Patrycja Ciosek

Sensor arrays for liquid sensing – electronic tongue systems

Electronic tongue systems are multisensor devices dedicated to automatic analysis of complicated composition samples and to the recognition of their characteristic properties. Recently, the number of publications covering this topic has significantly increased. Many possible architectures of such devices were proposed: potentiometric, voltammetric, as well as approaches embracing mass- and optical-sensors. For the analysis of sensor array data, various pattern recognition systems were proposed. All of these topics are summarized in this review. Moreover, additional problems are considered: miniaturization of electronic tongues and hybrid systems for liquid sensing.

Direct and two-stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue—Effect of supervised feature extraction

A novel strategy of data analysis for artificial taste and odour systems is presented in this work. It is demonstrated that using a supervised method also in feature extraction phase enhances fruit juice classification capability of sensor array developed at Warsaw University of Technology. Comparison of direct processing (raw data processed by Artificial Neural Network (ANN), raw data processed by Partial Least Squares-Discriminant Analysis (PLS-DA)) and two-stage processing (Principal Components Analysis (PCA) outputs processed by ANN, PLS-DA outputs processed by ANN) is presented. It is shown that considerable increase of classification capability occurred in the case of the new method proposed by the authors.

Polymeric membrane ion-selective and cross-sensitive electrode-based electronic tongue for qualitative analysis of beverages.

An electronic tongue based on the sensor array of polymeric membrane ion-selective electrodes combined with pattern recognition tools was applied to qualitative analysis of various brands of orange juice, tonic, and milk. The capability of this device to reliably discriminate between different brands of those products was presented. The tests of the system were performed using products of the same brand, but with different manufacture dates (and thus comparable by the term of taste). The fusion of two types of sensors-classical selective ones and partially selective in one versatile array, and working out the sensor arrays response by means of principal component analysis and back propagation neural network methods allowed the discrimination between different brands of various beverages with very high accuracy (90-100%). The real performance of the electronic tongue was evaluated applying testing samples from another manufacture lot, than the samples used in the learning set.

The recognition of beer with flow-through sensor array based on miniaturized solid-state electrodes

Flow-through electronic tongue based on miniaturized solid-state potentiometric sensors has been developed. A simple technique, i.e. membrane solution casting on the surface of the planar Au transducers was applied for the preparation of classical ion-selective and partially selective microelectrodes, introduced in the flow-through sensor array. The performance of the designed electronic tongue was tested in the qualitative analysis of various brands of beer. Samples of the same brand of beer but with different manufacture dates, originating from different manufacture lots, have been applied in the studies. The combination of PLS and ANN techniques allowed the discrimination between different brands of beer with 83% of correct classifications.

Potentiometric Electronic Tongues for Foodstuff and Biosample Recognition—An Overview

Potentiometric sensors are attractive tools for the fabrication of various electronic tongues that can be used in wide area of applications, ranging from foodstuff recognition to environmental monitoring and medical diagnostics. Their main advantages are the ability to modify their selectivity (including cross-sensitivity effects) and the possibility of miniaturization using appropriate construction methods for the transducer part (e.g., with the use of solid-state technology). In this overview various examples of the design, performance, and applications of potentiometric electronic tongues are presented. The results summarize recent research in the field conducted in the Department of Microbioanalytics, Warsaw University of Technology (WUT).

Miniaturized electronic tongue with an integrated reference microelectrode for the recognition of milk samples

This work presents the application of a new construction of an electronic tongue for the classification of milk originating from various producers. Integrated array of microelectrodes was fabricated from epoxy-glass laminate. PVC membranes with various additives were used as chemosensitive layers. The developed device is capable of recognition of milk samples with high correctness. Moreover, the application of miniaturized reference electrode, integrated on the same substrate, also provided satisfactory results, which can be helpful in future constructions of hand-held electronic tongue systems.

Performance of selective and partially selective sensors in the recognition of beverages.

A sensor array composed of selective and partially selective electrodes is applied to the recognition of beverages: milk, orange juice, and beer. The role of selective and partially selective sensors in classification results is discussed. For this reason two experiments are realized: distinguishing among various types of foodstuffs and the recognition of their origin (manufacturer). In both cases, the measurements were performed in stationary and flow mode. Classical ion-selective electrodes and miniaturized solid-state electrodes were considered. The best results are obtained when the two types of sensor are incorporated in the array, which leads to the highest correctness of the system response. It is however worth to notice, that in the case of some samples (for example, milk) satisfactory results were obtained when only selective or when only partially selective electrodes were applied.

Milk classification by means of an electronic tongue and Support Vector Machine neural network

The aim of this work was the classification of milk samples with the use of Support Vector Machine networks. An electronic tongue, based on a sensor array of miniaturized solid-state potentiometric electrodes, was used for measurements of milk originating from various dairies (i.e. various brands) and with different fat content. The sensors were mounted into the measurement flow-cell developed at Warsaw University of Technology. Their signals were input to the Support Vector Machine neural network without a pre-processing stage. The results of the classification of milk by trademark and by fat content proved the proposed system to be very efficient.

Monitoring of beer fermentation based on hybrid electronic tongue

Monitoring of biotechnological processes, including fermentation is extremely important because of the rapidly occurring changes in the composition of the samples during the production. In the case of beer, the analysis of physicochemical parameters allows for the determination of the stage of fermentation process and the control of its possible perturbations. As a tool to control the beer production process a sensor array can be used, composed of potentiometric and voltammetric sensors (so-called hybrid Electronic Tongue, h-ET). The aim of this study is to apply electronic tongue system to distinguish samples obtained during alcoholic fermentation. The samples originate from batch of homemade beer fermentation and from two stages of the process: fermentation reaction and maturation of beer. The applied sensor array consists of 10 miniaturized ion-selective electrodes (potentiometric ET) and silicon based 3-electrode voltammetric transducers (voltammetric ET). The obtained results were processed using Partial Least Squares (PLS) and Partial Least Squares-Discriminant Analysis (PLS-DA). For potentiometric data, voltammetric data, and combined potentiometric and voltammetric data, comparison of the classification ability was conducted based on Root Mean Squared Error (RMSE), sensitivity, specificity, and coefficient F calculation. It is shown, that in the contrast to the separately used techniques, the developed hybrid system allowed for a better characterization of the beer samples. Data fusion in hybrid ET enables to obtain better results both in qualitative analysis (RMSE, specificity, sensitivity) and in quantitative analysis (RMSE, R(2), a, b).

Miniaturized tools and devices for bioanalytical applications: an overview

This article presents an overview of various miniaturized devices and technologies developed by our group. Innovative, fast and cheap procedures for the fabrication of laboratory microsystems based on commercially available materials are reported and compared with well-established microfabrication techniques. The modules fabricated and tested in our laboratory can be used independently or they can be set up in different configurations to form functional measurement systems. We also report further applications of the presented modules e.g. disposable poly(dimethylsiloxane) (PDMS) microcuvettes, fibre optic detectors, potentiometric sensors platforms, microreactors and capillary electrophoresis (CE) microchips as well as integrated microsystems e.g. double detection microanalytical systems, devices for studying enzymatic reactions and a microsystem for cell culture and lysis.