STREAMLINE LC-MS/MS DATA PROCESSING FOR FOOD ANALYSIS WITH MS QUAN

Importance of the Topic

The rapid processing and review of LC-MS/MS results are crucial in food analysis to ensure reliable quantitation of nutrients and contaminants. Streamlined workflows reduce manual effort and error risk in high volume testing such as vitamin B profiling.

Objectives and Overview

This study highlights MS Quan, a module within waters_connect for quantitation, designed to simplify complex LC-MS/MS data processing and review tasks. Key aims include implementing task oriented workflows, batch level review, and exception focused individual data checks.

Methodology

Analyses targeted B vitamins in energy drinks and a vitamin B complex dietary supplement. Samples were prepared according to established protocols and analyzed using an Arc Premier UHPLC system coupled to a Xevo TQ-S micro mass spectrometer. Data acquisition was controlled with MassLynx 4.2 software and processed using the MS Quan application.

Instrumentation

• Arc Premier UHPLC system
• Xevo TQ-S micro triple quadrupole mass spectrometer
• MassLynx 4.2 control and acquisition software

Main Results and Discussion

MS Quan separates data handling into discrete tasks with dedicated user interfaces that present all relevant data, parameters, and chromatograms in context. A fast batch level review allows detection of integration errors or unusual values across the entire sample set. Analysts can then drill into exceptions with focused individual data review. These workflows accelerate processing, improve consistency, and reduce error rates compared to injection by injection evaluation.

Benefits and Practical Applications

• Enhanced throughput through streamlined batch processing and task specialization
• Improved data quality via consistent review methods and automatic exception flagging
• Reduced analyst workload with intuitive dashboards and interactive navigation
• Scalable solution suitable for routine food safety and nutritional assays

Future Trends and Opportunities

Emerging developments may include integration of machine learning for automated exception resolution, expansion of workflows to other compound classes, and cloud based collaboration features. Increased connectivity and advanced visualization tools will further accelerate decision making in high throughput laboratories.

Conclusion

MS Quan effectively transforms complex LC-MS/MS data review into manageable tasks by combining batch level screening and targeted individual analysis. This approach enhances processing speed, accuracy, and overall data quality, meeting the demands of modern food analysis workflows.

Reference

• Application note 720007264en Waters Corporation 2022