© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 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.
|Publication status||Published - 1 Aug 2018|
- Artificial neural network
- Metal nanoparticles
- Multi-walled carbon nanotubes
- Total sugars
Cardoso de Sá, A., Cipri, A., González-Calabuig, A., Stradiotto, N. R., & del Valle, M. (2018). Multivariate Determination of Total Sugar Content and Ethanol in Bioethanol Production Using Carbon Electrodes Modified with MWCNT/MeOOH and Chemometric Data Treatment. Electroanalysis, 30(8), 1688-1697. https://doi.org/10.1002/elan.201700725