Antimicrobial Coatings in the Food Industry
Bibliographic record
Abstract
Microbial food contamination has become a burden to public health and is regarded as a serious issue. Spoiled and contaminated foods become unfit for consumption and, in turn, increase the spread of pathogenic organisms and the accumulation of food waste. Awareness of hygiene and food safety among consumers raises the demand for quality in prepacked foods. Therefore, it is necessary to introduce novel strategies to overcome conventional packaging and sales limitations. In recent times, antimicrobial coatings have gained immense interest in food-based industries due to their multifunctional applications.Additionally, nanotechnology has augmented the production of inorganic and organic materials, matrices, and polymers with antimicrobial activity that can be effectively coated on foods and packaging supplies. The unique morphological characteristics of these particles improve their ability to inhibit the growth of microbes. Antimicrobial nanocoatings efficiently kill bacteria, fungi, and viruses, increasing the stability and reducing the perishability of the foods. Coated nanoparticles that can be directly delivered into the food and applied on the packaging material act as antifouling agents and extend their shelf life. Thus, this chapter will highlight the applications of different nanoparticles as antimicrobial coatings in the food industry, with an added focus on current trends and future perspectives. It will also emphasize the available resources, technologies used for coating, their safety concerns, and pros and cons.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".