Development of sugar- and fat-reduced pulse cookies with improved predicted glycemic behavior
Bibliographic record
Abstract
• The use of specialty fibres successfully reduced the sugar and saturated fat contents of cookies. • 100 % butter substitution with “fat block” induced cookie hardening. • 100 % pulse flour cookies were high in protein and fibre. • Pulse flour cookies had higher protein digestibility and lower predicted glycemic response compared to wheat cookies. • Sugar- and saturated fat-reduced lentil flour cookies had good overall acceptability by the elderly consumer panel. Cookies, a trendy snack, are traditionally characterized by a poor nutritional profile (high sugars and fats, low protein and fiber). To improve their nutritional profile, standard wheat “pasta-frolla” cookies were reformulated with 100 % pulse flour (chickpea and lentil), partial sugar reduction with a commercial fiber syrup (Meltec®), and full butter replacement using a structured fiber-sunflower oil-water emulsion (alone or in conjunction). Developed cookies had higher protein and fiber contents, reduced sugar (∼45 - 50 % reduction), and saturated fats (∼77 - 80 % reduction) and also had a lower predicted glycemic index compared to traditional cookies. Water activity and moisture content of the cookies were in line with those of the same product category, while they had a harder texture compared to their full butter counterparts due to the full substitution of butter (alone or in combination with sugar reduction). Lentil cookies showed slightly lower in vitro starch and remarkably higher protein digestibility than the control cookie, and the simultaneous application of sugar and fat substitution did not negatively affect their overall acceptability. The developed products are expected to be a suitable base for the development of snacks for elderly consumers, who are the population niche that, due to health issues, is most likely to be interested in this type of cookies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".