The Investigation of Benzoic Acid Amounts in Some Foodstuffs Consumed in Ankara Region
Bibliographic record
Abstract
Benzoic acid and its salts are commonly used as a preservatives in food products. Excess amounts of benzoic acid can be harmful to human health. Therefore, the determination of benzoic acid is important in routine analysis of foods. The aim of this study was to determine amounts ofbenzoic acids in some foodstuffs and to evaluate whether these amounts were within the Turkish Food Codex (TFC) values or not. For this purpose, total number of 80 samples consisting of 20 ketchup (A, B firms), 20 sauce (C, D firms) and 40 jam samples (E, F, G, H, I, J, K firms) were collected from supermarkets, Ankara Region. In this research, spectrophotometric method was used for the quantitative determination of benzoic acid in ketchup, sauce and jam samples. Mean amounts (X± S.E) of benzoic acidin ketchupsamples of A and B firm were found as 152.32±18.41 and 1008.21±30.74 mg/kg, respectively. Mean amounts (X± S.E) of benzoic acidin sauce samples of C and D firm were determined as 990.85±26.00 and 1148.19±43.62 mg/kg, respectively. Also, mean amounts (X± S.E) of benzoic acidwere found as 435.27±26.07mg/kg in 8 jam samples of E firm. Our data revealed that while mean amounts of benzoic acid of A and C firms were found within TFC values, benzoic acid amounts of B and D firms samples were higher than the TFC values. Furthermore, some jam samples of firm E was not found appropriate to TFC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".