Yu. G. Vlasov

Bio: Yu. G. Vlasov is an academic researcher from Saint Petersburg State University. The author has contributed to research in topics: Chalcogenide & Chalcogenide glass. The author has an hindex of 29, co-authored 113 publications receiving 2629 citations. Previous affiliations of Yu. G. Vlasov include National Taiwan University of Science and Technology.

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

Abstract: 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.

Electronic tongue: new analytical tool for liquid analysis on the basis of non-specific sensors and methods of pattern recognition

Abstract: Development of promising sensor instrument — “electronic tongue” based on sensor arrays with data processing by pattern recognition methods have been described. The attention is paid to “electronic tongue” based on an array of original non-specific (non-selective) potentiometric chemical sensors with chalcogenide glass membranes. Principles of research, criteria for the development of non-selective sensing materials, pattern recognition methods have been described. Possible applications and some results of integral qualitative analysis of beverages and of quantitative analysis of complex liquids, containing heavy metals are reported. Discriminating power obtained and possibility of multicomponent analysis permit to consider “electronic tongue” as a perspective analytical concept.

Application of electronic tongue for qualitative and quantitative analysis of complex liquid media

Abstract: The present work is devoted to the assessment of analytical applications of a new instrument for multicomponent analysis in liquid media — “electronic tongue”, based on an array of originally designed non-specific solution chemical sensors and pattern recognition tools for processing of multidimensional output of this sensor array. The “electronic tongue” is supposed to be capable to produce integral qualitative imaging (artificial sensing) of complex liquids such as food stuffs and beverages, comparable to human taste panel sensing and enhancing its capabilities by durable and reproducible operation, analysis and tasting of toxic and potentially dangerous media, applicability to long-term routine industry analysis. The method is also successfully applicable to quantitative analysis of different objects such from biological liquids to natural waters. Promising experimental results of “electronic tongue” application in different beverages are reported.

Surface plasmon resonance sensors: review

Abstract: Since the first application of the surface plasmon resonance (SPR) phenomenon for sensing almost two decades ago, this method has made great strides both in terms of instrumentation development and applications. SPR sensor technology has been commercialized and SPR biosensors have become a central tool for characterizing and quantifying biomolecular interactions. This paper attempts to review the major developments in SPR technology. Main application areas are outlined and examples of applications of SPR sensor technology are presented. Future prospects of SPR sensor technology are discussed.

Modeling wine preferences by data mining from physicochemical properties

Abstract: We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.