PSI-18 Effect of Heat Processing Methods on Carbohydrate Subfractions and Degradation in Relation to Carbohydrate Molecular Spectral Profile of Barley Grain Using Advanced Molecular Spectroscopy in Ruminants
Bibliographic record
Abstract
Abstract To our knowledge, there are few studies on the association between carbohydrate molecular structure spectral profiles of barley grain and ruminant-relevant nutritional characteristics. This study aimed to study associate processing-induced changes in carbohydrate molecular structure with changes in ruminant-relevant carbohydrate nutritional profiles. The heat processing methods included: dry roasting, autoclaving, and microwave irradiation. The ruminant-relevant carbohydrate nutritional profiles were determined which included carbohydrate chemical profiles, carbohydrate subfractions, ruminant-relevant carbohydrate digestion. The molecular structure spectral profiles were determined using vibrational molecular spectroscopy (ATR-FT/IR). The results showed that heat related processing significantly induced carbohydrate molecular spectral profiles. The heat related processing also significantly changed ruminant-relevant nutritional characteristics. There was an association between processing induced carbohydrate molecular structure changes and ruminant-relevant carbohydrate nutritional profiles. The advanced vibrational molecular spectroscopic technique (ATR-FTIR) shows the great potential as a fast analytical tool to predict ruminant-relevant carbohydrate nutritional characteristics.
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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".