Detection of Carbonated Beverages using a PCF SPR Sensor
Bibliographic record
Abstract
A Photonic Crystal Fiber (PCF) Surface Plasmon Resonance (SPR) sensor has been proposed in this work. The sensor is designed for detecting the refractive index of carbonated beverages, such as Coca-Cola, Pepsi, 7UP, Sprite, Fanta, Mountain Dew, beer, stout, Canada Dry, and Schweppes. These beverages are widely consumed for their effervescence and refreshing taste. Ensuring their quality is crucial due to variations in alcohol content, sugar content, water mixture, aging process, and additional ingredients. The sensor, designed with a circular gold layer coating over a PCF with a symmetrical structure, facilitates the SPR phenomenon. Numerical exploration using the finite element method revealed a maximum amplitude sensitivity of 787 RIU<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>and a maximum wavelength sensitivity of 33,333.3 nm/RIU, with a wavelength sensitivity resolution of 3 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup>RIU and a high figure of merit of 555.56 RIU<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>, confirming the sensor's superior performance. Demonstrating exceptional sensitivity, the sensor offers a costeffective and time-saving alternative to existing methods, also applicable in security settings such as airports and border checkpoints for detecting illegal substances. This study highlights the sensor's potential in ensuring the quality of carbonate beverages by providing valuable insights into their composition and authenticity, as well as enhancing security measures against illegal drug supply.
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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".