Nontargeted Screening of Water Samples Using Data-Dependent Acquisition with Similar Partition Searching.

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Mass Spectrometry

Background:

• Surface water quality is crucial for ecosystem health and human use.
• Detecting unknown contaminants requires advanced analytical techniques.
• Nontargeted screening offers a comprehensive approach to identifying emerging pollutants.

Purpose of the Study:

• To develop and optimize a rapid screening method for nontargeted compound detection in surface water.
• To enhance the identification of diverse chemical characteristics in environmental samples.
• To establish an efficient workflow for analyzing large datasets from mass spectrometry.

Main Methods:

• Utilized MS-MS high-resolution mass spectrometry with data-dependent acquisition.
• Optimized key acquisition parameters using model compounds.
• Employed a combinatorial approach, similar partition searching (SPS), for analyte identification against a large compound database.
• Processed raw data using MSConvert and stored results in an Access relational database.

Main Results:

• Successfully analyzed 18 surface water samples, generating over 41,000 MS-MS spectra in approximately 14 hours.
• Putatively identified analytes by comparing accurate mass and MS-MS fragmentation spectra against a database of 250,000 compounds.
• Developed a database system for efficient sorting, searching, filtering, and querying of identification results.

Conclusions:

• The developed method provides a rapid and effective means for nontargeted screening of surface water.
• The combinatorial approach combined with high-resolution mass spectrometry enables confident identification of unknown compounds.
• This workflow significantly improves the capacity for environmental monitoring and contaminant assessment in water bodies.