Evaluation of rapid evaporative ionization mass spectrometry (REIMS) for the prediction of slice shear force and biochemical markers of tenderness in beef Longissimus lumborum steaks
Notice bibliographique
Résumé
The objective of this study was to evaluate rapid evaporative ionization mass spectrometry (REIMS) as a rapid method to predict slice shear force (SSF) and biochemical markers of beef tenderness. Steak samples were randomly collected from beef carcasses (Canada AA, n = 1505; Canada AAA, n = 1363) over a three-year period. Steaks were aged for 14 d, then tenderness was determined using SSF. Metabolomic profiling of beef samples was performed using REIMS (N = 2,853). A subset of samples (n = 600) were selected to evaluate sarcomere length, myofibril fragmentation index (MFI), desmin, and troponin-T degradation. Thirteen machine learning algorithms were used to build several predictive models. Data were reduced using feature selection (FS) and principal component analysis followed by FS (PCA-FS). No models could predict SSF tenderness category with a higher accuracy than the no information rate (NIR, 59.5%) for FS and PCA-FS datasets (P ≥ 0.05). Population mean and standard deviation (SD) were used to generate 4 SD categories (± 2) for further predictions. No models could predict SD category with a higher accuracy than the NIR using the FS dataset (P > 0.05). Accuracies to predict SD category using the PCA-FS dataset ranged from 55.0% to 83.0%. Top accuracies of 82.8% and 83.0% were generated from the treebag and random forest (RF) algorithms (NIR = 55.0%, P < 0.001). Accuracies to predict quality grade using the FS dataset ranged from 52.5% to 85.3%. Top accuracies of 84.6% and 85.3% were generated from SVM Radial and XGBoost, respectively (NIR = 52.5%, P < 0.001). Using the PCA-FS dataset, all models could predict quality grade with a higher accuracy than the NIR (P < 0.001). The top accuracies of 82.8% and 84.2% were generated from SVM Radial and RF (P < 0.001). A stepwise regression model was built to evaluate the relationship between SSF values and the spectra data generated from REIMS (N = 2,853). The selected REIMS bins accounted for 7.2% of the variation in predicted SSF value (R2 = 0.072; P < 0.001). Stepwise regression models were built to evaluate the relationship between sarcomere length, MFI, intact desmin, degraded desmin, intact troponin- T, and degraded troponin- T and the spectra data generated from REIMS (n = 600). The selected REIMS bins accounted for 64.4% of the variation in predicted sarcomere length (R2 = 0.644, P < 0.001), 31.6% in predicted MFI (R2 = 0.316, P < 0.001), 58.3% in predicted intact desmin (R2 = 0.583, P < 0.001), 50.9% in predicted degraded desmin (42kDa) (R2 = 0.509, P < 0.001), 54.2% in predicted degraded desmin (38kDa) (R2 = 0.542, P < 0.001), 12.8% in predicted intact troponin- T (R2 = 0.128, P < 0.001), 6.9% in predicted degraded troponin- T (30kDa) (R2 = 0.069, P < 0.001), and 8.4% in predicted degraded troponin- T (28kDa) (R2 = 0.084, P < 0.001). Segregation of samples based on SSF, sarcomere length, MFI, desmin, and troponin- T degradation was performed using K- means clustering, resulting in 3 clusters. The top accuracy using the FS dataset to predict cluster was 50.9%, generated from the XGBoost algorithm (NIR = 41.5%, P = 0.03). No models could predict k- means cluster with a higher accuracy than the NIR using the PCA-FS dataset (P > 0.05 for all models). Overall, REIMS showed an ability to predict the most tender and toughest steaks with a relatively high degree of accuracy. The RF and Treebag algorithms performed well in identifying tenderness and quality grade, so these algorithms could be further developed to improve prediction accuracies, allowing REIMS to be used as a rapid assessment of carcass quality.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».