The Potential of Baobab (Adansonia digitata L.) Extracts as Biocontrol on the Growth and Aflatoxin Production by Aspergillus flavus and A. parasiticus
Bibliographic record
Abstract
Moulds and associated mycotoxins, especially aflatoxins, are important factors that advesely affect food and feed produced from contaminated plant and animal prodcuts. They are lethal to humans and animals, which emphasizes the great concern in food and feed production. In this study, the effects of baobab (Adansonia digitata) extracts on the vegetative growth and aflatoxin secretion by A. flavus (SQU21) and A. parasiticus (CBS921.7) strains were exzmined. Different concentrations of baobab fruit extract (1.5, 3, 5, and 7% w/v) and essential oil (0.5, 1, 3 and 5% v/v) was used. Fruit extract of baobab apparently inhibited the total aflatoxin secretion up to 20.4-68.5% for A. flavus and 11.9-69.1% for A. parasiticus, whereas the inhibition of aflatoxin B1 production ranged between 29.9-79.2% and 13-68% for the two strains, respectively. The highest inhibition levels of total aflatoxin and aflatoxin B1 secretion by A. flavus (47.2-95.7%; 28.1-89.7%) and A. parasiticus (42.7-93.3%; 25.9-80.2%) were obtained with essential oil extracted from baobab seeds. The two extracts significantly reduced the vegetative growth and the mycelial dry weights of selected fungi. This indicates the antifungal activity and inhibitory effect of baobab on the growth and aflatoxin production by the two toxigenic strains. Thus, fruit extract and essential oil of A. digitata can be suggested as potentially effective biocontrol and biopreservative substrates against food and feed contamination by aflatoxigenic moulds.
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) | 0.000 | 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".