Thermal Stability and Molecular Microstructure of Heat-Induced Cereal Grains, Revealed with Raman Molecular Microspectroscopy and Differential Scanning Calorimetry
Bibliographic record
Abstract
The objectives of the present study were to use Raman molecular microspectroscopy and differential scanning calorimetry (DSC) to reveal molecular thermal stability and thermal degradation behavior of heat-induced cereal grains and reveal the molecular chemistry of the protein structures of cereal grain tissues affected by heat processing and to quantify the protein secondary structures using multicomponent peak modeling Gaussian and Lorentzian methods. Hierarchical cluster analysis (CLA) and principal components analysis (PCA) were also conducted to identify molecular differences in the Raman spectra. Three cereal grain seeds, wheat, triticale, and corn, were used as the model for feed protein in the experiment. The specimens were autoclaved (moist heating) and dry-heated (roasted) at 121 °C for 80 min, respectively. Raman spectroscopy results revealed that there are marked differences in the secondary structures of the proteins subjected to various heating treatments of different cereals. The sensitivity of cereals to moist heating was much higher than the sensitivity to dry heating. The multivariate analyses (CLA and PCA) showed that heat treatment was significantly isolated between the different Raman raw spectra. The DSC study revealed that the thermal degradation behavior of cereals was significantly changed after moist- and dry-heat treatments. The position of the major endothermic peak of dry-heated cereals shifted toward a higher temperature, from 131.7 to 134.0 °C, suggesting the high thermal stability of dry-heated cereals. In contrast, the endothermic peak position was slightly decreased to 132.1 °C in the case of moist autoclaved heating. The digestive behavior and nutritive value of rumen-undegradable protein in animals may be related to the changes of the protein secondary molecular structure and thermal stability of the cereal grain materials, which is attributed by Raman microspectroscopy and DSC endotherm profiles.
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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".