Nutritional Quality and Functional Properties of Baobab (Adansonia digitata) Pulp from Tanzania
Bibliographic record
Abstract
Baobab (Adansonia digitata L.) is a majestic tree associated with human habitation in some of the semi-arid regions of Africa and establishes an enormous economic and nutritional importance to the rural residential districts. The fruit pulp is considered to be of high nutritional significance; particularly vitamin C and calcium, also possess antioxidant functions as well as high dietary fiber content. Although it is a potential fruit for improving local diets and livelihoods,this fruit is underutilized and its potential not yet fully acknowledged. This work was contracted with the aim of defining the nutritional quality and functional properties of baobab pulp harvested from some selected parts of Tanzania.Results indicated that the pulp from the three locations had moisture content which ranged between 9.16% to 10.30%, fat 0.46%-1.98%, ash 4.75%-5.21%, fiber 5.91%-9.65%, protein 3.23%-3.53%, carbohydrate 80.49%-85.19, vitamin C 169.74mg/100g-231.57mg/100g, beta-carotene 2.16 mg/100g-3.19mg/100g.Fructose 0.56±0.15-0.81±0.17g/100g, glucose 0.77±0.26-0.87±0.31g/100g and sucrose 0.75±0.25-0.84±0.29g/100g. The substantial differences (p≤ 0.05) between locations were observed in fat, crude fiber, carbohydrates, and fructose. Vitamin C, beta-carotene, protein, ash, moisture, sucrose and glucose showed no significance difference (p≤ 0.05) among locations. The functional properties included emulsification, foaming and gelling properties which ranged between 37.9-45.15%, 1.85-6.57% and 11-12% respectively and were significantly different (p≤ 0.05) among locations. The results show that baobab pulp has a good content of nutrients and functional properties which can be useful in food industries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".