Impact of Corn Fiber on the Physicochemical/Technological Properties, Emotions, Purchase Intent and Sensory Characteristics of Gluten Free Bread with Novel Flours
Bibliographic record
Abstract
Gluten-free products present huge advantages for individuals with gluten intolerance or celiac disease. The most influential strategy to develop a product comparable to wheat-containing gluten products is to promote its nutritional and sensorial attributes. Recently, rice flour and teosinte flour were used to develop gluten-free bread. As a result, this work aimed to compare the physical/technological attributes of bread made with rice flour and teosinte flour with corn fiber fortification and examine consumer acceptance and purchase intent. An extreme vertices mixture design decided that a teosinte flour (43.4%), high protein white rice flour (55%), and high protein brown rice flour (1.6%) ratio of gluten-free flour mixture for bread was best based on the sensory attributes utilized for the desirability function methodology. After optimizing the gluten-free flour mixture, the bread was fortified with corn fiber under 1.7 g and 3.8 g per serving, and it was evaluated to examine its physical/technological attributes, consumer perception, liking, emotions, and purchase intent. Incorporating fiber into gluten-free bread impacted the color and texture, giving lighter and yellower colors with a firmer texture. Likewise, concerning health benefit claims, gluten-free bread with fiber claims only improves the purchase intent and overall liking of bread containing 1.7 g per serving of fiber. The flavor attribute was the main driver for consumers’ purchase decisions. This product could be beneficial for consumers who are seeking fiber in their diet.
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".