ArticleAnalytical chemistry2022
Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography.
Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Prediction of Thermal Modulated Comprehensive Two-Dimensional Gas Chromatographic Separation Using a Modular, Graph-Based Simulation Platform.Journal of separation science · 2026Article
- The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives.Chromatographia · 2025Article
- Current Role and Potential of Triple Quadrupole Mass Spectrometry in Biomedical Research and Clinical Applications.Molecules (Basel, Switzerland) · 2024Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations (
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Registered trials
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