一个三级波长选择算法用于近红外光谱校准近红外光谱学
Xi-Yao Feng1, Zheng-Guang Chen1, Shu-Juan Yi2
1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
概括
一个新的三级波长选择算法 (第三阶段) 有效地减少近红外 (NIR) 光谱数据的冗余性和对线性. 与现有技术相比,这种方法提高了玉米分析的预测模型准确度.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
背景情况:
- 近红外 (NIR) 光谱数据是高维的,通常包含多余的信息.
- 现有的波长选择方法与对线性作斗争,影响模型的准确性和可靠性.
- 有效的波长选择对于开发光谱学中强大的预测模型至关重要.
研究的目的:
- 为NIR光谱数据提出和评估一种新的三级波长选择算法 (第三阶段).
- 为了减少NIR光谱中的数据冗余和波长间对线性.
- 开发一种更简单,更准确的玉米样本分析预测模型.
主要方法:
- 开发了一种三阶段算法,包括相关性分析和逐步回归.
- 第一个阶段:选择与度向量高度相关的波长.
- 第二阶段:选择低相互关联的波长.
- 第三阶段:使用渐进回归来识别用于建模的重要波长.
- 使用所选波长开发了一种多重线性回归 (MLR) 模型.
主要成果:
- 与全频谱,I阶段和II阶段模型相比,III阶段-MLR模型表现出更高的性能.
- 第三阶段的MLR模型在测试组上实现了高的确定系数 (0.9360).
- 第三阶段在预测准确性方面超过了SPA,UVE和CARS等既有方法.
结论:
- 拟议的三阶段波长选择算法对于NIR光谱数据分析是有效的.
- 第三阶段成功地减少了对线性,简化了模型的复杂性,提高了预测精度.
- 这种算法提供了一种有效的方法,以提高精度来建模NIR光谱.
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