Retention time prediction for 653 pesticides on a biphenyl liquid chromatography stationary phase using machine learning

Suspect screening large numbers of analytes by a single LC-MS/MS method has become more widespread in recent years with new advances in high speed data-dependent (DDA) or data-independent (DIA) acquisition methods. The process of molecular identification can however be challenging when it is not possible to measure an authentic standard. Retention time verification (or prediction) is a critical tool in suspect screening. The ability to predict retention times on C18has recently been demonstrated using machine learning tools, but models have not been explored for other reversed-phase media which may offer alternative selectivity to enhance component identification. In this work, the prediction of retention times for a diverse chemical space is considered using artificial neural networks for a biphenyl stationary phase.

Content Type:
Document Number:
Product Type:
Liquid Chromatograph-Mass Spectrometry, Mass Spectrometry, triple quadrupole LC-MS/MS
molecular identification, biphenyl stationary phase, advances in high speed data-dependent(DDA), advances in high speed data-independent (DIA), artificial neural networks, pesticides, Food and Beverages, Agriculture, Food safety (Residues, Contaminants), LCMS-8060
File Name:
File Size:

Free Download

For Research Use Only. Not for use in diagnostic procedures.

This page may contain references to products that are not available in your country. Please contact us to check the availability of these products in your country.

Top of This Page