Simple and inexpensive microwave setup for industrial based applications: Quantification of flower honey adulteration as a case study
Bibliographic record
Abstract
Abstract A simple and inexpensive microwave measurement setup based on measurements of magnitudes of transmission properties ( $$|S_{21}|_{\text {dB}}$$ | S 21 | dB ) is proposed for industrial-based microwave aquametry (moisture or water content) applications. An easy-to-apply calibration procedure based on normalization is implemented to eliminate systematic errors in the measurement system. As a case study, we applied this setup for the quantification of water-adulteration in flower honey. After validating this system by distilled water and pure flower honey measurements, $$|S_{21}|_{\text {dB}}$$ | S 21 | dB measurements of the pure flower honey with various adulteration percentages ( $$\delta$$ δ ) up to 9% are conducted to examine the performance of the measurement setup for quantification of water adulteration. A multi-dimensional fitting procedure is implemented to predict $$\delta$$ δ using the proposed inexpensive microwave measurement setup. It is shown that it is possible to quantify an adulteration level with an accuracy better than $${\mp } 1$$ ∓ 1 % by the proposed measurement setup and the applied multi-dimensional fitting procedure.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".