Profiling Phlorotannins in Brown Macroalgae by Liquid Chromatography–High Resolution Mass Spectrometry
Bibliographic record
Abstract
INTRODUCTION: Phlorotannins, phenolic compounds produced exclusively by Phaeophyceae (brown algae), have recently been associated with a wide variety of beneficial bioactivities. Several studies have measured the total phenolic content in extracts from various species, but little characterisation of individual phlorotannin components has been demonstrated. OBJECTIVE: The purpose of this study was to develop a liquid chromatography-mass spectrometry (LC-MS) based method for rapid profiling of phlorotannins in brown algae. METHODOLOGY: Phlorotannin-enriched extracts from five phaeophyceaen species were analysed by ultrahigh-pressure liquid chromatography (UHPLC) operating in hydrophilic interaction liquid chromatography (HILIC) mode combined with high resolution mass spectrometry (HRMS). The method was optimised using an extract of Fucus vesiculosus; separation was achieved in less than 15 min. The basic mobile phase enhanced negative-ion electrospray ionisation (ESI), and generated multiply charged ions that allowed detection of high molecular weight phlorotannins. RESULTS: The phlorotannin profiles of Pelvetia canaliculata, Fucus spiralis, F. vesiculosus, Ascophyllum nodosum and Saccharina longicruris differed significantly. Fucus vesiculosus yielded a high abundance of low molecular weight (< 1200 Da) phlorotannins, while P. canaliculata exhibited a more evenly distributed profile, with moderate degrees of polymerisation ranging from 3 to 49. HRMS enabled the identification of phlorotannins with masses up to 6000 Da using a combination of accurate mass and ¹³C isotopic patterns. CONCLUSION: The UHPLC-HRMS method described was successful in rapidly profiling phlorotannins in brown seaweeds based on their degree of polymerisation. HILIC was demonstrated to be an effective separation mode, particularly for low molecular weight phlorotannins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".