Cuticular hydrocarbons as weathering biomarkers of empty puparia of the forensically important blowfly Calliphora vicina Robineau-Desvoidy, 1830 (Diptera: Calliphoridae) in soil v/s under room conditions

Advances and Current Status in the Use of Cuticular Hydrocarbons for Forensic Entomology Applications

Cuticular hydrocarbons (CHCs) are long-chain lipids found on the exoskeletons of insects, serving primarily as a protective barrier against water loss and environmental factors. In the last few decades, the qualitative and quantitative analysis of CHCs, particularly in blow flies, has emerged as a valuable tool in forensic entomology, offering promising potential for species identification and age estimation of forensically important insects. This review examines the current application of CHC analysis in forensic investigations and highlights the significant advancements in the field over the past few years. Studies have demonstrated that CHC profiles vary with insect development, and while intra-species variability exists due to factors such as age, sex, geographical location, and environmental conditions, these variations can be harnessed to refine post-mortem interval (PMI) estimations and improve the accuracy of forensic entomological evidence. Notably, CHC analysis can also aid in distinguishing between multiple generations of insects on a body, providing insights into post-mortem body movement and aiding in the interpretation of PMI in complex cases. Furthermore, recent studies have investigated the variability and degradation of CHCs over time, revealing how environmental factors-such as temperature, humidity, UV light exposure, and toxicological substances-affect CHC composition, providing valuable insights for forensic investigations. Despite the promise of CHC profiling, several challenges remain, and this review also aims to highlight future research directions to enhance the reliability of this technique in forensic casework.

Variations in cuticular hydrocarbons of Calliphora vicina (Diptera: Calliphoridae) empty puparia: Insights for estimating late postmortem intervals

Necrophagous flies, particularly blowflies, serve as vital indicators in forensic entomology and ecological studies, contributing to minimum postmortem interval estimations and environmental monitoring. The study investigates variations in the predominant cuticular hydrocarbons (CHCs) viz. n-C25, n-C27, n-C28, and n-C29 of empty puparia of Calliphora vicina Robineau-Desvoidy, 1830, (Diptera: Calliphoridae) across diverse environmental conditions, including burial, above-ground and indoor settings, over 90 days. Notable trends include a significant decrease in n-C25 concentrations in buried and above-ground conditions over time, while n-C27 concentrations decline in buried and above-ground conditions but remain stable indoors. Burial conditions show significant declines in n-C27 and n-C29 concentrations over time, indicating environmental influences. Conversely, above-ground conditions exhibit uniform declines in all hydrocarbons. Indoor conditions remain relatively stable, with weak correlations between weathering time and CHC concentrations. Additionally, machine learning techniques, specifically Extreme Gradient Boosting (XGBoost), are employed for age estimation of empty puparia, yielding accurate predictions across different outdoor and indoor conditions. These findings highlight the subtle responses of CHC profiles to environmental stimuli, underscoring the importance of considering environmental factors in forensic entomology and ecological research. The study advances the understanding of insect remnant degradation processes and their forensic implications. Furthermore, integrating machine learning with entomological expertise offers standardized methodologies for age determination, enhancing the reliability of entomological evidence in legal contexts and paving the way for future research and development.

Forensic and ecological significance of necrophagous insects: Insights from animal carcasses, human cadavers, and myiasis patients

Necrophagous insects, including flies and beetles, play pivotal roles in decomposition, ecology, and forensics. Their diversity and activities vary across environments, necessitating comprehensive studies for understanding and management. The aim of the study is to investigate insect infestation on animal carcasses, human cadavers, and myiasis patients to enhance ecological, forensic, and medical entomological understanding, aiding in ecosystem management, forensic investigations, and disease control. Various species of flies and beetles were found associated with animal carcasses, human cadavers, and myiasis patients, as indicated by the comprehensive study. On animal carcasses, notable fly species included Chrysomya rufifacies (Macquart, 1842), Chrysomya megacephala (Fabricius, 1794), Lucilia cuprina (Wiedemann, 1830), and Sarcophaga sp., while beetles such as Dermestes maculatus (De Geer, 1774), Necrobia rufipes (Fabricius, 1781), Saprinus quadrigatattus (Fabricius, 1798), Saprinus splendens (Paykull, 1811), Saprinus optabilis (Marseul, 1855), Saprinus chalcites (Iliger, 1807), and Omorgus sp. (Erichson, 1847) were also observed. Similarly, human cadavers exhibited a presence of flies like Chrysomya albiceps (Wiedemann, 1819), Chrysomya rufifacies (Macquart, 1842), Chrysomya megacephala (Fabricius, 1794), and Sarcophaga dux (Thomson, 1869). In cases of myiasis patients, flies including Chrysomya megacephala, Cochliomyia hominivorax (Coquerel, 1858), and Chrysomya bezziana (Villeneuve, 1914) were identified. These findings underscore the diverse range of insect species involved in carcass decomposition, forensic investigations, and medical entomology, illustrating their crucial roles in ecological processes, forensic assessments, and disease management.

Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae)

The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples. Initially, we used the scanning electron microscope (SEM) to observe the surface of the empty puparia, but it was challenging to identify significant markers to estimate weathering time. We then utilized attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to detect the puparia spectrogram. Absorption peaks were observed at 1064 cm-1, 1236 cm-1, 1381 cm-1, 1538 cm-1, 1636 cm-1, 2852 cm-1, 2920 cm-1. Three machine learning models were used to regress the spectral data after dimensionality reduction using principal component analysis (PCA). Among them, eXtreme Gradient Boosting regression (XGBR) showed the best performance in the wavenumber range of 1800-600 cm-1, with a mean absolute error (MAE) of 1.20. This study highlights the value of refining these techniques for forensic applications involving entomological specimens and underscores the considerable potential of combining FTIR and machine learning in forensic practice.

Discrimination of Diptera order insects based on their saturated cuticular hydrocarbon content using a new microextraction procedure and chromatographic analysis

The nature and proportions of hydrocarbons in the cuticle of insects are characteristic of the species and age. Chemical analysis of cuticular hydrocarbons allows species discrimination, which is of great interest in the forensic field, where insects play a crucial role in estimating the minimum post-mortem interval. The objective of this work was the differentiation of Diptera order insects through their saturated cuticular hydrocarbon compositions (SCHCs). For this, specimens fixed in 70 : 30 ethanol : water, as recommended by the European Association for Forensic Entomology, were submitted to solid-liquid extraction followed by dispersive liquid-liquid microextraction, providing preconcentration factors up to 76 for the SCHCs. The final organic extract was analysed by gas chromatography coupled with flame ionization detection (GC-FID), and GC coupled with mass spectrometry was applied to confirm the identity of the SCHCs. The analysed samples contained linear alkanes with the number of carbon atoms in the C9-C15 and C18-C36 ranges with concentrations between 0.1 and 125 ng g-1. Chrysomya albiceps (in its larval stage) showed the highest number of analytes detected, with 21 compounds, while Lucilia sericata and Calliphora vicina the lowest, with only 3 alkanes. Non-supervised principal component analysis and supervised orthogonal partial least squares discriminant analysis were performed and an optimal model to differentiate specimens according to their species was obtained. In addition, statistically significant differences were observed in the concentrations of certain SCHCs within the same species depending on the stage of development or the growth pattern of the insect.

Deciphering the impact of microenvironmental factors on cuticular hydrocarbon degradation in Lucilia sericata empty Puparia: Bridging ecological and forensic entomological perspectives using machine learning models

Blow flies (Calliphoridae) play essential ecological roles in nutrient recycling by consuming decaying organic matter. They serve as valuable bioindicators in ecosystem management and forensic entomology, with their unique feeding behavior leading to the accumulation of environmental pollutants in their cuticular hydrocarbons (CHCs), making them potential indicators of exposure history. This study focuses on CHC degradation dynamics in empty puparia of Lucilia sericata under different environmental conditions for up to 90 days. The three distinct conditions were considered: outdoor-buried, outdoor-above-ground, and indoor environments. Five predominant CHCs, n-Pentacosane (n-C25), n-Hexacosane (n-C26), n-Heptacosane (n-C27), n-Octacosane (n-C28), and n-Nonacosane (n-C29), were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS). The findings revealed variations in CHC concentrations over time, influenced by environmental factors, with significant differences at different time points. Correlation heatmap analysis indicated negative correlations between weathering time and certain CHCs, suggesting decreasing concentrations over time. Machine learning techniques Support Vector Machine (SVM), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) models explored the potential of CHCs as age indicators. SVM achieved an R-squared value of 0.991, demonstrating high accuracy in age estimation based on CHC concentrations. MLP also exhibited satisfactory performance in outdoor conditions, while SVM and MLP yielded unsatisfactory results indoors due to the lack of significant CHC variations. After comprehensive model selection and performance evaluations, it was found that the XGBoost model excelled in capturing the patterns in all three datasets. This study bridges the gap between baseline and ecological/forensic use of empty puparia, offering valuable insights into the potential of CHCs in environmental monitoring and investigations. Understanding CHCs' stability and degradation enhances blow flies' utility as bioindicators for pollutants and exposure history, benefiting environmental monitoring and forensic entomology.