E-Sniffer: Raw Meat Freshness Detection Tool Based on Odor Classification and Fuzzy Logic Utilizing Gas Fusion Sensor
Bibliographic record
Abstract
Traditional approaches to assessing meat freshness continue to depend on visual inspection, manual olfactory evaluation, or laboratory analyses that are often subjective, labor-intensive, and expensive.To address these gaps, this study developed E-Sniffer, an Internet of Things (IoT)-based meat freshness detection tool that identifies the freshness level of meat through odor analysis using SGP40, MQ-137, and DHT22 sensors.These sensors identify decay gases including Total Volatile Organic Compounds (TVOC), Ammonia (NH₃), Hydrogen Sulfide (H₂S), and Trimethylamine (TMA), temperature and humidity.The collected data is analyzed through an ESP32 microcontroller with Mamdani Fuzzy Logic algorithm to categorize the freshness of meat into three distinct levels: fresh, slightly spoiled, and not fresh.The analysis results display on a Nextion Touch Display and transmitted via Bluetooth, enabling monitoring through a Flutter-based app.Experiments conducted on beef, chicken, and fish revealed an impressive detection success rate of 80%, accompanied by an average response time of just 10 seconds.The accuracy of the results is ensured through a manual comparison method for validation.This innovation positions E-Sniffer as an objective, rapid, and portable solution for consumers, restaurants, and the food industry in assessing meat freshness, ultimately enhancing food safety and the quality of products consumed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".