Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics
Sarma, Munmi (Universitat Autònoma de Barcelona. Departament de Química)
Romero, Noelia (Universitat Autònoma de Barcelona. Departament de Química)
Cetó Alsedà, Xavier (Universitat Autònoma de Barcelona. Departament de Química)
Valle Zafra, Manuel del (Universitat Autònoma de Barcelona. Departament de Química)

Data:	2020
Resum:	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.
Ajuts:	Agencia Estatal de Investigación PID2019-107102RB-C21C
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Electronic tongue ; Voltammetric sensors ; Principal component analysis ; Artificial neural networks ; Discrete wavelet transform
Publicat a:	Sensors (Basel, Switzerland), Vol. 20, Issue 17 (September 2020) , art. 4798, ISSN 1424-8220

DOI: 10.3390/s20174798
PMID: 32854411

15 p, 7.0 MB

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