Visible and near Infrared Spectroscopy of Beef <i>Longissimus Dorsi</i> Muscle as a Means of Dicriminating between Pasture and Corn Silage Feeding Regimes
Bibliographic record
Abstract
Near infrared (NIR) reflectance spectroscopy was used as a tool to classify beef muscle samples according to their feeding regime. Seventy-eight beef longissimus dorsi muscle samples both intact and minced were scanned in a NIRS 6500 instrument (NIRSystems, MD, USA) in reflectance. A dummy regression technique was developed to differentiate beef muscle samples, which originated from beef feed exclusively on pasture or/and mainly on corn silage feeding regimes. Ninety percent of the pasture-fed beef muscle samples were correctly classified using principal component regression (PCR) and 86% of beef fed on corn silage were correctly classified. Both muscle chemical composition and physical characteristics explained the classification results. The results in the present study showed the potential of muscle optical properties for classification and traceability of meat muscles in the food chain.
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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) | 0.001 | 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".