Vibrational and fluorescence spectroscopy to study gluten and zein interactions in complex dough systems
Bibliographic record
Abstract
The volume-spanning network formed by gluten during breadmaking is crucial in the production of high-quality bakery products. Zein proteins are also capable of forming a protein network under specific conditions. Vibrational (Fourier transform infrared spectroscopy (FTIR) and Raman scattering) and fluorescence spectroscopy are powerful, non-invasive techniques capable of assessing protein structures and interactions. The main objective of this project was to explore the suitability of these techniques to study zein and gluten structures and interactions in complex dough systems. The dough samples were prepared by mixing 20 w/w% of protein (with different proportions of zein and gluten) and 80 w/w% of corn starch. The tyrosine (Tyr) fluorescence emission peak (λexc = 280 nm) was still present even in those zein-gluten samples containing the highest gluten concentration and lowest zein concentration. This suggests that the Tyr moieties (stemming from zein) are not in close proximity to tryptophan (Trp) of gluten and their fluorescence is not quenched efficiently. Raman scattering results also showed the presence of different Tyr residues, exposed and buried, as well as different conformations of disulfide bridges, in zein and gluten samples. Based on the results from spectroscopic measurements and scanning electron microscopy (SEM), two distinct network structures composed of gluten and zein were identified in the mixed dough systems. The present work illustrates how complementary vibrational (Raman scattering and FTIR) and fluorescence spectroscopy methods can be combined to non-invasively assess protein structure and interactions in a complex food matrix.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".