Near-Infrared Spectroscopic Separation of Green Chain Sub-Alpine Fir Lumber from a Spruce-Pine-Fir Mix

Amir Sohi, Stavros Avramidis, Shawn Mansfield

Abstract


The intention of this exploratory study was to determine whether near-infrared spectroscopy, combined with multivariate statistical modeling, could become a swift and accurate tool for identifying sub-alpine fir within a typical spruce-pine-fir (SPF) lumber mix in the green chain of a sawmill. This need arises from the difficulty encountered in the drying sub-alpine fir. Its identification and removal from the SPF mix before kiln drying may be quite beneficial for producing high quality lumber. Near-infrared spectra were obtained from scanning of small specimens that were prepared from freshly cut trees. The results of the initial principal component analysis indicated that all four components could be used for species differentiation with the help of partial least squares discriminant analysis. All specimens in the training set were fitted into the correct sub-group of either fir or spruce-pine groups. The test set was validated and it revealed that all specimens were correctly classified. The outcome also confirmed that near-infrared spectroscopy combined with multivariate statistical modeling could be a suitable prediction model for separation of sub-alpine fir from the SPF mix.

Keywords


Multivariate statistical modeling; Near-infrared spectroscopy; Principal component analysis; Spectra; Sub-alpine fir; Spruce-pine-fir; Kiln drying; SPF

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