Real‐Time Monitoring of Milk Fermentation Process Using Highly‐Sensitive Fibre Bragg Grating Stress Sensor
Bibliographic record
Abstract
ABSTRACT Continuous monitoring of milk fermentation process during industrial yogurt production using pH metres is often cumbersome and inefficient technique. A simple, cost‐effective and accurate alternative sensing technology is required for real‐time monitoring. In this paper, we report real‐time continuous monitoring of milk fermentation process and determine the fermentation end point during yogurt production using highly‐sensitive fibre Bragg grating (FBG) stress sensor. Milk fermentation is monitored in real‐time by analysing the shift in the Bragg wavelength of FBG stress sensor inserted into the milk corresponding to time‐dependent gradual increase in applied stress on FBG stress sensor because of yogurt coagulation. Required sensitivities for an FBG sensor used for milk having 0% and 2.5% fat are around 3.03 and 3.01 pm/Pa, respectively. A proof of the concept of a smart alarm system (AS) for determination of the fermentation end point of yogurt is discussed in this work. This study presents a cost‐effective, simple and non‐destructive method for continuous real‐time monitoring of milk fermentation process and determination of fermentation end point for small as well as large‐scale production of yogurt.
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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 | 0.000 |
| 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 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".