IoT-Sensor Integrated Smart Food Freshness Scoring System
Bibliographic record
Abstract
Monitoring food and detecting its freshness is crucial for ensuring food safety, reducing waste, and optimizing supply chains. Traditional methods rely on retroactive expiration dates, and recent models do not consider real-time temperature fluctuations, leading to inaccurate shelf-life estimation and unnecessary waste. This article presents an innovative framework for food monitoring and freshness scoring systems integrating the IoT and sensors. A unique freshness scoring model is developed to calculate the freshness score in numerical value and categorize the current state of the food into different classes based on its temperature. In addition, sensors and IoT devices are utilized to collect and analyze food temperature data. As IoT and sensors are integrated, this proposed system can continuously monitor temperature and calculate the freshness score at any stage of the supply chain. The result analysis shows that the IoT device can continuously collect the real-time temperature of food using the sensor. Then, the proposed system can effectively calculate the innovative freshness score using the proposed model. This proposed system can help businesses and consumers make informed decisions by providing real-time freshness insights and calculating the freshness score, ultimately improving safety and sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".