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%。
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.
作者
祁兴普
刘纯友
佀再勇
刘萍
傅晓雨
战旭梅
陈通
QI Xing-pu;LIU Chun-you;SI Zai-yong;LIU Ping;FU Xiao-yu;ZHAN Xu-mei;CHEN Tong(School of Food Science and Technology,Jiangsu Agri-animal Husbandry Vocational College,Taizhou 225300,Jiangsu,China;School of Biological and Chemical Engineering,Guangxi University of Science and Technology,Liuzhou 545006,Guangxi,China)
出处
《食品研究与开发》
CAS
北大核心
2021年第14期152-157,共6页
Food Research and Development
基金
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)。
关键词
庐山云雾茶
气相离子迁移谱
挥发性有机成分
等级判别
风味指纹
Lushan Cloud-fog tea
gas chromatography-ion mobility spectrometry(GC-IMS)
volatile organic compounds
grade discrimination
flavor fingerprint
