González-Calabuig Andreu, Cetó Xavier, Del Valle Manel
Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Barcelona, Spain.
Future Industries Institute, University of South Australia, SA 5095 Adelaide, Australia.
Sensors (Basel). 2018 Jan 13;18(1):216. doi: 10.3390/s18010216.
This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and ≥ 0.948.
这项工作报告了一种伏安传感器阵列的适用性，该阵列能够利用电子舌（ET）原理对人工样品中的2,4-二硝基苯酚、4-硝基苯酚和苦味酸含量进行定量分析。电子舌基于循环伏安法信号，这些信号来自金属圆盘电极阵列和石墨环氧复合电极，通过离散小波变换并结合化学计量学工具（如人工神经网络（ANN））进行压缩。人工神经网络用于建立定量预测模型。通过这种方式，制备了一组基于全因子设计的标准品，浓度范围为0至300 mg·L，用于建立模型；随后，用一组完全独立的标准品对模型进行验证。该模型成功预测了三种酚类物质的浓度，训练子集和测试子集的归一化均方根误差分别为0.030和0.076，相关系数≥0.948。
