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Identification of the Species Constituents of Maggot Populations Feeding on Decomposing Remains -- Facilitation of the Determination of Post Mortem Interval and Time Since Tissue Infestation Through Application of Machine Learning and Direct Analysis in Real Time-Mass Spectrometry

NCJ Number
Analytical Chemistry Volume: 92 Issue: 7 Dated: February 2020 Pages: 5439-5446
Date Published
February 2020
8 pages

This project demonstrated that direct analysis in real time-high resolution mass spectrometric (DART-HRMS) analysis of ethanol suspensions containing combinations of maggots representing Calliphora vicina, Chrysomya rufifacies, Lucilia coeruleiviridis, L. sericata, Phormia regina, and Phoridae exhibit highly reproducible chemical signatures.


The utilization of entomological specimens such as larvae (maggots) for the estimation of time since oviposition (i.e., egg laying) for post mortem interval determination, or for estimation of time since tissue infestation (in investigations of elder or child care neglect and animal abuse cases), requires accurate determination of insect species identity. Because the larvae of multiple species are visually highly similar and difficult to distinguish, it is customary for species determination of maggots to be made by rearing them to maturity so that the gross morphological features of the adult can be used to accurately identify the species. This is a time-consuming and resource-intensive process which also requires that the sample be viable. The situation is further complicated when the maggot mass being sampled is comprised of multiple species. Therefore, a method for accurate species identification, particularly for mixtures, is needed. The current research demonstrated that an aggregated hierarchical conformal predictor applied to a hierarchical classification tree that was trained against the DART-HRMS data enabled, for the first time, multispecies identification of maggots in mixtures of two, three, four, five, and six species. The conformal predictor provided label specific regions with confidence limits between 80 and 99 percent for species identification. The study demonstrates a novel, rapid, facile, and powerful approach for identification of maggot species in field-derived samples. (publisher abstract modified)

Date Published: February 1, 2020