Development of a synbiotic snack for gut–brain axis health
Bibliographic record
Abstract
Snacks are ideal vehicles to intervene in the diet of consumers as they are becoming a staple in the modern diet. This study aimed to develop a synbiotic snack capable of delivering both prebiotics and live probiotics with a potential positive impact on supporting the gut-brain axis system by incorporating chickpea and red lentil flour as a source of protein and fibre, specialty fibres as sugar and fat/saturated fat substitutes, and dark chocolate enriched with a probiotic formulation (SLAB51®) with proven neuroprotective properties in a mouse model. Compared with a conventional wheat snack, the developed snacks presented not only better nutritional composition (high in proteins, fibres, and unsaturated fatty acids and low in sugars and total/saturated fats) but were also characterised by high prebiotic potential towards the SLAB51® multi-strain probiotic formulation, low glycaemic index, and the ability to induce slightly increased satiety. The developed snacks had a good shelf life with minimal chocolate blooming and high probiotic viability after six months of storage at both 25 °C and 4 °C (41.7% and 88.0% survival, respectively). Positive consumer response was observed among the senior population (>65 years old), with moderate acceptability and high willingness to buy in the senior population upon disclosure of pulse ingredients and potential health benefits. This research provides comprehensive scientific evidence for developing nutritional and healthy food products with a potential synbiotic effect tailored to an ageing population, without neglecting the pleasure of treating yourself to a good chocolate-coated cookie.
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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 | 0.001 | 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".