A Novel Approach to Protect Brazil Nuts from Lipid Oxidation: Efficacy of Nanocellulose–Tocopherol Edible Coatings
Bibliographic record
Abstract
High levels of unsaturated fatty acids in Brazil nuts compromise their sensory quality through lipid oxidation. To mitigate this reaction, it is crucial to package nuts under a vacuum and in aluminate packaging. An alternative method is the application of an edible coating with antioxidant properties. This study aimed to develop an edible coating composed of carboxymethylcellulose and sorbitol, physically reinforced with nanocellulose, and chemically fortified with tocopherol. The edible coating was characterized based on its physical properties, mechanical strength, biodegradability, optical light transmission properties, color parameters, and water vapor permeability. Formulations CC5 (Carboxymethyl cellulose (CMC) + sorbitol + 5% nanocellulose) and CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% nanocellulose) showed enhanced mechanical strength. The combination of nanocellulose with tocopherol in formulations CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% nanocellulose) and CCT5 developed superior barriers to visible and ultraviolet light, a desired characteristic for coatings intended to increase the shelf life of Brazil nuts. The nuts coated with CC5 and CCT3 showed the lowest PV values at the end of the accelerated oxidation test conducted at 60 °C.
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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 | 0.000 | 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".