Investigation Into Shelf Life of Fresh Dates and Pistachios in a Package Modified With Nano-Silver
Bibliographic record
Abstract
AIMS: The aim of this study was to apply polymer films containing silver nanoparticles as a new method for increasing the shelf life and preserving the quality of export/commercial products of Kerman Province and determine the ideal temperature for preserving these products. METHODS: After preparing nano-composite films containing silver nanoparticles (3% and 5% by weight), Mazafati dates were packed in them and stored with their control samples under four temperatures. In the second series, the films were filled with fresh pistachios and stored at four temperatures. In date samples, after 2, 7, 21 and 53 days of storing the samples were examined under the certified test of Iran Institute of Industrial Standard for Dates, which includes pH, TSS, acidity and reducing sugars tests. In pistachio samples the color values and market-friendly quality were evaluated after 1, 2, 3, 6, 7 and 8 days of storage. RESULTS: In date samples, the pH value decreased with increasing acidity in 3 and 5 wt% of nano-silver and their control samples. In addition, in 5 wt% samples the acidity was higher than that in 3% samples, with pH being lower in the controls at almost all the intervals. Furthermore, pH values in 5% samples were higher in comparison with 3 wt% samples and controls. The amount of reducing sugars in the control samples was lower than those in 3 and 5 wt% samples. In relation to pistachio samples, the damage over time was greater in sample stored under higher temperatures. CONCLUSION: The maximum shelf life of the dates packaged in 5 wt% of silver nano-powder was 53 days and the best temperature to store samples was determined at 4°C. Packages containing nano-silver increased shelf life of fresh pistachios, with the best temperatures being 25°C and 0°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) | 0.000 | 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".