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Highlights • Polymeric membranes based on extracting agents with sensitivity to actinides. • Potentiometric multisensor system for PUREX-related solutions. • Quantification of UO 2 2+, Th4+, HNO 3 in complex mixtures with appropriate accuracy. Abstract A potentiometric multisensor system, composed of 12 cross-sensitive sensors, is proposed for uranium and thorium quantification in multi-component mixtures simulating PUREX process tailing solutions. Various ligands with high extraction capacity to lanthanides and actinides were used as active compounds in plasticized polymeric sensor membranes. The sensitivity of the individual sensors to tetra- and hexavalent actinides (UO 2 2+ and Th4+) in aqueous nitric acid solutions with pH = 2 has been studied for the first time. The sensors with pronounced sensitivity to actinides were comprised into the multisensor system that was applied for UO2+ 2 and Th4+ quantitative determination in presence of each other and lanthanides cations (La3+, Pr3+, Sm3+, Gd3+, Yb3+). The nitric acid quantification in the set of mixtures with UO2+ 2 and La3+ was also performed using the same system. Projection on latent structures (PLS) algorithm was used for multivariate regression models calculation. The results of PLS models validation with independent test set show the possibility of UO2+ 2 and Th4+ quantification with mean relative error (MRE) equal to 17% and 14% correspondingly, and HNO 3 quantification with MRE = 7%. This accuracy is appropriate for real-time technological control of PUREX process. [ABSTRACT FROM AUTHOR]

The most popular sensors in multisensor systems (electronic tongues) are voltammetric and potentiometric ones. Practical application of multisensor systems for evaluation of particular target parameters requires calibration step. Even if both types of sensors (voltammetric and potentiometric) can be sensitive towards the same parameter of an analyte and can be used for its quantification, corresponding multisensor systems cannot operate in the framework of a single unified calibration model interpreting the responses of both systems. This research is dedicated to experimental verification of calibration transfer feasibility between voltammetric and potentiometric multisensor systems. The algorithm of direct standardization suggested earlier in spectroscopy was applied for transformation of potentiometric data into voltammetric format, and vice versa. Such transformations allowed for interpretation of a system response by multivariate regression model built employing the data from another type of multisensor system. For example, concentration of the tartaric acid in the grape musts can be determined with the regression model developed for voltammetric system using the data obtained from potentiometric system. Only 20% decrease in precision was observed for the converted potentiometric data compared to initial voltammetric model. [ABSTRACT FROM AUTHOR]

Abstract: A simplified four-electrode multisensor prototype incorporating a homemade sensor based on chitosan-clay nanocomposite, and making use of Case-Based Reasoning (CBR) as pattern recognition technique, is effective for discerning among complex solutions of similar composition. The long-term stability of the sensors array together with the possibility of applying drift and weight corrections to the CBR database increase the efficiency of the CBR-based system, enhancing its ability to detect variations in the composition of fertigation solutions used in greenhouse crops. [Copyright &y& Elsevier]

Abstract: In this paper, a new variable selection technique inspired in sequential forward selection but specifically designed to work with support vector machines is introduced. The usefulness of the variable selection coupled to support vector machines for solving classification and regression problems is assessed by analysing two different databases. The first database corresponds to different concentrations of vapours and vapour mixtures measured with a metal oxide gas-sensor e-nose and the second database corresponds to different Iberian hams measured with a mass-spectrometry based e-nose. Using a reduced set of important variables (i.e. reducing the dimensionality of input space by the variable selection procedure) results in support vector machines with better performance. For example, the success rate in ham classification (11-class problem) rises from 79.91% (when all the variables available are used) to 90.30% (when a reduced set of input variables is used). Furthermore, a quantitative analysis of ham samples with good accuracy is shown to be possible: when the variable selection process introduced is coupled to support vector machine regression models, the correlation coefficients of actual versus predicted humidity, water activity and salt in ham samples are 0.975, 0.972 and 0.943, respectively. This compares favourably with the correlation coefficients obtained when no variable selection is performed (0.937, 0.924 and 0.894). [Copyright &y& Elsevier]

A multisensor with a four-sectional membrane based on two Li- and two K-ionophores with different selectivities has been developed based on the laser-scanned silicon transducer (LSST), where the responses of the sensor sections have been measured by scanning the potentials at different points on the membrane surface. An artificial neural network (ANN) was employed for the analysis of mixed solutions. It has been demonstrated that by applying this hybrid sensor as a laser-scanned multisensor system, the concentration regions for the simultaneous determination of lithium and potassium ions in complex solutions have been enlarged significantly. It has been shown that using such a kind of multisensor system makes it possible to overcome the restriction of the selectivity in comparison with single sensors. [Copyright &y& Elsevier]