Rapid screening of anthocyanins in berry samples by surfactant‐mediated matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry
Bibliographic record
Abstract
Surfactant-mediated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has been used for the identification of flavonoids from three berry extracts: lowbush blueberry (Vaccinium angustifolium), lingonberry (Vaccinium vitis-idaea), and blackberry (Rubus armeniacus). The addition of the surfactant led to suppression of matrix ions from both alpha-cyano-4-hydroxycinnamic acid (CHCA) and 2',4',6'-trihydroxyacetophenone (THAP). This is the first case of this method being successfully employed with a matrix other than CHCA. It was observed that CHCA led to a great deal of fragmentation of the sugar moiety from glycosides, whereas THAP produced more intact glycoside molecules, and thus leads to better characterization of the flavonoids in a berry sample. The flavonoids were characterized and quantified by liquid chromatography/electrospray ionization mass spectrometry (LC/ESI-MS) with UV detection. Although MALDI-TOF-MS did not lead to the identification of as many flavonoids, it did enable us to identify many anthocyanin glycosides. Quantification was achieved and demonstrated that use of the THAP matrix can enable quantification of the intact glycosides with relative standard deviation (RSD) values of less than 10% with surfactant addition. These results are comparable with LC results. MALDI-TOF-MS with THAP matrix thus provided a rapid method for the qualitative screening of these compounds. It took only a few minutes, greatly reducing the analysis time from that in traditional LC/MS methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".