通过多任务贝叶斯式优化增强LC×LC分离
Jim Boelrijk1, Stef R A Molenaar2, Tijmen S Bos2
1AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands; AMLab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands.
Journal of chromatography. A
|May 15, 2024
概括
多任务贝叶斯优化 (MTBO) 简化了全面的二维液态色谱 (LC×LC) 方法开发. 这种方法结合了保留建模和实验数据,与复杂分析的传统方法相比,大大减少了代.
科学领域:
- 分析化学 分析化学
- 染色体学 染色体学 是一种染色学.
背景情况:
- 在全面的二维液态色谱 (LC×LC) 中的方法开发是复杂的,因为相互依赖和梯度形状.
- 传统的试错方法耗时且依赖于经验.
- 保留建模和贝叶斯优化 (BO) 提供解决方案,但也有局限性.
研究的目的:
- 调查多任务贝叶斯优化 (MTBO) 对LC×LC方法开发的有效性.
- 将保留模型和实验测量的优势结合在一个优化框架中.
- 为了应对复杂样品和LC×LC中的众多可调节参数的挑战.
主要方法:
- 开发并测试了一种多任务贝叶斯优化 (MTBO) 算法.
- 使用合成保留模型测试案例,将MTBO与传统贝叶斯优化 (BO) 进行比较.
- 应用MTBO以优化农药分析的LC×LC方法.
主要成果:
- 在合成测试案例中,MTBO在较少的代过程中发现了比传统的BO更好的最佳.
- MTBO成功地改善了用于农药分析的初始扫描实验.
- 该算法在优化复杂的LC×LC方法方面表现出有效性.
结论:
- MTBO是优化LC×LC方法的一个有希望的技术,特别是当保留模型很难时.
- 在有限的实验预算下,MTBO为高参数LC×LC优化提供了实用的解决方案.
- 这种方法提高了染色学方法开发的效率和有效性.
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