Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici Cn, Barcelona, 08193 Bellaterra, Spain.
Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici Cn, Barcelona, 08193 Bellaterra, Spain.
Talanta. 2018 Mar 1;179:70-74. doi: 10.1016/j.talanta.2017.10.041. Epub 2017 Oct 22.
This work reports the applicability of a voltammetric sensor array able to evaluate the content of the metabolites of the Brett defect: 4-ethylphenol, 4-ethylguaiacol and 4-ethylcatechol in spiked wine samples using the electronic tongue (ET) principles. The ET used cyclic voltammetry signals, obtained from an array of six graphite epoxy modified composite electrodes; these were compressed using Discrete Wavelet transform while chemometric tools, among these artificial neural networks (ANNs), were employed to build the quantitative prediction model. In this manner, a set of standards based on a modified full factorial design and ranging from 0 to 25mgL on each phenol, was prepared to build the model; afterwards, the model was validated with an external test set. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.02 and 0.05, for the training and test subsets respectively, and correlation coefficients better than 0.958.
本工作报道了使用电化学生物传感器阵列评估布雷特缺陷代谢物（4-乙基苯酚、4-乙基愈创木酚和 4-乙基儿茶酚）含量的适用性，该阵列可评估强化葡萄酒样品中的代谢物含量，其原理基于电子舌（ET）。ET 使用循环伏安法信号，由六个石墨环氧树脂复合改性电极组成的阵列获得；对这些信号使用离散小波变换进行压缩，同时使用化学计量工具（包括人工神经网络（ANNs））构建定量预测模型。通过这种方式，基于改进的完全析因设计，在每个酚的 0 到 25mg/L 范围内，制备了一组标准品来构建模型；然后，使用外部测试集对模型进行验证。该模型成功地预测了三种考虑的酚类物质的浓度，对于训练集和测试集，归一化均方根误差分别为 0.02 和 0.05，相关系数优于 0.958。
