Table 1. A statistical summary of four-class models developed using the full (780–2500 nm) shorter (780–1100 nm) and longer (1100–2500 nm) NIR regions for simultaneously classifying empty, insect-attacked, shriveled and filled-viable seeds of J. polycarpos. | ||||||

A) PLS-DA | ||||||

Spectra (nm) | A | R^{2}X | R^{2}Y | Q^{2 }_{cv} | Pred_{test} (%) | |

780–2500 | 12 | 0.999 | 0.722 | 0.658 | 66 | |

780–1100 | 10 | 0.999 | 0.699 | 0.669 | 71 | |

1100–2500 | 14 | 0.999 | 0.750 | 0.686 | 70 | |

B) OPLS-DA | ||||||

Spectra (nm) | A | R^{2}X_{p} | R^{2}X_{o} | R^{2}Y | Q^{2 }_{cv} | Pred_{test} (%) |

780–2500 | 3 + 8 | 0.274 | 0.726 | 0.695 | 0.664 | 69 |

780–1100 | 3 + 6 | 0.289 | 0.711 | 0.698 | 0.667 | 71 |

1100–2500 | 3 + 8 | 0.275 | 0.725 | 0.671 | 0.638 | 64 |

A = number of significant components to build the model (for OPLS-DA models , the first value is for predictive component and the second value is for the orthogonal component), R^{2}X = the explained spectral variation (1 – SS(E)/SS(X)), R^{2}Y = the variation between seed classes explained by the model (1 – SS(F)/SS(Y)), R^{2}X_{p} = the predictive spectral variation; R2Xo = Y-orthogonal variation (spectral variation uncorrelated to class discrimination), Q^{2}_{cv} = the predictive power (the predicted variation) of a model according to cross validation, and Pred_{test} = the overall prediction accuracy of the models for the test set. |

Table 2. A matrix of predicted class membership of seed lot fractions in the test set (n = 40 for each seed lot fraction) by four-class PLS-DA and OPLS-DA (values in parenthesis) models developed using different NIR spectral regions. View in new window/tab. |

Table 3. A statistical summary of two-class models developed using the entire (780–2500 nm) shorter (780–1100 nm) and longer (1100–2500 nm) NIR regions for discriminating viable and non-viable seed of J. polycarpos. | ||||||

A) PLS-DA | ||||||

Spectra (nm) | A | R^{2}X | R^{2}Y | Q^{2 }_{cv} | Pred_{test} (%) | |

780–2500 | 11 | 0.999 | 0.913 | 0.871 | 99 | |

780–1100 | 6 | 0.999 | 0.927 | 0.915 | 99 | |

1100–2500 | 7 | 0.999 | 0.914 | 0.888 | 99 | |

B) OPLS-DA | ||||||

Spectra (nm) | A | R^{2}X_{p} | R^{2}X_{o} | R^{2}Y | Q^{2 }_{cv} | Pred_{test} (%) |

780–2500 | 1 + 10 | 0.0312 | 0.969 | 0.916 | 0.905 | 99 |

780–1100 | 1 + 9 | 0.0683 | 0.932 | 0.927 | 0.920 | 99 |

1100–2500 | 1 + 8 | 0.0121 | 0.988 | 0.892 | 0.878 | 99 |

A = number of significant components to build the model (for OPLS-DA models , the first value is for predictive component and the second value is for the orthogonal component), R^{2}X = the explained spectral variation, R^{2}Y = between-class variation explained by the model, R^{2}X_{p} = the predictive spectral variation; R^{2}Xo = spectral variation uncorrelated to class discrimination, Q^{2}_{cv} = the predictive power of a model according to cross validation, and Pred_{test} = the overall prediction accuracy of the models for the test set. |

Table 4. Discrimination of non-viable (empty, insect-attacked and shriveled) and viable seeds in the test set by two-class PLS-DA modelling of different NIR spectral region. | |||||||

Spectra (nm) | Class | Members | 1 | 2 | No class | 1 & 2 | Correct |

780–2500 | Non-viable (1) | 120 | 120 | 0 | 0 | 0 | 100% |

Viable (2) | 40 | 1 | 39 | 0 | 0 | 97.5% | |

780–1100 | Non-viable (1) | 120 | 120 | 0 | 0 | 0 | 100% |

Viable (2) | 40 | 1 | 39 | 0 | 0 | 97.5% | |

1100–2500 | Non-viable (1) | 120 | 120 | 0 | 0 | 0 | 100% |

Viable (2) | 40 | 1 | 39 | 0 | 0 | 97.5% |