Sensitive Microwave Sensor for Detection and Quantification of Water in Adulterated Honey
Bibliographic record
Abstract
Honey is a rich source of sugar and is one of the indispensable ingredients in infant foods. Thus, it can be subjected to adulteration due to its cost. Measurement techniques such as liquid chromatography and near-infrared spectroscopy, used for detecting any adulteration, are expensive and need to be conducted by highly trained personnel for off-line analysis. Microwave measurements, as a fast, simple, and relatively inexpensive analysis, have recently shown great potential in detecting adulteration within honey samples. Nonetheless, sensor types used in such measurements are conventional. In this study, a reflection-type sensitive microwave sensor terminated by a metal back is proposed for the first time in the literature for the detection and quantification of water percentage ($\delta $) level (mass-to-mass basis) within water-adulterated honey samples. When compared with other resonance-based microwave cavity sensors, thanks to its eight strips located at the centers of two closed circular loops, it demonstrates superior frequency selectivity and sensitivity ($S =5.13$%) validated by full-wave 3-D simulations performed by the CST Microwave Studio and equivalent circuit analysis carried out by the Advanced Design System (ADS) software. For example, for ethanol, the proposed sensor gives a frequency shift of more than 1 GHz in the X band. Resonance frequency shift and variation of the reflection coefficient amplitude ($|S_{11}|$) are measured at X band to detect honey samples with up to 8% adulteration level. Three different honey samples (flower honey, highland honey, and thyme honey) were examined to test the performance and applicability of the proposed sensor.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".