Multivariate analyses of visible/near infrared (VIS/NIR) absorbance spectra reveal underlying spectral differences among dried, ground conifer needle samples from different growth environments
Article first published online: 6 NOV 2003
Volume 161, Issue 1, pages 291–301, January 2004
How to Cite
Richardson, A. D., Reeves III, J. B. and Gregoire, T. G. (2004), Multivariate analyses of visible/near infrared (VIS/NIR) absorbance spectra reveal underlying spectral differences among dried, ground conifer needle samples from different growth environments. New Phytologist, 161: 291–301. doi: 10.1046/j.1469-8137.2003.00913.x
- Issue published online: 6 NOV 2003
- Article first published online: 6 NOV 2003
- Received: 11 June 2003 Accepted: 27 August 2003; doi: 10.1046/j.1469-8137.2003.00913.x
- balsam fir (Abies balsamea);
- conifer foliage;
- discriminant analysis;
- partial least squares (PLS) regression;
- principal components analysis;
- red spruce (Picea rubens);
- reflectance spectra
- • Absorbance of visible and near infrared (400–2500 nm) radiation by plant material is determined primarily by biochemical and structural components. We used three multivariate techniques to explore the spectral differences among dried, ground foliage samples of two conifer species from different montane growth environments (three elevations and two crown positions on three different mountains).
- • Principal components analysis indicated underlying spectral patterns strongly related to species and crown position, and the derived components were correlated with the chemical composition of the samples. Discriminant analysis showed that it was possible to perfectly separate samples by species, but much more difficult to discriminate among different elevations, using just the spectral information. Samples from low and high elevation were well-separated, but mid elevation samples were frequently misclassified.
- • Partial least squares regression produced results that were superior to those of discriminant analysis, in that all groups were better separated and there was less within-group variability.
- • These approaches do not directly reveal the biochemical basis of the spectral differences. However, such methods provide a solid foundation for hypothesizing the overall degree of biochemical similarity among diverse samples. Thus, samples from different growth elevations appeared to be biochemically more similar than samples from different species or crown positions. Other potential applications are discussed.