Investigation of Aflatoxin Content Using HPLC to Ensure the Sustainability of Rice Products in Jakarta City, Indonesia
Bibliographic record
Abstract
Aflatoxin exposure in foodstuff is a public health problem because it causes acute and chronic carcinogenic effects.Countries with hot and humid climates, aflatoxin exposure is a serious problem, and rice product is one of the foodstuffs that have the potential to be contaminated.Indonesia is a country that consume rice as a staple food.To ensure the sustained availability of safe rice products, initial identification of the aflatoxin content is necessary.This study aims to trace the aflatoxin content in rice obtained randomly from traditional market and a logistic agency in the Jakarta area, Indonesia.The number of samples is 20 types of rice grouped by price, area, and location category.Rice samples were tested for aflatoxin exposure by using a HPLC method in a laboratory that has been accredited by the Indonesian National Accreditation Committee.Results show that 95% of the samples have aflatoxin levels below 0.50 mg/kg as the limits of international standards and regulations for AFB1, AFB2, AG1, AG2, and AFT.However, 5% of samples show that AFB1 content is 2.45 mg/kg and AFT content is 2.62 mg/kg.These contents are higher than the limits regulation (maximum 2.0 mg/kg).The results indicate that there is a risk of aflatoxin contaminations in rice traded in the market within Jakarta area, Indonesia.It is important to maintain post-harvest processes with good procedures, including distribution, storage, and packaging to minimize the appearance of aflatoxin.
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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".