Spectroscopic impact on protein and carbohydrate inherent molecular structures of barley, oat and corn combined with wheat DDGS
Bibliographic record
Abstract
The objectives of this experiment were to use non-invasive and non-destructive infrared molecular spectroscopy as a novel approach to explore and identify protein and carbohydrate molecular structure spectral features of DDGS (dried distillers grain solubles from wheat,Triticum aestivum) and its combinations with barley (Hordeum vulgare), corn (Zea mays) and oat (Avena sativa). The spectral parameters assessed in this study included amides, protein molecular structures ofα-helix andβ-sheet, lignin, cellulosic compounds (CeC) and nonstructural carbohydrates (NSC). The results of the study show that the combinations of DDGS with cereal grains significantly changed (P< 0.05) protein and carbohydrate structures and protein secondary structure. The use of FT/IR molecular spectroscopy in terms of identification of inherent structural changes was remarkable. The combination of DGGS with different grains alters the constituents and intrinsic molecular structures. This change would improve the nutritional quality and digestive characteristics of the feed. Further studies are recommended to evaluate the effect on digestibility, availability and its structural correlation.
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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".