A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

Authors: Luo, Zinan; Thorp, Kelly; Abdel-Haleem, Hussein

Submitted to: Plant Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/9/2019
Publication Date: 12/17/2019
Citation: Luo, Z., Thorp, K.R., Abdel-Haleem, H.A. 2019. A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy. Plant Methods. 15. https://doi.org/10.1186/s13007-019-0544-3.
DOI: https://doi.org/10.1186/s13007-019-0544-3

Interpretive Summary: Guayule, a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to identify guayule cultivars with optimal levels of rubber and resin. We have successfully constructed reliable high-throughput prediction models for the determination of resin and rubber in dry-ground guayule biomass samples using near-Infrared spectroscopy. The prediction power of the models for resin content were better than rubber content and the increased spectral resolution of data. Samples collected from different growing conditions are suggested to be separated for independent model establishment. In general, the established models might be used in the future to form a simple, low-cost and efficient pipeline to maximize the phenotyping efficiency in determining guayule rubber content. The established models could enable guayule breeders to efficiently screen large populations for individuals with superior traits of interests.

Technical Abstract: Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure with- out losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified. Using two different NIR devices, ASD FieldSpec®3 performed better than Polychromix Phazir™ in improving R2 and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750–2500 nm), using a subset of wavelengths (1100–2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content. In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.