Development and UFLC-MS/MS Characterization of a Product-Specific Standard for Phenolic Quantification of Maple-Derived Foods
Bibliographic record
Abstract
The phenolic contents of plant foods are commonly quantified by the Folin-Ciocalteu assay based on gallic acid equivalents (GAEs). However, this may lead to inaccuracies because gallic acid is not always representative of the structural heterogeneity of plant phenolics. Therefore, product-specific standards have been developed for the phenolic quantification of several foods. Currently, maple-derived foods (syrup, sugar, sap/water, and extracts) are quantified for phenolic contents based on GAEs. Because lignans are the predominant phenolics present in maple, herein, a maple phenolic lignan-enriched standard (MaPLES) was purified (by chromatography) and characterized (by UFLC-MS/MS with lignans previously isolated from maple syrup). Using MaPLES and secoisolariciresinol (a commercially available lignan), the phenolic contents of the maple-derived foods increased 3-fold compared to GAEs. Therefore, lignan-based standards are more appropriate for phenolic quantification of maple-derived foods versus GAEs. Also, MaPLES can be utilized for the authentication and detection of fake label claims on maple products.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".