Abstract
Electronic tongues are bioinspired sensing schemes that employ an array of sensors for analysis, recognition or identification in liquid media. An especially complex case happens when the sensors used are of the voltammetric type, as each sensor in the array yields a 1-dimensional data vector. This work presents the use of a Wavelet Neural Network (WNN) with multiple outputs to model multianalyte quantification from an overlapped voltammetric signal. WNN is implemented with a feedforward multilayer perceptron architecture, whose activation functions in its hidden layer neurons are wavelet functions, in our case, the first derivative of a Gaussian function. The neural network is trained using a backpropagation algorithm, adjusting the connection weights along with the network parameters. The principle is applied to the simultaneous quantification of the oxidizable aminoacids tryptophan, cysteine and tyrosine, from its differential-pulse voltammetric signal. WNN generalization ability was validated with training processes of k-fold cross validation with random selection of the testing set.
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References
Aboufadel, E., Schlicker, S.: Discovering wavelets. Wiley, New York (1999)
Addison, P.S.: The illustrated wavelet transform handbook. Institute of Physics Publishing, Bristol (2002)
Akay, M.: Time Frequency and wavelets. In: Akay, M. (ed.) Biomedical Signal Processing: IEEE Press Series in Biomedical Engineering. Wiley—IEEE Press, Piscataway (1997)
Alsberg, B.K., Woodward, A.M., Kell, D.B.: An introduction to wavelet transform for chemometricians: a time-frequency approach. Chemometr. Intell. Lab. Syst. 37, 215–239 (1997)
Artursson, T., Holmberg, M.: Wavelet transform of electronic tongue data. Sens. Actuators B 87, 379–391 (2002)
Bachman, G., Narici, L., Beckenstein, E.: Fourier and wavelet analysis. Springer, New York (2000)
Blatter, C.: Wavelets, a primer. A K Peters Ltd, Natick MA (1988)
Beale, R., Jackson, T.: Neural computing, an introduction. IOP Publishing Ltd., Bristol (1992)
Cannon, M., Slotine, J.E.: Space-frequency localized basis function networks for nonlinear system estimation and control. Neurocomputing 9, 293–342 (1995)
Ciosek, P., Augustyniak, E., Wroblewski, W.: Polymeric membrane ionselective and cross-sensitive electrode-based electronic tongue for qualitative analysis of beverages. Analyst. 129, 639–644 (2004)
Cocchi, M., Hidalgo-Hidalgo-de-Cisneros, J.L., Naranjo-Rodriguez, I., Palacios-Santander, J.M., Seeber, R., Ulrici, A.: Multicomponent analysis of electrochemical signals in the wavelet domain. Talanta 59, 735–749 (2003)
Daubechies, I., Grossmann, A., Meyer, Y.: Painless nonorthogonal expansions. J. Math. Phys. 27, 1271–1283 (1986)
Daubechies, I.: Ten Lectures on wavelets. In: CBMS-NSF Regional Conference Series In Applied Mathematics, Philadelphia, PA. Society for Industrial and Applied Mathematics, vol. 61 (1992)
Deisingh, A.K., Stone, D.C., Thompson, M.: Applications of electronic noses and tongues in food analysis. Int. J. Food. Sci. Technol. 39, 587–604 (2004)
Di Lorenzo, P.M., Lemmon, C.H.: The neural code for taste in the nucleus of the solitary tract of the rat: effects of adaptation. Brain. Res. 852, 383–397 (2000)
Distante, C., Leo, M., Siciliano, P., Persaud, K.C.: On the study of feature extraction methods for an electronic nose. Sens. Actuators B 87, 274–288 (2002)
Ensafi, A.A., Khayamian, T., Tabaraki, R.: Simultaneous kinetic determination of thiocyanate and sulfide using eigenvalue ranking and correlation ranking in principal component-wavelet neural network. Talanta 71, 2021–2028 (2007)
Erickson, R.P., Doetsch, G.S., Marshall, D.A.: The gustatory neural response function. J. Gen. Physiol. 49, 247–263 (1965)
Fine, T.L.: Feedforward neural network methodology. Springer, New York (1999)
Frank, M.: An analysis of hamster afferent taste nerve response functions. J. Gen. Physiol. 61, 588–618 (1973)
Freeman, J.A., Skapura, D.M.: Neural networks: algorithms, applications and programming techniques. Addison-Wesley, Redwood City (1992)
