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投稿时间：2020-08-25
投稿时间：2020-08-25
中文摘要: 为建立一种快速、无损的庐山云雾茶等级判别方法，采用气相离子迁移谱（gas chromatography-ion mobility spectrometry，GC-IMS）联用设备对3个等级共63个庐山云雾茶样的挥发性有机成分进行分析检测，并采用Otsu自动阈值分割算法对GC-IMS二维谱图中特征峰进行特征提取，以特征峰的峰面积为变量进行主成分分析，再结合K-最邻近（K-nearest neighbor，KNN）算法对主成分得分进行模式识别。结果表明，采用KNN方法能够很好地区分不同等级的庐山云雾茶，预测集样品识别率可达94.73%。
Abstract:To identify a method for discriminating among three different grades of Lushan Cloud-fog tea，a rapid and nondestructive method for analysis of volatile organic compounds was established using gas chromatography combined with ion mobility spectrometry（GC-IMS）.In this experiment，a total of 63 samples representing three different grades were evaluated by GC-IMS，and the Otsu algorithm was used to extract characteristic peaks from two-dimensional data profiles.Areas under the selected peaks were used as characteristic variables for principal component analysis，and a K-nearest neighbor（KNN）algorithm was used for pattern recognition.The results showed that the KNN pattern recognition method could effectively distinguish between different grades of Lushan Cloud-fog tea samples，achieving a discrimination rate of 94.73% in the prediction set.
keywords: Lushan Cloud-fog tea gas chromatography-ion mobility spectrometry（GC-IMS） volatile organic compounds grade discrimination flavor fingerprint
文章编号：202114024 中图分类号： 文献标志码：
基金项目:泰州311高层次人才计划；广西科技大学博士基金项目（校科博20Z34）
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