Background 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 without 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. Results 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. Conclusions 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.

中文翻译：

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使用近红外 (NIR) 光谱对银白菊中树脂和橡胶含量进行高通量定量。

背景 Guayule (Parthenium argentatum A. Gray) 是一种原产于美国墨西哥北部和德克萨斯州南部半干旱地区的植物，是天然橡胶 (NR) 的替代来源。需要快速筛选工具来替代目前用于量化橡胶和树脂含量的劳动密集型且成本低效的方法。近红外 (NIR) 光谱是一种很有前途的技术，它可以简化和加快量化过程而不会损失精度。在这项研究中，使用两种光谱仪器快速量化 315 个地面样品中的树脂和橡胶含量，这些样品来自亚利桑那州马里科帕在不同灌溉条件下生长的银胶菊种质资源。研究并比较了八种不同预处理方法对使用偏最小二乘回归 (PLSR) 改进预测模型的影响。确定了导致显着吸收峰的重要特征波长。结果 使用两种不同的 NIR 设备，ASD FieldSpec®3 在提高 PLSR 模型的 R2 和剩余预测偏差 (RPD) 值方面的表现优于 Polychromix Phazir™。与基于全范围光谱 (750-2500 nm) 的模型相比，使用对银胶菊橡胶和树脂含量具有高灵敏度的波长子集 (1100-2400 nm) 可以提高预测精度。量化树脂含量模型的预测能力优于橡胶含量。结论 总之，针对树脂和橡胶含量的校准 PLSR 模型已成功开发用于多种银胶菊种质资源收集，并以低成本和高效的方式应用于样品粗略筛选。这种提高的效率可以使育种者快速筛选大量银胶菊种群，以确定橡胶和树脂含量高的品种。