## 1 Introduction  
The Northwestern United States accounts for a large proportion of the structural lumber produced in the United States, to which Douglas-fir (_Pseudotsuga menziesii_ (Mirb.) [PERSON]) is the most important species [1, 2]. Douglas-fir is increasingly being sourced from forest plantations that yield excellent growth, which allows for shorter rotation lengths than in the past [3, 4, 5, 6]. The wood from stands harvested from short rotations have a higher proportion of corewood (juvenile wood), which has a lower modulus of elasticity (MOE or stiffness), lower modulus of rupture (MOR or strength), and lower dimensional stability compared to wood harvested from older stands [7, 8, 9]. Due to the variable nature of wood, there is a need to better segregate high-quality material from low-quality material. Prior to sawing logs into lumber, logs can be evaluated by measuring the acoustic velocity, with high velocity indicating stiffer wood than logs with low velocity [10; 11; 12; 13; 14]. Segregating logs by acoustic velocity has its limitations in that logs with low acoustic velocity can contain lumber with high stiffness and vice versa, because much of the variation in stiffness within a log is due to the radial variability associated with corewood and outerwood variation [15; 16]. Once sawn into lumber, the stiffness of a piece can be assessed by measuring the density and the acoustic velocity [8; 17]; however, at this point, the lumber has already been sawn and thus the product has been determined. Changing the product after sawing would generate significant waste and thus is not common. Thus, improved methods for measuring wood properties are needed at the log level for more informed decision-making within a lumber mill. At a veneer mill, the same challenges exist and the use of segregating logs by acoustic velocity has been applied successfully to poplar (_Populus eramericana_) [18] and trembling aspern (_Populus tremuloides_ Michx.) [19]. Near-infrared (NIR) spectroscopy has emerged as an alternative approach for the estimation of mechanical properties of lumber. In studies based on short, defect-free samples, the potential of NIR spectroscopy for predicting density, MOE, and MOR in both softwood [20; 21; 22; 23] and hardwoods [24] has been demonstrated. These studies on small clear samples represent a \"best-case\" scenario, as a small number of spectra can adequately represent a sample due to the limited radial and vertical variation found in a small clear sample. Owing to size and variability, the collection of representative spectra from dimensional lumber of standard size presents a far greater challenge. [PERSON] et al. [25] reviewed publications that had explored estimation of the mechanical properties of softwood lumber [26; 27; 28; 29; 30]. Of these studies, only [PERSON] and [PERSON] [26] collected spectra from the transverse surface; all others scanned along the length of the sample with spectra collected from the tangential and/or radial face. Calibration and prediction statistics were variable, with the strongest predictions (\(\text{R}_{\text{P}}\)\({}^{2}\) = 0.82) reported for hinoki cypress (_Channacepparis obtusa_ ([PERSON]) number [30]. Only [PERSON] et al. [29] reported a calibration for MOR, and it was much weaker than that reported for MOE using the same set of Japanese larch (_Larix kaempfer_ ([PERSON] [PERSON]) lumber. [PERSON] et al. [25] utilized transverse face spectra collected from a laboratory-grade NIR spectroscopy instrument to predict the SG, MOE, and MOR of No. 2 grade 2 \(\times\) 4 southern pine lumber from six commercial mills in the Southeast. The Duplex sample selection technique was used to identify samples for the calibration (539) and prediction (179) sets. Moderate results were reported for the prediction set for lumber SG (\(\text{R}_{\text{P}}\)\({}^{2}\) = 0.53), MOE (\(\text{R}_{\text{P}}\)\({}^{2}\) = 0.58), and MOR (\(\text{R}_{\text{P}}\)\({}^{2}\) = 0.4).