Morphological and Molecular Characterization of Ochratoxin A Producing Black Aspergilli from Grape Pomace
Bibliographic record
Abstract
Grape pomace (GP), a winery by-product is increasingly being explored as food ingredients. Ochratoxin A (OTA), a natural toxigenic metabolite frequently found in wine and its by-products. Black Aspergilli are mainly responsible for OTA build-up and contamination of grapes and winery by-products. The fungal population in GP of five grape cultivars were enumerated and characterized. Fungal population ranged from 4.27±0.05 to 5.35±0.04 Log CFU/mL with GP from Chardonnay being the most contaminated. Aspergillus niger (81.1%) was found to be the major source of contamination and most frequently isolated fungal species. Other fungal isolates were A. carbonarius (13.51%) and A fumigatus (5.39%). Fungal contamination of GP correlated with the type of grape cultivars used for the pomace. Fourteen identified mold isolates were confirmed by PCR using primer pairs ITS1/NIG, ITS1/CAR and ITS1/FUM. Random amplified polymorphic DNA (RAPD) analysis with eight primers OPC-04, OPC-06, OPC-08, OPC-10, OPC-11, OPC-12, OPC-13 and OPC-14 revealed similarity in band patterns between the isolates and the control. Clustering of banding patterns generated from amplification with primer OPC-12 using Pearson’s coefficient detected similarity at 99.10%, 97.60%, 86.30% and 99.40%, 99.10%, 87.60%, 78.50% among Aspergillus niger and Aspergillus carbonarious strains, respectively, confirming the identification of potential ochratoxigenic black Aspergillus strains in the GP. The findings from this study suggest that GP obtained from some grape cultivars could be unsafe as food ingredients due to contamination by ochratoxigenic-producing molds, which is an indicative factor for the presence of ochratoxin A and other mycotoxins.
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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 | 0.000 |
| 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) | 0.000 | 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".