Isolation of Filamentous Fungi Species Contaminating Some Nigerian Food Commodities
Bibliographic record
Abstract
Samples of maize, rice, cocoa and cocoa-based powder beverage) collected from different stores and markets in south-western Nigeria were screened for filamentous fungi contamination using conventional and molecular methods. Samples were cultured aseptically on potato dextrose agar (PDA), ohio agricultural experimental agar (OEASA), Malt Extract Agar (MEA) and Czapek Yeast Agar (CYA) prior to fungi isolation. Conventional methods comprising of macroscopic and microscopic evaluation of isolated fungi species were implemented in the analysis for identification of fungi species. Molecular method of identification involved DNA extraction, Polymerase chain Reaction (PCR) using ITS-1/ITS-4 primer pair and nucleotide sequencing. Results obtained indicated a range of filamentous fungi genus including Aspergillus, Penicillium, Fusarium, Alternaria, Cladosporium and Rhizopus contaminating the food commodities with Aspergillus and Penicillium species dominating most of the samples. High incidences were recorded for Aspergillus flavus, Aspergillus niger and Aspergillus fumigatus in most of the samples screened. The occurrence of these filamentous fungal species pose a reason for concern as most of these fungal species are known producers of toxic substances. This study was carried out to contribute to mycological screening of different Nigerian food commodities for a variety of filamentous fungi species.
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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".