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Expert Algorithm for Substance Identification (EASI)

Award Information

Award #
15PNIJ-21-GG-04179-COAP
Location
Congressional District
Status
Awarded, but not yet accepted
Funding First Awarded
2021
Total funding (to date)
$327,405

Description of original award (Fiscal Year 2021, $327,405)

This proposal addresses several defined research needs identified by NIJ’s Forensic Science Research and Development Technical Working Group (TWG), including “The ability to identify NPS by comparison to spectra from a different instrument rather than a reference standard” and “Error rate studies on qualitative analysis.” The major goal of this project is to develop an Expert Algorithm for Substance Identification (EASI) that will both improve the confidence of drug identifications from mass spectra and enable reliable inter-laboratory identifications without the need to acquire contemporaneous spectra of standards. Initial algorithm development will employ more than 57,000 replicate spectra of more than 70 fentanyl analogs that have already been collected from nine participating laboratories. The project will also include the collection of additional spectra of fentanyl analogs and other novel psychoactive substances (NPSs). The new data will be collected in-house and by our partnering crime laboratories. The new algorithm, which is based on basic multivariate linear regression, will be developed and tested primarily using electron ionization (EI) mass spectra. However, the algorithm will also be extended to drug identifications using electrospray ionization-tandem mass spectrometry (ESI-MS/MS) data using data collected on a high-resolution quadrupole/time-of-flight (Q-TOF) instrument. Tandem mass spectrometry on an ESI-Q-TOF instrument is more likely to be used in a toxicological setting, and extending the algorithm to MS/MS data of drugs and drug metabolites in biological fluids opens the door to a wider variety of forensic applications.     The algorithm relies on robust fundamental theories of physical chemistry, including the Rice–Ramsperger–Kassel–Marcus (RRKM) theory of unimolecular dissociation. The developed algorithm will also rely on a vast quantity of crime lab data and robust linear regression models, all of which will lend credence and legitimacy to the results. Finally, the error rate of the algorithm will be presented in a variety of probabilistic forms, including estimates of the sensitivity, selectivity, and likelihood ratios, thereby helping to address NIJ’s request for error rates of qualitative analysis. This research will ultimately benefit both forensic chemists and forensic toxicologists at the national level. The results will be presented at national forensic science conferences, published in respected peer-reviewed journals and incorporated in GC-MS workshops that the PI regularly teaches to forensic practitioners. Upon completion of the grant, the master database containing more than 57,000 spectra will be made available to the public through open access data repositories.

Date Created: December 9, 2021