Preservation Mechanism of Chitosan-Based Coating with Cinnamon Oil for Fruits Storage Based on Sensor Data
Bibliographic record
Abstract
The chitosan-based coating with antimicrobial agent has been developed recently to control the decay of fruits. However, its fresh keeping and antimicrobial mechanism is still not very clear. The preservation mechanism of chitosan coating with cinnamon oil for fruits storage is investigated in this paper. Results in the atomic force microscopy sensor images show that many micropores exist in the chitosan coating film. The roughness of coating film is affected by the concentration of chitosan. The antifungal activity of cinnamon oil should be mainly due to its main consistent trans-cinnamaldehyde, which is proportional to the trans-cinnamaldehyde concentration and improves with increasing the attachment time of oil. The exosmosis ratios of Penicillium citrinum and Aspergillus flavus could be enhanced by increasing the concentration of cinnamon oil. Morphological observation indicates that, compared to the normal cell, the wizened mycelium of A. flavus is observed around the inhibition zone, and the growth of spores is also inhibited. Moreover, the analysis of gas sensors indicate that the chitosan-oil coating could decrease the level of O₂ and increase the level of CO₂ in the package of cherry fruits, which also control the fruit decay. These results indicate that its preservation mechanism might be partly due to the micropores structure of coating film as a barrier for gas and a carrier for oil, and partly due to the activity of cinnamon oil on the cell disruption.
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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 | 0.001 | 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".