Moore, H, Butcher, J, Day, CR and Drijfhout, FP (2017) Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species. Forensic Science International, 280. pp. 233-244. ISSN 1872-6283

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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.

Item Type: Article
Additional Information: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Elsevier at - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: adult blowflies artificial neural networks cuticular hydrocarbons post mortem interval principal component analysis
Subjects: Q Science > Q Science (General)
Q Science > QD Chemistry
Divisions: Faculty of Natural Sciences > School of Chemical and Physical Sciences
Depositing User: Symplectic
Date Deposited: 16 Oct 2017 13:50
Last Modified: 08 Oct 2018 01:30

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