High-Performance Liquid Chromatography Characterization and Identification of Antioxidant Polyphenols in Maple Syrup
Bibliographic record
Abstract
Maple syrup of four grades (extra-light, light, medium, and dark) of the 2007 crop was provided by three local (St. Joseph's Island, Ontario, Canada) producers. Twenty-four phenolic compounds were isolated from a medium-grade syrup and identified on the basis of spectral and chemical evidence. They were (a) benzoic acid and several hydroxylated and methoxylated derivatives (gallic acid, 1-O.-galloyl-β-d-glucose, γ-resorcylic acid); (b) cinnamic acid derivatives (p.-coumaric acid, 4-methoxycinnamic acid, caffeic acid, ferulic acid, sinapic acid, and the ester chlorogenic acid); (c) flavonoids, the flavanols catechin and epicatechin, and the flavonols kaempferol and its 3-O.-β-d-glucoside, 3-O.-β-d-galactoside, quercetin and its 3-O.-β-d-glucoside, 3-O.-β-L-rhamnoside and 3-O.-rhamnoglucoside (rutin). Traces obtained at 280 and 350 nm in HPLC runs of the ethyl acetate–soluble fractions of eight samples indicated the presence of many more phenolic substances, most at very low concentration with some varibilities in peak heights, but not in retention times, among the syrups. In view of the well-established antioxidant activity these substances possess, it is suggested that it is the complexity of the mixture rather than any one compound that may serve to counter the unhealthful presence of the high concentration of sugars in the syrup.
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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 | 0.000 | 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".