Determination of pork quality attributes using hyperspectral imaging technique
Bibliographic record
Abstract
Meat grading has always been a research topic because of large variations among meat products. Many subjective assessment methods with poor repeatability and tedious procedures are still widely used in meat industry. In this study, a hyperspectral-imaging-based technique was developed to achieve fast, accurate, and objective determination of pork quality attributes. The system was able to extract the spectral and spatial characteristics for simultaneous determination of drip loss and pH in pork meat. Two sets of six significant feature wavelengths were selected for predicting the drip loss (590, 645, 721, 752, 803 and 850 nm) and pH (430, 448, 470, 890, 980 and 999 nm). Two feed-forward neural network models were developed. The results showed that the correlation coefficient (r) between the predicted and actual drip loss and pH were 0.71, and 0.58, respectively, by Model 1 and 0.80 for drip loss and 0.67 for pH by Model 2. The color levels of meat samples were also mapped successfully based on a digitalized Meat Color Standard.
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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) | 0.000 | 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".