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Statistical profiling for identifying transformation products in an engineered treatment process.

Minkyu Park1, Shane A Snyder2

  • 1Department of Chemical & Environmental Engineering, University of Arizona, 1133 E James E Rogers Way, Harshbarger 108, Tucson, AZ, 85721-0011, USA.

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|March 9, 2020
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Summary

Statistical profiling using ANOVA and fold change effectively identified carbamazepine (CBZ) transformation products during ozonation. This method enhances data analysis by reducing false negatives and can be applied to complex environmental samples.

Keywords:
Blank subtractionCarbamazepineOzonePower analysisQuadrupole time-of-flight mass spectrometryStatistical profiling

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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Mass Spectrometry

Background:

  • Ozonation is a common method for treating organic contaminants like carbamazepine (CBZ).
  • Identifying transformation products is crucial for understanding degradation pathways and environmental impact.
  • Accurate identification of these products can be challenging due to complex sample matrices.

Purpose of the Study:

  • To develop and demonstrate a statistical profiling method for identifying carbamazepine (CBZ) transformation products in ozonation.
  • To assess the efficiency of statistical profiling in differentiating true transformation products from background noise.
  • To explore the potential of this method for complex environmental sample analysis.

Main Methods:

  • Liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was used to detect CBZ transformation products.
  • Statistical profiling, including analysis of variance (ANOVA) and fold change, was applied to identify significant features.
  • Post hoc power analysis was conducted to evaluate the statistical significance of identified products.

Main Results:

  • Statistical profiling successfully identified several CBZ transformation products formed during ozonation.
  • A novel transformation product, 2-quinazolinone, was tentatively identified.
  • An "anomalous" transformation product, with one less carbon than CBZ, was also detected.
  • Identified products showed significant statistical power (1-β > 0.8), indicating reliable detection.

Conclusions:

  • Statistical profiling is an efficient method for identifying organic transformation products in ozonation processes.
  • The approach effectively reduces false negatives in data analysis.
  • This methodology holds promise for application in non-targeted screening (NTS) and non-target analysis (NTA) of complex environmental samples.