Quality evaluation of value-added nutritious biscuit with high antidiabetic properties from blends of wheat flour and oyster mushroom
Bibliographic record
Abstract
The aim of this study was to produce biscuit from blend of mushroom flour and wheat flour. The composite flour was at 0, 10, 20, 30 and 40% level of mushroom inclusion. The biscuit was analyzed for proximate composition, physical properties, mineral content, antioxidant properties, carbohydrate inhibitory enzyme activities, glycemic index, trend in blood glucose concentration, microbiological and sensory evaluation. Result from the proximate analysis showed that as the proportion of mushroom flour increased in the blend the protein con also increased. The protein content ranged from 10.36 to 17.92 % while ash content ranged from 0.63 to 3.31%. Result from the physical properties of the biscuit showed that the spread ratio increased with increase in incorporation of the mushroom flour. The result from the mineral analysis revealed that sodium and calcium content increased with the increase in the level of the mushroom flour. Result from carbohydrate inhibitory enzyme activities showed that the biscuit inhibited key digestive enzymes such as (α- amylase and α- glucosidase). The result of glycemic index showed that the biscuit possessed low glycemic index and glycemic load. The trend in blood glucose concentration of rats fed with the biscuit showed that increasing level of the mushroom flour has a positive effect to lower the postprandial blood glucose response with WFMRB4 being the lowest. The microbial count ranged from 2 × 102 to 5.0 × 102 Cfu/g for bacteria count and 1.0×102 to 3.0 × 102 cfu/g for mould count. E coli was not detected in the mushroom biscuit. Sensory evaluation result showed that the biscuits were rated above average. The mushroom biscuit can therefore be used to manage diabetes mellitus and overcome protein-energy deficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".