Li Yanjie, Sun Honggang, Tomasetto Federico, Jiang Jingmin, Luan Qifu
Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, Zhejiang 311400, China.
AgResearch Ltd., Christchurch 8140, New Zealand.
Plant Phenomics. 2022 Jan 12;2022:9892728. doi: 10.34133/2022/9892728. eCollection 2022.
The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R and R of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the content in plant tissues. This study evaluates a rapid and efficient method for the estimation of content in different tissues that can help to serve as a tool for tree storage and recompilation study.
树木中氮（N）储存与消耗的内部循环是一种与树木生长相关的重要生理机制。在此，我们研究了近红外光谱（NIR）在无需耗费时间和成本的情况下对不同组织类型（针叶、树干、树枝和根系）中氮浓度进行量化的能力。收集了湿地松不同组织的近红外光谱数据，并在实验室中使用标准分析方法测定了每个组织中的氮浓度。对随机选择的一组训练数据进行了偏最小二乘回归（PLSR）模型分析。使用全长光谱和选择的显著多元相关（sMC）变量光谱进行模型校准。使用全长光谱和sMC选择的近红外光谱时，树枝、针叶和树干的PLSR模型对氮浓度的预测效果良好。通用模型使用全长光谱时的R²和R²分别为0.62和0.66，使用sMC选择光谱时分别为0.61和0.65，具有可靠的准确性。当在其他组织中使用时，单个组织模型的表现不佳。发现五个显著重要区域，即1480、1650、1744、2170和2390 nm，与植物组织中的氮含量高度相关。本研究评估了一种快速有效的方法来估算不同组织中的氮含量，该方法有助于作为树木氮储存和重新编制研究的工具。
