## 4 Discussion  
In efforts to utilize NIR spectroscopy to estimate lumber properties, various approaches have been employed, with the majority of studies collecting spectra along the length of the sample from either the radial face, tangential face, or somewhere between the two where neither a perfect radial or tangential face is available for examination, as is common in flatsawn lumber. The quality of information collected with reference to its suitability for estimation of whole lumber quality will vary between the two (radial vs tangential) extremes, with the radial face providing the most representative spectra and the tangential face the least [69]. In sawmill lumber with a very wide variety of orientations, ranging from  
Figure 9: **Top left**: RGB image, **Top Right**: single hyperspectral channel image, **Bottom**: prediction of specific gravity (SG), modulus of elasticity (MOE) (GPa), and modulus of rupture (MOR) (MPa) using the PLS models and applied to one sample in the study with narrow growth rings. quatersawn to flatsawn and everything in between can be expected. Hence, scanning along the length of a board can provide spectra that potentially represent the properties of a board very well (when the radial face is scanned) to one that does not (when the tangential face is scanned), or does only partially (somewhere between radial and tangential), if scanning is done on only one face. Current automated grading systems employ cameras and sensors on all four faces, and given the cost of a hyperspectral camera, this type of setup is likely not feasible. For these reasons, we chose to scan the transverse face. The transverse face also demonstrates the variation in orientation described, but with this face, we can still accurately capture the variation in wood properties across rings; for example, the proportion of earlywood to latewood is visible on this face, which ultimately is an important factor in determining lumber properties such SG, MOE, and MOR. To scan the transverse face, we utilized a FOSS benchtop NIR and NIR hyperspectral imaging (HSI) system. Scanning lumber on a benchtop NIR had been described previously [25], with promising results in that the NIR spectra was better correlated with wood stiffness than lumber SG. However, the use of the benchtop system presented problems because the authors calculated that the spectra collected represented less than 10% of the transverse area of the sample and thus failed to fully capture sample variability. Rather than collect more spectra per sample on the benchtop instrument in an attempt to better represent a board, a NIR-HSI system, which can quickly capture an image of the entire transverse surface, was explored. As each pixel has its own NIR spectrum, a representative whole-board spectrum can be obtained by averaging spectra across all pixels, which in principle should be more representative of the entire transverse surface. Despite improved representation of the surface, the two different approaches gave calibration/prediction results that were similar (Table 2), with FOSS NIR having slightly better calibration statistics, while predictions based on NIR-HSI data were marginally better, perhaps due to the greater area covered in the measurement. In a similar comparative study (FOSS XDS probe vs NIR-HSI) based on loblolly pine logs, similar results (SG and moisture content calibrations/predictions) for the two scanning options were also reported [48]. While we may have achieved greater coverage of the surface using NIR-HSI, we know the spectra are not of the same quality as a benchtop instrument, hence the lack of a substantial improvement in model performance. We also explored the relationships between the SG, MOE, and modulus of rupture (MOR) and observed moderate results. For hard pines including southern pine (_Pinus_ spp. ), the relationship between SG and MOE, and to a lesser degree MOR, is known to be quite strong, particularly for short clear samples.