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Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: A method of fabrication

机译:基于比色传感器阵列的人工嗅觉系统,用于感应中国绿茶的质量:一种制造方法

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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.
机译:茶叶质量通常由经验丰富的茶叶品尝师进行评估;但是,他们的评估是主观的,受其个人生理和心理状况的影响。在这里,我们制造了一种基于比色传感器阵列的人工嗅觉系统,用于感测中国绿茶的质量。首先,比色传感器阵列是在硅胶平板上印制12种化学响应性染料(9种卟啉,金属卟啉和3种pH指示剂)制成的。将该板暴露于挥发性有机化合物,并且通过区分与茶样品接触之前和之后的传感器阵列的图像来获得每个样品的颜色变化。颜色组成变化的值是从染料的颜色部分提取的。通过主成分分析和反向传播人工神经网络(BP-ANN)应用多变量校准进行建模。获得了具有9个主要成分的最优BP-ANN模型,在校正和预测集中的鉴别率分别等于85%和86%。因此,我们得出结论,基于低成本比色传感器阵列的人工嗅觉技术在智能评估绿茶质量方面具有巨大的潜力。

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