Optimization of sensors to be used in a voltammetric electronic tongue based on clustering metrics

Munmi Sarma, Noelia Romero, Xavier Cetó, Manel Del Valle*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

Original languageEnglish
Article number4798
Pages (from-to)1-16
Number of pages16
JournalSensors (Switzerland)
Issue number17
Publication statusPublished - 1 Sep 2020


  • Artificial neural networks
  • Discrete wavelet transform
  • Electronic tongue
  • Principal component analysis
  • Voltammetric sensors


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