New probabilistic method enables forensic scientists to determine the classes of substances found in fire debris
UCF researchers have developed a new computer-based decision tool that forensic scientists and analysts can use to identify and classify components of complex mixtures found in the debris of fires and explosions. The invention employs a likelihood ratio approach that combines the use of gas chromatography and mass spectrometry (GC-MS) databases with factor analysis. A likelihood ratio is a measure of the strength of evidence. Using the new approach, analysts can report likelihood ratios for complex mixtures and component classifications.
In forensics, a scientist may need to identify the class of a particular combination of chemicals, for example, the class of ignitable liquid (such as gasoline or normal alkane) in a fire debris sample. Gasoline comprises a combination of chemicals, and that combination constitutes a component of the mixture. The mixture in turn contains the component and additional chemicals that may comprise other components. Unfortunately, existing identification methods do not enable component classifications for such complex mixtures, especially after combustions.
The new invention offers a solution to this dilemma. It gives analysts the methodology needed to identify targeted components and likely classifications in complex mixtures. Instrument manufacturers and software providers can also automate the process to give the forensic community and legal system a capability that is not available in the marketplace today. Additionally, medical laboratories and security analysts can use the inventive concepts to interpret complex data.
The invention consists of a system and methods for assessing the likelihood that a recovered debris sample (from a fire or an explosion) is a member of at least two distinct classes of a substance, based on the presence of a specified set of targeted components. The invention combines the use of GC-MS databases and factor analysis to identify targeted components. A methodology for calculating the likelihood ratios for the class membership of a substance includes data on how frequently the targeted components occur in debris samples and estimates of the frequency of unobserved targeted components. As an example setup, the system can comprise an ion intensity quantification system (a combined GC-MS) that connects to a computer. The ion intensity quantification system is configured to quantify the intensity of ions resulting from compounds, such as those contained in test samples. The computer comprises a processing device, memory, an operating system, a substance classification system, a user interface, and at least one input/output device connected to a local interface. As part of the computer setup, the ion spectrum generator configuration identifies the chemicals in a sample based on the ion intensities identified by the ion intensity quantification system for all fractions of separated test samples components.
- Reduces error rates (false positives and/or false negatives)
- Provides a measure of the strength of collected evidence
- Objectively assesses the evidential value of a pyrolysis debris sample
- Forensic and medical diagnostics
- Law enforcement
- GC-MS instruments