Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species.

Forensic Sci Int

Chemical Ecology Group, School of Chemical and Physical Sciences, Keele University, Staffordshire, ST5 5BG, UK.

Published: November 2017

AI Article Synopsis

  • Blowflies are crucial for forensic studies as they are among the first insects to invade human remains, with their larval stages mainly utilized for age estimation; adult flies are rarely used due to their similar appearances post-hatching.
  • This study focused on analyzing the cuticular hydrocarbons (CHC) of three blowfly species using Gas Chromatography-Mass Spectrometry (GC-MS) to identify chemical changes as the flies mature.
  • The research demonstrated that analyzing CHCs can effectively establish the age of adult blowflies, enhancing forensic entomology methods and allowing automatic classification using advanced techniques like Principal Component Analysis (PCA) and Artificial Neural Networks (ANN).

Article Abstract

Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult to age because they remain morphologically similar once they have gone through the initial transformation upon hatching. However, being able to age them is of interest and importance within the field. This study examined the cuticular hydrocarbons (CHC) of Diptera: Calliphoridae species Lucilia sericata, Calliphora vicina and Calliphora vomitoria. The CHCs were extracted from the cuticles of adult flies and analysed using Gas Chromatography-Mass Spectrometry (GC-MS). The chemical profiles were examined for the two Calliphora species at intervals of day 1, 5, 10, 20 and 30 and up to day 10 for L. sericata. The results show significant chemical changes occurring between the immature and mature adult flies over the extraction period examined in this study. With the aid of a Principal Component Analysis (PCA) and Artificial Neural Networks (ANN), samples were seen to cluster, allowing for the age to be established within the aforementioned time frames. The use of ANNs allowed for the automatic classification of novel samples with very good performance. This was a proof of concept study, which developed a method allowing to age post-emergence adults by using their chemical profiles.

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Source
http://dx.doi.org/10.1016/j.forsciint.2017.10.001DOI Listing

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