Age estimation of Calliphora (Diptera: Calliphoridae) larvae using cuticular hydrocarbon analysis and Artificial Neural Networks

Abstract

Cuticular hydrocarbons were extracted daily from the larvae of two closely related blowflies Calliphora vicina and Calliphora vomitoria (Diptera:Calliphoridae). The hydrocarbons were then analysed using Gas Chromatography-Mass Spectrometry (GC-MS), with the aim of observing changes within their chemical profiles in order to determine the larval age. The hydrocarbons were examined daily for each species from 1 day old larvae until pupariation. The results show significant chemical changes occurring from the younger larvae to the post-feeding larvae. With the aid of a multivariate statistical method (Principal Component Analysis and Artificial Neural Networks), samples were clustered and classified, allowing for the larval age to be established. Results from this study allowed larvae to be aged to the day with at worst, 87% accuracy, which suggests there is great potential for the use of cuticular hydrocarbons present on larvae to give an indication of their age and hence potentially a valuable tool for minimum PMI estimations.

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

“…These results show that the SOM offers very good classification performance when using the average of 5 samples compared to using individual samples for testing. This to be expected due to the reduction in variability during training, which was also the case when analyzing larvae of the same species [13] [28].…”

“…The same training and testing approaches detailed in Butcher et al [13] and Moore et al [28] were used to train a SOM to classify previously unseen hydrocarbon profiles of adult L. sericata, C. vicina and C. vomitoria flies. Very briefly, training of a SOM involves presenting every input pattern to the input layer where the weighted connection between the input neuron and the output neuron whose activation closely matches the input pattern (the winning neuron) are modified by a standard Hebbian learning rule [29].…”

“…The data processing used in this study is the same as described previously (Butcher et al. [13] and Moore et al [28]). First, the data was preprocessed using PCA to reduce…”

Cuticular Hydrocarbon Analysis in Forensic Entomology: A Review

“…ANNs can reduce the analysis time required by clarifying novel data based on their knowledge of the domain that they have acquired during training (Moore 2013). The results showed significant promise for ageing the larval stages and this was replicated by a study from Moore et al (Moore et al 2016), successfully ageing C. vomitoria and C. vicina larvae. Pechal et al (2014) investigated the use of cuticular hydrocarbons for the chemical ageing of adult flies.…”

Combining domain filling with a self-organizing map to analyze multi-species hydrocarbon signatures on a regional scale

“…SOM analyses have been used for atmospheric classification of synoptic patterns (e.g., Huth et al 2008), towards diagnosing air quality events (e.g., Pearce et al 2011), and for various analyses of VOCs. VOC SOM analyses have included Calliphora age estimation (Moore et al 2016), analysis of oil spill emissions (Fernández-Varela et al 2010), aiding in sensor detection of petroleum at low ppb (Sugimoto et al 1999) or of VOCs in sea water (Tonacci et al 2015), for validation of tropospheric VOC degradation models (Papa and Gramatica 2008) and even in an urban benzene spatial map analysis (Strebel et al 2013). We believe that we are presenting the SOM's first use combining a meteorological model with a multi-species VOC spatial assessment.…”