Physico Chemical Properties and in-vitro Protein Digestibility of Non-Wheat Cookies Prepared From Plantain Flour and Bambara Groundnut Protein Concentrate
Bibliographic record
Abstract
Plantain flour was prepared from matured-unripe fruits of Agbagba cultivar, protein concentrate was prepared from bambara groundnut seeds using the alkaline extraction method, plantain cookies were produced using different levels of plantain flour substituted with bambara groundnut protein concentrate ranging from 0-25% and using 100% wheat flour as control. Physical characteristics, proximate composition, sensory properties and in-vitro protein digestibility of the cookie samples were determined. Cookies prepared from 15% bambara groundnut protein concentrate and 85% plantain flour compared favourably in physical characteristics (weight, height, diameter and hardness) with the control (100% wheat flour). Addition of bambara groundnut protein concentrate significantly improved the crude protein content (17.8%), ash content (2.8%), crude fibre (9.2%) and energy (434.0 kcal/100g) of the cookies compared to values obtained from 100% wheat flour. Sensory evaluation showed that cookies with 15% bambara groundnut protein concentrate and 85% plantain flour was preferred in terms of colour, flavor and general acceptability with mean scores of 8.1, 8.3 and 7.8, respectively and showed no significant difference (P ? 0.05) with the control with mean scores of 8.6, 8.5 and 8.0, respectively. However, in-vitro protein digestibility of the cookies increased from 2.74% in cookies with 100% plantain flour to 62.81% in samples with 25% bambara groundnut protein concentrate and 75% plantain flour.
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 | 0.000 | 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".