J. D. Sauerländer's Verlag: (13) Humphreys
   

Abstract

Identification of plant hybrids produced from closely related species can be difficult using morphological characteristics alone, particularly when identifying young seedlings. In this study, we compared the performance of three calibration models developed to discriminate between seedlings of Eucalyptus globulus, E. nitens and their first-generation hybrid using either foliar oil chemistry or near-infrared reflectance spectral data from fresh, whole leaves. Both oil and near-infrared reflectance spectroscopy (NIRS) models were developed using partial least-squares discriminant analysis and showed high classification accuracy, all correctly classifying more than 91% of samples in cross-validation. Additionally, we developed a larger, “global” and independently validated NIRS model specifically to discriminate between E. globulus and F1 hybrid seedlings of different ages. This model correctly classified 98.1% of samples in cross-validation and 95.1% of samples from an independent test set. These results show that NIRS analysis of fresh, whole leaves can be used as a rapid and accurate alternative to chemical analysis for the purpose of hybrid identification.

Key words: eucalypt, spectra, 1,8-cineole, NIRS, fresh leaves, discriminant analysis, partial least-squares regression.

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