黄山毛峰茶贮藏时间电子鼻检测方法研究

Study on analysis method of storage time of Huangshanmaofeng tea by electronic nose

利用电子鼻对6个贮藏时间5个等级的黄山毛峰茶进行检测,首先获取反映茶叶香气的原始特征向量,再通过主成分分析法(PCA)提取出前5个主成分作为主特征向量,然后以主特征向量作为BP神经网络(BPNN)的输入,建立黄山毛峰茶贮藏时间预测模型(PCA-BPNN)。结果表明:PCA-BPNN对于贮藏0 d的茶叶,最大预测误差为11 d,5个(6.67%)样本预测误差超过13 d;对于贮藏60 d的茶叶,最大预测误差为13d,4个(5.33%)样本预测误差超过10 d;对于贮藏120 d的茶叶,最大预测误差为16 d,7个(9.33%)样本预测误差超过10 d;对于贮藏180 d的茶叶,最大预测误差为19 d,8个(10.67%)样本预测误差超过10 d;对于贮藏240 d的茶叶,最大预测误差为21 d,8个(10.67%)样本预测误差超过10 d;对于贮藏300 d的茶叶,最大预测误差为14 d,6个(8.00%)样本预测误差超过10 d。该研究所建立的PCA-BPNN预测模型可用于检测黄山毛峰茶贮藏时间,且与以原始特征变量作为输入的BPNN模型相比,性能更好。

Five levels of Huangshanmaofeng tea with 6 varied storage time were detected by electronic nose. Firstly,the original feature vectors presenting the tea odor were acquired. Then,the first 5 principal components were extracted as the principal feature vectors by principal component analysis（ PCA）. With the principal feature vectors used as BPNN input,a new model called PCA-BPNN for storage time analysis of Huangshanmaofeng tea was built. After experimental test,it was shown that for the tea of 0 d storage,the maximum prediction error（ MPE） was 11 d,and the samples of prediction error exceeding 10 d was 5（ 6. 67%）; for the tea of 60 d storage,MPE was 13 d,and the samples of prediction error exceeding 10 d was 4（ 5. 33%）; for the tea of 120 d storage,MPE was 16 d,and the samples of prediction error exceeding 10 d was 7（ 9. 33%）; for the tea of 180 d storage,MPE was 19 d,and the samples of prediction error exceeding 10 d was 8（ 10. 67%）; for the tea of 240 d storage,MPE was 21 d,and the samples of prediction error exceeding 10 d was 8（ 10. 67%）; for the tea of 300 d storage,MPE was 14 d,and the samples of prediction error exceeding 10 d was 6（ 8. 00%）. In conclusion,PCA-BPNN model could be used to analyze the storage time of Huangshanmaofeng tea,and the proposed model was better than BPNN,which used original feature vectors as the input.