应用FT-NIR光谱法来通过进化化学计量预测土壤中污染
Shaoyong Hong1, Zhanhong Liang1, Huazhou Chen2
1School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, China.
PloS one
|January 27, 2026
概括
富里埃变换近红外 (FT-NIR) 技术与新的二元修改差异进化-参数缩放支持矢量机 (BDE-PSSVM) 系统相结合,使用更少的光谱变量有效估计土壤 (Cr) 污染.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 传统的土壤 (Cr) 检测方法耗时,可能无法捕获复杂的光谱关系.
- 福利埃变换近红外 (FT-NIR) 谱法提供了一个快速的替代方案,但需要有效的光谱特征选择和模型优化.
- 非线性动态和FT-NIR光谱中的数据冗余使精确的Cr含量估计变得复杂.
研究的目的:
- 开发和验证一种新的综合优化系统,用于使用FT-NIR光谱数据快速估计土壤Cr含量.
- 为了同时优化建模参数和选择有效的光谱特征变量.
- 将拟议系统的性能与土壤Cr检测的其他建模方法进行比较.
主要方法:
- 对于光谱特征变量选择,使用了二进制修改差分进化 (BDE) 算法.
- 用一个参数缩放支持向量机 (PSSVM) 来优化建模参数.
- BDE-PSSVM系统集成了这些方法,将PSSVM模型嵌入到BDE代过程中,用于同时优化.
- 萨维茨基-戈莱平滑和SPXY算法分别用于光谱数据预处理和样本集分割.
主要成果:
- 开发的BDE-PSSVM模型实现了8.114.4的最小根平均平方误差.
- 最佳模型只识别并使用了56个离散的光谱变量.
- 与其他建模方法相比,BDE-PSSVM系统展示了优越的预测结果.
- 该系统使用FT-NIR光谱数据有效地估计了土壤中的Cr含量,使用了功能变量减少的特征变量.
结论:
- 拟议的BDE-PSSVM建模系统提供了一种高效且准确的方法,可以通过FT-NIR光谱学快速估计土壤中的Cr含量.
- 这种综合优化策略有效地解决了光谱数据冗余和复杂的非线性动态.
- 该系统在未来应用中检测额外的分析物的潜力是有希望的.
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