A Strategy for an Unknown Screening Approach on Environmental Samples Using HRAM Mass Spectrometry

Importance of the Topic

The detection of known and unknown organic contaminants in environmental water is critical for safeguarding public health and ecosystems. High-resolution accurate mass (HRAM) mass spectrometry allows simultaneous targeted quantification and non-targeted screening, reducing blind spots inherent in conventional targeted analyses.

Objectives and Study Overview

This study aims to demonstrate an automated workflow integrating targeted, suspect, and unknown screening in a single dataset. Surface water samples from Berlin were analyzed using an Exactive Plus Orbitrap mass spectrometer, with data processed by TraceFinder 3.1 and SIEVE 2.1.

Methodology

• Sample Preparation
  Four surface water sources, a tap water reference, and a neat standard were injected directly (1 mL) without pretreatment.
• Chromatography
  Online solid-phase extraction employed a Hypersil GOLD preconcentration column followed by separation on an Accucore RP-MS C18 column using a 6.7 min gradient (total cycle time 15 min).
• Data Acquisition
  Full-scan and all-ion fragmentation (AIF) were alternated on an Exactive Plus mass spectrometer at 70,000 resolving power (m/z 200) over m/z 103–900.

Instrumentation

• Thermo Scientific EQuan LC-MS system with Accela pumps and autosampler
• Thermo Scientific Exactive Plus Orbitrap mass spectrometer
• TraceFinder 3.1 and SIEVE 2.1 software for automated data processing

Main Results and Discussion

• Suspect Screening
  Using a built-in database of ~1,000 compounds and a spectral library of ~4,000 MS2 spectra, several contaminants were confirmed by isotopic pattern, fragment matching, and retention time, although some target analytes remained undetected.
• Unknown Screening
  Unconditioned feature detection in SIEVE identified ~5,000 components; filtering against tap water reduced the list to ~1,829 features. Principal component analysis highlighted sample-specific variations, and further filtering yielded ~1,671 features, with ChemSpider matching providing ~1,529 identifications.
• Integrated Reporting
  Results were looped back into TraceFinder for combined targeted, suspect, and unknown screening output, streamlining reporting and archiving in a single application.
• Resolution Advantage
  High resolving power (R=70,000) enabled clear separation of analyte signals (e.g., loxoprofen) from matrix interferences, ensuring accurate identification at trace levels.

Benefits and Practical Applications

• Single-run workflows reduce analysis time and manual data handling
• Comprehensive coverage of both known and unexpected contaminants
• Automated data transfer between targeted and non-targeted software ensures consistency and traceability
• High resolution HRAM reduces false positives from matrix interferences

Future Trends and Applications

• Expansion of spectral libraries and suspect lists for broader screening
• Integration of machine learning for automated feature prioritization
• Real-time monitoring and feedback loops in environmental surveillance
• Quantitative calibration routines embedded within unknown screening workflows

Conclusion

An automated HRAM workflow combining targeted, suspect, and unknown screening within a single application can enhance the detection and confirmation of environmental contaminants. This approach leverages the resolving power of Orbitrap technology and advanced software to deliver comprehensive and efficient analysis.

References

• Scheibner O, van Baar P, Wode F, Dünnbier U, Akervik K, Humphrie J, Bromirski M. A Strategy for an Unknown Screening Approach on Environmental Samples Using HRAM Mass Spectrometry. Thermo Fisher Scientific Poster Note PN ASMS13_T578_OSheibner_E. 2013.