Gallardo, J., Alegret, S., de Roman, M.A., Muñoz, R., Hernandez, P.R., Leija, L., del Valle, M.: Determination of ammonium ion employing an electronic tongue based on potentiometric sensors. Anal. Lett. 36, 2893–2908 (2003)
Gardner, J.W., Bartlett, P.N.: Electronic noses: Principles and Applications. Oxford University Press, Oxford (1999)
Garson, J.: Connectionism. In: Zalta, E.N. (ed.) The Stanford Encyclopaedia of Philosophy (2007), http://plato.stanford.edu/
Goswami, J.C., Chan, A.K.: Fundamentals of wavelets. Wiley, New York (1999)
Graps, A.: An introduction to wavelets. Comput. Sci. Eng. 2, 50–61 (1995)
Guo, Q.X., Liu, L., Cai, W.S., Jiang, Y., Liu, Y.C.: Driving force prediction for inclusion complexation of α-cyclodextrin with benzene derivatives by a wavelet neural network. Chem. Phys. Lett. 290, 514–518 (1998)
Gutés, A., Céspedes, F., Cartas, R., Alegret, S., del Valle, M., Gutierrez, J.M., Muñoz, R.: Multivariate calibration model from overlapping voltammetric signals employing wavelet neural networks. Chemometr. Intell. Lab. Syst. 83, 169–179 (2006)
Hallock, R.M., Di Lorenzo, P.M.: Temporal coding in the gustatory system. Neurosci. Biobehavioral. Rev. 30, 1145–1160 (2006)
Hassoun, M.H.: Fundamentals of artificial neural networks. The MIT Press, Cambridge (1995)
Haykin, S.: Neural networks, a comprehensive foundation. Prentice Hall, Upper Saddle River (1999)
Hebb, D.: The organization of behavior. In: Anderson, A., Rosenfield, E. (eds.) Neurocomputing, foundations of research. The MIT Press, Cambridge (1949)
Heil, C.E., Walnut, D.F.: Continuous and discrete wavelet transforms. SIAM Review 31, 628–666 (1989)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley, Redwood City (1991)
Holmberg, M., Eriksson, M., Krantz-Rülcker, C., Artursson, T., Winquist, F., Lloyd-Spetz, A., Lundström, I.: Second workshop of the second network on artificial olfactory sensing (NOSE II). Sens. Actuators B 101, 213–223 (2004)
Hornik, K.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)
Ionescu, R., Llobet, E., Vilanova, X., Brezmes, J., Suegras, J.E., Calderer, J., Correig, X.: Quantitative analysis of nitrogen dioxide in the presence of carbon monoxide using a single tungsten oxide semiconductor sensor and dynamic signal processing. Analyst 127, 1237–1246 (2002)
Ionescu, R., Llobet, E., Brezmes, J., Vilanova, X., Correig, X.: Dealing with humidity n the qualitative analysis of carbon monoxide and nitrogen dioxide using a tungsten trioxide sensor and dynamic signal processing. Sens. Actuators B95, 177–182 (2003)
Iyengar, S.S., Cho, E.C., Phoha, V.V.: Foundations of wavelet neural networks. Chapman & Hall/CRC, Boca Raton (2002)
Jetter, K., Depczynski, U., Molt, K., Niemöller, A.: Principles and applications of wavelet transform to chemometrics. Anal. Chim. Acta. 420, 169–180 (2000)
Jones, L.M., Fontanini, A., Katz, D.B.: Gustatory processing: a dynamic system approach. Current Opinion in Neurobiology 16, 420–428 (2006)
Kaiser, G.: A friendly guide to wavelets. Birkhäuser, Basel (1994)
Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of neural science, 4th edn. McGraw Hill, New York (2000)
Katz, D.B., Nicolelis, M., Simon, S.A.: Gustatory processing is dynamic and distributed. Current Opinion in Neurobiology 12, 448–454 (2002)
Khayamian, T., Ensafi, A.A., Benvidi, A.: Extending the dynamic range of copper determination in differential pulse adsorption cathodic stripping voltammetry using wavelet neural network. Talanta 69, 1176–1181 (2006)
Kugarajah, T., Zhang, Q.: Multidimensional wavelet frames. IEEE Trans. Neural Netw. 6, 1552–1556 (1995)
Legin, A.V., Rudnitskaya, A.M., Vlasov, Y. G., Di Natale, C., D’Amico, A.: The features of the electronic tongue in comparison with the characteristics of the discrete ion-selective sensors. Sens. Actuators B 58, 464–468 (1999)
Leung, A.K., Chau, F., Gao, J.: A review on applications of wavelet techniques in chemical analysis: 1989-1997. Chemometr. Intell. Lab. Syst. 43, 165–184 (1998)
Llobet, E., Brezmes, J., Ionescu, R., Vilanova, X., Al-Khalifa, S., Gardner, J.W., Barsan, N., Correig, X.: Wavelet transform and fuzzy ARTMAP-based pattern recognition for fast gas identification using a micro-hotplate gas sensor. Sens. Actuators B 83, 238–244 (2002)
Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)
Mallat, S.: A wavelet tour of signal processing, 2nd edn. Academic Press, San Diego (1999)
Meyer, Y.: Wavelets: Algorithms and Applications. Society for Industrial and Applied Mathematics. SIAM, Philadelphia (1993)
Moreno, L., Cartas, R., Merkoçi, A., Alegret, S., Gutiérrez, J.M., Leija, L., Hernández, P.R., Muñoz, R.: Data Compression for a Voltammetric Electronic Tongue Modelled with Artificial Neural Networks. Anal. Lett. 38, 2189–2206 (2005)
Moreno, L., Cartas, R., Merkoçi, A., Alegret, S., Leija, L., Hernández, P.R., Muñoz, R.: Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue. Sens. Actuators B 113, 487–499 (2006)
Ogawa, H., Sato, M., Yamashita, S.: Multiple sensitivity of chorda tympani fibres of the rat and hamster to gustatory and thermal stimuli. J. Physiol 199, 223–240 (1968)
Oussar, Y., Rivals, I., Personnaz, L., Dreyfus, G.: Training wavelet networks for nonlinear dynamic input-output modeling. Neurocomputing 20, 173–188 (1998)
Palacios-Santander, J.M., Jimenez-Jimenez, A., Cubillana-Aguilera, L.M., Naranjo-Rodriguez, I., Hidalgo-Hidalgo-de-Cisneros, J.L.: Use of artificial neural networks, aided by methods to reduce dimensions, to resolve overlapped electrochemical signals. A comparative study including other statistical methods. Microchim. Acta 142, 27–36 (2003)
Rioul, O., Vetterli, M.: Wavelets and signal processing. IEEE SP Magazine 8, 14–38 (1991)
Rudnitskaya, A., Ehlert, A., Legin, A., Vlasov, Y., Büttgenbach, S.: Multisensor system on the basis of an array of non-specific chemical sensors and artificial neural networks for determination of inorganic pollutants in a model groundwater. Talanta 55, 425–431 (2001)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations, vol. 1. MIT, Cambridge (1986)
Sarkar, T.K., Su, C.: A tutorial on wavelets from an Electrical Engineering Perspective, Part 2: The Continuous Case. IEEE Antennas and Propagation Magazine 40, 36–49 (1998)
Scarcelli, F., Tsoi, A.C.: Universal Aproximation using Feedforward Neural networks: A survey of some existing methods and some new results. Neural Networks 11, 15–37 (1998)
Simon, S.A., De Araujo, I.E., Gutierrez, R., Nicolelis, M.A.: The neural mechanisms of gustation: a distributed processing code. Nature Rev. Neurosci. 7, 890–901 (2006)
Tabaraki, R., Khayamian, T., Ensafi, A.A.: Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressure in supercritical carbon dioxide. J. Molec. Graphics Model 25, 46–54 (2006)
Vlasov, Y., Legin, A.A.: Non-selective chemical sensors in analytical chemistry: from electronic nose to electronic tongue. Fresenius J. Anal. Chem. 361, 255–260 (1998)
Winquist, F., Holmin, S., Krants-Rülcker, C., Wide, P., Lundström, I.: A hybrid electronic tongue. Anal. Chim. Acta 406, 147–157 (2000)
Zhang, J., Walter, G.G., Miao, Y., Lee, W.N.W.: Wavelet neural networks for function learning. IEEE Trans. Signal Processing 43, 1485–1497 (1995)
Zhang, Q., Benveniste, A.: Wavelet Networks. IEEE Trans. Neural Netw. 3, 889–898 (1992)
Zhang, X., Oi, J., Zhang, R., Liu, M., Hu, Z., Xue, H., Tao Fan, B.: Prediction of programmed-temperature retention values of naphthas by wavelet neural networks. Comput. Chem. 25, 125–133 (2001)
Zhong, H., Zhang, J., Gao, M., Zheng, J., Li, G., Chen, L.: The discrete wavelet neural network and its application in oscillographic chronopotentiometric determination. Chemometr. Intell. Lab. 59, 67–74 (2001)
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Cartas, R. et al. (2009). Multivariate Calibration Model for a Voltammetric Electronic Tongue Based on a Multiple Output Wavelet Neural Network. In: Gutiérrez, A., Marco, S. (eds) Biologically Inspired Signal Processing for Chemical Sensing. Studies in Computational Intelligence, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00176-5_9
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DOI: https://doi.org/10.1007/978-3-642-00176-5_9
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