Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals

Abstract:

ROMERO BONILLA, Hugo; RAMIREZ-MORALES, Iván and ROMERO FLORES, Cinthia. AUTOMATIC CLASSIFICATION OF WATER SAMPLES USING AN OPTIMIZED SVM MODEL APPLIED TO CYCLIC VOLTAMMETRY SIGNALS. Vitae [online]. 2019, vol. 26, n. 2, pp. 94-103. ISSN 0121-4004. https://doi. org/10.17533/udea. vitae. v26n2a05. Background: concern about the quality of the water for human consumption has become widespread among the population. The taste and some problems associated with drinking water have been the cause of increased demand for bottled water. Due to this, day to day, a large number of companies has manifested their interest in the production of bottled water. Objective: to evaluate a novel automatic classification model that differentiates bottled water from tap water. Methods: the voltammetric technique consisted of three electrode setup. The output current has been considered for data analysis. From the results of grid search, six pairs of values were pre-selected for the parameters of σ and C whose results were similar. High values of accuracy, specificity and sensitivity were achieved in test dataset. The final decision was made after performing an ANOVA test of 100 repetitions of 5-fold cross-validation, 3000 models were evaluated with the parameter combinations described above for the SVM. Results: the oxidation and reduction peaks of the water samples have been observed to be prominent. Absolute values of current (I) increased in the case of public water samples, possibly due to the largest concentration of chloride ions which have higher contributions to the conductivity. 5-fold cross-validation test …

Año de publicación:

2019

Keywords:

Fuente:

google