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                            Funding & Awards
 Expert Algorithm for Substance Identification (EASI)
Award Information
Awardee
              WEST VIRGINIA UNIVERSITY RESEARCH CORPORATION
Award #
              15PNIJ-21-GG-04179-COAP
Funding Category
              Competitive Discretionary
Location
                    MORGANTOWN, WV
Congressional District
              2
Status
              Past Project Period End Date
Funding First Awarded
              2021
Total funding (to date)
              $327,405
Original Solicitation
              Research  and Development in Forensic Science for Criminal Justice Purposes, Fiscal Year 2021
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.



  
Grant-Funded Publications
        
                      Forensic Mass Spectrometry: Scientific and Legal Precedents
                  Expert Algorithm for Substance Identification (EASI) using Mass Spectrometry: Application to the Identification of Cocaine on Different Instruments using Binary Classification Models
                  Expert Algorithm for Substance Identification (EASI) using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory
                  Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory

  
Date Created: December 9, 2021
    
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