# Chemical Analysis of Gunpowder and Gunshot Residues
## 3 Analysis of Gunpowder and Gunshot Residue
### Liquid Chromatography
The extraction protocol for OGSR analysis consisted of liquid extraction with acetone and preconcentration before instrumental analysis, which potentially increases the probable value of GSR when using the developed sampling method in combination with SEM-EDX and UHPLC methodologies [30]. [PERSON] et al. [37] developed and validated an LC-MS/MS method to separate and detect both IGSR (used SEM-EDX) and OGSR in the same sample, increasing the confidence of the obtained chemical profile. [PERSON] et al. [28] developed and employed a fast UPLC-MS/MS methodology to separate and identify analytes in GSR samples. The authors were successful in identifying 21 OGSR compounds. The authors were able to differentiate between brands and lots by analysing the compositional differences [28]. [PERSON] et al. [54] optimised a UHPLC-UV method to separate and identify 32 OGSR compounds with lower limit of detection (LOD) than previously reported. This increased sensitivity allowed for the detection of trace compounds, significantly augmenting method applicability. In a different approach, [PERSON] et al. [22] used HPLC-DAD and statistical models to predict the age of SG. The authors chose HPLC-DAD with an isocratic water/acetonitrile mobile phase to avoid the thermal degradation of N-nitroso-DPA when analysed by GC. The authors focused on derivates of DPA, mainly N-nitroso-DPA, since the DPA nitration process is stable during the ageing of SG [22; 55; 129]. Also focusing on the additives of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **Objectives** & **Target Analytes** & **Matrix** & **Conclusions** & **Ref.** \\ \hline \multirow{8}{*}{UHPLC-UV} & Separation and & & & & \\ identification of 32 & & & & & Separation and \\ target OGSR & & & & dichloromethane; & identification of 32 OGSR, \\ compounds, with & & & & & faster and with lower \\ the aid of Artificial & & & & & & LOD, thanks to ANN \\ Neural Networks & & & & & \\ (ANN) & & & & & optimisation \\ \hline \multirow{8}{*}{HPLC} & Prediction of the & & & & & \\ age of gunpowder, & & & & & \\ mainly & & & & & \\ solutions (in & & & & \\ \cline{1-1}  & statistical models & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{8}{*}{LC-MS/MS} & Development of & & & & \\ methodologies for & & & & & \\ the analysis of & & & & & \\ \cline{1-1}  & OGSR to & & & & \\ determine their & & & & & \\ application in & & & & & \\ \cline{1-1}  & chemical ballistic & & & & \\ \hline \multirow{8}{*}{UHPLC-MS} & Comparison of the & & & & & \\ efficiency of & & & & & \\ \cline{1-1}  & various sampling & & & & \\ materials in & & & & & \\ \cline{1-1}  & collecting OGSR & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._gunpowder, [PERSON] et al. [57] proposed an LC-MS/MS method for analysing OGSR stabilisers, such as DPA. With a different objective, [PERSON] and [PERSON] [56] developed a UHPLC-MS methodology to compare the efficiency of various sampling materials for collecting OGSR. The authors showed that modern instrumentation and an efficient sample preparation technique facilitated detecting and identifying OGSR from discharged material a few hours after discharge [56]. Liquid chromatography (LC) is a valuable tool for analysing GSR in forensic ballistics due to its excellent separation capabilities, sensitive detection methods, flexibility in sample preparation, and automation potential [126], enabling accurate identification and quantification of GSR components for criminal investigations. Gas chromatography (GC) is among the most used techniques in the forensic science, especially when coupled with mass spectroscopy (MS) [51; 93; 100; 130]. Within the field of forensic ballistics, GC was already employed in the analysis of unburnt SG [26; 104; 131] and GSR [27; 102] (Table 3). This method presents many advantages, such as low analysis time, low detection limits (at the nanogram level), and high selectivity and sensitivity [14; 16; 93].