Agilent MassHunter Optimizer for Ultivo LC/TQ - Automated MS Method Development Software - Quick Start Guide

Importance of Topic

The automated development of MS methods for multiple reaction monitoring (MRM) streamlines quantitative and qualitative analysis in complex matrices. Manual optimization of precursor and product ions, fragmentor voltages, and collision energies is labor-intensive and time-consuming. Agilent MassHunter Optimizer for Ultivo LC/TQ provides a fully integrated solution to accelerate method development, improve reproducibility, and ensure consistent sensitivity and selectivity in targeted analyses.

Objectives and Overview

This guide introduces key features and workflows of MassHunter Optimizer:

• Automate selection of optimal precursor ions and product fragments.
• Optimize fragmentor voltage and collision energy for each transition.
• Store optimized parameters in a compound database and import into acquisition methods.
• Support different sample introduction modes (injection, direct infusion, manual infusion).
• Integrate seamlessly with MassHunter Acquisition and Qualitative Analysis tools.

Methodology and Instrumentation

MassHunter Optimizer interacts with an Agilent Ultivo LC/TQ system under defined acquisition parameters:

• Sample introduction: column-based injection or infusion via syringe pump or loop.
• Acquisition methods imported from MassHunter Acquisition for LC and source conditions.
• Automated optimization sequence: SIM scan to optimize fragmentor voltage, product-ion scan to discover fragments, MRM scan to optimize collision energy, final focused product-ion scan for validation.
• Database management via Compound Browser for compound lists, transitions, and libraries.

Main Results and Discussion

In typical workflows, optimization on Ultivo LC/TQ yields:

• A clear graphical profile of fragmentor and collision energy sweeps per compound.
• Identification of top-ranked product ions (up to 10 for triggered MRM, up to 4 for standard MRM).
• Fine‐tuning of fragmentor voltages for maximum precursor intensity.
• Collision energy tuning to maximize product ion responses.

The built-in Review Results tool permits manual overrides and resets, ensuring flexibility over strictly automated parameters.

Benefits and Practical Applications

Automated optimization delivers:

• Substantial time savings compared to manual tuning.
• Improved method consistency and reproducibility.
• Efficient transition selection for dynamic MRM (dMRM) or triggered MRM (tMRM).
• Seamless transfer of optimized parameters into acquisition methods with correct retention time windows.

This workflow is ideal for high‐throughput bioanalysis, environmental monitoring, and quality control in pharmaceutical or food laboratories.

Future Trends and Applications

Advancements may include:

• Expanded support for real-time machine learning to predict optimal parameters based on compound structure.
• Enhanced automation for isotope-ratio monitoring and high-mass accurate fragment optimization.
• Integration with cloud databases for cross-laboratory sharing of optimized methods.

Conclusion

Agilent MassHunter Optimizer for Ultivo LC/TQ significantly reduces the manual effort required for MS method development. By automating precursor and product ion selection, fragmentor and collision energy optimization, and database integration, it ensures consistent, high‐quality MRM methods suitable for diverse analytical challenges.

Reference

Agilent Technologies. MassHunter Optimizer for Ultivo LC/TQ Automated MS Method Development Software Quick Start Guide. Revision A, November 2017.