根据LIBS和HSI的多来源数据融合,精确的定量检测 (Na) 含量在根中
Xiaohong Li1, Zhuopin Xu2, Pengfei Zhang2
1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China.
Food chemistry
|July 15, 2025
概括
一个新的FusionNet模型结合了激光诱导分解光谱 (LIBS) 和近红外高光谱成像 (NIR-HSI) 准确地检测了在根中的. 数据增强改善了盐土壤改善的模型性能.
科学领域:
- 植物科学 植物科学
- 分析化学 分析化学
- 土壤科学 土壤科学
背景情况:
- 植物含量对于盐土壤修复至关重要.
- 目前的金属元素检测方法复杂且耗时.
研究的目的:
- 开发一种定量检测模型,用于 (Na) 元素含量在根.
- 整合激光诱导分解光谱 (LIBS) 和近红外高光谱成像 (NIR-HSI) 进行增强的检测.
- 用数据增强来解决小样本数据集限制.
主要方法:
- 开发了FusionNet,这是一个整合LIBS和NIR-HSI的定量检测模型.
- 雇佣生成对抗网络 (GAN) 用于数据增强以增加样本多样性.
- 使用交叉验证 (cv) 和预测 (p) 度量 (R2,RMSE) 验证的模型性能.
主要成果:
- 数据增强显著提高了数据集多样性和模型性能.
- 通过FusionNet实现了高精度,R2cv为0.9915和R2p为0.9808.
- 与LIBS单独相比,FusionNet表现出显著的改善,R2cv增加了4.94%和R2p增加了5.61%.
结论:
- FusionNet提供了一种有效和高效的方法来检测植物中的金属元素.
- 集成的LIBS和NIR-HSI方法为植物分析提供了一个有前途的解决方案.
- 这项研究推进了监测植物健康和土壤状况的技术.
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