Assessment of Important Sensory Attributes of Millet Based Snacks and Biscuits
Bibliographic record
Abstract
There is an increasing push by consumers for new food products that can provide health benefits. To develop these products, sometimes it is necessary to look to alternative crops, 1 of which is millet. For millet to be successfully adopted by consumers, it is necessary to identify and develop product types that are acceptable to North Americans. Biscuits and extruded snacks were produced using varying amounts of refined proso millet flour (0%, 25%, 75%, and 100%). Sensory analysis was conducted on 8 products (4 types of biscuits and 4 types of extruded snack) in 2 separate tests (1 for biscuits and 1 for snacks). Preferred Attribute Elicitation (PAE), a relatively new sensory method, was used to determine attributes affecting liking of the products. Results indicated that as the amount of millet in the biscuits and extruded snacks increased, the liking of the flavor, texture and overall liking decreased. Millet contributed to a bitter taste and bitter aftertaste, and resulted in gritty and dry food products. Further work is required to refine the products tested as well as to identify further products that can be added to the diet in order to take advantage of the health benefits that millet provides.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".