THE EFFECT OF SASKATOON BERRY (AMELANCHIER ALNIFOLIA NUTT.) ADDITION ON THE TECHNOLOGICAL PROPERTIES OF WHEAT FLOUR AND THE QUALITY OF BISCUITS
Bibliographic record
Abstract
Nowadays, it is very popular to enrich biscuits with less typical ingredients and thus increase their nutritional value. The aim of this work was to monitor the effect of the addition of lyophilized Saskatoon berries (Amelanchier alnifolia Nutt.) mixed with wheat flour T650 in different proportions on the technological quality of the flour (moisture, ash, falling number, crude protein, wet gluten, acidity) and to compare the chemical (aw, dry matter, crude protein, ash) and sensory parameters (appearance, aroma, taste, consistency, overall perception) of biscuits prepared without and with 5, 10 a 15 % additions of this fruit. With the increased addition of Saskatoon berry to the flour mixture, its moisture content decreased. Compared to the control sample, the ash content increased from 0.59% (control) to 0.87 % (15%) with the addition of fruits, as did the acidity of the flour (p < 0.05). On the contrary, with the increasing proportion of the addition of the powder of this fruit in the mixture with wheat flour, some technological parameters decreased (p < 0.05) (gluten content and its quality, falling numbers). The ash content of the biscuits increased from 0.88 (control) to 1.14 % (15 %), but the crude protein content decreased with increasing Saskatoon berry addition. In biscuits with the addition of this fruit, the value of water activity was significantly reduced from 0.57 (control) to 0.31 (15 %) (p < 0.05). Based on organoleptic evaluation, the replacement of up to 10 % and 15 % of wheat flour with Saskatoon berry powder improved the sensory properties (appearance and taste) of the prepared biscuits compared to control wheat biscuits. We can recommend this non-traditional fruit for application in the production of biscuits (adding 10 and 15 %), for its color and nutritional composition it can increase biscuits attractiveness and become a perspective when designing new 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".