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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.
Sharif, Swaima; Wunder, Cora; Amendt, Jens; Qamar, Ayesha.
Affiliation
  • Sharif S; Institute of Legal Medicine, Forensic Biology, University Hospital, Goethe University, Frankfurt am Main, Germany. Electronic address: swaimasharif@gmail.com.
  • Wunder C; Institute of Legal Medicine, Forensic Biology, University Hospital, Goethe University, Frankfurt am Main, Germany. Electronic address: wunder@uni-mainz.de.
  • Amendt J; Institute of Legal Medicine, Forensic Biology, University Hospital, Goethe University, Frankfurt am Main, Germany. Electronic address: amendt@em.uni-frankfurt.de.
  • Qamar A; Section of Entomology, Department of Zoology, Aligarh Muslim University, Aligarh 202002, U.P., India. Electronic address: ayesha.zo@amu.ac.in.
Sci Total Environ ; 913: 169719, 2024 Feb 25.
Article de En | MEDLINE | ID: mdl-38171456
ABSTRACT
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.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Diptera / Entomologie médico-légale Type d'étude: Prognostic_studies Limites: Animals Langue: En Journal: Sci Total Environ Année: 2024 Type de document: Article

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Diptera / Entomologie médico-légale Type d'étude: Prognostic_studies Limites: Animals Langue: En Journal: Sci Total Environ Année: 2024 Type de document: Article