| 李 卓 1
，张雄峰 1
，孙瑞玲 2，杏朝刚 3，张 棋 4，张启明 1
，焦绍赫 1.顶空 - 气相色谱 - 质谱联用法结合化学计量学分析赣州烤烟样品中的挥发性化合物[J].广东农业科学,2019,46(4):130-137 |
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顶空 - 气相色谱 - 质谱联用法结合化学计量学分析赣州烤烟样品中的挥发性化合物 |
| Analysis of Volatile Compounds of Flue-cured Tobacco Samples from Ganzhou by Head space - Gas Chromatography - Mass Spectrometry with Chemometrics |
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| DOI：10.16768/j.issn.1004-874X.2019.04.019 |
| 中文关键词: 烤烟样品 顶空 - 气质联用 挥发性化合物 化学计量学 产地预报 |
| 英文关键词: flue-cured tobacco headspace-gas chromatography-mass spectrometry volatile compounds chemometrics origin-identified prediction |
| 基金项目:江西省烟草专卖局重点项目（赣烟 201301006） |
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| 摘要点击次数: 1482 |
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| 中文摘要: |
| 【目的】挥发性香气成分作为烟气组分的前体物质，影响着烟草感官评吸质量，通过对赣州烤
烟样品中的挥发性化合物含量进行分析，挖掘不同产地烟叶中挥发性化合物的含量规律并进行产地预报分类，
从而实现对烟草的质量控制。【方法】通过静态顶空 - 气相色谱 - 质谱联用测定赣州 6 个产烟区县（石城、
瑞金、安远、会昌、兴国、信丰）共 62 个烤烟样品中的 54 种挥发性化合物含量，采用主成分分析（Principal
component analysis, PCA）对样品产地进行分类，用遗传算法（Genetic algorithm, GA）进行变量筛选，用线性判
别分析（Linear discriminant analysis, LDA）、反传 - 人工神经网络（Back propagation-artificial neural network, BPANN）、最小二乘 - 支持向量机（Least squares-support vector machines, LS-SVM）3 种有监督模式识别方法进行
产地预报。【结果】直接对样品进行 PCA，分类效果并不理想；采用 GA 对挥发性化合物含量进行变量筛选后
得到 11 个变量，分类效果得到显著改善，结合分类结果分析了来自不同区县的样品中挥发性化合物的含量规律。
BP-ANN 和 LS-SVM 的预报正确率分别达到 96.8% 和 98.4%，展现了较好的实际使用价值，据此建立了赣州烤
烟样品的产地鉴别模型。【结论】该方法可以较为准确地判定赣州烤烟样品的来源，同时可以找到不同产地烤
烟挥发性化合物的含量规律，亦可用于全国各地烤烟的香型风格监测和质量控制。 |
| 英文摘要: |
| 【Objective】 As precursor substances of flue gas components, volatile aroma components can affect
the quality of tobacco sensory evaluation. The aims of this study were to analyze the content of volatile compounds in fluecured tobacco samples from Ganzhou, to explore the content regulation of volatile compounds in tobacco leaves of different producing areas, and to predict the origin classification of samples so as to achieve quality control of tobacco. 【Methods】
54 kinds of volatile compounds of 62 flue-cured tobacco samples from 6 tobacco-producing counties(Shicheng, Ruijin,
Anyuan, Huichang, Xingguo, Xinfeng) in Ganzhou were measured by static headspace-gas chromatography-mass spectrometry
method(HS-GC-MS). Subsequently, principal component analysis(PCA) was used for classification of sample origins, genetic
algorithm (GA) was used for variable screening, and three supervised pattern recognition methods, namely, linear discriminant
analysis (LDA), back propagation-artificial neural network (BP-ANN) and least squares-support vector machines (LS-SVM)
were used for origin prediction. 【Results】 The classification effect by PCA, which was contributed to explore the content
regulation of volatile compounds of samples in Ganzhou, was significantly improved after obtaining 11 volatile compounds
(variables) by GA. The prediction accuracy rates of BP-ANN and LS-SVM reached 96.8% and 98.4%, respectively, which
showed good practical application value. On this basis, the identification model for the origin of flue-cured tobacco of Ganzhou
was established. 【Conclusion】 The method proposed in this paper can accurately determine the origins of flue-cured tobacco
samples from Ganzhou, find the content regulation of volatile compounds among different origins, and be used for the monitoring
of flavor patterns and the quality control of flue-cured tobacco across the country. |
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