Authors: Lavine, B.K.; Davidson, C.E.; Moores, A.J.; Griffiths, P.R.

Source: Applied Spectroscopy, Volume 55, Issue 8, Pages 260A-294A and 955-1111 (August 2001) , pp. 960-966(7)

Publisher: Society for Applied Spectroscopy

Abstract:

Raman spectroscopy and pattern recognition techniques are used to develop a potential method to characterize wood by type. The test data consists of 98 Raman spectra of temperate softwoods and hardwoods, and Brazilian and Honduran tropical woods. A genetic algorithm (GA) is used to extract features (i.e., line intensities at specific wavelengths) characteristic of the Raman profile of each wood-type. The spectral features identified by the pattern recognition GA allow the wood samples to cluster by type in a plot of the two largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by these spectral features is about differences between wood types. The predictive ability of the descriptors identified by the pattern recognition GA and the principal component map associated with them is validated using an external prediction set consisting of tropical woods and temperate hard and softwoods.

Keywords: PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; GENETIC ALGORITHMS; MACHINE LEARNING; WOOD TYPING; RAMAN SPECTROSCOPY

Document Type: Research article

DOI: http://dx.doi.org/10.1366/0003702011953108

Publication date: 2001-08-01

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  Previously published as Bulletin (Society for Applied Spectroscopy)
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