Prediction of beef tenderness using hyperspectral imaging
Bibliographic record
Abstract
Cuts from different types of beef muscles [e.g., infraspinatus (Top blade, TB), gluteus medius (Top sirloin, TS), psoas major (Tenderloin, TL), and longissimus thorasis (Rib eye, RE)] were dry-aged for up to 21 days and then subjected to near-infrared hyperspectral imaging to gauge its usefulness in evaluating beef tenderness.Imaged in reflection mode using a hyperspectral (900 nm< λ <1700 nm) imaging system, samples were then cooked (grilled one side to internal temperature of 40ºC, turned and grilled to a final internal temperature of 71ºC) and examined for tenderness by the Warner-Bratzler shear force (WBSF) method.Stepwise regression of mean spectral data collected from the samples was used to determine wavebands that can be used assess beef tenderness.Multiple Linear Regression (MLR) was used to assess the relative advantage of the selected wavebands to predict tenderness of beef samples.An overall correlation coefficient (R) for each muscle type (R=0.89 for TS, R=0.86 for RE, R=0.81 for TB, and R=0.83 for TL) shows the possibility of using hyperspectral imaging for predicting beef tenderness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".