Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
Sensors (Basel). 2019 Jul 26;19(15):3286. doi: 10.3390/s19153286.
In the present work, ternary mixtures of Acetaminophen, Ascorbic acid and Uric acid were resolved using the Electronic tongue (ET) principle and Cyclic voltammetry (CV) technique. The screen-printed integrated electrode array having differentiated response for the three oxidizable compounds was formed by Graphite, Prussian blue (PB), Cobalt (II) phthalocyanine (CoPc) and Copper oxide (II) (CuO) ink-modified carbon electrodes. A set of samples, ranging from 0 to 500 µmol·L, was prepared, using a tilted (3) factorial design in order to build the quantitative response model. Subsequently, the model performance was evaluated with an external subset of samples defined randomly along the experimental domain. Partial Least Squares Regression (PLS) was employed to construct the quantitative model. Finally, the model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 1.00 and 0.99 for the training and test subsets, respectively, and R ≥ 0.762 for the obtained vs. expected comparison graphs. In this way, a screen-printed integrated electrode platform can be successfully used for voltammetric ET applications.
在本工作中，采用电子舌（ET）原理和循环伏安法（CV）技术对氨基酚、抗坏血酸和尿酸的三元混合物进行了拆分。通过石墨、普鲁士蓝（PB）、酞菁钴（CoPc）和氧化铜（II）（CuO）油墨修饰碳电极，形成了对三种可氧化化合物具有差异化响应的丝网印刷集成电极阵列。使用倾斜（3）因子设计，制备了一组范围从 0 到 500 µmol·L 的样品，以构建定量响应模型。随后，使用沿实验域随机定义的外部子集样品评估模型性能。采用偏最小二乘回归（PLS）构建定量模型。最后，该模型成功地对三种化合物的浓度进行了预测，训练集和测试集的归一化均方根误差（NRMSE）分别为 1.00 和 0.99，获得的与预期比较图的 R ≥ 0.762。通过这种方式，可以成功地将丝网印刷集成电极平台用于伏安电化学 ET 应用。
