Detecting and Grouping In-Source Fragments with Low-Energy Stepped HCD, Together with MS3, Increases Identification Confidence in Untargeted LC–Orbitrap Metabolomics of Plantago lanceolata Leaves and P. ovata Husk

<b>Background:</b> Comprehensive and accurate compound composition characterization in natural sources has high relevance in food and nutrition, health and medicine, environmental and agriculture research areas, though profiling of plant metabolites is a challenging task due to the structural complexity of natural products. This study delves into the identification and characterization of compounds within the Plantago genus, leveraging state-of-the-art analytical techniques. <b>Methods:</b> Utilizing an ultra-high-performance liquid chromatography (UHPLC) system in conjunction with Orbitrap™ IQ-X™ Tribrid™ mass spectrometer (MS), we employed a Phenyl-Hexyl HPLC column alongside optimized extraction protocols to analyze both husk and leaf samples. To maximize compound identification, we implemented data-dependent acquisition (DDA) methods including MS² (ddMS2), MS³ (ddMS3), AcquireX™ deep scan, and real-time library search (RTLS). <b>Results:</b> Our results demonstrate a significant increase in the number of putatively yet confidently assigned compounds, with 472 matches in <i>P. lanceolata</i> leaves and 233 in <i>P. ovata</i> husk identified through combined acquisition methods. The inclusion of an additional fragmentation level (MS³) noticeably enhanced the confidence in compound annotation, facilitating the differentiation of isomeric compounds. Furthermore, the application of low-energy fragmentation (10 normalized collision energy (NCE) for higher-energy collisional dissociation (HCD)) improved the detection and grouping of MS¹ fragments by 55% in positive mode and by 16% in negative mode, contributing to a more comprehensive analysis with minimal loss in compound identification. <b>Conclusions:</b> These advancements underscore the potential of our methodologies in expanding the chemical profile of plant materials, offering valuable insights into natural product analysis and dereplication of untargeted data.