## 1 Introduction
There, the spectra were collected from approximately 8% of the area of the transverse face, but the prediction statistics were quite favorable compared to full-length density information collected from the lumber (MOE \(R^{2}\) = 0.46, MOR \(R^{2}\) = 0.51). These NIR results were promising, especially given the limited area in which the NIR spectra was collected, and the authors proposed that the use of an NIR instrument that allowed a larger area of the transverse surface to be scanned could provide better results. To this end, NIR hyperspectral imaging (HSI) is an emerging methodology whose use has been reported in several recent reviews across a range of fields, including medical applications [31], food quality and safety [32; 33], and plants and biological materials [34; 35]. As noted by [PERSON] and [PERSON] [36], \"HSI combines spectroscopy and imaging, resulting in three-dimensional multivariate data structures ('hypercubes'). Each pixel in a hypercube contains a spectrum representing its light absorbing and scattering properties. This spectrum can be used to estimate the chemical composition and/or physical properties of the spatial region represented by that pixel.\". NIR-HSI can thus be used to capture information on the spatial distribution of wood properties [34], and to this end, it provides an option for the collection of spectra over the entire transverse surface of the lumber. In addition, all spectra collected from the surface of an individual sample can be averaged to a single spectrum that represents the whole area scanned. The earliest related HSI studies on wood used visible light [37; 38] and wavelengths from 400-1000 nm [39] to detect compression wood. [PERSON] et al. [40] utilized NIR-HSI data from 1250-1650 nm to estimate density, microfibril angle (MFA), and stiffness pith-to-bark variation at 2-mm spatial resolution for loblolly pine (_Pinus taeda_ L.). More recently, [PERSON] et al. [41; 42] estimated density for stone pine (_Pinus pinea_ L.), while [PERSON] et al. [43] reported maps of radial strips showing density and MFA variation at high spatial resolution for Sugi (_Cryptomeria japonica_ (L.f.) [PERSON]). [PERSON] et al. [44; 45] utilized NIR-HSI to examine the spatial variation of several radiata pine (_Pinus radiata_ D. [PERSON]) wood properties (lignin, cellulose, hemicellulose, galactose, glucose) across discs, while for the same species, NIR-HSI has also been used to detect compression wood [46] and resin defects [47]. [PERSON] et al. [48] utilized NIR-HSI to estimate loblolly pine whole-log density and moisture content. Due to the flexibility of NIR-HSI, the system has many possible applications in manufacturing environments. Whole-board maps showing variation in moisture content [49] and density [50] were reported for subalpine fir (_Abies lasiocarpa_ Hook) lumber. [PERSON] et al. [51] used NIR-HSI to identify wood chips with high levels of extractives, which would allow for biomass to be directed towards a process stream that would maximize its utilization. The advantages of NIR-HSI are apparent, but compared to benchtop NIR systems, there is typically a higher interval between wavelengths and the signal-to-noise ratio may not be as high, thus the calibrations may not be as accurate as those of a benchtop system. Hence, the primary goals of this preliminary study were to (1) compare the use of NIR spectroscopy with NIR-HSI on Douglas-fir lumber properties with the transverse face scanned on both instruments, (2) compare the predictions with models constructed to predict MOE and MOR using specific gravity, and (3) utilize the NIR-HSI for the spatial prediction of properties on transverse surfaces. If successful and with additional studies, the flexibility of the NIR-HSI could potentially be used in a mill, whereby logs could be scanned and areas within a log that do not meet product specifications could have their sawing pattern changed to a more suitable product.