Raman Spectroscopy and Genetic Algorithms for the Classification of Wood Types

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.