Advances in bio-based smart food packaging for enhanced food safety
Bibliographic record
Abstract
The increasing environmental concerns surrounding conventional plastic packaging and the demand for higher food safety and quality have led to a surge in the development of bio-based smart food packaging. These novel packaging materials are featured with renewability and biodegradability, and at the same time, possess active and intelligent functionalities to enable extended food shelf life. Despite numerous advances, challenges remain in the feasibility of industrial production. This review examines the current state of bio-based smart food packaging, focusing on the most used raw materials and fabrication methods and their unique functionalities. Special attention is given to innovative production techniques like 3D printing and electrospinning, exploring their potential scalability and enhanced properties. This review also delves into the key applications of bio-based smart packaging materials in pH/gas, temperature, humidity, enzyme-responsive systems, and their multi-responsive capabilities. Significant progress has been made in developing bio-based smart packaging materials that can respond to environmental stimuli. Particularly, pH- and gas-responsive packaging offers promising solutions for food spoilage detection. However, the commercial applications of novel packaging materials and production techniques need to be promoted by considering the cost, the scalability, the potential benefits, and the regulations. • Advances in bio-based smart packaging address sustainability and food safety. • Stimuli-responsive systems react to pH, gas, temperature, and humidity changes. • 3D printing and electrospinning enable advanced fabrication. • Biopolymers such as PLA, PHB, cellulose, and starch are highlighted. • Multi-responsive systems and cost-effective innovations are key future directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".