食品科学 ›› 2019, Vol. 40 ›› Issue (10): 279-284.doi: 10.7506/spkx1002-6630-20180504-039

• 安全检测 • 上一篇    下一篇

电子鼻/舌融合技术的信阳毛尖茶品质检测

邹光宇,王万章,王淼森,肖焱中,张红梅*   

  1. 河南农业大学机电工程学院,河南 郑州 450002
  • 出版日期:2019-05-25 发布日期:2019-05-31
  • 基金资助:
    国家自然科学基金青年科学基金项目(31501213);河南省现代农业产业技术体系建设专项(S2017-02-G07);河南省高等学校青年骨干教师资助计划项目(2015GGJS-077)

Quality Detection of Xinyang Maojian Tea Using Electronic Nose and Electronic Tongue

ZOU Guangyu, WANG Wanzhang, WANG Miaosen, XIAO Yanzhong, ZHANG Hongmei*   

  1. College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China
  • Online:2019-05-25 Published:2019-05-31

摘要: 为实现茶叶品质和化学成分快速鉴别和预测,采用电子鼻与电子舌联用技术对信阳毛尖茶茶叶挥发性气味和茶汤滋味成分进行检测分析。对电子鼻与电子舌联用的响应值进行主成分分析,结果显示电子鼻与电子舌数据融合可提高对茶叶样品区分度。通过电子鼻与电子舌响应的融合数据,对茶叶样品中茶多酚、咖啡碱含量建立预测模型。结果表明,多元线性回归、多元线性逐步回归、二次多项式逐步回归模型中回归系数效果显著(P<0.01),其中二次多项式逐步回归模型效果最佳,茶多酚建模集和验证集的决定系数分别为0.999、0.975,均方根误差分别为0.083、0.174;咖啡碱建模集和验证集的决定系数分别为0.985、0.978,均方根误差分别为0.015、0.048。电子鼻/舌联用可对茶叶品质和理化成分进行很好地分析和预测。

关键词: 电子鼻, 电子舌, 主成分分析(PCA), 理化成分

Abstract: In order to rapidly identify and predict tea quality, the volatile odor components of Xinyang Maojian tea and the taste components of its infusion were analyzed with an electronic nose and an electronic tongue. Principal component analysis (PCA) indicated that the fusion of the electronic nose and tongue data allowed a better discrimination among tea samples. The fused data were used to develop mathematical models to predict the contents of tea polyphenol and caffeine. Results showed that the regression coefficients of the multiple linear regression (MLR), multivariate linear stepwise regression (MLSR) and quadratic polynomial stepwise regression (QPSR) models were all statistically significant (P < 0.01). The QPSR model presented the best prediction performance among these models. The determination coefficients of calibration and validation of this model were 0.999 and 0.975 for tea polyphenol, and 0.985 and 0.978 for caffeine, respectively; the root mean square error of calibration (RMSEC) were 0.083 and 0.174 for tea polyphenol, and 0.015 and 0.048 for caffeine, respectively. The combination of electronic nose and tongue is feasible to predict the quality and chemical composition of tea.

Key words: electronic nose, electronic tongue, principal component analysis (PCA), chemical composition

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