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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".