首页> 外文期刊>Asian Journal of Chemistry: An International Quarterly Research Journal of Chemistry >Calibration of a Potentiometric Multi-Sensor Array for the Determination of Na~+, K~+ and NFH_4~+ Ions by Using Artificial Neural Networks
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Calibration of a Potentiometric Multi-Sensor Array for the Determination of Na~+, K~+ and NFH_4~+ Ions by Using Artificial Neural Networks

机译:电位多传感器阵列的校准,用于通过人工神经网络测定Na〜+,K〜+和NFH_4〜+离子

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摘要

A potentiometric multi-sensor array, comprising of all-solid-state selective and non-selective electrodes, was constituted and calibrated via artificial neural networks (ANNs) for the determinations of NH_4~+, Na~+ and K~+ ions in aqueous model solutions. Various artificial neural network configurations were constituted and their root mean square errors of calibration (RMSEC) and root mean square errors of prediction (RMSEP) values were calculated. The model which has the lowest RMSECxRMSEP value was preferred as the best model. Artificial neural network models calculated for Na~+ and K~+ have nearly similar prediction ability to univariate calibrations which were performed using the main ion concentrations of the calibration solutions and respective ion-selective electrode responses. Artificial neural network model calculated for NH4~+ had the superior prediction ability when compared with its univariate calibration model.
机译:构成了一个由全固态选择性和非选择性电极组成的电位多传感器阵列,并通过人工神经网络(ANN)进行了校准,用于测定水溶液中的NH_4〜+,Na〜+和K〜+离子。模型解决方案。构造了各种人工神经网络配置,并计算了它们的校准均方根误差(RMSEC)和预测均方根误差(RMSEP)值。 RMSECxRMSEP值最低的模型被认为是最佳模型。为Na〜+和K〜+计算的人工神经网络模型具有与单变量校准几乎相似的预测能力,该校准使用校准溶液的主离子浓度和相应的离子选择电极响应进行。与单变量标定模型相比,人工计算的NH4〜+神经网络模型具有更好的预测能力。

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