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APPLICATION OF CHEMOMETRICS FOR DATA PROCESSING OF THE ELECTRONIC TONGUE

APPLICATION OF CHEMOMETRICS FOR DATA PROCESSING OF THE ELECTRONIC TONGUE. Alisa Rudnitskaya, Andrey Legin, Kirill Legin, Andrey Ipatov, Yuri Vlasov Laboratory of Chemical Sensors, Chemistry Department, St. Petersburg University, St. Petersburg, Russia http://www.electronictongue.com.

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APPLICATION OF CHEMOMETRICS FOR DATA PROCESSING OF THE ELECTRONIC TONGUE

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  1. APPLICATION OF CHEMOMETRICS FOR DATA PROCESSING OF THE ELECTRONIC TONGUE Alisa Rudnitskaya, Andrey Legin, Kirill Legin, Andrey Ipatov, Yuri Vlasov Laboratory of Chemical Sensors, Chemistry Department, St. Petersburg University, St. Petersburg, Russia http://www.electronictongue.com

  2. ELECTRONIC TONGUE RESEARCH GROUP CHEMISTRY FACULTY RADIOCHEMISTRY DEPARTMENT LABORATORY OF CHEMICAL SENSORS Head of the Laboratory prof. Yuri Vlasov Project leaderDr. Andrey Legin PermanentDr. Alisa Rudnitskaya staff Dr. Andrey Ipatov M.Sc. Boris Seleznev Associated researchers, currently 3 Ph.D. students, several students a year WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  3. Research directions 1. New sensing materials Solid-state materials (chalcogenide glasses) Organic polymers Thin films Electrochemical characteristics Cross-sensitivity study Sensing mechanism 2.Chemical sensors Multisensor arrays Chemometrics tools Recognition & Analysis 3.Sensor systems – electronic tongue 4.Application of chemical sensors and sensor systems Industrial analysis Environmental control Medical analysis Foodstuff analysis WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  4. Advantages and drawbacks of potentiometric chemical sensors ·Advantages 1.A wide range of available sensing materials and sensors. 2.Wide variations of sensor properties, some unique features. 3.A wide knowledge about composition/properties relationship. 4.Simple installation. Easy, direct measurements. 5.Different configuration (static, flow) and size (bulk, micro). 6.Easy applicability for automatic routine analysis. 7.Low cost. ·Drawbacks 1.Insufficient selectivity of many sensors. 2.The number of available sensors is far smaller than the variety of analytes. WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  5. Electronic tongue Electronic tongue is an analytical instrument comprising an array of non-specific, poorly selective, chemical sensors with partial specificity (cross-sensitivity) to different components in solution, and an appropriate chemometrics tool (method of pattern recognition and/or multivariate calibration) for the data processing. Of primary importance is stability of sensor behaviour and enhanced cross-sensitivity, which is understood as reproducible response of a sensor to as many species as possible. If properly configured and trained (calibrated), the “electronic tongue" is capable to recognise quantitative and qualitative composition of multicomponent solutions of different nature. WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  6. Potentiometric electronic tongue WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  7. Electronic tongue – laboratory version WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  8. Composition of chemical sensor array for electronic tongue • Chalcogenide glass sensors • As2S3, GeS2, AsSe with various additives • Polymer based • PVC, plastisizer and active substances • Chrystalline based • Ag2S with different additives, LaF3 • Totally: up to 40 sensors WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  9. Methods for the ET data processing • Quantitative analysis (concentrations/parameters prediction) • Modeling using MLR, PLS-regression, artificial neural networks, N-PLS • Data exploration, recognition • PCA • Classification • SIMCA, LDA, PLS-regression WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  10. Electronic tongue applications Types of analysis Classification and discrimination (identification, recognition) Quantitative analysis of multiple components simultaneously Process control Taste assessment and correlation with human perception Objects Food - fruit juices, coffee, soft drinks, milk, mineral water, wine, vodka, cognac, meat, fish, onion Medical analysis - dialyses solution for artificial kidney, pharmaceuticals, urine Environmental - groundwater, seawater, dirty water from farms Industrial analysis - galvanic baths, waste purification systems, control of biotechnology processes WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  11. Selected applications of the electronic tongue • Discrimination of substances eliciting different taste and different substances eliciting the same taste • Determination of ultra low activity of transition metals in seawater • Determination of ammonium and organic acids content in the model growth media • New approach to the data for flow-injection electronic tongue - determination of zinc and lead concentration in mixed solutions WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  12. Discrimination of taste substances • Objective • Discrimination of substances eliciting different tastes (i.e. bitter, sweet and salty) and substances eliciting the same taste • Samples: 10mmolL-1 individual solutions of substances • bitter: quinine, caffeine, drugs