## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **Calibration** & & & & & & **Prediction** & \\ \hline
**Tool 1** & **Property 2** & **No. Factors** & **R2** & **SECV** & **RMSECV** & **RPDcv** & \(\text{R}_{\text{p}}\)2 & **SEP** & **RPDp** & **Bias** \\ \hline \multirow{4}{*}{NIR} & SGblock & 10 & 0.83 & 0.027 & 0.035 & 2.4 & 0.78 & 0.031 & 2.1 & 0.00075 \\  & SGlumber & 10 & 0.74 & 0.026 & 0.032 & 2.0 & 0.65 & 0.029 & 1.7 & 0.0033 \\  & MOE & 6 & 0.67 & 1.6 & 1.8 & 1.7 & 0.56 & 1.9 & 1.5 & 0.16 \\  & MOR & 7 & 0.57 & 10.7 & 12.2 & 1.5 & 0.40 & 13.7 & 1.3 & 0.75 \\ \hline \multirow{4}{*}{NIR-HSI} & SGblock & 6 & 0.75 & 0.033 & 0.037 & 2.0 & 0.82 & 0.029 & 2.3 & 0.0029 \\  & SGlumber & 7 & 0.67 & 0.03 & 0.033 & 1.7 & 0.70 & 0.027 & 1.8 & 0.0013 \\ \cline{1-1}  & MOE & 5 & 0.65 & 1.6 & 1.7 & 1.7 & 0.62 & 1.8 & 1.6 & 0.15 \\ \cline{1-1}  & MOR & 6 & 0.51 & 11.3 & 12.2 & 1.4 & 0.49 & 12.7 & 1.4 & 0.79 \\ \hline \end{tabular}  
* NIR = near-infrared spectroscopy, NIR-HSI = near-infrared spectroscopy hyperspectral imaging. 2 SGblock = block specific gravity, SGlumber = lumber specific gravity, MOE = modulus of elasticity (GPa), MOR = modulus of rupture (MPa). SECV: standard error of cross-validation; RPDcv: ratio of performance to deviation for the cross-validation; R\({}_{\text{p}}\)2: R2 for predictions of each property; SEP: standard error of prediction; RPDp: ratio of performance to deviation for the prediction set.  
\end{table} 
Table 2: Near-infrared spectroscopy and hyperspectral imaging results of block specific gravity, lumber specific gravity, lumber modulus of elasticity, and lumber modulus of rupture for the calibration and prediction sets. Figure 3: Quadratic and linear models and plots to predict modulus of elasticity (MOE) and modulus of rupture (MOR) with lumber specific gravity and MOE. RMSE: root mean square error. For MOE, six factors were chosen for the cross-validated NIR model and five factors for the NIR-HSI model. When used to predict MOE, the NIR model gave a \(\text{R}_{\text{p}}{}^{2}\) of 0.56 and a SEP of 1.9 GPa, while the NIR-HSI model was slightly better (\(\text{R}_{\text{p}}{}^{2}\) = 0.62, SEP = 1.8 GPa) (Figure 6). The bias was similar for the NIR and the NIR-HSI models. For the NIR-based MOR calibration, seven factors were chosen,  
Figure 4: Calibration and prediction plots of block specific gravity for near-infrared spectroscopy and hyperspectral imaging. The dashed blue line indicates the line of equivalence. Figure 5: Calibration and prediction plots of lumber specific gravity for near-infrared spectroscopy and hyperspectral imaging. The dashed blue line indicates the line of equivalence. while six factors were recommended for the NIR-HSI model. When used to predict MOR, the NIR model gave a \(\text{R}_{\text{p}}\)\({}^{2}\) of 0.40 and a SEP of 13.7 MPa, which was inferior to the NIR-HSI MOR model (\(\text{R}_{\text{p}}\)\({}^{2}\) = 0.49, SEP = 12.7 MPa) (Figure 7). The bias was similar for the NIR and the NIR-HSI models. Figure 6: Calibration and prediction plots of modulus of elasticity (MOE) for near-infrared spectroscopy and hyperspectral imaging. The dashed blue line indicates the line of equivalence. Figure 7: Calibration and prediction plots of modulus of rupture (MOR) for near-infrared spectroscopy and hyperspectral imaging. The dashed blue line indicates the line of equivalence. The PLS model for the NIR-HSI system was then used to visualize the spatial variation in SG, MOE, and MOR. Figure 8 illustrates the predictions on a sample containing the pH with wide growth rings, thus being indicative of low-stiffness corewood, while Figure 9 illustrates the predictions on a sample containing narrow growth rings, thus being indicative of high-stiffness outerwood.