eCite Digital Repository

Developing near infrared spectroscopy models for predicting chemistry and responses to stress in Pinus radiata (D. Don)


Nantongo, JS and Potts, BM and Rodemann, T and Fitzgerald, H and Davies, NW and O'Reilly-Wapstra, JM, Developing near infrared spectroscopy models for predicting chemistry and responses to stress in Pinus radiata (D. Don), Journal of Near Infrared Spectroscopy pp. 1-12. ISSN 0967-0335 (2021) [Refereed Article]

Copyright Statement

Copyright The Author(s) 2021

DOI: doi:10.1177/09670335211006526


Incorporating chemical traits in breeding requires the estimation of quantitative genetic parameters, especially the levels of additive genetic variation. This requires large numbers of samples from pedigreed populations. Conventional wet chemistry procedures for chemotyping are slow, expensive and not a practical option. This study focuses on the chemical variation in Pinus radiata, where the near infrared (NIR) spectral properties of the needles, bark and roots before and after exposure to methyl jasmonate (MJ) and artificial bark stripping (strip) treatments were investigated as an alternative approach. The aim was to test the capability of NIR spectroscopy to (i) discriminate samples exposed to MJ and strip assessed 7, 14, 21 and 28 days after treatment from untreated samples, and (ii) quantitatively predict individual chemical compounds in the three plant parts. Using principal components analysis (PCA) on the spectral data, we differentiated between treated and untreated samples for the individual plant parts. Based on partial least squares–discriminant analysis (PLS-DA) models, the best discrimination of treated from non-treated samples with the smallest root mean square error cross-validation (RMSECV) and highest coefficient of determination (r2) was achieved in the fresh needles (r2 = 0.81, RMSECV= 0.24) and fresh inner bark (r2 = 0.79, RMSECV = 0.25) for MJ-treated samples 14 days and 21 days after treatment, respectively. Using partial least squares regression, models for individual compounds gave high (r2), residual predictive deviation (RPD), lab to NIR error (PRL) or range error ratio (RER) for fructose (r2 = 0.84, RPD = 1.5, PRL = 0.71, RER = 7.25) and glucose (r2 = 0.83, RPD = 1.9, PRL = 1.14, RER = 8.50) and several diterpenoids. This provides an optimistic outlook for the use of NIR spectroscopy-based models for the larger-scale prediction of the P. radiata chemistry needed for quantitative genetic studies.

Item Details

Item Type:Refereed Article
Keywords:near infrared, chemistry, Pinus radiata, plant defence
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Forestry sciences
Research Field:Forestry management and environment
Objective Division:Plant Production and Plant Primary Products
Objective Group:Forestry
Objective Field:Softwood plantations
UTAS Author:Nantongo, JS (Mrs Judith Nantongo)
UTAS Author:Potts, BM (Professor Brad Potts)
UTAS Author:Rodemann, T (Dr Thomas Rodemann)
UTAS Author:Fitzgerald, H (Mr Hugh Fitzgerald)
UTAS Author:Davies, NW (Associate Professor Noel Davies)
UTAS Author:O'Reilly-Wapstra, JM (Professor Julianne O'Reilly-Wapstra)
ID Code:145974
Year Published:2021
Funding Support:Australian Research Council (LP140100602)
Web of Science® Times Cited:5
Deposited By:Plant Science
Deposited On:2021-08-14
Last Modified:2021-09-02

Repository Staff Only: item control page