Identification of plastic toys contaminated with volatile organic compounds using QCM gas sensor array

In this paper, we propose a fast and easy-to-use analytical method to identify the children toys contaminated with potentially dangerous substances from the class of volatile organic compounds (VOCs). It is shown that the use of cross-sensitive gas sensor array based on piezoelectric sensors, modified with different sorbents, allows reliable recognition of items with the elevated levels of VOCs. Applying chemometric methods for processing of the sensor array data, it is possible to classify the toys into clean and hazardous ones with sensitivity and accuracy around 96%. Taking into account the simplicity of the suggested procedure, it appears to be an attractive option for cost-effective pre-screening of potentially dangerous plastic toys in comparison with the expensive and time-consuming chromatographic methods.

Three-point multivariate calibration models by correlation constrained MCR-ALS: A feasibility study for quantitative analysis of complex mixtures

When it comes to address quantitative analysis in complex mixtures, Partial Least Squares (PLS) is often referred to as a standard first-order multivariate calibration method. The set of samples used to build the PLS regression model should ideally be large and representative to produce reliable predictions. In practice, however, the large number of calibration samples is not always affordable and the choice of these samples should be handled with care as it can significantly affect the accuracy of the predictive model. Correlation constrained multivariate curve resolution (CC-MCR) is an alternative regression method for first-order datasets where, unlike PLS, calibration and prediction stages are performed iteratively and optimized under constraints until the decomposition meets the convergence criterion. Both calibration and test samples are fitted into a unique bilinear model so that the number of calibration samples is no longer a critical issue. In this paper we demonstrate that under certain conditions CC-MCR models can provide for reasonable predictions in quantitative analysis of complex mixtures even when only three calibration samples are employed. The latter are defined as samples having the minimum, the maximum and the average concentration, providing for a simple and rapid strategy to build reliable calibration model. The feasibility of three-point multivariate calibration approach was assessed with several case studies featuring mixtures of different analytes in presence of interfering species. Satisfactory predictions with relative errors in the range 3-15% were achieved and good agreement with classical PLS models built from a larger set of calibration samples was observed.

1,10-Phenanthroline-2,9-dicarboxamides as ligands for separation and sensing of hazardous metals

1,10-Phenanthroline-2,9-dicarboxamides of various structures were synthesized and studied as ligands for separation and sensing of d- and f-metals.

Approach to on-line monitoring of PUREX process using chemometric processing of the optical spectral data

Optical spectroscopic measurements in the UV-Vis and IR ranges were performed in model solutions of aqueous and organic phases of the PUREX process for spent nuclear fuel (SNF) reprocessing. Chemometric processing of the spectral data with PLS (partial least squares) regression allowed simultaneous quantification of several key components (uranium, neptunium, plutonium, nitric acid) in these mixtures in an effective and elegant way. The content of all key components was quantitively determined with mean relative errors not exceeding 10%. It was shown that the employment of the whole spectra or their certain continuous regions for a PLS calibration enables to decrease the analytical errors compared to the use of a single wavelength in an ordinary least squares approach. The results of this research imply that the development of on-line techniques for SNF reprocessing monitoring is fully possible and can be based on optical spectroscopy methods combined with multivariate data processing techniques.

Combination of optical spectroscopy and chemometric techniques—a possible way for on-line monitoring of spent nuclear fuel (SNF) reprocessing

UV-Vis spectroscopic measurements were performed in model mixtures simulating solutions obtained in the course of PUREX spent nuclear fuel reprocessing. Mixtures contained constant concentrations of nitric acid and uranium with varied content of neptunium and plutonium. Spectral data were processed with classical chemometric technique — PLS regression. This approach has shown significant improvement in analytical precision of actinides determination compared to standard univariate data processing. The combination of spectroscopic measurements with chemometric data processing shows good promise for the development of on-line spectroscopic techniques for SNF reprocessing monitoring.

Differentiation of four Aspergillus species and one Zygosaccharomyces with two electronic tongues based on different measurement techniques

Two electronic tongues based on different measurement techniques were applied to the discrimination of four molds and one yeast. Chosen microorganisms were different species of Aspergillus and yeast specie Zygosaccharomyces bailii, which are known as food contaminants. The electronic tongue developed in Linköping University was based on voltammetry. Four working electrodes made of noble metals were used in a standard three-electrode configuration in this case. The St. Petersburg electronic tongue consisted of 27 potentiometric chemical sensors with enhanced cross-sensitivity. Sensors with chalcogenide glass and plasticized PVC membranes were used. Two sets of samples were measured using both electronic tongues. Firstly, broths were measured in which either one of the molds or the yeast grew until late logarithmic phase or border of the stationary phase. Broths inoculated by either one of molds or the yeast was measured at five different times during microorganism growth. Data were evaluated using principal component analysis (PCA), partial least square regression (PLS) and linear discriminant analysis (LDA). It was found that both measurement techniques could differentiate between fungi species. Merged data from both electronic tongues improved differentiation of the samples in selected cases.

Nonspecific sensor arrays ("electronic tongue") for chemical analysis of liquids (IUPAC Technical Report)

The history of the development of potentiometric sensors over the past century demonstrates progress in constructing single, discrete (i.e., separate, to distinguish from sensor arrays) ion sensors, which have been made as selective as possible. Only a few types reveal high selectivity. However, easy measurement procedure, with low cost and availability, give rise to the search for new ways for their successful application. The present document describes a new concept for application of potentiometric multisensor systems, viz., sensor arrays for solution analysis, and the performance of this new analytical tool - the "electronic tongue". The electronic tongue is a multisensor system, which consists of a number of low-selective sensors and uses advanced mathematical procedures for signal processing based on the pattern recognition (PARC) and/or multivariate analysis [artificial neural networks (ANNs), principal component analysis (PCA), etc.]. Definitions of the multisensor systems and their parameters are suggested. Results from the application of the electronic tongue, both for quantitative and qualitative analysis of different mineral water and wine samples, are presented and discussed.