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

• Published in: Analytical chemistry (Washington) Vol. 92; no. 7; pp. 5439 - 5446
• Main Authors: Beyramysoltan, Samira, Ventura, Mónica I, Rosati, Jennifer Y, Giffen-Lemieux, Justine E, Musah, Rabi A
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 07.04.2020
• Subjects: Analytical chemistry; Animals; Chemistry; Child abuse & neglect; Confidence limits; Cruelty to animals; Egg laying; Ethanol; Forensic science; Forensic Sciences; Identification; Infestation; Insecta - classification; Insecta - physiology; Insects; Larva - physiology; Larvae; Learning algorithms; Machine Learning; Maggots; Mass Spectrometry; Mass spectroscopy; Oviposition; Real time; Species; Time Factors
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.0c00199

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. It is demonstrated here 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. 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% for species identification. The study demonstrates a novel, rapid, facile, and powerful approach for identification of maggot species in field-derived samples.