Matrix interference remains a chief concern for laboratories analyzing food and environmental samples. Excess fats, proteins, and pigments often obscure target analytes and degrade data quality in LC-MS/MS assays. Gavin Fischer, Vice President of Chromatography at PerkinElmer, highlights this problem during a recent interview. “Mass specs generally don’t like all of the long fats or anything that smells,” advises Fischer, stressing how these naturally occurring compounds present serious obstacles for routine and high-throughput LC-MS/MS workflows.
Understanding the Problem of Matrix Interference
Matrix interference stems from the diverse chemical components found in food and environmental samples. Oils, sugars, and naturally occurring pigments can cling to internal instrument surfaces or co-elute with analytes, complicating detection. Fischer explains that all of the solids, fats, and carbohydrates—typically the components that make foods taste good—can quickly coat instrumentation and hamper performance. These unwanted matrix elements reduce sensitivity, increase maintenance demands, and slow sample throughput.
Common consequences of matrix interference include:
- Instrument contamination: Residue buildup within the source or ion path forces labs to stop and clean equipment more often.
- Extended downtime: Frequent cleaning disrupts workflows, lowers productivity, and leads to scheduling bottlenecks.
Data variability: Inconsistent cleanup steps and instrument fouling can skew detection limits and reproducibility.
The complexity intensifies in environmental testing, where samples may contain petroleum products or other viscous contaminants that overwhelm standard LC-MS/MS setups.
Rethinking Sample Preparation
Traditional sample preparation strategies, such as time-consuming cleanup protocols, focus on removing contaminants before analysis. Fischer describes these methods as laborious, often involving multiple solvent steps and manual pipetting. Each step introduces the risk of human error and adds operational costs.
Fischer notes that by employing an LC-MS/MS system specifically designed to handle dirtier samples, labs can reduce time-consuming preparation. “For example, if you’re looking at avocados, you can skip hours of cleanup.” This streamlines the workflow and facilitates faster turnaround times.
Fewer manual steps mean fewer opportunities for mistakes in measuring solvents or reagents. And simple filtration or centrifugation may be all that’s needed to introduce samples directly into the LC-MS/MS, boosting sample throughput. What’s more, labs cut down on solvent use and disposables, aligning with sustainability goals.
Building a Robust LC-MS/MS Workflow
Improved instrumentation design plays a major role in mitigating matrix interference. Fischer describes how innovative source components can trap or divert unwanted particles. When combined with routine maintenance and automated checks, these advanced front-end strategies prevent excessive buildup within the mass spectrometer.
Key elements of a robust system include:
- Advanced front-end source: New source technology prevents contaminants from entering the instrument.
- Protective curtain gases: Curtain or shielding gas flows can block large molecules and aerosols from entering the detector.
- Easy-clean design: Accessible components allow quick, simple wiping of problem areas instead of major teardown procedures.
“We want to remove the black arts from LC-MS/MS,” says Fischer, explaining that instruments should run reliably with minimal human intervention. Analysts should be able to trust the process without second-guessing chromatographic peaks or performing multiple re-runs.
The Future of LC-MS/MS: Automation and AI
Looking ahead, artificial intelligence (AI) and automation promise to refine LC-MS/MS for matrix-heavy samples. Fischer sees a future where the instrument automatically primes pumps, checks baseline stability, and flags suspicious data. “If you’re running the same assay all day, every day, chromatograms shouldn’t be a thing,” he notes, emphasizing that routine QC checks and repetitive tasks could be delegated to AI-driven software.
Automation would also extend into sample preparation, allowing robotic systems to handle mixing, filtration, and injection with no human intervention. Such systems reduce variability, save time, and lower labor costs.
Conclusion
Matrix interference in LC-MS/MS remains a barrier for labs testing complex food and environmental samples. However, new source designs, simplified sample prep protocols, and evolving AI-driven workflows help laboratories streamline operations. Fischer’s comments underscore how robust instrumentation, reduced manual cleanup, and forward-thinking automation can transform LC-MS/MS into a reliable, high-throughput tool. By cutting downtime and minimizing errors, analysts can confidently produce consistent, high-quality results—even when dealing with the most challenging matrices.
