食性苍蝇,尤其是苍蝇,作为法医昆虫学和生态学研究的重要指标,有助于最小的死后间隔估计和环境监测。该研究调查了主要表皮碳氢化合物(CHCs)的变化。CalliphoravicinaRobineau-Desvoidy的空p的n-C25,n-C27,n-C28和n-C29,1830年,(双翅目:Calliphoridae)在不同的环境条件下,包括埋葬,地上和室内设置,超过90天。值得注意的趋势包括随着时间的推移,在掩埋和地上条件下,n-C25浓度显着下降,n-C27浓度在埋藏和地上条件下下降,但在室内保持稳定。埋葬条件显示n-C27和n-C29浓度随时间显著下降,表明环境影响。相反,地上条件表现出所有碳氢化合物的均匀下降。室内条件保持相对稳定,风化时间与CHC浓度之间的相关性较弱。此外,机器学习技术,特别是极端梯度提升(XGBoost),被用来估计空p的年龄,在不同的室外和室内条件下产生准确的预测。这些发现突出了CHC谱对环境刺激的微妙反应,强调在法医昆虫学和生态学研究中考虑环境因素的重要性。该研究促进了对昆虫残留物降解过程及其法医学意义的理解。此外,将机器学习与昆虫学专业知识相结合,为年龄确定提供了标准化的方法,提高昆虫学证据在法律环境中的可靠性,为未来的研究和发展铺平道路。
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.