精的主体香韵是花香韵、果香韵和甜香韵。解析结果与调香师的品评结果一致。     

梗丝形态对细支卷烟主流烟气及燃烧特性影响

为探寻细支卷烟的降本增效路径,开展了微波膨胀梗丝、薄压细切及常规气流梗丝在细支卷烟中的应用效果评价,考察了三种梗丝对细支卷烟物理特性、混合均匀度、常规主流烟气成分、动态吸阻稳定性、烟气粒径分布、燃烧锥体积及速率、7种有害成分及感官品质的影响。结果表明:①相对于常规传统气流梗丝而言,微波膨胀梗丝对细支卷烟吸阻影响更大,在相同试验条件下,其掺配样吸阻均值较常规传统气流梗丝样大113Pa。②微波膨胀梗丝和薄压细切气流梗丝的掺配均匀度均值分别为97.674%、97.005%,均大于常规气流梗丝的掺配均匀度均值94.846%。③常规主流烟气成分释放量的相对标准偏差(RSD)为评价指标,细支卷烟样品稳定性排序为:微波膨胀梗丝掺配样薄压细切气流梗丝掺配样常规气流梗丝掺配样,感官品质得分与此排序一致,常规气流梗丝掺配样的抽吸均衡性差、木质气重,感官品质得分最低;④较常规气流梗丝掺配样,微波膨胀梗丝及薄压细切气流梗丝掺配样动态吸阻稳定性更优,这与三种梗丝的掺配均匀度均值排序一致;⑤三种梗丝掺配样的中值粒径(CMD)均值基本一致,均分布在146nm附近。其中微波膨胀梗丝掺配样的逐口抽吸粒子浓度最大;⑥薄压细切气流梗丝掺配样的燃烧速率及燃烧锥体积均最大,其燃烧更充分;⑦7项有害成分方面,常规气流梗丝降低苯酚、巴豆醛及NNK的效果最优,其卷烟危害性指数均低于微波膨胀梗丝与薄压细切气流梗丝掺配样,表明前者在细支烟中的减害作用较优。     

卷烟化学指标的因子分析

运用因子分析法对A牌号卷烟的18个化学指标进行分析,结果表明:烟丝的总糖、还原糖、总氮、总植物碱、水分、氯离子、钾离子、葡萄糖、麦芽糖、苹果酸、异戊酸、苯甲醇、茄酮、β-大马酮14个指标,及主流烟气的水分、CO、烟碱、焦油4个指标所含的信息可由8个因子来表示。该8个因子模型解释了试验数据总方差的86.7%;烟丝的致香成分、水分、糖类物质、有机酸类物质对卷烟A的质量风格具有重要影响,特别是致香成分在进行卷烟质量表征时应该重点关注。因子分析结果为卷烟的化学表征和质量控制提供了有用的参考。     

闪蒸-气相色谱指纹图谱及系统聚类分析用于烟用香精香料的测定

采用闪蒸-气相色谱法(FE-GC)对不同黏度的烟用香精香料样品进行测定,考察了影响测定的主要因素。结果表明,0.40 mg样品在350 ℃下进行FE-GC分析,可以得到重现性良好的色谱图。与超声辅助液-液萃取-气相色谱法(ULLE-GC)分析结果的比较表明,FE-GC适用于不同黏度样品的分析。采用该法检测了中等黏度的烟用香精香料1184号8个批次样品,并建立其指纹图谱。利用系统聚类分析法可以明显区分1184号样品及掺兑10%～30%其他种类烟用香精香料的样品。FE-GC法简便、快速、灵敏,适用于不同黏度的烟用香精香料样品的检测和质量控制分析。     

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.     

Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine

It is important to monitor quality of tobacco during the production of cigarette. Therefore, in order to scientifically control the tobacco raw material and guarantee the cigarette quality, fast and accurate determination routine chemical of constituents of tobacco, including the total sugar, reducing sugar, Nicotine, the total nitrogen and so on, is needed. In this study, 50 samples of tobacco from different cultivation areas were surveyed by near-infrared (NIR) spectroscopy, and the spectral differences provided enough quantitative analysis information for the tobacco. Partial least squares regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The quantitative analysis models of 50 tobacco samples were studied comparatively in this experiment using PLSR, ANN, radial basis function (RBF) SVM regression, and the parameters of the models were also discussed. The spectrum variables of 50 samples had been compressed through the wavelet transformation technology before the models were established. The best experimental results were obtained using the (RBF) SVM regression with gamma=1.5, 1.3, 0.9, and 0.1, separately corresponds to total sugar, reducing sugar, Nicotine, and total nitrogen, respectively. Finally, compared with the back propagation (BP-ANN) and PLSR approach, SVM algorithm showed its excellent generalization for quantitative analysis results, while the number of samples for establishing the model is smaller. The overall results show that NIR spectroscopy combined with SVM can be efficiently utilized for rapid and accurate analysis of routine chemical compositions in tobacco. Simultaneously, the research can serve as the technical support and the foundation of quantitative analysis of other NIR applications.     

热脱附/冷阱聚焦/气相色谱/质谱法卷烟香气分析

利用热脱附装置(TDS)对烟丝粉末进行快速升温,模拟卷烟燃吸过程中蒸馏区的部分反应,用冷阱收集所脱附的物质,以GC/MS对进行定性分析.成功的分离出了有机酸、饱和烃、芳香烃、烯烃以及含氮、氧杂原子的化合物等系列超过100种有机化合物,并且还发现了典型的存在于环境中的化学污染物——邻苯二甲酸二丁酯.     

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.     

Comparative analysis of the volatile components in cut tobacco from different locations with gas chromatography-mass spectrometry (GC-MS) and combined chemometric methods

A combined approach of subwindow factor analysis and orthogonal projection resolution was used to analyze the volatile components of cut tobacco samples from different sources. After extracted with simultaneous distillation and extraction method, the volatile components in cut tobacco from five different locations were detected by GC-MS. Then, the qualitative and quantitative analysis of the volatile components of cut tobacco from Changde area was completed with the help of subwindow factor analysis resolving two-dimensional original data into pure mass spectra and chromatograms. One hundred and two volatile components among 138 separated peaks were identified and quantified, accounting for about 88.90% of the total content. Finally, orthogonal projection method was used to extract the common peaks from different locations. Among the identified components, there were 74 components coexisting in five studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprints. It was the first time to apply orthogonal projection method to compare different cut tobacco samples, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex cut tobacco samples. The developed method can be used to compare the sameness and differences of cut tobacco from different sources and for quality control of cigarette production and materials.     

烘丝前后烟丝多元酸和高级脂肪酸变化研究

烟草中的多元酸对抽吸品质有重要影响,它们能与生物碱结合成盐,调节烟草中碱性成分的挥发性。高级脂肪酸可分成饱和与不饱和两类,饱和脂肪酸能增加烟气的脂肪味、腊味并使之圆和;不饱和脂肪酸增加烟气的丰满度和粗糙感。卷烟在烘丝过程中由于温度、湿度的变化而引起烟草内部化学成分的变化。本文应用甲酯衍生化和GC-MS法,对卷烟加工过程中烘丝工序前后的多元酸和高级脂肪酸进行了定性定量分析。为卷烟配方和加工工艺参数的优化提供理论依据。