LC–MS

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Researchers have introduced MCnebula, a new framework for untargeted LC–MS/MS data analysis. MCnebula focuses on critical chemical classes and utilizes visualization techniques to streamline the analysis process, enabling efficient pathway analysis, biomarker discovery, and classification of unknown compounds beyond the limits of spectral libraries.

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In a presentation at HPLC 2023, Kevin Pagel of Freie Universität in Berlin, Germany described methods he has found most effective for better understanding the roles of glycans and glycoconjugates in biological processes.

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Researchers have developed an innovative strategy for compound annotation in offline 2D-LC–MS analysis. The approach combines hand-in-hand alignment with targeted molecular networking (TMN) and was successfully applied to analyze the chemical profile of Yupingfeng (YPF), a traditional Chinese medicine prescription, resulting in the identification of 497 compounds and demonstrating its efficiency and scalability in complex sample analysis.