Hyperspectral Imaging for Beef Tenderness Assessment
Bibliographic record
Abstract
Tenderness is one of the principal properties of meat quality. The traditional way to measure tenderness the beef is time consuming and also destructive, and therefore not appropriate for rapidly identifying quality parameters on the processing line, with the minimum of human intervention. The objective of the present research was to measure the tenderness of cooked beef samples obtained from four types of muscles (i.e. infraspinatus (TB), gluteus medius (TS), psoas major (TL), and longissimus thorasis (RE)) at three different durations of dry aging (Fresh (0 days), 14 days, and 21 days), using near infrared hyperspectral imaging. Hyperspectral reflectance spectra (900 nm <l < 1700 nm) were acquired for a total of 260 beef steak samples with dry-ages of 0, 14 or 21 days. After imaging, samples were cooked and the Warner-Bratzler shear force (WBSF), a parameter inversely related to meat tenderness, was measured. After reflectance calibration, a region of interest (ROI) was selected from each acquired hyperspectral image and stepwise regression was applied to the ROI to select wavelengths that were strongly related to cooked meat tenderness. Multiple Linear Regression (MLR) calibration models were developed for quantitative evaluation of beef tenderness. The correlation coefficient (R) and the root mean square error (RMSE) were employed to evaluate the calibration model’s predictive ability for each group. The calibration model developed predicted tenderness with R values of 0.89, 0.86, 0.81 and 0.83 for TS, RE, TB, and TL, respectively. The results revealed that the HSI could be used for non-destructive measurement of beef tenderness in beef having undergone three different durations of aging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".