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                            Funding & Awards
 Research and Development of a Rapid Approach for Identification of Calliphoridae Species Entomological Forensic Evidence Based on DART-HRMS-derived Chemical Signatures and Chemometrics
Award Information
Awardee
              RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE
Award #
              2020-MU-MU-0016
Funding Category
              Competitive Discretionary
Location
                    ALBANY, NY
Congressional District
              20
Status
              Open
Funding First Awarded
              2020
Total funding (to date)
              $731,306
Original Solicitation
              Research and Development in Forensic Science for Criminal Justice Purposes, Fiscal Year 2020
Description of original award (Fiscal Year 2020, $731,306)
In death investigations, carrion insects collected on or near a body can be used as indicators for estimating the post-mortem interval. However, to utilize entomological evidence in this fashion, accurate species identification is critical.
Traditional methods of insect identification often rely on rearing larvae to adulthood to identify gross morphological features to make the final identification. Newer techniques aimed at identifying species in various life stages, such as DNA-typing, are expensive, time-consuming, and limited by a lack of a full, comprehensive database of insect profiles for comparisons. Moreover, there may be instances in which the eggs or juvenile insects cannot be reared because they are nonviable, as occurs when they have been exposed to drugs, toxins or extreme conditions such as freezing, high temperatures, submergence in fluids, or anoxic conditions. With no quick and reliable method to use for species attribution, entomological evidence often remains an underutilized forensic investigative tool.
It is hypothesized here that members of the Calliphoridae family exhibit unique species-specific metabolome signatures, and that a database of these can be used as a screening device against which the metabolome signatures of sample unknowns can matched, in order to rapidly determine species identity with a statistical level of certainty. These metabolomic signatures can be readily and rapidly acquired from samples that are in the form in which they are generated and stored by forensic entomologists, by exploiting the unique capabilities of direct analysis in real time-high resolution mass spectrometry (DART-HRMS). Development of machine learning algorithms for the processing of this data will enable rapid species identification of unknowns, and with this approach, the current challenges associated with full utilization of entomological evidence can be overcome. Thus, the ultimate goal of the proposed work is to develop a DART-HRMS spectral database capable of matching data acquired from sample unknowns for the identification of necrophagous insect evidence. It will be achieved through pursuit of the following Objectives/Specific Aims:
Specific Aim I: Collection, validation and application of species' identities for forensically relevant Calliphoridae species endemic to the United States.
Specific Aim II: Determination of the species-specific chemical signatures of forensically relevant Calliphoridae species for eggs, larvae, pupae, and adults using DART-HRMS
Specific Aim Ill: Development of a machine learning approach to differentiate species-specific chemical signatures for species identification, database and software development, beta testing  and validation.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). CA/NCF



  
Grant-Funded Publications
        
                      Distinguishing Infested Flour from Uninfested Flour through Chemometric Processing of DART-HRMS Data─Revealing the Presence of Tribolium castaneum, the Red Flour Beetle
                  Coral Genus Differentiation Based on Direct Analysis in Real Time-High Resolution Mass Spectrometry-Derived Chemical Fingerprints

  
Date Created: October 22, 2020
    
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