Efficiency of linear and non-linear classifiers for gas identification from electrocatalytic gas sensor

• Autorzy: Kalinowski P. , Woźniak Ł. , Strzelczyk A. , Jasinski P. , Jasinski G.
• Języki publikacji: EN

Abstrakty

EN

Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling.

Słowa kluczowe

EN

electrocatalytic sensor; cyclic voltammetry; data pre-processing; support vector machine (SVM); Partial Least Squares Discriminant Analysis

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2013
• Tom: Vol. 20, nr 3
• Strony: 501--512
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr., wzory

Twórcy

autor

Kalinowski P.

• Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Woźniak Ł.

• Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Strzelczyk A.

• Gdańsk University of Technology, Faculty of Chemistry, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Jasinski P.

• Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Jasinski G.

• Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland

Bibliografia

• [1] Persaud K., Dodd G. (1982). Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299, 352-355.
• [2] Scott S.M, James D., Ali Z. (2006). Data Analysis for electronic nose systems. Microchimica Acta, 156(3-4), 183-207.
• [3] Capone S., Forleo A., Francioso L., Presicce D.S., Rella R., Siciliano P., Spadavecchia J. (2003). Solid state gas sensors: state of the art and future activities. J. Optoelectron. Adv. M., 5, 1335-1348.
• [4] Guth U., Vonau W., Zosel J. (2009). Recent developments in electrochemical sensor application and technology - a review. Meas. Sci. Technol., 20, 042002+14.
• [5] Jasinski P., Nowakowski A. (2000). Simultaneous detection of sulphur dioxide and nitrogen dioxide by Nasicon sensor with platinum electrodes. Ionics, 6, 230-234.
• [6] Chachulski B., Jasinski G., Jasinski P., Nowakowski A. (2006). Electrocatalytic gas sensors based on Nasicon and Lisicon. Materials Science-Poland, 24, 261-268.
• [7] Jasinski P., Strzelczyk A.,. Chachulski B. A, Gazda M., Jasinski G. Investigation of sensing mechanism of Nasicon electrocatalytic sensors in nitrogen dioxide and ammonia. Sensor Actuat. B-Chem., in print.
• [8] Jasinski P., Nowakowski A., Weppner W. (2000). Kinetic studies of Nasicon based sensors with cyclic voltammetry. Sensors and Materials, 12, 89-97.
• [9] Schoemaker E.L., Vogt M.C., Dudek F.J. (1996). Cyclic voltammetry applied to an oxygen ion-conducting solid electrolyte as an active electrocatalytic gas sensor. Solid State Ionics, 92, 285-292.
• [10] Shoemaker E.L., Vogt M.C, Dudek F.J., Turner T. (1997). Gas microsensor using cyclic voltammetry with a cermet electrochemical cell. Sensor Actuat. B-Chem., 42, 1-9.
• [11] Jasinski G., Jasinski P., Nowakowski A., Chachulski B. (2006). Properties of a lithium solid electrolyte gas sensor based on reaction kinetics. Meas. Sci. Technol., 17, 17-21.
• [12] Chachulski B., Jasinski G., Jasinski P., Nowakowski A. (2005). Lisicon solid electrolyte electrocatalytic gas sensor. J. Eur. Ceram. Soc., 25, 2969-2972.
• [13] Pearce T.C., Gardner J.W., Schiman S.S., Nagle H.T. (2003). Handbook of machine olfaction. Wiley-VCH.
• [14] Kotarski M. M., Smulko J. M. (2010). Hazardous gases detection by fluctuation-enhanced gas sensing. Fluctuation and Noise Letters, 9(04), 359-371.
• [15] Nesbitt W., Quon D.H.H., Wheat T.A. (1980). Synthesis, characterization and fabrication of Na1+xZr2SixP3- xO12. Mater. Res. Bull., 15, 1533-1539.
• [16] Jasinski G., Jasinski P. (2011). Solid electrolyte gas sensors based on cyclic voltammetry with one active electrode. IOP Conference Series: Materials Science and Engineering 18, 212007
• [17] Palit M., Tudu B., Bhattacharyya N., Dutta A., Dutta P.K., Jana A., Bandyopadhyay R., Chatterjee A. (2010). Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea. Anal. Chim. Acta, 675(1), 8-15.
• [18] Ciosek P., Wróblewski W. (2006). The analysis of sensor array data with various pattern recognition techniques. Sensor Actuat. B-Chem., 114(1), 85-93.
• [19] Schreyer S.K., Mikkelsen S.R. (2000) Chemometric analysis of square wave voltammograms for classification and quantitation of untreated beverage samples. Sensor Actuat. B-Chem., 71(1-2), 147-153.
• [20] Yu H., Wang J., Xiao H., Liu M.(2009). Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals. Sensor Actuat. B-Chem., 140(2), 378-382.
• [21] Tiwari K., Tudu B., Bandyopadhyay R., Chatterjee A.(2013). Identification of monofloral honey using voltammetric electronic tongue. J. Food Eng., 117(2), 205-210.
• [22] Richards E., Bessant C., Saini S. (2002). Optimisation of a neural network model for calibration of voltammetric data. Chemom. Intell. Lab. Syst., 61(1-2), 35-49.
• [23] Chang C.C, Lin C.J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:21-27:27.
• [24] Winquist F., Wide P., Lundström I. (1997). An electronic tongue based on voltammetry. Anal. Chim. Acta, 357(1-2), 21-31.
• [25] Wold S., Esbensen K., Geladi P. (1987). Principal component analysis, Chemom. Intell. Lab. Syst., 2(1-3), 37-52, ISSN 0169-7439
• [26] Di Natale C. Martinelli E. D’Amico A. (2005). Pre-processing and pattern recognition methods for artificial olfaction systems: a review. Metrology and Measurement Systems, 12(1), 3-26.
• [27] Ben-Hur A, Weston J. (2010). A user’s guide to support vector machines. Methods in molecular biology(Clifton, N.J.) In Data Mining Techniques for the Life Sciences, Springer Protocols, 609, 223-239.
• [28] Wold S., Sjöström M., Eriksson E. (2001). PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst., 58(2), 109-130.
• [29] Ciosek P., Brzózka Z., Wróblewski W., Martinelli E., Di Natale C., D’Amico A. (2005). Direct and twostage data analysis procedures based on PCA. PLS-DA and ANN for ISE-based electronic tongue-Effect of supervised feature extraction. Talanta, 67(3), 590-596.
• [30] Vapnik V.N. (1995). The Nature of Statistical Learning Theory. Springer.
• [31] Pardo M., Sberveglieri G. (2005) Classification of electronic nose data with support vector machines. Sensor Actuat. B-Chem., 107, 730-737.
• [32] Kumar R., Bhondekar A.P., Kaur R., Vig S., Sharma A., Kapur P. (2012). A simple electronic tongue. Sensor Actuat. B-Chem., 171-172, 1046-1053.
• [33] Kaur R., Kumar R., Gulati A., Ghanshyam C., Kapur P., Bhondekar A.P. (2012). Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). Sensor Actuat. B-Chem., 166-167, 309-319.

Uwagi

EN

This study was partially supported by the Polish Ministry of Science and Higher Education under grant No. N N515 243437.