Comparison of ELISA, UHPLC-MS/MS, and Development of UHPLC-HRMS Method for Ergot Alkaloid Quantification in Wheat
Bibliographic record
Abstract
Background & Objective: Wheat is critical to Canada’s economy, contributing approximately $10.2 billion annually, with 80% of wheat exported. However, ergot contamination caused by Claviceps purpurea poses a significant food safety risk due to the presence of toxic ergot alkaloids (EAs). This study aimed to evaluate the performance of an enzyme-linked immunosorbent assay (ELISA) for EA detection compared to ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Additionally, an ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) method was developed and validated to improve the accuracy and sensitivity of EA detection. Methods: The ELISA kit was validated using standard and in-house reference materials. CR was determined as the ratio of IC50 values of individual alkaloids to ergotamine. Spiked solvent and wheat matrix were analyzed using ELISA and UHPLC-MS/MS, followed by regression analysis and Bland-Altman plots. A UHPLC-HRMS method was developed and validated for enhanced EA quantification. Results: ELISA showed a strong correlation with UHPLC-MS/MS (r=0.8793) but underestimated total EA concentrations by a factor of two. CR varied widely, with ergometrine exhibiting the highest CR in solvent and wheat matrix, indicating substantial matrix effects. UHPLC-HRMS provided superior resolution, sensitivity, and mass accuracy, enabling comprehensive EA profiling and identification of previously undetected alkaloids. Conclusion: While ELISA offers a rapid screening approach, its accuracy is highly influenced by matrix effects and CR variability. The validated UHPLC-HRMS method enhanced analytical capabilities by providing high sensitivity and specificity, complementing UHPLC-MS/MS for regulatory compliance and improved food safety in wheat monitoring, ensuring more reliable and comprehensive analysis.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".