可见光和近红外光谱测定甘的叶绿素含量,使用修改波长选择方法进行多变量校准
Pauline Ong1, Jinbao Jian2, Xiuhua Li3
1College of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Minzu University, Nanning 530006, China; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor.
概括
一种新的修改回归系数 (MRC) 方法有效地选择可见光和近红外 (Vis-NIR) 光谱的波长. 这提高了甘叶中的叶绿素含量预测,优于其他方法.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 频谱学是一种光谱学.
背景情况:
- 可见和近红外 (Vis-NIR) 光谱对于植物叶绿素评估至关重要.
- 频谱变化和干扰对准确的Vis-NIR分析构成挑战.
- 具有特征的波长选择对于提高诊断准确性至关重要.
研究的目的:
- 引入一种新的修改回归系数 (MRC) 波长选择方法.
- 提高使用Vis-NIR光谱学对甘叶中的叶绿素含量量定量的准确性.
- 将MRC的性能与其他波长选择技术和回归模型进行比较.
主要方法:
- 从不同生长阶段的甘叶收集了光谱反射率数据 (220-1400 nm).
- 应用了修改后退系数 (MRC) 方法来进行最佳波长选择.
- 使用选定的波长开发了部分最小平方回归 (PLSR) 和高斯过程回归 (GPR) 模型.
主要成果:
- 简化的MRC-GPR模型使用291个波长,显示出卓越的性能.
- 实现了0.9665 (校准) 和0.8659 (预测) 的高确定系数 (R2).
- 已证明的下根平均平方误差 (RMSE) 为1.7624 (校准) 和3.2029 (预测).
- 高斯过程回归 (GPR) 的表现优于部分最小平方回归 (PLSR).
结论:
- 在Vis-NIR光谱学中,MRC方法对于选择特征波长是有效的.
- 该MRC-GPR模型提供了精确可靠的甘叶绿素含量的预测.
- 像GPR这样的非线性回归方法对于复杂的光谱数据分析是有利的.
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