Blackseed Oil Supplemented Caseinate–Carboxymethyl Chitosan Film Membrane for Improving Shelf Life of Grape Tomato
Bibliographic record
Abstract
Blackseed oil supplemented with caseinate (CA)–carboxymethyl chitosan (CMCH) composite membranes were evaluated for their functional properties and as edible coating for extending the shelf life of grape tomatoes. Composite films were prepared from equal parts of (CaCa or NaCa) and (CMCH) with or without supplemented 3% blackseed oil (BO) and evaluated for their functional properties. Subsequently, the edible membrane coating was evaluated to extend the shelf life of grape tomatoes (Solanum lycopersicum L.). The water vapor permeability (WVP) of the films was the lowest for the calcium caseinate–carboxymethyl chitosan–blackseed oil (CaCa-CMCH-BO) film (3.01 g kPa−1 h−1 m−2). Adding blackseed oil to the edible film matrix also led to a significant increase in its mechanical properties, resulting in tensile strength values of 12.5 MPa and 10.2 MPa and elongation at break values of 90.5% and 100% for NaCa-CMCH-BO and CaCa-CMCH-BO, respectively. The composite films also exhibited good compatibility through hydrogen bonding and hydrophobic interactions, as confirmed by FTIR spectroscopy. The particle size and zeta potential of CaCa-CMCM-BO were 117 nm and −40.73 mV, respectively, while for NaCa-CMCH-BO, they were 294.70 nm and −25.10 mV, respectively. The incorporation of BO into the films resulted in greater antioxidant activity. When applied as an edible film membrane on grape tomatoes, the coating effectively delayed the deterioration of tomatoes by reducing weight loss, microbial spoilage, and oxidative degradation. Compared to the control, the coated fruits had delayed ripening, with a shelf life of up to 30 days, and reduced microbial growth over the entire storage period.
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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".