自扩展全信息优化策略:用于近红外频谱自动分析的方便和高效方法
Shenghao Wang1, Manman Lin1, Yanhong Meng1
1School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China.
概括
本研究引入了一种用于近红外 (NIR) 频谱预处理的新自动化方法. 自扩展的完整信息优化策略提高了准确性,并简化了化学测量应用的模型开发.
科学领域:
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
- 数据分析 数据分析
背景情况:
- 频谱预处理对于近红外 (NIR) 光谱的精度至关重要.
- 手动预处理方法耗时且容易出现错误,特别是对于经验较少的用户来说.
- 现有的自动化方法在确定预处理序列和提供统计信息方面存在局限性.
研究的目的:
- 开发用于NIR数据的自动化频谱分析方法.
- 为了应对预处理序列确定,优化结果波动和统计信息方面的挑战.
- 建立一个可靠和有效的近红外自动建模方法.
主要方法:
- 引入自我扩展的完整信息优化策略.
- 使用内置模块进行信息生成和频谱处理.
- 使用蒙特卡洛交叉验证来搜索预处理技术和波长选择算法.
- 使用优化的预处理方案构建集成校准模型.
主要成果:
- 提出的方法成功地提取了光谱信息,并开发了校准模型.
- 在两组现实世界NIR光谱数据上证明了有效性.
- 该战略为模型评估提供客观的统计信息.
结论:
- 自扩展的全信息优化策略为NIR频谱自动分析提供了一个强大的开源解决方案.
- 它可以同时解决预处理序列,优化和统计信息问题.
- 该方法表明,它可以广泛应用于其他光谱技术,如拉曼光谱和红外光谱.
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