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首页> 外文会议>Annual International Meeting of The American Society of Agricultural and Biological Engineers >The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye signals combining withchemometrics methods
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The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye signals combining withchemometrics methods

机译：基于电子鼻，电子舌和E-Ey眼信号的茶度质量的定性和定量评估与CopeMometrics方法相结合

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In this work, electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) were jointly applied as intelligent instruments to acquire aroma, taste and color signals of tea samples. Features were severally extracted from E-nose, E-tongue and E-eye signals and were fused for analysis. The polyphenols, catechins, caffeine and amino acid as quality indices were detected by traditional methods as reference. For qualitative identification, support vector machine (SVM) and random forest(RF) were comparatively employed in modeling severally based on individual and fusion signals. The SVM and RF models based on the fusion signals achieved perfect classification results with the accuracy of 100%. For quantitative prediction of tea quality indices, partial least squares regression (PLSR), SVM and RF were applied based on individual and fusion signals to establish regression models between electronic signals and the amount of polyphenols, catechins, caffeine and amino acid. The RF prediction models reached higher correlation coefficients (R2) and lower root mean square errors (RMSE) than the PLSR and SVM models did. Meanwhile, the fusion signals had a better performance than the individual signals in PLSR, SVM and RF regression models. This work indicated that the simultaneous utilization of E-nose, E-tongue and E-eye based on appropriate chemometrics method could be successfully applied for qualitative and quantitative analysis of tea quality.
机译：在这项工作中，电子鼻子（电子鼻），电子舌（电子舌）和电子眼（E-Eye）共同应用于智能仪器以获得茶样品的香气，味道和彩色信号。从电子鼻部，电子舌和电子眼信号中分别提取特征，并融合用于分析。通过传统方法作为参考方法检测多酚，儿茶素，咖啡因和氨基酸作为质量指标。对于定性识别，支持向量机（SVM）和随机森林（RF）相对适用于基于个体和融合信号的分别建模。基于融合信号的SVM和RF模型实现了完美的分类结果，精度为100％。对于茶度质量指标的定量预测，基于个体和融合信号施加部分最小二乘回归（PLSR），SVM和RF，以建立电子信号与多酚，儿茶素，咖啡因和氨基酸之间的回归模型。 RF预测模型达到比PLSR和SVM模型更高的相关系数（R2）和较低的根均线误差（RMSE）。同时，融合信号具有比PLSR，SVM和RF回归模型中的各个信号更好的性能。这项工作表明，基于适当的化学计量方法的电子鼻，电子舌和E-ey的同时使用可以成功地应用于茶品质的定性和定量分析。

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