Simple Fiber-Optic-Based Sensors for Process Monitoring: An Application in Wine Quality Control Monitoring
Bibliographic record
Abstract
The goal of this research was to develop a simple and economical fiber-optic sensor technology for agrifood process monitoring. Toward this end, two fiber-optic sensors were developed to be used in combination: a single reflection V-bend sensor and a single fiber air-gap probe. The former is designed to be sensitive toward refractive index and the latter towards absorption. Experiments indicate that the micromachined V-bend fiber refractometer is most sensitive when the bend angle is centered around 140 degrees, at which angle the sensor may resolve changes in refractive index as small as 0.00015. Additionally, the V-bend sensor was found to be non-responsive toward sample absorption even in extremely absorbing solutions. The air-gap design absorption sensor, most commonly used for measurements in highly colored media, was found to be slightly sensitive towards refractive index. When the two sensors are used together, the response of the absorption sensor may be corrected for. This sensor combination is able to provide accurate measurements in situations where Beer's law is not obeyed. Results are presented that show that the sensor pair was successfully used to monitor wine sugar content (Brix), and color density and hue, parameters related to the age of the wine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".