Gutiérrez Manuel, Alegret Salvador, del Valle Manel
Sensors & Biosensors Group, Department of Chemistry, Autonomous University of Barcelona, Edifici Cn, 08193 Bellaterra, Catalonia, Spain.
Biosens Bioelectron. 2008 Jan 18;23(6):795-802. doi: 10.1016/j.bios.2007.08.019. Epub 2007 Sep 6.
Urea and creatinine biosensors based on urease and creatinine deiminase, respectively, covalently immobilized onto ammonium selective electrodes, were included in an array together with sensors sensitive to ammonium, potassium and sodium. Generic sensors to alkaline ions were also included. All the sensors used were of all-solid-state type, employing polymeric membranes and having rather nonspecific response characteristics. A response model based on artificial neural networks was built and tested for the simultaneous determination of urea, creatinine, ammonium, potassium and sodium. The results show that it is possible to obtain a good multivariate calibration model. In this way, the developed bioelectronic tongue was successfully applied to multidetermination of the five species in raw and spiked urine samples. Predicted concentrations showed a good agreement with reference methods of analysis, allowing a simple direct method for determining urea and creatinine in real samples. At the same time, this method permitted to obtain the concentrations of the alkaline interferences (endogenous ammonium, potassium and sodium) without the need of eliminating them.
分别基于脲酶和肌酐脱亚氨酶共价固定在铵选择性电极上的尿素和肌酐生物传感器,与对铵、钾和钠敏感的传感器一起被纳入一个阵列中。还包括对碱性离子的通用传感器。所有使用的传感器均为全固态类型,采用聚合物膜且具有相当非特异性反应特性。建立了基于人工神经网络的响应模型,并对其进行测试以同时测定尿素、肌酐、铵、钾和钠。结果表明,可以获得良好的多元校准模型。通过这种方式,所开发的生物电子舌成功应用于原尿和加标尿样中这五种物质的多组分测定。预测浓度与参考分析方法显示出良好的一致性,从而提供了一种用于测定实际样品中尿素和肌酐的简单直接方法。同时,该方法无需去除碱性干扰物(内源性铵、钾和钠)即可获得其浓度。