FTIR spectroscopy combined with chemometrics for species identification of eggs from 14 necrophagous insect species

The intensive oviposition by multiple necrophagous insect species shortly after carcass appearance poses a significant challenge to forensic practice, particularly for accurate postmortem interval (PMI) estimation, due to the considerable deficiency in egg species identification capabilities. To address this, this study obtained and established colonies of 14 common necrophagous insect species, systematically collected egg samples, and performed attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, acquiring 1680 spectra. Both the molecular phylogenetic tree and spectral principal component analysis (PCA) validated the morphological taxonomic relationships among the species. The absorption peak characteristics of the average spectra explained the structural and functional properties of the insect eggs. Finally, four established machine learning classification models—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), and Partial Least Squares Discriminant Analysis (PLS-DA)—achieved satisfactory classification performance. Notably, the test set accuracy of the RF model was 96.43%, with most misclassified samples predicted as closely related species within the same genus. In summary, this work provides a large biochemical information database for insect eggs. Beyond aiding PMI estimation in forensic entomology, the combination of ATR-FTIR and chemometrics, with its objective, rapid, repeatable, and cost-effective advantages, holds immense potential for future applications in agriculture, public health, customs quarantine, and food safety where pest control is needed.