水稻叶片理化特性对LIBS信号的影响机制及重金属化学成像方法研究

水稻重金属运移机理研究是重金属抗性品种培育和重金属调控技术开发的基础，有利于保障粮食产量和粮食安全。传统实验室化学分析技术缺少定量检测的空间分析能力，难以获得水稻重金属含量空间分布信息。针对上述问题，以水稻叶片重金属（镉和铬）为研究对象，以常规-显微多尺度激光诱导击穿光谱（LIBS）定量化学成像技术为手段，开展水稻叶片样本理化特性影响机制及重金属LIBS定量化学成像方法研究。项目分别研究水稻叶片基体特性、异质特性和水分特性对常规-显微LIBS信号的影响规律和作用机制，提出谱线信号解析挖掘方法、等离子体信号补偿方法和特征信号辅助增强方法，提高LIBS定量化学成像的精确性、稳定性和灵敏性，建立水稻叶片“整体叶面和局部微区”LIBS重金属定量反演模型，实现水稻叶片重金属分布可视化。本项目拟取得成果可为重金属运移机理研究提供技术支撑，对水稻重金属污染防治和粮食安全具有重要意义。

Study on heavy metals transport mechanism in rice is the foundation for metal-resistance variety cultivation and prevention strategies, which is helpful in guaranteeing grain yield and safety. Due to the lack of spatial quantification capability, the distribution of heavy metals in rice is hard to be obtained by traditional analytical methods. Focusing on this question, we use routine-micro multi-scale laser-induced breakdown spectroscopy (LIBS) to analyze the heavy metals (Cd and Cr) in rice leaves. The influence of rice physicochemical property on LIBS signal is studied, and the distribution of heavy metals in rice leaves is visualized. This project is engaged in investigating the influence mechanism of sample matrix, sample inhomogeneity, moisture content of rice leaves on LIBS signal and detection capability, studying spectral data mining, plasma signal compensation and feature signal enhancement methods to improve detection accuracy, stability and sensitivity. In addition, we concentrate on establishing the LIBS quantification models for whole leaf and micro-region, and visualizing heavy metals in rice leaves. The project provides an approach for heavy metals transport mechanism study, which is important for the heavy metal prevention and food safety in rice.