Influence of Quinoa Flour on Quality Characteristics of Cookie, Bread and <scp>C</scp>hinese Steamed Bread
Bibliographic record
Abstract
Abstract Quinoa has unique physicochemical and nutritional properties among diverse food grains. Quinoa flour (QF) was blended into wheat flour (WF) at weight ratios of 85/15, 70/30, 55/45, 40/60, 25/75 and 10/90 to formulate composite flour for the production of cookie, bread and Chinese steamed bread (CSB). Physicochemical properties of quinoa–wheat composite flour (QWCF) and quality characteristics of the bakery products were characterized. The feasibility of using QF in CSB making was explored for the first time. Compared with products of WF, the resulting products from QWCF had reduced specific volume, and increased density, hardness and chewiness of the texture, darkness, redness, and yellowness of the color. The mold‐free shelf life of bread and CSB increased as a function of QF level. The influence of QF addition on the physicochemical properties of bakery products is product‐type sensitive. Practical Applications Addition of quinoa flour diversifies wheat flour products with certain modification on physicochemical and nutritional qualities, and thus could expand both flours in overall food applications. These findings could provide some insights for industrial research and development using quinoa grain for novel bakery products.
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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".