主成分分析
传感器阵列
绿茶
生物系统
制作
人工神经网络
校准
色谱法
材料科学
化学
模式识别(心理学)
人工智能
计算机科学
食品科学
数学
生物
机器学习
病理
统计
医学
替代医学
作者
Luqing Li,Shimeng Xie,Fengyuan Zhu,Jingming Ning,Quansheng Chen,Zhengzhu Zhang
标识
DOI:10.1080/10942912.2017.1354021
摘要
Tea quality is often evaluated by experienced tea tasters; however, their assessments are subjective, being influenced by their individual physiological and psychological condition. Herein, we fabricated a colorimetric sensor array-based artificial olfactory system for sensing the quality of Chinese green tea. First, the colorimetric sensors array was man-made using printing 12 chemically responsive dyes (9 porphyrins, metalloporphyrins and 3 pH indicators) on silica-gel flat plate. The plate was exposed to volatile organic compounds, and the colour changes in each sample were obtained by distinguishing between the images of sensor array before and after contact with tea sample. The values of colour composition changes were extracted from the dyes' colour sections. Multivariate calibrations were applied through principal component analysis and back propagation artificial neural network (BP-ANN) for modelling. The optimum BP-ANN model was obtained with nine principal components, and the discrimination rate was equal to 85% and 86% in the calibration and prediction sets, respectively. We thus conclude that the low cost colorimetric sensor array-based artificial olfactory technique has great potential for application in intelligent evaluation of the quality of green tea.
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