Method for quantification of antioxidant capacity of processed fruit juices exploring the formation of the Fe(II)/3-hydroxy-4-nitroso-2,7-naphthalenedisulfonic complex
Bibliographic record
Abstract
The formation of the Fe(NRS)34− complex was used in a modified spectrophotometric method to quantify the total antioxidant capacity (TAC) of processed fruit juice samples. The procedure is based on the reduction of Fe(III) to Fe(II) in aqueous buffered solution (Tris; pH 8.0) containing the 3-hydroxy-4-nitroso-2,7-naphthalenedisulfonic acid (H2NRS). The absorbance values at 730 nm (A730 nm) of the Fe(NRS)34− complex obtained with juice samples were compared with A730 nm values obtained with a standard ascorbic acid solution and then subsequently used to quantify and express the TAC of the samples. Good positive relationship between TAC values in the analysed samples (grape, orange, passion fruit, cashew, peach, and strawberry) was found for both the Fe(NRS)34− complex method (proposed method) and ABTS•+ radical-free method (used as reference method). The TAC values obtained with Fe(NRS)34− and ABTS•+ also showed a positive correlation with the Folin–Ciocalteu index values. Fructose, glucose, BHT, and BHA did not interfere in the TAC quantification of the processed fruit juice samples with the proposed method. However, ascorbic acid (natural or added as additive, E300) and its derivatives (E301, E302, E303, E315, and E316) did interfere positively. As a 10-fold dilution is required and the absorbance measurements are performed at 730 nm, the proposed method was not subject to colour interference from the tested samples. The procedure suggested here is simple, easy to perform, reproducible, does not use organic solvents, and does not need expensive equipment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".