通过调整源校准模型进行本地建模,以分析没有参考值的转移目标域样本
Jordan M J Peper1, John H Kalivas1
1Department of Chemistry, Idaho State University, Pocatello, Idaho, USA.
Applied spectroscopy
|June 6, 2024
概括
这项研究介绍了NARCA,一种用于光谱校准的新型回归转移学习方法. 纳尔卡有效地将模型更新到新的样本域,使用未标记的重复光谱,提高预测准确性.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 光谱多变量校准模型预测分析剂量,但当目标样本从校准领域转移时失败.
- 用新的参考值进行重新校准可能是昂贵和具有挑战性的.
- 现有的方法,如回归域适应,与分析物分布的重大变化作斗争.
研究的目的:
- 开发一种更强大的回归转移学习方法,用于更新光谱校准模型以转移目标领域.
- 解决分析化学中现有的模型更新技术的局限性.
主要方法:
- 介绍了NARCA (Null Augmentation Regression Constant Analyte),一种回归转移学习方法. 这是一种回归转移学习方法.
- 纳尔卡利用单个目标样本的未标记的重复光谱来调整现有的校准模型.
- 纳尔卡作为局部建模方法运行,假设重复光谱的分析量几乎是恒定的.
主要成果:
- 在5个近红外 (NIR) 数据集中,NARCA被评估为回归转移学习方法.
- 该方法在更新模型以转移目标域的过程中表现出有效性,而不需要新的分析基准值.
结论:
- 与完全重新校准相比,NARCA提供了一种更简单,可能更具成本效益的模型更新方法.
- 这种方法有望提高光谱校准模型在各种分析场景中的适用性.
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