基于LIBS和NIR-HSI联用的植物金属元素快速可视化检测方法研究

植物生长发育需要多种金属元素的参与，实现植物体内金属元素动态分布的可视化检测有着极为重要的生物学意义。然而，目前的检测手段普遍存在过程繁琐、价格昂贵等不足，制约了其在植物研究中的推广使用。激光诱导击穿光谱（LIBS）可视化检测具有精确快速、价格低廉、操作简单等优点，但应用于植物分析会产生严重的基体效应并导致光谱复杂化。基于此，本项目的目标是：1）将近红外高光谱（NIR-HSI）与LIBS联用以解决基体效应影响；2）采用高效数据处理算法解析光谱复杂性。对应的研究如下：①设计微聚焦系统，确定各参数对空间分辨率影响；②联用NIR-HSI和LIBS数据，开发基体效应校正方法；③比对研究3种融合算法，获取最优算法；④结合数据库、数据处理和化学计量学，解析复杂光谱；⑤基于检测平台，研究Cd在水稻中运转和积累规律。上述研究旨在实现植物金属元素的精确、快速和可视化检测，为微尺度植物元素研究提供重要支撑。

The growth and development of plants require the participation of multiple metal elements, and the visual detection of the dynamic distribution of metal elements in plants has extremely important biological significance. However, the current detection methods generally have the disadvantages of tedious processes and high prices, which restrict their popularization and use in plant research. The laser-induced breakdown spectroscopy (LIBS) visual detection has the advantages of accurate, fast, low-cost, and simple operation, but applied to plant analysis will produce serious matrix effects and cause spectrum complication. Accordingly, the aims of this project are: 1) to combine Near Infrared Hyperspectral Imaging (NIR-HSI) with LIBS to resolve matrix effects; 2) to analyze spectral complexity using efficient data processing algorithms. The corresponding research is as follows: ① Design a micro-focusing system to determine the influence of various parameters on spatial resolution; ② Use NIR-HSI and LIBS data to develop matrix effect correction methods; ③ Compare the three fusion algorithms to obtain the optimal algorithm; ④ Combine databases, data processing, and chemometrics to analyze complex spectra; ⑤ Based on the detection platform, the law of Cd operation and accumulation in rice is studied. The above research aims to achieve accurate, rapid, and visual detection of plant metal elements, and to provide important support for micro-scale plant element research.