Article
Full entry |
PDF
(0.7 MB)
Feedback
PDF
(0.7 MB)
Feedback
Keywords:
locally weighted regression; artificial neural network; modelling; biosensor
locally weighted regression; artificial neural network; modelling; biosensor
Summary:
This paper presents a semi-global mathematical model for an analysis of a signal of amperometric biosensors. Artificial neural networks were applied to an analysis of the biosensor response to multi-component mixtures. A large amount of the learning and test data was synthesized using computer simulation of the biosensor response. The biosensor signal was analyzed with respect to the concentration of each component of the mixture. The paradigm of locally weighted linear regression was used for retraining the neural networks. The application of locally weighted regression significantly improved the quality of the prediction of the concentrations.
References:
[1] Artursson T., Eklöv T., Lundström I., Mårtensson P., Sjöström, M., Holmberg M.: Drift correction for gas sensors using multivariate methods. J. Chemometrics 14 (2000), 711–723 DOI 10.1002/1099-128X(200009/12)14:5/6<711::AID-CEM607>3.0.CO;2-4
[2] Atkeson C. G., Moore A. W., Schaal S.: Locally weighted learning. Artificial Intelligence Rev. 11 (1997), 11–73 DOI 10.1023/A:1006559212014
[3] Baronas R., Christensen J., Ivanauskas, F., Kulys J.: Computer simulation of amperometric biosensor response to mixtures of compounds. Nonlinear Anal. Model. Control 7 (2002), 3–14 Zbl 1062.93500
[4] Baronas R., Ivanauskas, F., Kulys J.: The influence of the enzyme membrane thickness on the response of amperometric biosensors. Sensors 3 (2003), 248–262 DOI 10.3390/s30700248
[5] Baronas R., Ivanauskas F., Maslovskis, R., Vaitkus P.: An analysis of mixtures using amperometric biosensors and artificial neural networks. J. Math. Chem. 36 (2004), 281–297 DOI 10.1023/B:JOMC.0000044225.76158.8e | MR 2105018 | Zbl 1053.92024
[6] Chan L. W., Szeto C. C.: Training recurrent network with block-diagonal approximated Levenberg–Marquardt algorithm. In: Proc. IEEE Internat. Joint Conference on Neural Networks, IJCNN ’99, pp. 1521–1526, 1999
[7] Devroye L., Gyorfi, L., Lugosi G.: A Probabilistic Theory of Pattern Recognition. Springer–Verlag, New York 1996 MR 1383093
[8] Haykin S.: Neural Networks: A Comprehensive Foundation. Second edition. Prentice Hall, New York 1999 Zbl 0934.68076
[9] INTELLISENS: Intelligent Signal Processing of Biosensor Arrays Using Pattern Recognition for Characterisation of Wastewater: Aiming Towards Alarm Systems. EC RTD project. 2000 – 2003
[10] Malkavaara P., Alén, R., Kolehmainen E.: Chemometrics: an important tool for the modern chemist, an example from wood-processing chemistry. J. Chem. Inf. Comput. Sci. 40 (2000), 438–441 DOI 10.1021/ci990444i
[11] Martens H., Næs T.: Multivariate Calibration. Wiley, Chichester 1989 MR 1029523 | Zbl 0732.62109
[12] Moore A. W., Schneider J. G., Deng K.: Efficient Locally Weighted Polynomial Regression Predictions. In: Proc. Fourteenth International Conference on Machine Learning, pp. 236–244, 1997
[13] Nakamoto T., Hiramatsu H.: Study of odor recorder for dynamical change of odor using QCM sensors and neural network. Sens. Actuators B 85 (2002), 98–105 DOI 10.1016/S0925-4005(02)00130-2
[14] Patterson D.: Artificial Neural Networks, Theory and Applications. Prentice Hall, Upper Saddle River 1996 Zbl 0839.68079
[15] Rao C. R.: Linear Statistical Inference and its Application. Wiley, New York 1973 MR 0346957
[16] Rogers K. R.: Biosensors for environmental applications. Biosens. Biolectron. 10 (1995), 533–541 DOI 10.1016/0956-5663(95)96929-S
[17] Ruppert D., Wand M. P.: Multivariate locally weighted least squares regression. Ann. Statist. 22 (1994), 1346–1370 DOI 10.1214/aos/1176325632 | MR 1311979 | Zbl 0821.62020
[18] Ruzicka J., Hansen E. H.: Flow Injection Analysis. Wiley, New York 1988
[19] Samarskii A. A.: The Theory of Difference Schemes. Marcel Dekker, New York – Basel 2001 MR 1818323 | Zbl 0971.65076
[20] Schaal S., Atkeson C. G.: Assessing the quality of learned local models, In: Advances in Neural Information Processing Systems 6 (J. Cowan, G. Tesauro, J. Alspector, eds.), Morgan Kaufmann 1994, pp. 160–167
[21] Schaal S., Atkeson C. G.: Constructive incremental learning from only local information. Neural Comput. 10 (1998), 2047–2084 DOI 10.1162/089976698300016963
[22] Scheller F., Schubert F.: Biosensors, Vol. 7. Elsevier, Amsterdam 1992
[23] Schulmeister T.: Mathematical modelling of the dynamics of amperometric enzyme electrodes. Selective Electrode Rev. 12 (1990), 260–303
[24] Turner A. P. F., Karube, I., Wilson G. S.: Biosensors: Fundamentals and Applications. Oxford University Press, Oxford 1987
[25] Wang Z., Isaksson, T., Kowalski B. R.: New approach for distance measurement in locally weighted regression. Anal. Chem. 66 (1994), 249–260 DOI 10.1021/ac00074a012
[26] Wollenberger U., Lisdat, F., Scheller F. W.: Frontiers in Biosensorics 2: Practical Applications. Birkhauser Verlag, Basel 1997
[27] Ziegler C., Göpel W., Hämmerle H., Hatt H., Jung G., Laxhuber L., Schmidt H.-L., Schütz S., Vögtle, F., Zell A.: Bioelectronic noses: A status report. Part II. Biosens. Bioelectron. 13 (1998), 539–571
Search
Partner of
