Environment-Compensated Gas Sensor Time-Series Analysis for Tracking Food Spoilage
Bibliographic record
Abstract
Food safety and kitchen use independence are key concerns for older adults aging in place and their care partners. With aging, there is a decline in sense of smell that appears worsened in individuals with neuro-cognitive decline; this can increase the risk of missing the signs that food is beginning to spoil. Automated detection of spoiled food by sensors in a supportive smart home could help keep older adults safe from food that is spoiling, and tracking spoilage events may help identify changes in food preparation capacity. Inexpensive metal-oxide (MOX) gas sensors can identify the volatile gases associated with food spoilage microbes; however, they are affected by sensor variation, aging, and environmental conditions. This work focuses on identifying change points in the gas sensor time-series that indicate shifts in volatile gas concentration or composition associated with microbial growth. To adjust for sensor variability and variations in ambient temperature and humidity, we develop a compensation method based on a power-law model of the sensor's response to water vapor, with field calibration of the model parameters. This compensation is shown to reduce the impact of environmental fluctuations that can mask changes in the signals of interest. Using milk and tofu as representative foods, we show that our proposed processing can characterize the spoilage process, and may be able to identify early signs of microbial growth before spoilage is evident.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".