Processed cranberry bean (<i>Phaseolus coccineus</i> L.) seed flour for the African diet
Bibliographic record
Abstract
With a view to supplementing protein-calorie in a developing country such as Nigeria, a study was conducted to determine the suitability of a little known crop, cranberry bean (Phaseolus coccineus L.). For this purpose, proximate analyses were done on mineral and amino acid composition of raw and processed seeds (roasted, sprouted, boiled and cooked) using standard analytical techniques. The processing methods showed deviations in nutrients from the raw seeds. Crude fat was found to be reduced by different processing methods, while crude protein was enhanced by roasting and sprouting. Processing significantly (P ≤ 0.05) affected the content of some minerals in P. coccineus seed flour. Roasting and sprouting reduced potassium content by 67.4 and 47.2%, respectively, while boiling and cooking increased the same mineral by 35.0 and 24.9%, respectively. All the processing methods reduced calcium content. Generally, processed cranberry bean seed flour was found to be a good source of essential minerals, and harmful heavy metals such as lead and cadmium were not detected. The amino acid profile revealed that roasting and sprouting enhanced total amino acid (TAA), total essential amino acid (TEAA) and total sulphur-containing amino acid (TSAA), while boiling and cooking reduced TAA, TEAA and TSAA. The limiting amino acid for raw and cooked seeds was Val, whereas TSAA were limiting in roasted, sprouted and boiled seeds. Sufficient proportions of the essential amino acids were retained after processing of the cranberry bean seed to meet FAO dietary requirement, so this crop is considered to be a valuable protein source for the African diet. Key words: African, chemical composition, cranberry bean, domestic processing, flour, Phaseolus coccineus L. seed
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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".