Abstract
This paper presents an evolutionary approach to estimate the sugar concentration inside compound bodies based on spectroscopy measurements. New European regulation will shortly forbid the use of established chemical methods based on mercury to estimate the sugar concentration in sugar beet. Spectroscopy with a powerful regression technique called PLS (Partial Least Squares) may be used instead. We show that an evolutionary approach for selecting relevant wavelengths before applying PLS can lower the error and decrease the computation time. It is submitted that the results support the argument for replacing the PLS scheme with a GP technique.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
H. Swierenga, W.G. Haanstra, AP de Weijer, and LMC Buydens. Comparison of two different approaches toward model transferability in nir spectrocopy. Applied Spectroscopy, 52:7–16, 1998.
H. Swierenga, PJ de Groot, AP de Weijer, MWJ Derksen, and LMC Buydens. Improvement of PLS method transferability by robust wavelength selection. Chemom. Intell. Lab. Syst., 14:237–248, 1998.
Maria Martin-Bautista and Maria-Amparo Vila. A survey of genetic feature selection in mining issues. In 1999 Congress on Evolutionary Computation, pages 1314–1321, 1999.
P.M. Narendra and K. Fukunaga. A branch and bound algorithm for feature subset selection. IEEE Trans. on Computer, 26(9):917–922, sep 1977.
H. Vafaie and K. De Jong. Robust feature selection algorithms. pages 356–363. IEEE Computer Society Press, 1993.
Michael L. Raymer, William F. Punch, Erik D. Goodman, Paul C. Sanschagrin, and Leslie A. Kuhn. Simultaneous feature scaling and selection using a genetic algorithm. In [11], pages 561–567, 1997.
Kan Chen and Huan Liu. Towards an evolutionary algorithm: A comparison of two feature selection algorithms. In 1999 Congress on Evolutionary Computation, pages 1309–1313, 1999.
J. Eggermond, A.E. Eiben, and J.I. van Hemert. Adapting the fitness function in GP for data-mining. In [12], pages 193–202, 3 1999.
Alex Freitas. A genetic programming framework for two data mining tasks: Classification and generalized rule induction. In [13], pages 96–101, 1997.
Celia Bojarczuk. Discovering comprehensible classification rules using genetic programming: a case study in a medical domain. In [14], pages 953–958, 1999.
R. Poli, P. Nordin, W.B. Langdon, and T.C. Fogarty, editors. Genetic Programming, proceedings of EuroGP’99, volume 1598 of LNCS, Goteborg, Sweden, may 1999.
John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors. Proceedings of the Second Annual Conference on Genetic Programming, Stanford University, CA, USA, Jul 1997. Morgan Kaufmann.
Wolfgang Banzhaf, Jason Daida, Agoston Eiben, Max Garzon, Vasant Honavar, Mark Jakiela, and Robert Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, july 1999. Morgan-Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fonlupt, C., Cahon, S., Robilliard, D., Talbi, EG., Duponchel, L. (2002). Using EAs for Error Prediction in Near Infrared Spectroscopy. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_21
Download citation
DOI: https://doi.org/10.1007/3-540-46004-7_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43432-0
Online ISBN: 978-3-540-46004-6
eBook Packages: Springer Book Archive