Design of FBG‐Based Sensing System for Meat Quality Assessment and Steak Cooking Optimisation
Bibliographic record
Abstract
ABSTRACT Water presence in raw meat not only degrades its quality by diluting the natural flavours, but also increases the risk of bacterial growth, altering texture, and juiciness which ultimately affects the quality of end products. Therefore, quality assessment of raw meat in markets as well as in eateries is important to ensure the consumer's safety and satisfaction. In this paper, we propose a nondestructive technique to detect water presence in raw meat and steak cooking temperature optimisation using a fibre Bragg grating (FBG) temperature sensor inserted into the piece of raw meat. The contribution of this paper is twofold: First, the water presence in the piece of raw meat is detected by analysing the shift in Bragg wavelength corresponding to time‐dependent gradual decrease in temperature of heated probe of FBG sensor due to convective heat transfer from probe to meat. Secondly, the optimum cooking temperature of a particular type of steak is achieved after quality assessment of meat by analysing the shift in Bragg wavelength corresponding to time‐dependent gradual increase in probe temperature due to convective heat transfer from meat to probe. The results show that the proposed sensing system can complete the error‐free detection of water in 250 g piece of raw meat within 30 s for water contents of 50, 75, and 100 g which account for 20%, 30%, and 40% of the net weight of meat, respectively. Moreover, the simulated and experimental values of temperature sensitivity (TS) of FBG sensor used in this work are around 13 and 10 pm/°C, respectively. The proof of the concept of a smart restaurant based upon the proposed work is also discussed. This study provides a nondestructive, simple, and fast solution for detection of water in raw meat and achieving the optimum cooking temperature for different types of steaks.
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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) | 0.001 | 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 | 0.000 |
| 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".