Robust Bayesian Algorithm for Targeted Compound Screening in Forensic Toxicology.

Area of Science:

• Forensic Toxicology
• Analytical Chemistry
• Computational Chemistry

Background:

• Forensic toxicological investigations of unexpected deaths rely on xenobiotic screening of post-mortem samples.
• Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is the standard technique for xenobiotic detection.
• Current data analysis predominantly uses threshold-based frequentist algorithms, which struggle to integrate multiple data sources effectively.

Purpose of the Study:

• To introduce a novel Bayesian probabilistic algorithm for xenobiotic screening in forensic toxicology.
• To address the limitations of frequentist methods in combining diverse analytical evidence.
• To provide a probabilistic output that aids toxicologists in decision-making.

Main Methods:

• Development of a Bayesian probabilistic algorithm for analyzing LC-HRMS data.
• The algorithm integrates multiple sources of evidence, including chromatographic peak shape and mass spectrometry data.
• Validation of the Bayesian method against existing vendor-supplied software.

Main Results:

• The Bayesian algorithm calculates the posterior probability of xenobiotic presence, rather than a binary outcome.
• It effectively combines various pieces of analytical evidence from chromatograms.
• Validation demonstrated superior performance compared to vendor-supplied frequentist software, with reduced false positives and negatives.

Conclusions:

• The novel Bayesian algorithm offers a more robust and accurate approach to xenobiotic screening in forensic toxicology.
• Probabilistic outputs enhance the interpretation of complex analytical data.
• This method has the potential to improve the reliability of forensic toxicological investigations.