Vibrational Spectroscopy最新文献

Every year, more than 5 million deaths are attributed to injuries worldwide. However, accurately identifying and distinguishing the types of injuries in decomposed corpses is a significant challenge in forensic identification. Determining the cause of death in cases involving decomposed cadavers is particularly difficult, because traditional methods often lack conclusive evidence. To address this gap, this study aimed to explore the potential of attenuated total reflection/Fourier-transform infrared (ATR-FTIR) spectroscopy in analyzing the molecular composition changes in tissue samples from putrefied corpses. To simulate different environmental conditions, 54 experimental mice were randomly divided into three groups: ante-mortem injury (AI), post-mortem injury (PI), and non-injury (NI) groups, and their bodies were monitored at different time points. Subsequently, we conducted comprehensive analyses of these tissue samples using ATR-FTIR. The results indicate that under winter conditions, PC1 explained 78.3 % of the variance, whereas PC2 explained 15.4 %. Similarly, under summer conditions, PC1 explained 75.3 % of the variance, whereas PC2 explained 16.1 %. The results under both conditions, the AUC values of the ROC curve exceeded 0.9, indicating the reliability and accuracy of this method in discriminating ante-mortem injuries from post-mortem injuries on decomposed bodies, highlighting its significance in forensic investigations. This demonstrates the capability of ATR-FTIR technology to identify distinct molecular changes linked to ante-mortem and post-mortem injuries in decomposed corpses. The findings of this study underscores the forensic significance of understanding the molecular composition changes in decomposed cadavers. Therefore, ATR-FTIR is a valuable tool for differentiating ante-mortem and post-mortem injuries while also considering environmental factors.

The utilization of the soil pedotransfer functions (PTFs) developed based on the basic soil propertied is an alternative, fast, cost-effective and applicable approach for the prediction of field capacity (FC) and permanent wilting point (PWP). In addition, the Visible–Near-Infrared (Vis-NIR) spectra in soil science has gained prominence due to its practicality and relevance. In this paper, we used the data of the soil PWP and FC of 135 soil samples, easily measurable soil properties and Vis-NIR spectroscopy. The multiple linear regression (MLR) model was utilized to formulate PTFs model and Vis-NIR spectroscopy combined with MLR and partial least-squares (PLSR) was used to develop Spectrotransfer Function (STF). Results showed that among the easily measurable soil properties, particle-size diameter (dg) with Beta of −0.72 and −0.63 the most influential parameters for predicting FC and PWP, respectively, followed by the clay content. Developed PTFs for both FC and PWP with a R² of 0.71 and 0.68, respectively, had a better performance than other previous developed PTFs. Results also revealed that the PLSR with a higher R² (0.81) and lower RMSE (4 %) significantly performed better in comparison to STF for both FC and PWP prediction.

Tuna fish oil (TO) is a valuable source of omega fatty acids and polyunsaturated fatty acids required for human growth and development. Triggered by economic reasons, TO can potentially be adulterated with pork oil (PO), which has a lower price. The adulteration is a serious problem because PO is a non-halal oil, which is truly prohibited to be consumed, especially for Muslim. This research aimed to develop an effective and efficient analytical technique for detecting PO adulteration in TO using Fourier transform infrared (FT-IR) spectroscopy aided by machine learning techniques. Various machine learning techniques were developed, including linear regression, support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and gradient boosting. The result showed that SVM at the fingerprint region (1400–900 cm⁻¹) demonstrated the best model to detect and predict PO in TO with the highest R² (0.993) and the lowest root mean square error (RMSE) of 2.719 %. All levels of PO contained in TO could be accurately predicted, as indicated by the closeness between the actual value and predicted value of PO levels predicted by the model. In conclusion, machine learning could be a promising tool for detecting adulterants in fish oil samples. Further research on method standardization is important to propose the method as the method of choice for fish oil authentication, including halal authentication.