Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures

A methodological approach to age estimation of the intra-puparial period of the forensically relevant blow fly Calliphora vicina via Fourier transform infrared spectroscopy

Estimating the age of immature blow flies is of great importance for forensic entomology. However, no gold-standard technique for an accurate determination of the intra-puparial age has yet been established. Fourier transform infrared (FTIR) spectroscopy is a method to (bio-)chemically characterise material based on the absorbance of electromagnetic energy by functional groups of molecules. In recent years, it also has become a powerful tool in forensic and life sciences, as it is a fast and cost-effective way to characterise all kinds of material and biological traces. This study is the first to collect developmental reference data on the changes in absorption spectra during the intra-puparial period of the forensically important blow fly Calliphora vicina Robineau-Desvoidy (Diptera: Calliphoridae). Calliphora vicina was reared at constant 20°C and 25°C and specimens were killed every other day throughout their intra-puparial development. In order to investigate which part yields the highest detectable differences in absorption spectra throughout the intra-puparial development, each specimen was divided into two different subsamples: the pupal body and the former cuticle of the third instar, that is, the puparium. Absorption spectra were collected with a FTIR spectrometer coupled to an attenuated total reflection (ATR) unit. Classification accuracies of different wavenumber regions with two machine learning models, i.e., random forests (RF) and support vector machines (SVMs), were tested. The best age predictions for both temperature settings and machine learning models were obtained by using the full spectral range from 3700 to 600 cm-1. While SVMs resulted in better accuracies for C. vicina reared at 20°C, RFs performed almost as good as SVMs for data obtained from 25°C. In terms of sample type, the pupal body gave smoother spectra and usually better classification accuracies than the puparia. This study shows that FTIR spectroscopy is a promising technique in forensic entomology to support the estimation of the minimum post-mortem interval (PMImin), by estimating the age of a given insect specimen.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy

Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000-2800 cm-1 and 1800-1300 cm-1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800-1300 cm-1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.

Analysis of the Influence of Changing and Fixed Temperatures on the Growth and Pteridine Content in the Head of Adults Sarcophaga crassipalpis (Diptera: Sarcophagidae)

Flesh flies (Diptera: Sarcophagidae) are regarded as significant in medical and veterinary entomology, and their development models can be utilized as considerable markers to ascertain the minimum postmortem interval (PMImin). In this research, we explored the growth cycle and larval body length of Sarcophaga crassipalpis Macquart 1839 (Diptera: Sarcophagidae) reared under variable temperatures ranging from 15.7 to 31.1 °C, with an average of 24.55 °C and relative humidity ranges from 31.4 to 82.8% and at six fixed temperatures of 15, 20, 25, 30, 32, and then 35 °C. Moreover, pteridine from the head was used to assess adult age grading. Our results allowed us to provide three development models: the isomorphen chart, the isomegalen chart, and the thermal summation models. The time taken for S. crassipalpis to complete its development from larviposition to adult emergence at constant temperatures of 15, 20, 25, 30, 32, and 35 °C was 1256.3 ± 124.2, 698.6 ± 15.1, 481.8 ± 35.7, 366.0 ± 13.5, and 295.8 ± 20.5 h, respectively, except 35 °C, where all pupae were unable to attain adulthood. They lasted 485.8 ± 5.4 h under variable temperatures. The minimum developmental limit (D0) temperature and the thermal summation constant (K) of S. crassipalpis were 9.31 ± 0.55 °C and 7290.0 ± 388.4 degree hours, respectively. The increase in pteridine content exhibited variations across different temperatures. There was quite a considerable distinction in the pteridine contents of male and female S. crassipalpis at 15 °C (p = 0.0075) and 25 °C (p = 0.0213). At 32 °C and variable temperatures, the pteridine content between female and male S. crassipalpis was not statistically divergent. However, temperature and gender remain the main factors influencing the pteridine content in the head of S. crassipalpis. We aim to provide detailed developmental data on S. crassipalpis that can be used as a valuable resource for future research and PMI estimation.