Interactive association between processing induced molecular structure changes and nutrient delivery on a molecular basis, revealed by cutting-edge vibrational biomolecular spectroscopy
Bibliographic record
Abstract
BACKGROUND: This study was conducted to determine protein molecular structure profiles and quantify the relationship between protein structural features and protein metabolism and bioavailability of blend pelleted products (BPP) based on co-products (canola or carinata) from processing with different proportions of pulse pea screenings and lignosulfonate chemical compound. METHOD: The protein molecular structures were determined using the non-invasive advanced vibrational molecular spectroscopy (ATR-FT/IR) in terms of chemical structure and biofunctional groups of amides (I and II), α-helix and β-sheet. RESULTS: The results showed that increasing the level of the co-products in BPP significantly increased the spectral intensity of the amide area and amide height. The products exhibited similar protein secondary α-helix to β-sheet ratio. The protein molecular structure profiles (amides I and II, α-helix to β-sheet) were highly associated with protein degradation kinetics and intestinal digestion. In conclusion, the non-invasive vibrational molecular spectroscopy (ATR-FT/IR) could be used to detect inherent structural make-up characteristics in BPP. CONCLUSION: The molecular structural features related to protein biopolymer were highly associated with protein utilization and metabolism.
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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".