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