A and B • sweet: acesulfam K, aspartame, sucrose • salty: sodium chloride, sodium benzoate, drug D • Measurements • ET comprising 20 sensors • at least 3 replicas of each sample in random order • Data processing • discrimination • LDA • PCA WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  13. Discrimination of taste substances WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  14. Determination of ultra low activities of transition metals • Objective • Determination of ultra low activities of transition metals in waste waters and seawater • Solutions • Individual and mixed binary buffered solutions of Cu, Zn, Cd and Pb • Total concentration of metals 1 M to 0.3mM, activity - 1nM to 0.1M • Background of 0.01M of NaCl and 0.01M citrate, pH 8 • Measurements • ET comprising 8 sensors • Data processing • Calibration and activity prediction of transition metals • PLS-regression WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  15. Determination of ultra low activities of transition metalsMeasurements in individual buffered solutions WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  16. Determination of ultra low activities of transition metals WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  17. Determination of ammonium and organic acids content in the model growth media • Objective • Quantification of main substances consumed / produced during microorganisms’ growth – monitoring of the fermentation processes • Samples • Set of 22 solutions modeling growth media • Components: MgSO4, KCl, KH2PO4, citrate, pyruvate, oxalate, glucose, glycerol, mannitol, erythritol, NH4Cl • Measurements • ET comprising 8 sensors • At least 3 replicas of each solution • Data processing • Calibration and concentration prediction w.r.t. ammonium, oxalate and citrate • Artificial neural network WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  18. Determination of ammonium and organic acids in the growth media WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  19. Determination of zinc and lead concentrations in mixed solutions using flow-injection electronic tongue • Objectives • Evaluate relevance of different types of signals produced using flow-injection ET • Evaluate relevance of different multivariate calibration methods for processing of the flow-injection electronic tongue data WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  20. Schematic of flow-injection electronic tongue KNO3 0,1M \ WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  21. Flow-through cell WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  22. Sensor response parameters in FIA ta- time before sample enters measuring cell Т– time of sample pass through the cell tb– peak width t- recovery time Н– peak height WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  23. Data produced by flow-injection ET 1. Peak height measured for each sensor: one signal from each sensor, I x J 2. Time-dependent response for each sensor: Unfolded data set, I x JK 3. Time-dependent response for each sensor: 3-dimensional data set, I x J x K Time Sensors Samples WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  24. Calibration methods • Data sets 1 and 2: • Partial least square regression • Artificial neural network (back-propagation neural network) • Data set 3 • N-way partial least square regression WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  25. N-PLS regression PLS-regression: X = TP’ + E; Y = TQ’ + E N-PLS regression: X = TWj(Wk)’ + E; Y = TQ’ + E WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  26. Experimental set-up • ET – 7 sensors with PVC plasticized membranes • Set of mixed solutions containing zinc and lead; • Background solution - 0.1M KNO3 • Sensor potentials measured every 4 s for 2 minutes, 30 points for each solutions • Four replicas of each solutions • Three types of data sets • Data processing using PLS-1 and N-PLS regression WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  27. Sensors’ response in the individual solutions of zinc and lead WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  28. Determination of zinc and lead in individual solutions using flow-injection ET Calibration was done using PLS regression with test set validation, only pick height being used as sensor signals. Concentration range of both zinc and lead 10-6 – 10-3 molL-1 WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  29. Sensors’ response in the mixed solutions of zinc and lead WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  30. Results of zinc and lead concentrations’ prediction using three different types of data sets WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  31. Peak heights Time dependent response (unfolded data) X-loadings weights WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  32. X-loadings weights Time dependent response (3-d data) WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

  33. Conclusions • Use of time-dependent response of flow-injection ET instead of peak heights allows higher accuracy of concentrations’ determination in mixed solutions • Use of 3-dimensional data set and N-PLS regression for calibration leads to simpler model and the same prediction errors compared to unfolded 2-dimensional data set and PLS regression for calibration WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004

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