# Chemical Analysis of Gunpowder and Gunshot Residues
## 3 Analysis of Gunpowder and Gunshot Residue
### Emerging Applications
The researchers used likelihood ratios to develop a probabilistic model and applied it to a hypothetical scenario, claiming that all the required parameters for such a model could be easily estimated from seized material [33]. In another approach, [PERSON] et al. [109] used chemometrics to estimate the time since shooting from spent cartridges based on their previously optimised and validated HSSE/GC-MS methodology. The authors tested PLS, MARS, ANN, RF, KNN, and SVM regression algorithms and found the most suitable models to be those trained with RF and PLS [109]. [PERSON] et al. [121] combined ATR-FTIR with statistical analysis, PCA, and PLS-DA. The PCA analysis revealed that samples from the same calibre were grouped. At the same time, PLS-DA was able to differentiate between the three calibres analysed in the study. The authors did not discuss the reason for obtaining better results using this system [121]. However, it is known that PLS-DA can effectively reduce the dimensionality of the data by identifying the most relevant set variables for the problem in question. This is particularly advantageous for gunshot residue analysis, as it enables the identification of key elemental or spectral characteristics that distinguish various types of residues. [PERSON] et al. [54] applied ANN under a predictive data-processing program to predict the retention time of 32 OGSR compounds analysed via UHPLC-UV using several gradients, allowing the detection of all of the studied compounds. [PERSON] and [PERSON] [148] have also used ANN to differentiate samples from shooters and non-shooters based on ion mobility spectra obtained from hand swabs. Regarding visual comparison of firearm-related elements, [PERSON] et al. [7] used computer-based methods to evaluate the validity and reliability of judgments and conclusions of forensic experts. The procedure is based on data acquisition in 2D and 3D, followed by data pre-processing and comparing striation patterns to calculate the likelihood ratio [7]. The study showed the high sensitivity and specificity of the conclusions presented by the examiners. Still, those conclusions seem slightly less proficient at relating samples from the same source and better at distinguishing samples from a different source when compared with the computer-based method [7]. These chemometric tools and applications offer significant potential for advancing forensic ballistics research by enabling more advanced and efficient analysis of GSR data. They have the capability to contribute to the establishment of comprehensive databases and classification systems for GSR samples, assist in the identification of firearm characteristics, facilitate the linking of evidence to specific firearms or crime scenes, and provide valuable insights into the factors influencing GSR characteristics. Moreover, these tools can aid in the interpretation of forensic evidence, enhance the accuracy of forensic examinations, and potentially pave the way for the development of automated or semi-automated GSR analysis systems. Ongoing developments in this field aim to refine and optimise these techniques, integrate multiple methods to ensure more robust analysis, and explore the application of emerging chemometric tools.