Phenolic Acid Profiles in Some Small Berries
Bibliographic record
Abstract
The composition of phenolic acids in several small berries grown in Northeastern Poland, namely, low-bush blueberries, black mulberries, European juneberries, black currants, fruits of blue-berried honeysuckle, and blackberries, was determined by gas chromatography (GC) and mass spectrometry (MS). The total content of phenolic acids, identified by GC-MS, ranged from 2845.8 ± 141.0 (black mulberries) to 5418.2 ± 228.0 (blue-berried honeysuckle). Twenty phenolic acids were identified in the berries. Of these, hydroxycaffeic, m- and p- coumaric, and 3,4-dimethoxycinnamic acids were the major phenolic acids in blackberries and blueberries, m -coumaric acid was the major phenolic acid in blue-berried honeysuckle and black currant fruits, while salicylic, caffeic, and m- and p- coumaric acids were the predominant phenolic acids in European juneberries. Syringic and veratric acids were detected only in blueberries, while p -hydroxybenzoic and sinapic acids were present only in black currants and o -coumaric acid was present in blueberries and black mulberries. The phenolic acids liberated from esters and glycosidic bonds were the major fractions of phenolic acids in the berries. Keywords: Phenolics acids; low-bush blueberry ( Vaccinium myrtillus ); black mulberry ( Morus nigra ); blue-berried honeysuckle ( Lonicera caerulea ); black currant ( Ribes nigrum ); blackberry ( Rubus plicatus ); European juneberry ( Amelanchier ovalis )
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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) | 0.000 | 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".