Analysis of <i>Staphylococcus</i> enterotoxin B using differential isotopic tags and liquid chromatography quadrupole ion trap mass spectrometry
Bibliographic record
Abstract
Staphylococcus aureus produces enterotoxins, which are causative agents of foodborne intoxications. Enterotoxins are single-chain polypeptides and have a molecular weight of about 26-28 kDa. The consumption of food contaminated with Staphylococcus aureus enterotoxins results in the onset of acute gastroenteritis within 2-6 h. The objective of this study was the development of a new method for the quantification of Staphylococcal enterotoxin B (SEB) in food matrices. Tryptic peptide map was generated and nine proteolytic fragments were clearly identified (sequence coverage of 35%). Among these, three specific tryptic peptides were selected to be used as surrogate peptides and internal standards for quantitative analysis using an isotopic tagging strategy along with analysis by LC-MS/MS. The linearity of the measurement by LC-MS/MS was evaluated by combining mixtures of both isotopes at 0.1, 0.2, 0.5, 1.0 and 2.0 ¹H/²H molar ratios with a slope near to 1, values of R² above 0.98 and %CV obtained from six repeated measurement was below 8%. The precision and accuracy of the method were assessed using SEB spiked in chicken meat homogenate samples. SEB was fortified at 0.2, 1 and 2 pmol/g. The accuracy results indicated that the method can provide accuracy within a 84.9-91.1% range. Overall, the results presented in this manuscript show that proteomics-based methods can be effectively used to detect, confirm and quantify SEB in food matrices.
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 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 | 0.001 |
| 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 | 0.001 | 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 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".