Effect of Spirulina in Bread Formulated with Wheat Flours of Different Alveograph Strength
Bibliographic record
Abstract
Consumers within the EU are increasingly asking for natural and healthier food products, which are additive-free and environmentally friendly. The aim of this study was to assess the effects of Spirulina (Arthrospira sp.) in bread formulated with four wheat flours with different alveograph strengths. The flours used were Manitoba Flour (00/251), Ground-force wholemeal (Whole/126), Standard Bakery Flour (0/W105), and Organic Bakery Flour (2/W66). Powdered Spirulina biomass was used as a new ingredient with a high nutritional profile and bioactive compounds; incorporation was made at two levels: 1.5% and 2.5% of the flour amount. The same bread recipe was used for all formulations, but for the 1.5 and 2.5% variations, 6 g and 10 g of Spirulina were added, respectively. Antioxidant capacity increased with increasing microalgal biomass. The visual and taste attributes of the breads with microalgae underwent noticeable changes compared to their counterparts without microalgae. Biomass addition significantly (p < 0.05) affected bread weight and volume, and different trends were found based on the type of wheat flour. Spirulina-containing breads showed a greener coloration while the microalgae concentration was augmented. The moisture and texture were slightly affected by the addition of the biomass at both levels. The 2.5% concentration samples were well accepted in most cases by consumers, emphasizing the salty flavor as a pleasant feature. No significant sensory differences were observed between samples, and the acceptability index was always higher than 72%. The results show that Spirulina could be an environmentally friendly ingredient for the reformulation of nutritionally enhanced bread with a good texture that is well-accepted by consumers.
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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) | 0.001 | 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) | 0.001 | 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".