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Development of a Simplified User Interface for SRM Method Creation

Posters | 2021 | Thermo Fisher Scientific | ASMSInstrumentation
LC/MS, LC/MS/MS, LC/QQQ
Industries
Other
Manufacturer
Thermo Fisher Scientific

Summary

Importance of the Topic


The development of user-friendly interfaces for selected reaction monitoring (SRM) method creation addresses a critical need in modern analytical laboratories. Simplified tools speed up method setup, reduce the risk of errors, and allow laboratories to move rapidly from instrument installation to routine sample analysis. Integrating reliable spectral databases with triple-quadrupole workflows also enhances confidence in method performance, especially for multi-residue and low-abundance compound assays.

Study Objectives and Overview


This study presents the design and evaluation of a conversion tool and enhanced method editor that leverages the high-resolution mzCloud spectral database to automate SRM list creation. Key goals included:
  • Translating Orbitrap MS/MS data into SRM transitions (precursor/product masses and collision energies) for triple quadrupole instruments.
  • Implementing an NCE-to-CE conversion formula to predict collision energies within 1–2 volts of empirical values.
  • Introducing dwell time prioritization and visualization to optimize data quality for low- and high-abundance compounds.

Methodology and Instrumentation


The conversion tool extracts curated MS and MS/MS spectra from mzCloud entries and applies a mathematical equation to convert normalized collision energy (NCE) values into collision energy (CE) settings compatible with TSQ, TSQ Plus, and TSQ Altis Plus mass spectrometers. Users assign priority levels to analytes, which automatically reassign dwell times based on desired points per peak and cycle time constraints. Method visualization displays the number of transitions per cycle and the distribution of dwell times across the run.

Main Results and Discussion


Collision energy values imported from mzCloud were generally within 1–2 volts of those obtained by traditional syringe-infusion optimization, yielding nearly identical SRM chromatograms and ion ratios. Dwell time prioritization trials with a 284-pesticide panel demonstrated that raising priority for low-abundance compounds (e.g., 2,4-D) increased dwell time from 3.9 ms to 26 ms and reduced %CV from ~35% to below 15%. Conversely, lowering dwell times for highly abundant analytes had minimal impact on data quality, with %CV remaining within acceptable regulatory limits.

Benefits and Practical Applications


  • Substantial reduction in SRM method development time through direct use of high-quality spectral data.
  • Improved reproducibility and sensitivity for low-abundance analytes without extensive manual calculation.
  • Streamlined laboratory workflow enabling faster implementation of multi-residue assays in environmental, food safety, and pharmaceutical QC applications.

Future Trends and Potential Uses


Ongoing expansion of spectral libraries and integration with cloud-based databases will further automate SRM method creation. Future enhancements may include AI-driven prediction of optimal fragmentation pathways, dynamic real-time method adaptation, and support for additional instrument platforms. These advances will continue to improve throughput, robustness, and method transferability across laboratories.

Conclusion


The integration of mzCloud spectral data into an enhanced SRM method editor simplifies collision energy selection and dwell time optimization, delivering performance equivalent to empirical optimization while saving considerable time. Dwell time prioritization ensures higher data quality for trace analytes and maintains reliable results for abundant compounds, making this approach valuable for a wide range of targeted LC-MS/MS applications.

Used Instrumentation


  • Thermo Scientific TSQ Plus and TSQ Altis Plus triple quadrupole mass spectrometers
  • Orbitrap-based mass spectrometers for spectral acquisition
  • Thermo Scientific mzCloud advanced mass spectral database

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