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

Abstract

The intention of this exploratory study was to determine whether nearinfrared 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.

Ways to increase the production efficiency of hardwood blanks

“…Scanning logs in industrial computed tomography provides detailed information about the quality of wood before sawing [16][17][18]. And near-infrared spectroscopy in combination with multidimensional statistical modeling can be a suitable forecasting model for determining the manufacturing quality [19].…”

Neural network for classification of Chinese zither panel wood via near-infrared spectroscopy

“…Hwang et al (2016) extended NIR to classify and identify pine trees traditionally used in Korean architecture. Sohi et al (2017) successfully applied NIR and multivariate statistical modeling to separate green chain sub-alpine fir lumber from a spruce-pine-fir mix. Tham et al (2018) applied capacitance and NIR to predict the solid wood density and water content.…”