Table 2.
Optimal number of principal components and explained total variance of principal component analysis model
| NIR data type | Larch | Red pine | Korean pine | Cedar | Cypress |
| PCs* | stot** | PCs | stot | PCs | stot | PCs | stot | PCs | stot |
| Raw | 2 | 99.33% | 2 | 99.10% | 2 | 99.28% | 2 | 98.71% | 1 | 98.98% |
| SNV*** | 7 | 97.55% | 7 | 96.73% | 6 | 96.05% | 5 | 97.00% | 6 | 98.32% |
| SG 2nd**** | 7 | 91.30% | 9 | 91.11% | 9 | 91.93% | 8 | 89.54% | 6 | 91.11% |
optimal number of principal components
explained total variance containing in optimal number of principal components
Standard normal variate
Savitzky−Golay 2nd derivatives