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.

在育种中纳入化学性状需要估计定量遗传参数，尤其是加性遗传变异的水平。这需要来自纯血统种群的大量样本。用于化学分型的常规湿化学程序缓慢，昂贵且不是实际的选择。这项研究侧重于辐射松的化学变化，其中研究了将茉莉酸甲酯（MJ）和人工剥皮（剥离）处理前后的针，树皮和根的近红外（NIR）光谱特性，作为替代方法。目的是测试NIR光谱仪的能力（i）区分暴露于MJ的样品并在处理后第7、14、21和28天与未处理的样品进行剥离评估，以及（ii）定量预测三个工厂部分中的单个化合物。使用光谱数据的主成分分析（PCA），我们可以区分出植物各个部分的处理过的样品和未处理过的样品。根据偏最小二乘判别分析（PLS-DA）模型，²）在新鲜的针（取得- [R ²  = 0.81，RMSECV = 0.24）和新鲜的内树皮（R ² 分别= 0.79，RMSECV = 0.25）为MJ处理的样品处理14天，后21天，。使用偏最小二乘回归，单个化合物的模型给出了果糖的高（r ²），残留预测偏差（RPD），实验室对NIR误差（PRL）或范围误差比（RER）（r ²  = 0.84，RPD = 1.5， PRL = 0.71，RER = 7.25）和葡萄糖（r ²  = 0.83，RPD = 1.9，PRL = 1.14，RER = 8.50）和几个二萜。这为基于近红外光谱的模型用于辐射假单胞菌的大规模预测提供了乐观的前景 定量遗传研究所需的化学。