Curcumin as Bioactive Agent in Active and Intelligent Food Packaging Systems: A Comprehensive Review
Bibliographic record
Abstract
The incorporation of bioactive agents into food packaging systems represents a promising approach to enhance and monitor food safety, quality, and shelf life. Among bioactive agents, curcumin, a polyphenolic compound derived from Curcuma longa, has garnered significant interest due to its multifunctional properties, including antimicrobial, antioxidant, anti-inflammatory, antidiabetic, and pH-responsive color-changing properties. This review offers a comprehensive analysis of the latest advancements in curcumin-based food active and intelligent packaging systems, beginning with an exploration of curcumin chemical composition, derivatives, and extraction methods from plant materials followed by a thorough discussion of the functional attributes of curcumin, such as ability to inhibit microbial growth, neutralize free radicals, and regulate inflammatory responses, and glucose levels are thoroughly discussed. Despite its significant potential, challenges such as poor stability, low solubility, and scalability issues have hindered the widespread adoption of curcumin in packaging applications. This review also addresses these limitations by examining the development of curcumin-infused packaging materials, including active films, active coatings, and intelligent films/labels that can respond to environmental changes or food spoilage indicators. The practical applications of these curcumin-based packaging systems in food preservation are evaluated, highlighting their effectiveness in reducing microbial contamination, preventing oxidative degradation, and serving as real-time indicators of food freshness. These findings emphasize the transformative potential of curcumin in advancing food packaging technologies, with significant implications for global food safety and sustainability initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".