Using advanced vibrational molecular spectroscopy to detect moist heating induced protein structure changes in cool-climate adapted barley grain
Bibliographic record
Abstract
Different techniques have been applied in feed processing to improve ruminal degradation and nutrient utilization in ruminant. There are limited studies investigating how moist heating process impacts barley protein utilization and internal molecular structures. The objectives of this study were to investigate: 1) how moist heating affects barley protein chemical profiles and Cornell Net Carbohydrate and Protein System (CNCPS) subfractions, in situ rumen degradation parameters, and predicted intestinal protein supply and feed milk value; 2) how moist heating affects protein molecular spectral features; and 3) the relationship between protein molecular structure spectral features and protein chemical profiles and metabolic characteristics. The barley variety CDC cowboy samples collected from the research farm during two consecutive years were used. Half of each sample was kept as raw and the other half underwent moist heating. The advanced molecular spectroscopy (attenuated total reflectance-fourier transform infrared, ATR-FTIR) was used to detect the barley protein molecular structure spectral features. It was found that moist heating decreased the fractions of soluble protein and increased the moderately degradable protein and ingestible protein fractions. This further resulted in the changes of in situ rumen degradation parameters and intestinal protein digestion characteristics. The protein molecular structure spectral features detected by using ATR-FTIR spectroscopy can be used as potential predictors for protein related chemical and metabolic parameters.
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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) | 0.000 | 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".