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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals
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Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals

机译：基于电子鼻传感器信号主成分分析特征值的线性判别分析和反向传播神经网络技术识别茶叶贮藏时间

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An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively.
机译：使用电子鼻（E-nose）检测不同保存时间后的绿茶香气。用电子鼻分别检测龙井绿茶干叶，饮料和残留物。为了降低数据维数并优化特征向量，通过主成分分析（PCA）对电子鼻传感器响应数据进行了分析，并提取了五个主要主成分值作为判别分析的输入。储存时间（0、60、120、180和240天）可以通过线性判别分析（LDA）更好地区分，并可以通过反向传播神经网络（BPNN）方法进行预测。结果表明，基于茶叶的鉴别和检测结果优于基于茶饮料和茶渣的鉴别和检测结果。茶叶数据的平均误差分别为9、2.73、3.93、6.33和6.8天。

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