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Record W4296416922 · doi:10.1080/23311932.2022.2123769

Risk assessment of aflatoxin in red peppers from selected districts of Amhara region, Ethiopia

2022· article· en· W4296416922 on OpenAlexaff
Efrata Adugna, Yohannes Abebe, Muluken Dejen, Marye Alemu, Atnafu Guadie, Mengistu Mulu, Endalkachew Bizualem, Mintesnot Worku, Molla Tefera

Bibliographic record

VenueCogent Food & Agriculture · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAflatoxinGeographyBiologyBiotechnology

Abstract

fetched live from OpenAlex

Red pepper is most widely consumed food in the world and easily damaged by mycotoxic fungi during growing, harvesting, transporting, storage and processing stages. Red pepper is susceptible to degradation by aflatoxins-producing fungi which have serious health effects on humans and livestock. Therefore, in this study, the levels of aflatoxin (B1, B2, G1 and G2) were determined and the potential health risk in red pepper samples collected from selected districts of Amhara Region, Ethiopia, were evaluated. The levels of AFB1, AFG1, AFB2 and AFG2 were ranged from 2.51–63.20, 0.96–5.29, 1.71–32.79 and 0.53–2.04 µg/kg, respectively. Among 18 investigated red pepper samples, 44.5% (8 samples) contaminated with aflatoxins. All the contaminated red peppers contained AFB1 and AFG1. Three-fourth (75%) of the total aflatoxins detected in red pepper were greater than maximum permissible levels set by European Union (10 µg/kg). Estimated Daily Intake (EDI) ranged from 0.00013 to 0.015800 µg/kg b.w/day. The Margins of Exposure (MOE) values for all aflatoxins were far from the safe margin (< 10,000), indicating potential health risk due to red pepper consumption. Therefore, public institutions, non-governmental and private organizations should give attention in order to raise awareness of aflatoxins’ effects on human health.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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