scholarly journals DETERMINATION OF ADULTERANT IN MILK THROUGH THE USE OF A PORTABLE VOLTAMMETRIC ELECTRONIC TONGUE

Author(s):  
A.A. Arrleta ◽  
2005 ◽  
Vol 382 (2) ◽  
pp. 471-476 ◽  
Author(s):  
A. Gutés ◽  
A. B. Ibáñez ◽  
F. Céspedes ◽  
Salvador Alegret ◽  
M. del Valle

2012 ◽  
Vol 732 ◽  
pp. 172-179 ◽  
Author(s):  
Xavier Cetó ◽  
Juan Manuel Gutiérrez ◽  
Manuel Gutiérrez ◽  
Francisco Céspedes ◽  
Josefina Capdevila ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5002 ◽  
Author(s):  
Anna Herrera-Chacón ◽  
Farzad Torabi ◽  
Farnoush Faridbod ◽  
Jahan B. Ghasemi ◽  
Andreu González-Calabuig ◽  
...  

The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.


2018 ◽  
Vol 243 ◽  
pp. 36-42 ◽  
Author(s):  
Nadia El Alami El Hassani ◽  
Khalid Tahri ◽  
Eduard Llobet ◽  
Benachir Bouchikhi ◽  
Abdelhamid Errachid ◽  
...  

2020 ◽  
Vol 2 (1) ◽  
pp. 62
Author(s):  
Luis F. Villamil-Cubillos ◽  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Diego A. Tibaduiza

An electronic tongue is a device composed of a sensor array that takes advantage of the cross sensitivity property of several sensors to perform classification and quantification in liquid substances. In practice, electronic tongues generate a large amount of information that needs to be correctly analyzed, to define which interactions and features are more relevant to distinguish one substance from another. This work focuses on implementing and validating feature selection methodologies in the liquid classification process of a multifrequency large amplitude pulse voltammetric (MLAPV) electronic tongue. Multi-layer perceptron neural network (MLP NN) and support vector machine (SVM) were used as supervised machine learning classifiers. Different feature selection techniques were used, such as Variance filter, ANOVA F-value, Recursive Feature Elimination and model-based selection. Both 5-fold Cross validation and GridSearchCV were used in order to evaluate the performance of the feature selection methodology by testing various configurations and determining the best one. The methodology was validated in an imbalanced MLAPV electronic tongue dataset of 13 different liquid substances, reaching a 93.85% of classification accuracy.


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