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                            Funding & Awards
 Determination of Key Factors in Particle Combination Analysis to Enable Systematic Improvement, Optimization and Transition to Practice
Award Information
Awardee
              STONEY FORENSIC, INC.
Award #
              2017-IJ-CX-0030
Funding Category
              Competitive Discretionary
Location
                    CHANTILLY, VA
Congressional District
              11
Status
              Closed
Funding First Awarded
              2017
Total funding (to date)
              $951,264
Original Solicitation
              NIJ FY17 Research and Development in Forensic Science for Criminal Justice Purposes
Description of original award (Fiscal Year 2017, $951,264)
As submitted by the proposer:
This project will determine key factors affecting particle combination analysis as applied to very small particles (VSP). Identification of these factors is a necessary step to enable systematic improvement, optimization, and transition to practice.
VSP show exceptional promise to (1) expand the numbers of cases where trace evidence can be used, and (2) provide quantitative measures of evidential value. The laboratory analyses are highly efficient, utilizing existing crime laboratory personnel and equipment.
Prior research, employing reasonable choices of analytical and statistical parameters, has (1) demonstrated the presence of highly discriminating VSP profiles on the surfaces of common items of physical evidence; (2) characterized VSP combinations using analytical instrumentation and expertise commonly available in forensic laboratories; (3) developed statistically rigorous measurements of correspondence between VSP profiles; and (4) produced objective measures for the resulting probative value.
It is now appropriate to more critically examine these analytical and statistical parameters. The available particle combination analysis methods, and VSP specimens from physical evidence (both resulting from prior NIJ-funded research) provide the means to do so.
Six experiments, varying parameters while measuring the performance of particle combination analysis, will address: (1) Reproducibility of VSP Analyses at Given Parameters; (2) Effects of SEM/EDS Particle Detection Parameters; (3) Effects of SEM/EDS X-Ray Analysis Parameters; (4) Effects of Number and Choice of Elements; (5) Contribution of Alternative Particle Size Fractions; and (6) Effects of Data Filtration Parameters.
Statistical analyses will identify which of the many individual parameters are important factors influencing performance. Based on the results, existing particle combination analysis methods will be revised, improved and applied to VSP specimens from common items of physical evidence and key factors affecting particle combination analysis of VSP will be identified.
This project will result in a significantly improved capability. More importantly, by identifying the key factors affecting the performance of the capability, it will enable systematic method improvement, optimization and transition to practice.
The project will also contribute to related forensic science research by (1) providing datasets allowing the testing and improvement of computational methods; and (2) addressing issues encountered in the analysis and interpretation of other large, complex combinations of particles (or other mixtures). These include current efforts focused on the analysis of pollen, microbes, isotopes, and multi-species DNA.
This is a new approach, highly-significant for its potential to expand the number of cases to which trace evidence can meaningfully contribute and for its ability to include a quantitative statistical approach to data interpretation.
This project contains a research and/or development component, as defined in applicable law. - See 2 CFR  200.210(a)(14).
ca/ncf



  
Grant-Funded Publications
        
                      Determination of Key Factors in Particle Combination Analysis to Enable Systematic Improvement, Optimization and Transition to Practice
                  Identification of factors affecting SEM/EDS analysis for discrimination and classification among common items of evidence using particle combination profiles

  
Date Created: September 30, 2017
    
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