Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Barcelona, Spain.
Analyst. 2012 Jan 21;137(2):349-56. doi: 10.1039/c1an15456g. Epub 2011 Nov 21.
This work reports the application of a Bio-Electronic Tongue (BioET) system made from an array of enzymatic biosensors in the analysis of polyphenols, focusing on major polyphenols found in wine. For this, the biosensor array was formed by a set of epoxy-graphite biosensors, bulk-modified with different redox enzymes (tyrosinase and laccase) and copper nanoparticles, aimed at the simultaneous determination of the different polyphenols. Departure information was the set of voltammograms generated with the biosensor array, selecting some characteristic features in order to reduce the data for the Artificial Neural Network (ANN). Finally, after the ANN model optimization, it was used for the resolution and quantification of each compound. Catechol, caffeic acid and catechin formed the three-analyte case study resolved in this work. Good prediction ability was attained, therefore allowing the separate quantification of the three phenols with predicted vs. expected slope better than 0.970 for the external test set (n = 10). Finally, BioET has been also tested with spiked wine samples with good recovery yields (values of 104%, 117% and 122% for catechol, caffeic acid and catechin, respectively).
本工作报道了一种基于酶生物传感器阵列的生物电子舌(BioET)系统在多酚分析中的应用,重点是葡萄酒中发现的主要多酚。为此,生物传感器阵列由一组环氧石墨生物传感器组成,通过批量修饰不同的氧化还原酶(酪氨酸酶和漆酶)和铜纳米粒子,旨在同时测定不同的多酚。出发信息是生物传感器阵列产生的一系列伏安图,选择一些特征以减少人工神经网络(ANN)的数据。最后,在对 ANN 模型进行优化后,用于各化合物的解析和定量。儿茶酚、咖啡酸和儿茶素构成了本工作中解析的三分析物案例研究。获得了良好的预测能力,因此允许用预测 vs. 预期斜率对三种酚类物质进行单独定量,外部测试集(n = 10)的斜率值优于 0.970。最后,还对加标葡萄酒样品进行了 BioET 测试,回收率良好(儿茶酚、咖啡酸和儿茶素的回收率分别为 104%、117%和 122%)。