In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more complicated multivariate models. Therefore, the purpose of this paper was to model the strength, stiffness and density of mature and juvenile longleaf pine to NIR spectra with MLR and principal component regression (PCR). MLR performed almost as well as PCR when predicting density, modulus of rupture (MOR) and modulus of elasticity (MOE). Choosing wavelengths associated with wood chemistry and developing principal components gave better predictive models (PCR₂) than when all NIR wavelengths were used (PCR₁). Models developed from mature wood did not predict wood properties from juvenile wood adequately, suggesting that separate models are needed. However, for density prediction, the area under the spectral curve appeared to be insensitive to mature and juvenile wood differences. Five of the six wavelengths associated with MOE were also associated with MOR, perhaps accounting for how MOE and MOR might be related. For pith wood, MOE and MOR were poorly related to NIR spectra, while density was strongly correlated. This inability to predict mechanical properties in the pith-wood zone warrants attention for those manufacturers interested in using near infrared to stress rate lumber within a mill.

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e.,R2> 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.