Silva Fennica

Table 1. Data sets used for developing discriminant model to distinguish seeds of two birch species (Betula spp.), together with number of families included in each species, total number of seeds per species, validation sets and number of outliers excluded during model development.
Data sets	Betula pubescens	Betula pendula
No. of families	15	13
Total No. seeds	750	650
Calibration set	497	399
External test set*	250	250
Outliers	3	1
* External test set is composed of samples from five families that were not part of the calibration model.

Fig. 1. Mean raw reflectance spectra (log 1/R) of Betula pendula and B. pubescens seeds.

Table 2. A statistical summary of discriminant models computed to identify Betula pubescens and B. pendula seeds using full (400–2500 nm), visible (400–780 nm) and near infrared (780–2500 nm) reflectance spectral region.
Model Statistics	Wavelength region (nm)
Model Statistics	400–2500	400–780	780–2500
Number of significant components (A)*	1 + 11	1 + 7	1 + 16
Predictive spectral variation (R²X_p)	0.125	0.138	0.114
Y-orthogonal spectral variation (R²X_o)	0.875	0.861	0.886
Modelled class variation (R²Y)	0.884	0.833	0.924
Predictive power of a model (Q²)	0.877	0.828	0.917
* The first and second values represent the number of components for predictive and orthogonal variations, respectively.

Fig. 2. Score plot for the first predictive (tp_[1]) and orthogonal (to_[1]) components of OPLS-DA model developed in Vis+ NIR range, depecting clear-cut separation of seeds of Betula pubescens (●) and B. pendula (●). Note that the ellipse shows 95% confidence interval according to Hotelling’s test.

Fig. 3. Predicted Class membership of samples in the test set for seeds of Betula pendula and B. pubescens by OPLS-DA models fitted on Vis + NIR (panel A), visible (panel B) and NIR (panel C) regions. Note that the dashed line is threshold for classification (Y_pred > 0.5).

Table 3. Performance of classification model developed for identifying seeds of two birch species (Betula spp.) by multivariate discriminant analysis of NIR reflectance spectra. Sn is class sensitivity, Sp is class specificity, CA and ER are classification accuracy and error rate, respectively.
Species	test set	Sn	Sp	CA	ER
Betula pubescens	Seedlot-1	1.0	1.0	1.0	0.0
	Seedlot-2	1.0	1.0	1.0	0.0
	Seedlot-3	1.0	1.0	1.0	0.0
	Seedlot-4	1.0	1.0	1.0	0.0
	Seedlot-5	1.0	1.0	1.0	0.0
	Mean CA			1.0
	Mean ER				0.0
Betula pendula	Seedlot-1	1.0	1.0	1.0	0.0
	Seedlot-2	1.0	1.0	1.0	0.0
	Seedlot-3	1.0	1.0	1.0	0.0
	Seedlot-4	1.0	1.0	1.0	0.0
	Seedlot-5	1.0	1.0	1.0	0.0
	Mean CA			1.0
	Mean ER				0.0

Fig. 4. Variable influence on Projection (VIP) plots depicting absorption bands accounted for identification of seeds of Betula pendula and B. pubescens by discriminant modelling of VIS (Panel A) and NIR (Panel B) spectral regions. The dashed line shows the threshold of significant contribution in model (VIP = 1).