Rapid prediction of beef colour evolution and myoglobin forms using near-infrared spectroscopy (NIRS)
Bibliographic record
Abstract
• NIRS is capable to evaluate colour and myoglobin parameters of beef products. • Chemometrics methods (PLS-R and SVM-R) are used to analysis the NIRS data. • Wavelengths selection improve the predictive performance for meat analysis. • R 2 Val values of myoglobin parameters were over 0.95 using PLS-R model. Meat offers essential nutrients and protein, with some vitamins and minerals rare in plant-based diets. Its colour, an essential quality indicator, influences consumer choices, shelf life, and economic aspects of meat products. Conventional measurements include an objective description of instrumental meat colour (CIELAB) and evaluation of myoglobin profiles, which are usually resource intensive and time consuming. This study aimed to expand the use of spectral techniques as a screening tool for efficient evaluation of colour and myoglobin profile of beef products. NIR spectroscopy (NIRS) was used to evaluate colour related meat quality parameters of beef products over long storage days, including CIELAB colour (L*, a*, b*, ΔE), total myoglobin content (mg/g), and three myoglobin forms (Deoxymyoglobin - DeoMb, Oxymyoglobin - MbO 2 , and Metmyoglobin - MetMb). Results have shown that the use of NIR spectroscopy for evaluating colour parameters in beef products shows great promise as a reliable and efficient method. At the validation stage, the RPD values following PLS-R modelling of these quality parameters (L*, a*, b*, ΔE, Total Myoglobin, DeoMb, MbO 2 , MetMb) were 7.03, 7.03, 6.84, 1.12, 7.79, 4.18, 7.09, 25.38, and 16.27, respectively. This study demonstrates that the NIR spectroscopy coupled with chemometrics methods is a promising approach for rapid quantitative analysis of colour and myoglobin parameters in meat products.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".