贝卢加优化算法用于近红外光谱变量选择复杂样品的近红外光谱变量.
Javaria Kousar1, Liping Yang1, Jiale Xiang2
1State Key Laboratory of Advanced Separation Membrane Materials, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China.
Foods (Basel, Switzerland)
|December 30, 2025
概括
使用部分最小平方 (PLS) 的离散白优化 (DBWO) 改进了复杂样本的定量分析. 这种光谱分析方法,DBWO-PLS,优于其他变量选择技术,提高了预测准确度.
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) 光谱对于复杂样品的定量分析至关重要.
- 高维的光谱数据往往含有冗余性,需要为准确的建模选择变量.
- 优化算法对于提高多变量校准的效率和准确性至关重要.
研究的目的:
- 评估离散白优化 (DBWO) 算法在NIR光谱定量分析中对变量选择的有效性.
- 为了比较DBWO与部分最小方程 (PLS) 结合的预测性能与其他已建立的变量选择方法.
- 证明DBWO-PLS用于分析小麦,片剂和可可豆等复杂样品的应用.
主要方法:
- 实现了对变量选择的离散白优化 (DBWO) 算法.
- 使用选定变量开发了部分最小平方 (PLS) 模型.
- 将DBWO-PLS与全频谱PLS,随机化试验 (RT) -PLS,无信息变量消除 (UVE) -PLS和蒙特卡罗无信息变量消除 (MC-UVE) -PLS进行比较.
- 优化了DBWO参数,包括代数和转移函数.
主要成果:
- 所有测试的变量选择方法 (DBWO,RT,UVE,MC-UVE) 与全频谱分析相比,提高了PLS模型的预测准确性.
- DBWO-PLS模型在小麦,平板电脑和可可豆样本分析中显示出优异的预测性能.
- DBWO表现出快速的融合,高精度,并且需要最小的参数,使其对光谱数据分析高效.
结论:
- 离散的白优化算法是NIR光谱定量分析中变量选择的高效方法.
- DBWO-PLS为分析复杂样品提供了强大而准确的方法,性能优于传统方法.
- 这项研究突出了生物灵感优化算法的潜力,以推进光谱数据的化学计量建模.
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