Gao Zhen, Zhao Chunjiang, Dong Daming, Liu Songzhong, Wen Xuelin, Gu Yifan, Jiao Leizi
College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Front Plant Sci. 2023 Jan 12;13:1079660. doi: 10.3389/fpls.2022.1079660. eCollection 2022.
Owing to iron chlorosis, pear trees are some of the most severely impacted by iron deficiency, and they suffer significant losses every year. While it is possible to determine the iron content of leaves using laboratory-standard analytical techniques, the sampling and analysis process is time-consuming and labor-intensive, and it does not quickly and accurately identify the physiological state of iron-deficient leaves. Therefore, it is crucial to find a precise and quick visualization approach for metabolites linked to leaf iron to comprehend the mechanism of iron deficiency and create management strategies for pear-tree planting. In this paper, we propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves. According to our findings, iron deficiency significantly decreased the Raman peak intensities of chlorophylls and lipids in leaves. The spatial distributions of chlorophylls and lipids in the leaves changed significantly as the symptoms of iron insufficiency worsened. The technique offers a new, prospective tool for rapid recognition of iron deficiency in pear trees because it is capable of visual detection of plant physiological metabolites induced by iron deficiency.
由于缺铁黄化病,梨树是受缺铁影响最严重的树种之一,每年都遭受重大损失。虽然可以使用实验室标准分析技术测定叶片中的铁含量,但采样和分析过程既耗时又费力,而且无法快速准确地识别缺铁叶片的生理状态。因此,找到一种精确、快速的与叶片铁相关代谢物的可视化方法对于理解缺铁机制和制定梨树种植管理策略至关重要。在本文中,我们提出了一种用于对梨树叶中与缺铁相关代谢物进行无损、快速、精确视觉表征的显微拉曼光谱成像方法。根据我们的研究结果,缺铁显著降低了叶片中叶绿素和脂质的拉曼峰强度。随着缺铁症状加重,叶片中叶绿素和脂质的空间分布发生了显著变化。该技术能够可视化检测缺铁诱导的植物生理代谢物,为快速识别梨树缺铁提供了一种新的、有前景的工具。