预测土壤中的度使用分数顺序导数和可见近红外光谱指数
Jianfei Cao1, Wei Liu2, Yongyu Feng3
1College of Geography and Environment, Shandong Normal University, Jinan, China.
PloS one
|August 1, 2024
概括
分数顺序导数 (FOD) 预处理增强了可见近红外 (Vis-NIR) 光谱和土壤 (Ni) 度之间的相关性. 这种方法改善了重金属检测和土壤污染监测.
科学领域:
- 环境科学 环境科学
- 频谱学是一种光谱学.
- 地质化学 地质化学
背景情况:
- 准确的土壤重金属监测对于污染控制至关重要.
- 由于弱光谱重金属相关性和光谱干扰,如基线漂移和重叠的峰值,光谱学面临挑战.
- 使用传统的光谱方法很难预测重金属污染风险.
研究的目的:
- 提出一种用于检测土壤中的 (Ni) 的新方法,使用分数顺序导数 (FOD) 预处理和光谱指数.
- 改善可见近红外 (Vis-NIR) 光谱与土壤Ni度之间的相关性.
- 为土壤Ni检测开发一个准确的估计模型.
主要方法:
- 采集了土壤样本的可见近红外 (Vis-NIR) 光谱.
- 分数顺序导数 (FOD) 预处理 (顺序0到2,间隔0.1) 应用于Vis-NIR光谱,以减少光谱干扰.
- 计算和比较了包括产品指数 (PI),比率指数 (RI),总和指数 (SI),差异指数 (DI),规范差异指数 (NDI) 和亮度指数 (BI) 在内的各种光谱指数.
- 在FOD处理后使用表现最好的光谱指数构建了一个估计模型.
主要成果:
- FOD预处理有效地增强了光谱细节,并减少了基线漂移和重叠的峰值干扰.
- 规范差异指数 (NDI) 显示出与Ni度的最强相关性 (r=0.803) 在FOD级别为1.9.的Ni度.
- 基于FOD处理的NDI数据的估计模型实现了高性能,验证准确度为0.735,RMSE为3.848,RPD为2.423.
结论:
- FOD预处理显著提高了对Ni度的光谱灵敏度,增强了Vis-NIR光谱和土壤Ni之间的相关性.
- 拟议的方法结合了FOD和NDI,为土壤Ni估计提供了有效和准确的方法.
- 这种技术在土壤污染评估中对其他重金属元素的监测具有实用性和适应性.
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