## 3 Results  
Table 1 contains the property measurements for the lumber utilized in the current study, broken down by calibration data set and prediction data set. The SG, MOE, and MOR data is highly variable, which reflects the variability that these properties have in commerce. Both data sets contained samples with similar summary statistics for each property, which meant that the Duplex sample selection algorithm successfully selected calibration and prediction samples across the range of values only using information from the NIR spectra. A reference point for comparing the relative performance of the NIR and NIR-HSI systems to model MOE and MOR is to compare them against predictions using the measured SG of the lumber, and for MOR, the measured static MOE (Figure 3). A quadratic regression model without the linear term was used to model MOE versus lumber SG (intercept = 3.7, parameter = 37.1), which had a coefficient of determination of 0.42 and a root mean square error (RMSE) of 2.1 GPa. The same model form was used to model MOR versus lumber SG (intercept = \(-\)3.6, parameter = 215.4), which had a coefficient of determination of 0.41 and a RMSE of 12.8 MPa. For both models, the quadratic model form instead of a linear model was used because of the differences in the radial variation trends for wood properties of Douglas-fir. Wood density decreases for approximately 8 years from jth to bark and then increases, whereas stiffness and strength increases from jth to bark because of decreasing microfibril angle [67]. Thus, for samples near the jth, a lower density value does not necessarily \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} \\ \hline
**Property 1** & **N** & **Mean** & **Min** & **Max** & **SD** & **N** & **Mean** & **Min** & **Max** & **SD** \\ \hline SG\({}_{\text{block}}\) & & 0.455 & 0.326 & 0.667 & 0.066 & & 0.459 & 0.343 & 0.626 & 0.067 \\ SG\({}_{\text{lumber}}\) & & 0.47 & 0.346 & 0.619 & 0.051 & 0.472 & 0.374 & 0.603 & 0.05 \\ MOE & 292 & 12 & 6.3 & 19.9 & 2.8 & 98 & 12 & 6.5 & 20.4 & 2.9 \\ MOR & 44.6 & 14.8 & 93.6 & 16.2 & 44.5 & 13.4 & 94.3 & 17.8 \\ \hline \end{tabular} 1  
\end{table} 
Table 1: Summary statistics of block specific gravity, lumber specific gravity, lumber modulus of elasticity, and lumber modulus of rupture for calibration and prediction sets. mean a decrease in wood stiffness or strength. A linear model was used to model MOR versus MOE (intercept = \(-\)14.8, parameter = 4.9), which had a coefficient of determination value of 0.69 and a root mean square error (RMSE) value of 9.2 MPa. Table 2 contains the results of the calibration models and their predictions for the various properties measured in the study using second-derivative NIR spectra. The PLS coefficients for the intercept and the wavelengths are included as supplementary data. All of the RPDp results are lower than what is frequently cited by AACC Method 39-00 [68] as the recommended values for screening in breeding programs (RPD > 2.5), but these guidelines were set up for cereal grains and not for translating wood chemistry information as detected by the NIR spectra to that of the physical and mechanical properties of wood. For the block SG (SGblock), 10 factors were chosen for the cross-validation for the NIR model, and 6 factors for the NIR-HSI model. When used to predict SGblock, the NIR model gave a \(\text{R}_{\text{p}}\)2 of 0.78 and a SEP of 0.031, while the NIR-HSI gave a \(\text{R}_{\text{p}}\)2 of 0.82 and a SEP of 0.029 (Figure 4). The bias was slightly higher for the NIR-HSI model than for the NIR model. For the lumber SG (SGlumber), 10 factors were chosen for the cross-validation for the NIR model and 7 factors for the NIR-HSI model. When used to predict SGlumber, the NIR model gave a \(\text{R}_{\text{p}}\)2 of 0.65 and a SEP of 0.029, while the NIR-HSI gave a \(\text{R}_{\text{p}}\)2 of 0.70 and a SEP of 0.027 (Figure 5). The bias was slightly lower for the NIR-HSI model than the NIR model.