Article Figures & Data
Figures
- Figure 1.
Distribution of lesion lengths on Austrian pine shoots measured seven days following inoculation with Diplodia sapinea. Trees belonging to the first and fourth quartiles are marked, which correspond to resistant and susceptible groups, respectively.
- Figure 2.
Comparison of lesion lengths (± standard error) on Austrian pine shoots measured seven days following inoculation with Diplodia sapinea. Inoculated trees belonged to the resistant (first quartile) and susceptible (fourth quartile) groups based on lesion length distribution. Lettering denotes that resistant trees (N = 21) have significantly smaller lesions than susceptible trees (N = 21) as determined using Welch’s two sample t-test at the α = 0.05 level.
- Figure 3.
Transformed spectra from 698.6 to 1868.0 cm−1 (left axis: blue = resistant trees and gray = susceptible trees) overlaid with SIMCA discriminating power (right axis: dashed line)(N = 29). Spectral features with higher discriminating powers are more important for distinguishing between resistant and susceptible trees.
- Figure 4.
Coomans (i.e., class distances) plot showing the dimensionless distance between each sample and class—resistant and susceptible. Dashed lines indicate critical sample residual thresholds. Blue diamonds = resistant trees (N = 18). Gray diamonds = susceptible trees (N = 11). Samples in the top left quadrant are always classified as resistant. Samples in the bottom right quadrant are always classified as susceptible. Samples in the bottom left quadrant could be either resistant or susceptible. Every sample could be classified by the model, so the top right quadrant is empty.
- Figure 5.
Measured versus predicted lesion lengths (in mm) based on PLSR analysis of spectral data from 802.8 to 1852.6 cm−1. Blue diamonds = resistant trees (N = 16). Green diamonds = intermediate trees (N = 30). Gray diamonds = susceptible trees (N = 10). The correlation coefficient of cross-validation (rval) is 0.60.
- Figure 6.
Receiver operating characteristic (ROC) curve for support vector machine analysis showing the relationship between the false positive rate and the true positive rate. The area under the curve (AUC) is 0.929.
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