Research

LC-MS/MS data processing is currently carried out based on the software packages provided by the instrument’s manufacturer (In our laboratory raw files are processed in Sciex OS using the MQ4 integration algorithm) through a semi-automated yet labor-intensive workflow. While the software packages provide a starting point, manual review and re-integration by analysts remain essential to correct for peak tailing, background noise, matrix interferences, and retention time shifts. Although this manual curation step ensures data quality, it substantially limits throughput. In a full batch of 100 samples, data processing alone can take up to three working days. This project aims to replace labor-intensive LC–MS/MS data processing with a robust, AI-assisted workflow that accelerates throughput while preserving the rigor required in accredited environments. The primary goals are to: (1) automate peak detection and integration for scheduled MRM data to reduce processing time from multiple days per batch to hours; (2) minimize human error and inconsistency by standardizing decisions across large datasets; and (3) maintain full analyst oversight through an intuitive, responsive GUI that enables rapid batch-level review, transparent adjustments, and efficient curation. Success will be measured by reductions in processing time and re-integration rates, reproducibility gains across batches and matrices, and maintenance of accuracy at or above manual curation benchmarks—delivering a trustworthy, high-throughput solution that supports expanding analytical demands.

Over the past two decades, our group has pioneered a multi analyte LC–MS/MS platform that enables high throughput, dilute and shoot analysis and broad spectrum quantification of hundreds of mycotoxins and other fungal secondary metabolites, including emerging and masked forms. Through method harmonization, matrix robust calibration, and rigorous validation, we have established routine, cost effective surveillance across complex commodity streams in food, feed, and novel plant based materials. The need for expanded monitoring is urgent. Climate change is reshaping fungal ecology—altering species distributions, stress responses, and toxin profiles (e.g., Fusarium, Alternaria, Aspergillus)—while new plant based foodstuffs and feed ingredients introduce unfamiliar substrates and processing pathways that affect contamination risk. Continuous occurrence monitoring, co exposure assessment, and rapid response are essential to protect supply chains, inform risk management, and guide mitigation strategies. Beyond surveillance, comprehensive fungal metabolite profiling advances molecular biology by linking metabolomes to biosynthetic gene clusters, regulatory networks, and environmental triggers. These datasets accelerate discovery of pathway regulation, enable functional annotation, and provide biomarkers for strain selection and process control. This internal project will capitalize on our established platform to sustain and expand comprehensive monitoring. It will provide flexible support for personnel and materials not covered elsewhere, leveraging institutional resources. Expected outputs include (i) extended, validated high throughput LC–MS/MS methods; (ii) occurrence datasets focused on climate sensitive and innovation relevant matrices; and (iii) targeted metabolomic workflows to underpin collaborative molecular studies—ensuring agility, quality, and impact across our research portfolio.

Certain weather conditions can lead to increased mycotoxin levels during grain harvest. This, in turn, can result in elevated levels in feed or food products, which is why a contaminated harvest may only be partially usable or require specific treatment before being used for production. Early countermeasures are essential to prevent high pollutant levels, and it is crucial to assess the contamination at the time of harvest based on the current situation. By combining weather data with data from experimental grain cultivation or other mycotoxin measurement results, mathematical models can be developed to estimate mycotoxin contamination early. This aims to enable grain producers to respond promptly to the conditions and potentially reduce contamination in the grain through appropriate countermeasures. An example of such a countermeasure could be an earlier harvest. Additionally, it will be analyzed whether it is possible to make predictions for new cultivation sites where no historical measurement data on mycotoxin contamination is available.