semiquantification

semiquantification predicts ionization efficiencies and provides semiquantitative analysis of emerging contaminants in environmental samples using gas chromatography coupled with atmospheric pressure chemical ionization-quadrupole time-of-flight mass spectrometry (GC-APCI-QTOF-MS / GC-APCI-HRMS) for matrices such as soil, indoor dust, and sediments.

Key Features:

• Instrument Platform: Uses GC-APCI-QTOF-MS (GC-APCI-HRMS) with an atmospheric pressure chemical ionization (APCI) source for high-resolution mass spectrometric measurements.
• Ionization Efficiency Modeling: Models ionization efficiencies for 78 emerging contaminants to support semiquantitative analyses.
• QSPR Model: Implements a Quantitative Structure-Property Relationship (QSPR) model to predict ionization efficiencies of unknown compounds in the APCI source.
• Chemical Space Expansion: Expands detectable chemical space in environmental samples via high-resolution mass spectrometry to include previously unquantified chemicals.
• Matrix and Recovery Effects: Accounts for matrix effects and recovery rates when converting predicted ionization efficiencies to sample concentrations.
• Mechanistic Analysis: Employs a simple connectivity index and topological structure analysis to probe ionization mechanisms within the APCI source.

Scientific Applications:

• Environmental Exposure Assessment: Semiquantification of emerging contaminants in soil, indoor dust, and sediments to characterize environmental occurrence.
• Human Health and Fate Studies: Supports assessment of potential human exposure and environmental fate of newly identified chemicals using predicted ionization efficiencies.
• Regulatory Monitoring and Remediation: Provides semiquantitative data to inform environmental monitoring, risk assessment, and remediation decision-making.

Methodology:

The methodology uses ionization efficiency measurements obtained from GC-APCI-HRMS (QTOF) to test semiquantification workflows, integrates a simple connectivity index and topological structure analysis to investigate APCI ionization mechanisms, applies a QSPR-based model to predict ionization efficiencies for unknown compounds, and incorporates matrix effects and recovery rates when translating predicted efficiencies into sample concentration estimates.