<sup>1</sup>H Nuclear Magnetic Resonance (NMR) and Differential Scanning Calorimetry (DSC) Studies of Water Mobility in Dough Systems Containing Barley Flour
Bibliographic record
Abstract
ABSTRACT This study used 1H nuclear magnetic resonance (NMR) spin‐spin relaxation time (T2) and differential scanning calorimetric (DSC) measurements of unfreezable water content (UFW), to assess water behavior in freshly prepared (25°C), refrigerator‐stored (4°C, one day), or freezer‐stored (–35°C, one day) doughs containing 5, 10, or 30% whole grain, air‐classified β‐glucan‐diminished, and air‐classified β‐glucan‐enriched (BGB‐E) barley flours. Three populations of water were detected by NMR, depending on moisture content of dough, namely, tightly (T21, 2–5 msec), less tightly (T22, 20–50 msec), and weakly (T23, 100–200 msec) bound water. T22 peak was always detectable, and T22 peak time linearly correlated to moisture content of dough in a range of 0.7–2.0 g/g db (r = 0.99, P < 0.05). Freezer storage showed less effect on water mobility in dough compared with refrigerator storage, whereas cooking and cool storage of cooked dough significantly decreased the water mobility (P < 0.05). Adding barley flour steadily decreased the water mobility in dough, and the reduction was more significant with adding BGB‐E (P < 0.05). Immobile water content was calculated by extrapolating T22 peak time versus total moisture content in dough and significantly correlated to the UFW content measured by DSC (r = 0.72, P < 0.05).
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 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".