Improvement Quality and Shelf Life Assessment of Minimally Processed Apples by Use of Protein Coatings
Bibliographic record
Abstract
Minimally processed fruits offer a convenient and practical product to the consumer, with quality and freshness similar to the in natura product. The aim of this study was to apply coatings of croaker protein isolate (Micropogonias furnieri) with organoclay (montmorillonite) on minimally processed apples during 12 days of storage at 5 ± 1 °C. The apples were washed, peeled and cut into cubes. Coatings were then applied where T1 (control sample), T2 (croaker protein isolate coating) and T3 (croaker protein isolate coating and montmorillonite (MMT). The pieces were allowed to drain into sieves and these were stored in polyethylene terephthalete (PET) packaging for a period of 12 days at 5 ± 1 °C. Analyses of weight loss, firmness, color (Lightness (L*), Chroma a* and b*), pH, and microbiological analyses (psychrotrophic, yeasts and molds, Salmonella and Escherichia coli) were carried out. The analyses were carried out in triplicate and evaluated at the times of 1, 3, 5, 7, 9 and 12 days of storage. The use of croaker protein isolate coating and MMT proved promising in keeping weight loss, microbiological counts, enzymatic browning and firmness loss at minimum in minimally processed apples.
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 | 0.000 | 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 | 0.001 | 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".