This work reports the characterization and application of a voltammetric electronic tongue using an array of glassy carbon electrodes modified with multi‐walled carbon nanotubes containing metal (Pd, Au, Cu) and oxy‐hydroxide nanoparticles (MetalsOOH of Ni, Co) towards the determination of total sugar content in products related with sugarcane‐bioethanol production. The prediction model based on Artificial Neural Networks (ANN) has given satisfactory results for the carbohydrate sum and the obtained response had shown an adequate accuracy. Voltammetric data was first adapted for the computation using the Fast Fourier transform, and results from the electronic tongue approach were compared with use of different electrodes alone. Final performance was better using uniquely the Ni oxy‐hydroxide modified electrode, especially in the quantification of ethanol, a side‐effect of counter‐balancing its interference.

中文翻译：

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MWCNT / MeOOH修饰碳电极和化学计量学数据处理技术用于生物乙醇生产中总糖含量和乙醇的多变量测定

这项工作报告了伏安电子舌的表征和应用，该阵列使用玻璃碳电极阵列进行修饰，该电极由多壁碳纳米管修饰而成，该碳纳米管包含金属（Pd，Au，Cu）和羟基氢氧化物纳米粒子（Ni，Co的MetalsOOH），用于测定与甘蔗-生物乙醇生产有关的产品中总糖含量的百分比。基于人工神经网络（ANN）的预测模型对碳水化合物总量给出了令人满意的结果，并且所获得的响应已显示出足够的准确性。伏安数据首先通过快速傅立叶变换进行了计算，并将电子舌方法的结果与单独使用不同的电极进行了比较。使用独特的羟基氧化镍修饰电极，最终性能会更好，