Low-Cost Real-Time Non-Invasive Milk Quality Monitoring Using 60 GHz FMCW Radars
Bibliographic record
Abstract
Combining the vast growing field of Internet of Things (IoT) technologies and a low-cost electromagnetic radar solution for the real-time monitoring of liquid products during production may enable companies to address quality issues quickly and efficiently. This paper demonstrates a proof-of-concept that an electromagnetic radar can be implemented for milk monitoring. The Dielectric Assessment Kit (DAK) is used for the initial dielectric characterization of different milk fat concentrations. A 60 GHz radar sensor is used for the characterization of milk fat in cartons. AI models can be integrated into the data processing to correlate the radar signals to the milk fat concentration of a product. The real-time processing allows products to be flagged if they deviate too much from their reference sample. This work demonstrates a low-cost, non-invasive 60 GHz FMCW radar system for real-time milk quality monitoring. A classification accuracy of 87.5% was achieved for moving cartons, and 100% for stationary cartons. The results confirm the potential for scaling this approach in industrial production lines.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".