Intelligent Food Packaging: A Review of Smart Sensing Technologies for Monitoring Food Quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Food safety is a major factor affecting public health and the well-being of society. A possible solution to control food-borne illnesses is through real-time monitoring of the food quality throughout the food supply chain. The development of emerging technologies, such as active and intelligent packaging, has been greatly accelerated in recent years, with a focus on informing consumers about food quality. Advances in the fields of sensors and biosensors has enabled the development of new materials, devices, and multifunctional sensing systems to monitor the quality of food. In this Review, we place the focus on an in-depth summary of the recent technological advances that hold the potential for being incorporated into food packaging to ensure food quality, safety, or monitoring of spoilage. These advanced sensing systems usually target monitoring gas production, humidity, temperature, and microorganisms' growth within packaged food. The implementation of portable and simple-to-use hand-held devices is also discussed in this Review. We highlight the mechanical and optical properties of current materials and systems, along with various limitations associated with each device. The technologies discussed here hold great potential for applications in food packaging and bring us one step closer to enable real-time monitoring of food throughout the supply chain.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it