Study on determination of histamine in food by on-line automated pre-column derivatization coupled with HPLC
Bibliographic record
Abstract
This study proposed a new analytical method for the determination of histamine in food by on-line automated pre-column derivatization coupled with high performance liquid chromatography(HPLC).By optimizing the related parameters in determination such as the automated pre-column derivatization program,the amount of derivatization reagent,pH of the reaction system,the appropriate conditions for HPLC determination were set up.Under the established conditions,the limit of detection for histamine standard is 0.01 μg/mL,and good correlation coefficient(r20.999) can be achieved in the range of 0.05～100 μg/mL.By determination of the sipked samples,the limit of detection for real samples is 0.2 mg/kg based on s/n=5.The proposed method was applied to the determination of histamine in canned tuna fish,fumitory skipjack as well as frozen mackerel.Histamine at the level of 0.59～167 mg/kg was detected with the spiked recoveries over 97%,and the relative standard deviations(RSDs) were less than 5%.Because an automated derivatization program and the fluorescence detector were used in the proposed method,good reproducibility,high sensitivity and high analytical throughput were achieved.It is especially suitable for the routine analysis of great number of food samples.
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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 | 0.000 | 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) | 0.000 | 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".