
The scope of food residue testing is expanding more rapidly than many laboratories anticipated. Pesticide panels have grown from a few dozen compounds to well over a hundred. Mycotoxins associated with tropical climates are now appearing in global food supplies as weather patterns shift, adding pressure to expand analytical panels. Residues from livestock treatments add further complexity, as their chemical diversity often forces labs into fragmented preparation methods and lengthy validations, increasing the burden of meeting regulatory requirements.
This executive summary illustrates how flexible LC-MS/MS workflows are capable of capturing hundreds of pesticides, precise enough to quantify emerging mycotoxins, and versatile enough to confirm drug residues. The approaches highlighted by SCIEX experts demonstrate how laboratories can adapt to increasing residue demands while maintaining reliable and defensible results.
Download this executive summary to learn:
- How practical method choices, such as matrix-matched calibration, scheduled MRM, and EPI confirmation, strengthen data quality
- What the analysis of globally sourced toddler snacks revealed about the presence and prevalence of emerging mycotoxins
- How an AOAC-accepted workflow, developed in collaboration with Nestlé, enables routine screening of 152 animal-drug residues
Meet the Experts:
![]() | Holly Lee Staff Scientist, SCIEX Holly Lee is a staff scientist at SCIEX with extensive experience developing LC-MS/MS methods for food and environmental testing. Her work focuses on improving sensitivity, selectivity, and efficiency in routine residue analysis, particularly for complex matrices such as tea, juice, and plant extracts. |
![]() | Julie Brunkhorst |
![]() | Jianru Stahl-Zeng Senior Technical Marketing Manager, SCIEX Jianru Stahl-Zeng is a senior technical marketing manager at SCIEX, specializing in residue analysis workflows. She has led multiple international collaborations focused on veterinary drug screening, including method validation programs and implementation of scheduled MRM for high-throughput analysis. |




