LAUSR

Simple Summary The weathering time of cuticular hydrocarbons from the puparium could assist in estimating the postmortem interval (PMI) of decomposed corpses. However, the composition of cuticular hydrocarbons in the puparium is complicated and has not been well studied for sarcophagid species. Therefore, we examined the compounds of Sarcophaga peregrina (Robineau-Desvoidy, 1830) at varying temperatures and used various machine learning models to predict the weathering time. The artificial neural network (ANN) model may be optimal for weathering time estimation. Abstract Empty puparium are frequently collected at crime scenes and may provide valuable evidence in cases with a long postmortem interval (PMI). Here, we collected the puparium of Sarcophaga peregrina (Diptera: Sarcophagidae) (Robineau-Desvoidy, 1830) for 120 days at three temperatures (10 °C, 25 °C, and 40 °C) with the aim to estimate the weathering time of empty puparium. The CHC profiles were analyzed by gas chromatography-mass spectrometry (GC-MS). The partial least squares (PLS), support vector regression (SVR), and artificial neural network (ANN) models were used to estimate the weathering time. This identified 49 CHCs with a carbon chain length between 10 and 33 in empty puparium. The three models demonstrate that the variation tendency of hydrocarbon could be used to estimate the weathering time, while the ANN models show the best predictive ability among these three models. This work indicated that puparial hydrocarbon weathering has certain regularity with weathering time and can gain insight into estimating PMI in forensic investigations.