Abstract：
The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples. Initially, we used the scanning electron microscope (SEM) to observe the surface of the empty puparia, but it was challenging to identify significant markers to estimate weathering time. We then utilized attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to detect the puparia spectrogram. Absorption peaks were observed at 1064 cm-1, 1236 cm-1, 1381 cm-1, 1538 cm-1, 1636 cm-1, 2852 cm-1, 2920 cm-1. Three machine learning models were used to regress the spectral data after dimensionality reduction using principal component analysis (PCA). Among them, eXtreme Gradient Boosting regression (XGBR) showed the best performance in the wavenumber range of 1800-600 cm-1, with a mean absolute error (MAE) of 1.20. This study highlights the value of refining these techniques for forensic applications involving entomological specimens and underscores the considerable potential of combining FTIR and machine learning in forensic practice.
摘要：
空p的风化时间对于预测最小死后间隔（PMImin）可能很重要。随着尸体分解进入骨骼阶段,空p通常仍然是现场苍蝇活动的唯一证据。在这项研究中,我们使用在2019年1月至2023年2月之间的10个不同时间点收集的Sarcophagaperegrina（双翅目：Sarcophagidae）的空p作为我们的样本.最初，我们使用扫描电子显微镜(SEM)观察了空阴部的表面,但是确定重要的标记来估计风化时间是具有挑战性的。然后，我们利用衰减的全内反射傅里叶变换红外光谱（ATR-FTIR）来检测阴部光谱图。在1064cm-1、1236cm-1、1381cm-1、1538cm-1、1636cm-1、2852cm-1、2920cm-1处观察到吸收峰。使用三种机器学习模型对降维后的光谱数据进行回归，使用主成分分析（PCA）。其中,极限梯度提升回归（XGBR）在1800-600cm-1的波数范围内表现最佳，平均绝对误差（MAE）为1.20。这项研究强调了完善这些技术在涉及昆虫学标本的法医应用中的价值，并强调了在法医实践中结合FTIR和机器学习的巨大潜力。
