Application Potential of ATR-FT/IR Molecular Spectroscopy in Animal Nutrition: Revelation of Protein Molecular Structures of Canola Meal and Presscake, As Affected by Heat-Processing Methods, in Relationship with Their Protein Digestive Behavior and Utilization for Dairy Cattle
Bibliographic record
Abstract
Protein quality relies not only on total protein but also on protein inherent structures. The most commonly occurring protein secondary structures (α-helix and β-sheet) may influence protein quality, nutrient utilization, and digestive behavior. The objectives of this study were to reveal the protein molecular structures of canola meal (yellow and brown) and presscake as affected by the heat-processing methods and to investigate the relationship between structure changes and protein rumen degradations kinetics, estimated protein intestinal digestibility, degraded protein balance, and metabolizable protein. Heat-processing conditions resulted in a higher value for α-helix and β-sheet for brown canola presscake compared to brown canola meal. The multivariate molecular spectral analyses (PCA, CLA) showed that there were significant molecular structural differences in the protein amide I and II fingerprint region (ca. 1700-1480 cm(-1)) between the brown canola meal and presscake. The in situ degradation parameters, amide I and II, and α-helix to β-sheet ratio (R_a_β) were positively correlated with the degradable fraction and the degradation rate. Modeling results showed that α-helix was positively correlated with the truly absorbed rumen synthesized microbial protein in the small intestine when using both the Dutch DVE/OEB system and the NRC-2001 model. Concerning the protein profiles, R_a_β was a better predictor for crude protein (79%) and for neutral detergent insoluble crude protein (68%). In conclusion, ATR-FT/IR molecular spectroscopy may be used to rapidly characterize feed structures at the molecular level and also as a potential predictor of feed functionality, digestive behavior, and nutrient utilization of canola feed.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".