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We report on the use of Principal Component Regression (PCR), a least squares calibration method employing Principal Component Analysis (PCA) for the analysis of large sets of images obtained by wide angle X-ray diffractometry (WAXD) of wood. PCR multivariate models were created for prediction of microfibril angle (MFA), an important wood structural parameter. Multivariate regression techniques have previously been applied to Xray diffraction data for a variety of analytical problems. These include analysis of complex mineral mixtures (Karstang and Eastgate 1987) and for automated high-throughput analysis of powder diffraction profiles obtained by azimuthal averaging of 2D diffraction patterns (Gilmore, Barr et al. 2004). Multivariate classification from X-ray diffraction data has also been used for discrimination of insulin microcrystals (Norrman, Stahl et al. 2006) and brucite particle morphology (Matos, Xavier et al. 2007). The models created in this study used pixels from across the full area of the diffraction image as predictor variables, a technique which has not been found applied to diffraction data in the literature. A related technique, multivariate analysis of congruent images (MACI), was introduced by Eriksson, Wold et al. (2005) for discrimination and quantification of events within video image sequences. MACI uses wavelet analysis to decompose images and obtain predictive information which is correlated with frame-by-frame differences and that can describe properties such as the movement of an object or change in composition of a feature.

Application of Principal Components Regression for Analysis of X-Ray Diffraction Images of Wood