Tracking food spoilage in the smart home using odour monitoring
Bibliographic record
Abstract
Use of continuous odour monitoring in a smart home represents a novel sensor modality with the potential to recognize various activities of daily living and identify unsafe conditions for the occupant. In this paper, we focus on food spoilage as one such condition. Using a metal-oxide sensor (MOS) based electronic nose, we measured the odour signatures of two common foods (milk and yogurt) that were stored at 25°C during a week-long period. Feature vectors were constructed using the maximum absolute sensor responses, and their components exhibited a smooth trajectory as the samples aged. Applying principal component analysis (PCA) revealed that the two substances followed distinct trajectories during spoilage. We conclude that an electronic nose can be used to track the spoilage of various foods in a manner that is a) repeatable for a specific food, and b) unique for different foods. Additionally, we found that the sampling protocol used in this work resulted in better repeatability than our previous work in this area. This result demonstrates the potential of using an electronic nose for smart home monitoring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".