Simultaneous sensitive determination of benzenediol isomers using multiwall carbon nanotube–ionic liquid modified carbon paste electrode by a combination of artificial neural network and fast Fourier transform admittance voltammetry,New Journal of Chemistry

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Simultaneous sensitive determination of benzenediol isomers using multiwall carbon nanotube–ionic liquid modified carbon paste electrode by a combination of artificial neural network and fast Fourier transform admittance voltammetry
New Journal of Chemistry ( IF 2.7 ) Pub Date : 2018-03-15 00:00:00 , DOI: 10.1039/c7nj04073c
Mohammad Reza Moghaddam _{1,

2,

2,

3,

4} , Parviz Norouzi _{1,

2,

2,

3,

5} , Jahan B. Ghasemi _{2,

3,

4}

Affiliation

In this study, a combination of artificial neural networks (ANNs) and fast Fourier transform admittance voltammetry (FFTAdV) was used as a novel electrochemical method for the simultaneous determination of catechol (CT), hydroquinone (HQ), and resorcinol (RC). The electrochemical performance of the carbon paste working electrode toward the oxidation of isomers was improved by modification using multiwall carbon nanotube and 1-octyl-3-methylimidazolium hexafluorophosphate ionic liquid. FFTAdV responses were processed with artificial neural networks (ANNs). Before applying ANNs, data was preprocessed by fast Fourier transform to reduce the complexity, noise, and FFT coefficients used as input for ANNs. The ANNs model reveals excellent prediction ability of nanomolar level concentration (root mean square error range 2.01–3.21). The concentration range of each isomer was 5–240 nM with detection limits of 2.0, 2.5, and 1.1 nM (RSD 2.1, 2.6, and 2.3% for n = 5) for HQ, CT, and RC, respectively. The method was validated by determining the isomers in surface water and wastewater.

中文翻译：

人工神经网络与快速傅里叶变换导纳伏安法组合使用多壁碳纳米管-离子液体改性碳糊电极同时灵敏测定苯二酚异构体

在这项研究中，人工神经网络（ANNs）和快速傅里叶变换导纳伏安法（FFTAdV）的组合被用作同时测定邻苯二酚（CT），对苯二酚（HQ）和间苯二酚（RC）的新型电化学方法。通过使用多壁碳纳米管和1-辛基-3-甲基咪唑鎓六氟磷酸盐离子液体进行改性，改善了碳糊工作电极对异构体氧化的电化学性能。FFTAdV响应使用人工神经网络（ANN）处理。在应用人工神经网络之前，通过快速傅立叶变换对数据进行预处理，以减少用作人工神经网络输入的复杂性，噪声和FFT系数。人工神经网络模型显示出极好的纳摩尔浓度浓度预测能力（均方根误差范围2.01-3.21）。n = 5）分别针对HQ，CT和RC。通过测定地表水和废水中的异构体验证了该方法的有效性。

更新日期：2018-03-15

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