Ethanolic extract of mango seed used in the feeding of broilers: effects on phenolic compounds, antioxidant activity, and meat quality
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of including different levels of ethanolic extract of mango seed (EEMS) in broiler chicken rations on their phenolic compound levels, antioxidant activity, and meat quality. Initially, 756 one-day-old male chicks of the Ross 308 line were distributed in a completely randomized experimental design with seven treatments and six replicates of 18 birds. The treatments consisted of (i) a ration without the addition of antioxidants, (ii) a ration with the addition of 200 ppm of the antioxidant butylated hydroxytoluene (BHT), and (iii) a ration with the addition of 200, 400, 600, 800, or 1000 ppm of EEMS. According to the results, the values of phenolic compounds, the lipid stability of meat (measured by the value of thiobarbituric acid reactive substances), shear force, loss of cooking water, color, and pH of the meat differed significantly between the treatments. For the antioxidant activity of the meat, there was no significant difference between the treatments by the 2,2-diphenyl-1-picrylhydrazyl method; however, by the 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) method, the breast meat of broilers fed diets containing EEMS from 600 ppm and up presented higher antioxidant capacity when compared with the meat of the birds fed the control diet. In the ABTS method, there was no significant difference between the use of synthetic antioxidant BHT and the addition of EEMS at different levels. In conclusion, the addition of EEMS does not affect meat quality parameters of broilers, but when a dose of 600 ppm or more is added, the antioxidant capacity of meat measured by the ABTS method increases.
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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 | 0.000 | 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".