NIR SPECTROSCOPIC MODELS FOR PHENOTYPING WOOD TRAITS IN BREEDING PROGRAMS OF Eucalyptus benthamii

ABSTRACT Wood characterization must be done in huge populations of Eucalyptus breeding programs in order to efficiently select potential trees. In this study, Eucalyptus benthamii wood was non-destructively characterized and the performance of near infrared (NIR) spectroscopy in estimating the wood basic density, lignin, extractive, glucose, xylan contents and total carbohydrates was evaluated. NIR models for wood traits were performed from 481 trees from E. benthamii progeny test (4-year-old) managed for pulp cultivated in Santa Catarina state, Southern Brazil. Increment cores were sampled for chemical and physical characterization in laboratory, as well as for NIR spectroscopy analyses. Three 350 samples were selected from PCA for model calibrations whereas 131 were reserved for independent test validation. The E. benthamii wood presented the standards required for Kraft pulp processing. The predictive NIR models showed satisfactory ability for estimating the chemical properties of wood. The prediction models for total lignin, extractive and xylan contents and total carbohydrates showed coefficients of determination of 0.53, 0.65; 0.36 and 0.53, with RPD values for these traits ranging from 1.3 to 2.3. The predictive model for basic density of wood and glucose presented low coefficient of determination (0.13 and 0.10). However, isn’t possible to use these models for ranking in genetic selection because there was no correlation. Therefore, NIR spectroscopy can potentially be applied in breeding programs, as it enables an early, non-destructive selection of trees with adequate physical and chemical properties for pulp production process.

摘要 为高效筛选潜力林木，需对桉树育种项目中的大规模群体开展木材表征工作。本研究对本哈姆桉（Eucalyptus benthamii）的木材进行了无损表征，评估了近红外（NIR）光谱技术在预测木材基本密度、木质素、提取物、葡萄糖、木聚糖含量及总碳水化合物方面的性能。本研究基于巴西南部圣卡塔琳娜州纸浆林培育的4年生本哈姆桉子代测定林的481株林木，构建了木材性状的近红外预测模型。研究采集钻取木芯样本用于实验室化学与物理表征，同时开展近红外光谱分析。从主成分分析（PCA）筛选出350个样本用于模型校准，剩余131个样本留作独立测试验证。本哈姆桉木材符合牛皮纸浆（Kraft pulp）加工所需的标准。所构建的近红外预测模型对木材化学性质具备良好的预测能力：总木质素、提取物、木聚糖含量及总碳水化合物的预测模型的决定系数分别为0.53、0.65、0.36和0.53，对应性状的残差预测偏差（RPD）值介于1.3至2.3之间。木材基本密度与葡萄糖含量的预测模型决定系数较低，分别为0.13和0.10，但由于相关性不足，此类模型无法用于遗传选育中的林木排序。因此，近红外光谱技术可应用于桉树育种项目，实现对具备适配纸浆生产所需物理与化学性质的林木进行早期无损筛选。