Evaluation of pork quality by proton nuclear magnetic resonance
Bibliographic record
Abstract
Consumers today are offered a diversified meat section at their local retail outlets.As the number of meat sources grows, consumers favour meat of higher quality.Competition is coming not only from other meat products but also from meats being imported internationally.There are growing concerns that pork quality is in fact struggling.After decades of research, issues such as pale soft exudative (PSE) pork and dark firm dry (DFD) pork remain prevalent.If such products reach the consumer, the industry risks the consumer no longer choosing pork in the future.To prevent economic losses to the industry, these products need to be detected and withheld.The problem lies in the fact that conventional techniques for meat quality measurement are slow, expensive, and destructive.Effective quality assurance programs that can keep up with market trends require a fast, non-destructive, method for measuring meat quality.The purpose of this study is to investigate low-field time-domain proton nuclear magnetic resonance (NMR) as a tool for rapid, non-destructive, multidimensional meat quality assessment.In the first section, a review of current literature with regards to meat science, meat quality, and meat quality measurement tools and techniques.This is followed by a review of current literature regarding NMR as a tool for meat quality assessment.NMR is then used in an experiment to investigate its applicability in measuring cooking loss, drip loss, and thaw loss.This was performed by using a transverse relaxometry experiment using a benchtop NMR at 6 MHZ at 4 °C of samples averaging 539 g -a size and temperature closer to what might be seen in industrial applications.Currently, the literature has exclusively investigated NMR as a tool for measuring meat quality on samples often around 1 x 1 x 5 cm in size and at room temperature.This study seeks to close that knowledge gap.Using multivariate cm à température pièce.Cette étude cherche à approfondir nos connaissances dans le domaine de qualité de viande par RMN.Après une analyse multi variable, une corrélation de r = 0.686, 0.573, 0.452 a été obtenue en perte à la cuisson, perte en eau et ainsi qu'en cycle de gèle-dégèle.Dans la deuxième partie, RMN a été utilisée pour mesurer le pourcentage de gras en état solide à 9 températures différentes pour chaque échantillon dans le but de prédire la valeur d'iode.Une corrélation de r = 0.87 a été obtenue.Ceci démontre l'applicabilité de l'RMN pour l'industrie.Cela aussi nous permet d'investiguer quels autres facteurs pourraient influencer la consistance d'un gras.En conclusion, l'RMN pourrait devenir applicable dans l'industrie si les recherches et développements de l'RMN ainsi que la viande se poursuivent.Elle démontre le potentiel de mesurer la perte à la cuisson, perte en eau et en cycle de gèle-dégèle, ainsi que la consistance des gras.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".