Issue 2, 2024

Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning

Abstract

Machine learning is increasingly popular and promising in environmental science due to its potential in solving various environmental problems. One such worldwide issue is the pollution caused by the persistent chemicals – per- and polyfluoroalkyl substances (PFAS), threatening the environment and human beings. Here, we introduce a no-code machine learning approach for modelling the quantitative structure–retention relationship (QSRR) of liquid chromatographic retention time (LC-RT) for PFAS. This approach aims to streamline the modelling process, particularly for environmental professionals who may find intensive coding cumbersome. The QSRR models were developed using the no-code machine learning tool, Orange, employing simple 2D molecular descriptors as input features. Through a systematic analysis, 12 descriptors were identified as pivotal properties essential for developing optimal models (including multiple linear regression – MLR and support vector machine – SVM). These selected models demonstrate great internal validation metrics (R2 > 0.98, MAE < 6.5 s) and reasonable external robustness (R2 > 0.80, MAE ∼ 40 s). Furthermore, a concise model interpretation was conducted to elucidate the molecular factors influencing LC-RT. It is anticipated that our models, capable of predicting the LC-RT for over 2000 PFAS within the Norman Network, will be instrumental in addressing this environmental challenge. This study not only contributes valuable insights into PFAS LC behaviour but also serves as a catalyst for future endeavours in the development and applications of no-code machine learning models.

Graphical abstract: Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning

Supplementary files

Article information

Article type
Paper
Submitted
22 aug 2023
Accepted
30 nov 2023
First published
01 dec 2023
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Adv., 2024,3, 198-207

Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning

Y. Fan, Y. Deng, Y. Yang, X. Deng, Q. Li, B. Xu, J. Pan, S. Liu, Y. Kong and C. Chen, Environ. Sci.: Adv., 2024, 3, 198 DOI: 10.1039/D3VA00242J

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements