罗玉琴, 韦燕菊, 林琳, 林馥茗, 苏峰, 孙威江. 基于GC-IMS技术的福建白茶产地判别[J]. 农业工程学报, 2021, 37(6): 264-273. DOI: 10.11975/j.issn.1002-6819.2021.06.032
    引用本文: 罗玉琴, 韦燕菊, 林琳, 林馥茗, 苏峰, 孙威江. 基于GC-IMS技术的福建白茶产地判别[J]. 农业工程学报, 2021, 37(6): 264-273. DOI: 10.11975/j.issn.1002-6819.2021.06.032
    Luo Yuqin, Wei Yanju, Lin lin, Lin Fuming, Su Feng, Sun Weijiang. Origin discrimination of Fujian white tea using gas chromatography-ion mobility spectrometry[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(6): 264-273. DOI: 10.11975/j.issn.1002-6819.2021.06.032
    Citation: Luo Yuqin, Wei Yanju, Lin lin, Lin Fuming, Su Feng, Sun Weijiang. Origin discrimination of Fujian white tea using gas chromatography-ion mobility spectrometry[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(6): 264-273. DOI: 10.11975/j.issn.1002-6819.2021.06.032

    基于GC-IMS技术的福建白茶产地判别

    Origin discrimination of Fujian white tea using gas chromatography-ion mobility spectrometry

    • 摘要: 为了实现福建省白茶产地的快速鉴别,采用气相色谱-离子迁移谱(Gas Chromatography-Ion Mobility Spectrometry,GC-IMS)技术对福建不同产地白茶挥发性物质进行检测,结合化学计量学方法建立白茶产地判别模型。结果表明,福鼎、福安、政和、建阳和松溪各产地间白茶挥发性物质含量存在差异,政和、建阳和松溪3地制成的白茶样品相似度相对较高。GC-IMS谱图数据和241种标记物质数据均可用于白茶产地区分。GC-IMS谱图数据建立的K近邻线性判别分析(K-near Neighbor Linear Discriminant Analysis,LDA-KNN)、多层感知机线性判别分析(Multi-layer Perceptron Linear Discriminant Analysis,LDA-MLP)和支持向量机线性判别分析(Support Vector Machine Linear Discriminant Analysis,LDA-SVM)模型判别率分别为91.84%、93.88%和93.88%;标记物质建立的Adaboost线性判别分析(LDA-Adaboost)、决策树线性判别分析(LDA-Decison Tree)、LDA-KNN、LDA-MLP、随机森林线性判别分析(LDA-Random Forest)和LDA-SVM模型判别率均为100%。结果表明基于标记物质数据建立的6种模型能更有效对白茶产地进行区分。研究结果为福建白茶原产地保护提供技术支持。

       

      Abstract: Abstract: White tea is one of the six categories of tea. Fresh leaf picking, withering and drying are the three basic processing technology of white tea, which are relatively simple. White tea originated in Fujian Province, mainly produced in Fuding City, Zhenghe County, Jianyang county and Songxi County. Aroma is one of the important factors that determine the quality of tea. The main aroma components of Yunnan Yueyue white tea and Fujian Baihao Yinzhen tea were reported, but the differences of volatile aroma components of white tea from different main producing areas in Fujian Province were not clear. Gas Chromatography Ion Mobility Spectrometry (GC-IMS) is a new gas phase separation and detection technology in recent years, which has high resolution of gas chromatography and low detection limit of ion mobility spectrometry. In order to reveal the different volatile aroma components of white tea from different areas in Fujian Province, and to realize the rapid identification of white tea producing areas, GC-IMS technology was used to detect the volatile components of white tea from different areas in Fujian Province. Meanwhile, Linear Discriminant Analysis (LDA) was carried out to reduce the dimension of aroma data, and established a discrimination model of white tea producing areas combined with chemometrics method. The results showed that the contents of volatile compounds in white tea among the producing areas of Fuding, Fu'an, Zhenghe, Jianyang and Songxi were different. The white tea samples of Zhenghe, Jianyang and Songxi had higher similarity, and lower content of volatile aroma substances. Both GC-IMS spectrum data and 241 kinds of labeled aroma compounds data could be used to distinguish the origin of white tea, and LDA based on marker material data was better than it based on GC-IMS spectrum data. The discriminant rates of K Near Neighbor Linear Discriminant Analysis (LDA-KNN), Multi-Layer Perceptron Linear Discriminant Analysis (LDA-MLP) and Support Vector Machine Linear Discriminant Analysis (LDA-SVM) model based on the GC-IMS spectrum data were 91.84%,93.88% and 93.88%, respectively. By comparing the three patterns of misjudgment samples, it was found that the origin misjudgment occurred between Zhenghe white tea and Songxi Jianyang white tea, which was related to the small difference of volatile aroma components and high similarity of samples. The results showed that the discriminant rates of Adaboost Linear Discriminant Analysis (LDA-Adaboost), Decision Tree Linear Discriminant Analysis (LDA-Decison Tree), LDA-KNN, LDA-MLP, Random Forest Linear Discriminant Analysis (LDA-Random Forest) and LDA-SVM were 100%. The positive discrimination rate of the origin model based on the marker substance was higher than that based on the GC-IMS spectrum data. All six discriminant models based on the labelled substances data could effectively distinguish the origin of white tea. The results of this study can provide technical support for the origin protection of Fujian white tea.

       

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