Systematic evaluation of the Folin–Ciocalteu and Fast Blue BB reactions during the analysis of total phenolics in legumes, nuts and plant seeds
Bibliographic record
Abstract
Folin-Ciocalteu and the more recent Fast Blue BB (FBBB) reactions are used for the quantification of total phenolics in food matrices. Despite its known interferences, Folin-Ciocalteu is still widely employed, considering Solid Phase Extraction (SPE) as clean-up step only in a few cases. Meanwhile, FBBB has shown no interferences for the determination of total phenolics in fruits and cereals, although its utilization in other popular matrices containing potential interferences, such as legumes, remains unexplored. In this study, the total phenolic content of 24 flours from legumes, cereals, fruits, nuts and plant seeds was evaluated by Folin-Ciocalteu and FBBB, with and without SPE. Folin-Ciocalteu showed interferences for 75% of the flours (attributed to reducing sugars and enediols), whereas FBBB only for legumes and nuts (attributed to the presence of tyrosine), both methods in those matrices requiring SPE. Although both SPE-FBBB and SPE-Folin-Ciocalteu presented excellent reproducibility, SPE-FBBB displayed 1.5 times higher sensitivity.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".