Monitoring of food spoilage with electronic nose: potential applications for smart homes
Bibliographic record
Abstract
In ambient-assisted living environments, advanced sensors are used to detect potential problems that may affect the occupant. For a range of unsafe living conditions, characteristic odours arise that can provide early warning of a problem in the dwelling. In this paper, we investigate the concept of smell monitoring in the smart home environment, with particular attention paid to food spoilage. Using a commercially available electronic nose (e-nose) based on a metal-oxide sensor array, the odours associated with five common foods were captured over a seven day period. All foods were readily discriminated at the beginning of the measurement period. However, as the food spoiled, the odour profiles changed significantly. In several cases, the changes for a given food exhibited a clear trajectory in the PCA space. This preliminary work suggests that e-nose technology is a promising candidate for incorporation in the smart home. For widespread adoption, however, future e-nose development must continue to improve current shortcomings such as instability, user intervention, and high cost.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".