5 Key Takeaways

• 1

  Ansgar Korf’s team develops computational methods and open infrastructure through MZmine to reduce software bottlenecks in untargeted mass spectrometry.

• 2

  Korf is developing FAIR-MS to make mass spectrometry data findable and reusable for machine learning across laboratories and instruments.

• 3

  Korf expects untargeted measurement to become the default, allowing datasets to be revisited to answer questions not considered when they were acquired.

• 4

  Korf says analytical science is undervalued as a scientific contribution, limiting recognition of method development, expertise, and software infrastructure.

• 5

  Korf warns that AI cannot compensate for systematic errors in analytical workflows, including matrix effects, ion suppression, batch drift, and sample degradation.