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Spectrometric prediction of nitrogen content in different tissues of slash pine trees

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journal contribution
posted on 2023-05-04, 11:07 authored by Yanjie Li, Honggang Sun, Federico TomasettoFederico Tomasetto, Jingmin Jiang, Qifu Luan
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 R2C and R2CV 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.


Rights statement

Copyright © 2022 Yanjie Li et al. Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).


  • English

Does this contain Māori information or data?

  • No


American Association for the Advancement of Science

Journal title

Plant Phenomics




Li, Y., Sun, H., Tomasetto, F., Jiang, J., & Luan, Q. (2022). Spectrometric prediction of nitrogen content in different tissues of slash pine trees. Plant Phenomics, 2022, 9892728.

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