.png){kind=icon}
返回
Enhancing LC x LC separations through multi-task Bayesian optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.chroma.2024.464941.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Method development in comprehensive two-dimensional liquid chromatography (LC x LC) is a challenging process. The interdependencies between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi -segmented gradients or shifting gradients, make trial -and -error method development time-consuming and highly dependent on user experience. Retention modeling and Bayesian optimization (BO) have been proposed as solutions to mitigate these issues. However, both approaches have their strengths and weaknesses. On the one hand, retention modeling, which approximates true retention behavior, depends on effective peak tracking and accurate retention time and width predictions, which are increasingly challenging for complex samples and advanced gradient assemblies. On the other hand, Bayesian optimization may require many experiments when dealing with many adjustable parameters, as in LC x LC. Therefore, in this work, we investigate the use of multi -task Bayesian optimization (MTBO), a method that can combine information from both retention modeling and experimental measurements. The algorithm was first tested and compared with BO using a synthetic retention modeling test case, where it was shown that MTBO finds better optima with fewer method -development iterations than conventional BO. Next, the algorithm was tested on the optimization of a method for a pesticide sample and we found that the algorithm was able to improve upon the initial scanning experiments. Multi -task Bayesian optimization is a promising technique in situations where modeling retention is challenging, and the high number of adjustable parameters and/or limited optimization budget makes traditional Bayesian optimization impractical.
Keyword:
Bayesian optimization
2D-LC
Closed-loop method development
Machine learning
Shifting gradients
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.2W
被引数:
5.0W
机构
引用论文
Comparison of originator and biosimilar therapeutic monoclonal antibodies using comprehensive two-dimensional liquid chromatography coupled with time-of-flight mass spectrometry.png)
MABSOAAI
MABSIF7.3
OAAI分享收藏Active Solvent Modulation: A Valve-Based Approach To Improve Separation Compatibility in Two-Dimensional Liquid ChromatographyANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
分享收藏
查看更多内容