Susceptibility of the In-shell Brazil Nut Mycoflora and Aflatoxin Contamination to Ozone Gas Treatment during Storage
Bibliographic record
Abstract
The effect on fungi load, toxigenic Aspergillus strains and aflatoxin (AFL) contamination of stored in-shell Brazil nut (Bertholletia excelsa H.B.K.) ozone (O3) gas treated were evaluated. Groups of nuts obtained from retail market were submitted to O3 atmosphere at different concentrations (10, 14, 31.5 mg/l) and stored for 180 days. The O3 treatment affected Brazil nuts mycoflora growth, reduced their moisture content (mc) and degraded AFLs. From the three O3 concentrations applied, 31 mg/l (5 hours exposition) was able to successfully destroy fungi contamination (initial: 4.83 logcfu/g to ng-no grow), including the Aspergillus flavus and A. parasiticus species, since Day One after application. On the other hand, they were still able to grow, at the lower O3 concentrations (10; 14 mg/l), however only in the first days of storage and at reduced number though (from 4.83 to 3.5/3.3 logcfu/g, respectively). Despite of the O3 concentrations applied, AFLs were not detected in all nut samples O3 treated since Day One of application up to the method LOQ of 1.34 µg/kg except for 10 mg/kg). As expected, a reduction of mc (9.43 to 7.32 %) and aw (0.82 to 0.63) due to gas stream application was registered throughout the storage period, which increased with the O3 time of exposure resulting cruncher Brazil nuts. Apart from low cost and simple technology to be applied during storage in-land or in containers before shipping, O3 treatment it is a promising alternative for contamination control and is environment friendly.
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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".