Development of LC-HRMS Assay for the Measurement of 12 Mycotoxins in Urine
Bibliographic record
Abstract
Mycotoxins are the secondary metabolites of certain molds. These toxic compounds naturally contaminate food products and beverages and can cause severe health effects in humans and animals. Environmental changes and industrialization are currently promoting the spread of these mycotoxins worldwide. Health Canada regularly monitors the levels of specific mycotoxins in various food products. Complementarily to food monitoring, periodic biomonitoring is especially important to determine the exposure to mycotoxins in the Canadian population considering the variability of individual diets and metabolism. Urine biomonitoring is a non-invasive approach and sample collection is easy. The purpose of this study is to develop a sensitive and accurate LC-HRMS method for the detection in urine of 12 mycotoxins that impact human health. These mycotoxins are enniatin A (ENNA), enniatin A1 (ENNA1), enniatin B (ENNB), enniatin B1 (ENNB1), alternariol (AOH), alternariol monomethyl ether (AME), beauvericin (BEA), citrinin (CIT), fumonisin B1 (FB1), fumonisin B2 (FB2), ochratoxin A (OTA) and ochratoxin alpha (OTα). The final 24-min LC-HRMS method used CORTECS T3 reversed-phase separation and employed time-segmented polarity switching to cover the 12 analytes of interest in a single analysis. To allow high-throughput for large-scale monitoring, two sample preparation procedures were evaluated: "dilute-and-shoot" and solid-phase extraction with hydrophilic and lipophilic sorbent (HLB SPE). Evaluation of solubility and non-specific adsorption with the dilute-and-shoot method revealed that enniatins (ENNs) and BEA have low solubility in a highly aqueous solvent (H2O/ACN/FA 94/5/1 v/v) with a 70-98% decrease in signal intensity compared to a highly organic solvent (MeOH/ H2O/ FA 60/39/1 v/v). This issue also caused loss of ENNs and BEA during storage of urine samples in plastic containers. Rinsing the containers with MeOH allowed the recovery of 17, 30, 57, 44 and 67% of ENNB, ENNB1, ENNA, ENNA1, and BEA, respectively. Use of 20x dilution in the dilute-and-shoot method resulted in LOQs > 2 ng/mL for almost all mycotoxins, which are present at < 1 ng/mL in real samples. Thus, dilute-and-shoot is not sensitive enough for the intended application, so HLB SPE was used for sample clean-up and enrichment. This sample preparation method recovered 68 - 88% of all the mycotoxins tested with 10x enrichment, which led to significant ionization suppression of CIT, OTα, OTA, AOH, AME and FB1. Reducing their enrichment decreased the matrix effects for all the mycotoxins (77% - 150%) except for AOH (22%) and AME (66%), which was compensated for by the addition of an internal standard (AMEd3). An optimized HLB SPE LC-HRMS method is proposed for validation and further application to real samples in the biomonitoring of mycotoxins in urine.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".