In this paper, a computational intelligence approach has been adopted to optimize the voltammetric electrode array for discrimination of the overall quality of black tea. Electrochemical measurements have been performed using the three- electrode system that comprises of an Ag/AgCl reference electrode, a platinum counter electrode, and synthesized working electrodes (WE). Nine working electrodes (WE1 to WE9) have been synthesized by varying the compositions of polymer and graphite. The electrodes have been imbibed in black tea samples and subjected to triangular voltage waveform ranging from −0.04 V to 0.8 V. From the data set so obtained, the number of electrodes have been optimized using computational approaches followed by a polling technique. Features were extracted by four feature transformation techniques and samples were classified with five different classification methods. Polling score has been assigned to each WE based on the decisions obtained by different feature transformation and classification techniques. The average classification accuracy rate of two working electrodes (WE1 and WE8) optimized from the algorithms were 91.27% and 91.34%, respectively. Thus, the optimization technique implemented in the present work yielded acceptable results and this technique could be suitable for other real time applications as well.

中文翻译：

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使用计算智能方法优化红茶鉴别的伏安电极阵列


本文采用计算智能方法来优化伏安电极阵列，以判别红茶的整体品质。使用三电极系统进行电化学测量，该系统由 Ag/AgCl 参比电极、铂对电极和合成工作电极 (WE) 组成。通过改变聚合物和石墨的成分合成了九种工作电极（WE1至WE9）。电极已被吸入红茶样品中，并受到-0.04 V 至 0.8 V 范围内的三角电压波形的影响。根据如此获得的数据集，使用计算方法和轮询技术优化了电极的数量。通过四种特征转换技术提取特征，并使用五种不同的分类方法对样本进行分类。根据不同特征转换和分类技术获得的决策，将轮询分数分配给每个 WE。算法优化后的两个工作电极（WE1和WE8）的平均分类准确率分别为91.27%和91.34%。因此，本工作中实现的优化技术产生了可接受的结果，并且该技术也适用于其他实时应用。