Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools
Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools
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利用电子鼻技术和人体小组测试鉴别绿茶品质，线性和非线性分类工具的比较
DOI：
10.1016/j.snb.2011.07.009
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发表时间：
2011-11-28
影响因子：
8.4
通讯作者：
Zhao, De-An
中科院分区：
文献类型：
--
作者：
Chen, Quansheng;Zhao, Jiewen;Zhao, De-An
本工作尝试使用电子鼻（E-nose）技术来鉴别绿茶品质，以代替人体小组测试。实验中尝试了通过人体小组测试分类的四个等级的绿茶。首先，开发了具有八个金属氧化物半导体气体传感器阵列的电子鼻系统用于数据采集；然后，从传感器的响应中提取特征变量；接下来，通过主成分分析（PCA）提取主成分（PC）作为判别模型的输入；最后，在建立判别模型时对三种不同的线性或非线性分类工具，即K近邻（KNN）、人工神经网络（ANN）和支持向量机（SVM）进行了比较。通过交叉验证优化 PC 数量和其他模型参数。实验结果表明SVM模型的性能优于其他模型。当包含 4 台 PC 时，获得最佳 SVM 模型。训练集中的后向辨别率等于 100%，预测集中的预测辨别率等于 95%。总体结果表明，电子鼻技术结合SVM分类工具可以成功地用于绿茶品质判别，并且SVM算法在解决利用电子鼻数据对绿茶品质进行分类方面显示出其优越性。 (C) 2011 Elsevier B.V. 保留所有权利。
Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors: next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data. (C) 2011 Elsevier B.V. All rights reserved.