Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube

• Published in: Nanomaterials (Basel, Switzerland) Switzerland), 2019-06, Vol.9 (6), p.891
• Main Authors: Apetrei, Constantin, Iticescu, Catalina, Georgescu, Lucian Puiu
• Format: Article
• Language: English
• Subjects: Carbon; carbon nanofiber; carbon nanotube; Chemical composition; Chemical properties; Cobalt; Cobalt compounds; Collection; Correlation coefficient; Correlation coefficients; cyclic voltammetry; Danube; Data analysis; Discriminant analysis; Drinking water; Electrochemistry; Electrodes; Factor analysis; gold nanoparticle; Graphene; Ionic strength; Multi wall carbon nanotubes; Multivariate analysis; nanomaterial; Nanomaterials; Nanoparticles; Nanotechnology; Nanotubes; Nitrates; Organic chemistry; Physicochemical properties; Pigments; Pollutants; Pollution; Principal components analysis; Rivers; sensor; Sensor arrays; Sensors; Trends; Voltammetry; water; Water analysis; Water quality; Water sampling
• ISSN: 2079-4991; 2079-4991
• DOI: 10.3390/nano9060891

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola's Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.