Chromium and arsenic speciation analysis in meats by HPLC-ICP-MS in the presence of hydrolyzed oat proteins with radical scavenging activities
Bibliographic record
Abstract
Transition metals play an important role in a wide variety of biological processes, but their functions are dependent on the quantity and the type of species present. Specific forms of arsenic (As) and chromium (Cr) are associated with oxidative stress, cellular damage and inflammation. The aim of this research was to test in a food system whether, in the presence of hydrolyzed oat proteins, arsenic or chromium will exist predominantly in a specific oxidative state, and to evaluate the potential implication of promoting or decreasing oxidative stress. Eight hydrolyzed proteins with different degrees of radical scavenging activities were produced by combining two extraction methods and four proteases. The addition of hydrolysates to ground chicken meat decreased lipid hydroperoxides by up to 50% when stored at 4 °C but had no effect at -20 °C. The ratio of pentavalent arsenic (As(V)) to arsenobetaine (AsB) in meat was about 2:1 but in the presence of the hydrolysates, meanwhile, the amount of AsB detected was 3-fold higher depending on the storage condition. This was due to better extraction of AsB in the presence of hydrolysates rather than to the conversion of other species. Data on chromium showed that Cr(VI) contents decreased from 14.3 ± 0.1 to 6.3 ± 0.5 μg/g while concentrations of Cr(III) increased from 2.8 ± 0. 2 to 8.6 ± 0.7 μg/g. In summary, the addition of hydrolyzed oat proteins to chicken meat enhanced the extraction of AsB, and had little effect on arsenic speciation during storage meanwhile, there was a reduction of Cr(VI) to Cr(III) which was in part due to the relative content of thiol groups. Additionally, there was a reduction of lipid oxidation in meats that contained the oat protein hydrolysates.
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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) | 0.001 | 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) | 0.001 | 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".