Antimicrobial Nanomaterials for Food Packaging
Bibliographic record
Abstract
Food packaging plays a key role in offering safe and quality food products to consumers by providing protection and extending shelf life. Food packaging is a multifaceted field based on food science and engineering, microbiology, and chemistry, all of which have contributed significantly to maintaining physicochemical attributes such as color, flavor, moisture content, and texture of foods and their raw materials, in addition to ensuring freedom from oxidation and microbial deterioration. Antimicrobial food packaging systems, in addition to their function as conventional food packaging, are designed to arrest microbial growth on food surfaces, thereby enhancing food stability and quality. Nanomaterials with unique physiochemical and antibacterial properties are widely explored in food packaging as preservatives and antimicrobials, to extend the shelf life of packed food products. Various nanomaterials that are used in food packaging include nanocomposites composing nanoparticles such as silver, copper, gold, titanium dioxide, magnesium oxide, zinc oxide, mesoporous silica and graphene-based inorganic nanoparticles; gelatin; alginate; cellulose; chitosan-based polymeric nanoparticles; lipid nanoparticles; nanoemulsion; nanoliposomes; nanosponges; and nanofibers. Antimicrobial nanomaterial-based packaging systems are fabricated to exhibit greater efficiency against microbial contaminants. Recently, smart food packaging systems indicating the presence of spoilage and pathogenic microorganisms have been investigated by various research groups. The present review summarizes recent updates on various nanomaterials used in the field of food packaging technology, with potential applications as antimicrobial, antioxidant equipped with technology conferring smart functions and mechanisms in food packaging.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".