Dry and moist heating-induced changes in protein molecular structure, protein subfraction, and nutrient profiles in soybeans
Bibliographic record
Abstract
Heat processing has been used to improve protein utilization and availability of animal nutrition. However, to date, few studies exist on heat-induced protein molecular structure changes on a molecular basis. The aims of this study were to use molecular spectroscopy as a novel approach to determine heat-induced protein molecular structure changes affected by moist and dry heating and quantify protein molecular structures and nutritive value in the rumen and intestine in dairy cattle. In this study, soybean was used as a model for feed protein and was autoclaved at 120°C for 1h (moist heating) and dry heated at 120°C for 1h. The parameters assessed in this study included protein structure α-helix and β-sheet and their ratio, protein subfractions associated with protein degradation behaviors, intestinal protein availability, and energy values. The results show that heat treatments changed the protein molecular structure. Both dry and moist heating increased the amide I-to-amide II ratio. However, for the protein α-helix-to-β-sheet ratio, moist heating decreased but dry heating increased the ratio. Compared with dry heating, moist heating dramatically changed the chemical and nutrient profiles of soybean seed. It greatly decreased soluble crude protein, nonprotein nitrogen, and increased neutral detergent insoluble protein. Both dry and moist heating treatments did not alter digestible nutrients and energy values. Heating tended to decrease the nonprotein nitrogen fraction (soluble and rapidly degradable protein fraction) and true protein 1 fraction (fast-degradable protein fraction). Conversely, the true protein 3 fraction (slowly degradable fraction) significantly increased. The in situ rumen study showed that moist heating decreased protein rumen degradability and increased intestinal digestibility of rumen-undegradable protein. Compared with the raw soybeans, dry heating did not affect rumen degradability and intestinal digestibility. In conclusion, compared with dry heating, moist heating dramatically affected the nutrient profile, protein subfractions, rumen degradability, intestinal digestibility, and protein molecular structure (amide I-to-II ratio; α-helix-to-β-sheet ratio). The sensitivity of soybean seed to moist heating was much higher than that to dry heating in terms of the structure and nutrient profile changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".