The colonization of decomposing remains by necrophagous insects such as blow flies is of forensic importance because the progression through the various stages of insect development can be correlated to time of death. The ability to infer this information hinges on accurate determination of the fly species that are associated with the entomological evidence collected. This evidence can include eggs, larvae, pupae, and puparial casings. Determination of the egg's identity is particularly challenging because the eggs of multiple species are morphologically very similar. We report here that the species identity of fly eggs can be determined from their chemical fingerprint signatures acquired by direct analysis in real time high-resolution mass spectrometry (DART-HRMS). Thus, freshly laid eggs were collected and readily distinguished from multiple necrophagous fly species in the Manhattan area of New York City. These species included representatives from the blow fly family (Calliphoridae), specifically Calliphora vicina, Lucilia sericata, L. coeruleiviridis, and Phormia regina species as well as the Phoridae and Sarcophagidae families. Multivariate statistical analysis of their observed DART-HRMS spectra revealed intraspecies similarities and interspecies differences that were the basis of species differentiation. The chemical basis of discrimination was differences in amino acid profiles. This represents the first chemically based fly egg identification approach with applications to forensic entomology. The rapidity of the method makes feasible the generation of a fly egg chemical profile database against which the DART-HRMS spectra of unknown eggs can be screened to rapidly assess species identity without needing to rear the eggs to adulthood.
Species determination of the various life stages of flies (Order: Diptera) is challenging, particularly for the immature forms, because analogous life stages of different species are difficult to differentiate based on morphological features alone. It is demonstrated here that direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) combined with supervised Kohonen Self-Organizing Maps (SOM) enables accomplishment of species-level identification of larva, pupa, and adult life stages of carrion flies. DART-HRMS data for each life stage were acquired from analysis of ethanol suspensions representing Calliphoridae, Phoridae, and Sarcophagidae families, without additional sample preparation. After preprocessing, the data were subjected to a combination of minimum Redundancy Maximal Relevance (mRMR) and Sparse Discriminant Analysis (SDA) methods to select the most significant variables for creating accurate SOM models. The resulting data were divided into training and validation sets and then analyzed by the SOM method to define the proper discrimination models. The 5-fold venetian blind cross-validation misclassification error was below 7% for all life stages, and the validation samples were correctly identified in all cases. The multiclass SOM model also revealed which chemical components were the most significant markers for each species, with several of these being amino acids. The results show that processing of DART-HRMS data using artificial neural networks (ANNs) based on the Kohonen SOM approach enables rapid discrimination and identification of fly species even for the immature life stages. The ANNs can be continuously expanded to include a larger number of species and can be used to screen DART-HRMS data from unknowns to rapidly determine species identity.
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