Multi-mycotoxin Analysis of Finished Grain and Nut Products Using Ultrahigh-Performance Liquid Chromatography and Positive Electrospray Ionization–Quadrupole Orbital Ion Trap High-Resolution Mass Spectrometry
Bibliographic record
Abstract
Ultrahigh-performance liquid chromatography using positive electrospray ionization and quadrupole orbital ion trap high-resolution mass spectrometry was evaluated for analyzing mycotoxins in finished cereal and nut products. Optimizing the orbital ion trap mass analyzer in full-scan mode using mycotoxin-fortified matrix extracts gave mass accuracies, δM, of < ± 2.0 ppm at 70,000 full width at half maximum (FWHM) mass resolution (RFWHM). The limits of quantitation were matrix- and mycotoxin-dependent, ranging from 0.02 to 11.6 μg/kg. Mean recoveries and standard deviations for mycotoxins from acetonitrile/water extraction at their relevant fortification levels were 91 ± 10, 94 ± 10, 98 ± 12, 91 ± 13, 99 ± 15, and 93 ± 17% for corn, rice, wheat, almond, peanut, and pistachio, respectively. Nineteen mycotoxins with concentrations ranging from 0.3 (aflatoxin B1 in peanut and almond) to 1175 μg/kg (fumonisin B1 in corn flour) were found in 35 of the 70 commercial grain and nut samples surveyed. Mycotoxins could be identified at δM < ± 5 ppm by identifying the precursor and product ions in full-scan MS and data-dependent MS/MS modes. This method demonstrates a new analytical approach for monitoring mycotoxins in finished grain and nut products.
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 | 0.001 | 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 | 0.001 | 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 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".