Abstract
The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples.
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References
Ferraro J. R., Nakamoto K., Brown C. W., Introductory Raman Spectroscopy Second Ed., San Diego, Academic Press, 2003.
Shaver J.M Chemometrics for Raman Spectroscopy. Handbook of Raman Spectroscopy: Practical Spectroscopy Series Vol. 28, Marcel Dekker Inc, New York, 2001.
Bulkin B.J. The Raman effect: an introduction. Analytical Raman Spectroscopy, Chemical Analysis, volume 114. John Wiley and Sons, Inc, 1991; 1–19.
Goodacre R., Timmins E. M., Rooney P.J. et al. Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks. FEMS Microbiol Lett 1996; 140:233–239.
Yang H., Griffiths P. R., Tate J.D. Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra. Anal Chim Acta 2003; 489:125–136.
Schulze H.G., Greek L.S., Gorzalka B.B. et al. Artificial neural network and classical least squares method for neurotransmitter mixture analysis. J Neurosci Meth 1995; 56:155–167.
Benjathapanun N., Boyle W.J.O. and Grattan K.T.V. Classification of UV-Vis spectroscopic data using principal component analysis and neural network techniques. Measurement 1998; 24:1–7.
Goodacre R. Explanatory analysis of Spectroscopic data using machine learning of simple, interpretable rules. Vib Spectrosc 2003; 32:33–45.
Ellis D.I., Broadhurst D., Kell D.B. et al. Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning. Appl Environ Microb 2002; 68:2822–2828.
Taylor J., Goodacre R., Wade W.G., Rowland J.J. and Kell D.B. The deconvolution of pyrolysis mass spectra using genetic programming: application to identification of some Eubacterium species. FEMS Microbiol Lett 1998; 160:237–246.
Ryder A.G., O’Connor G.M. and Glynn T.J. Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods. J Raman Spectrosc 2000; 31:221–227.
Estienne F., Massart D.L. Multivariate calibration with Raman data using fast Principal component regression and partial least squares methods. Anal Chim Acta 2001; 450:123–129.
Madden M.G., Ryder A. G. Machine learning methods for quantitative analysis of Raman Spectroscopy data. Proceedings of SPIE, the International Society for Optical Engineering 2002; 4876:1130–1139.
Koza J.R., Bennett III F.H, Andre D. et al. Genetic Programming III. Morgan Kaufmann, 1999.
StatSoft, Inc. Electronic Statistics Textbook, http://www.statsoft.com/textbook/stathome.html, 2004.
Berthold M., Hand D. J. Intelligent Data Analysis, Springer, 2003.
Brierley P. Visual Neural Data Mining, http://www.philbrierley.com, 2004.
Witten I.H., Eibe F. Data Mining: Practical machine learning tools with Java implementations, Morgan Kaufmann, San Francisco, 2000.
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© 2005 Springer-Verlag London Limited
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Hennessy, K., Madden, M.G., Conroy, J., Ryder, A.G. (2005). An Improved Genetic Programming Technique for the Classification of Raman Spectra. In: Macintosh, A., Ellis, R., Allen, T. (eds) Applications and Innovations in Intelligent Systems XII. SGAI 2004. Springer, London. https://doi.org/10.1007/1-84628-103-2_13
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DOI: https://doi.org/10.1007/1-84628-103-2_13
Publisher Name: Springer, London
Print ISBN: 978-1-85233-908-1
Online ISBN: 978-1-84628-103-7
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