# Chemical Analysis of Gunpowder and Gunshot Residues
## 3 Analysis of Gunpowder and Gunshot Residue
### Emerging Applications  
The adoption of new chemical analytical techniques for detecting and identifying organic compounds in SG and GSR usually results in the acquisition of a large amount of data. Most of these data points are not useful for the comparison of questioned samples within forensic ballistics. However, this opens the possibility for the exploration of novel avenues of approach that may allow responding to other vital questions within the field of forensic ballistics. In fact, over the last decade, several researchers have explored new applications for this data, taking advantage of the most recent statistical and machine learning tools currently available for model development [13] and of modern computational database systems for efficient storage and data access [135]. Within the forensic sciences, several research works have already proved the usefulness of these chemometric tools, as visible in Table 4. These helped in the clustering, interpretation, and optimisation of several analytical procedures, particularly those that rely on visual comparisons of spectra, chromatograms or other similar data [116; 117; 136; 137; 138; 139]. Chemometric tools, which cover a wide range of applications, can be divided into three main categories: (i) pattern recognition, which can be supervised or unsupervised, and is oriented toward the automated recognition of relations in datasets; (ii) regression methods, which predict sample characteristics quantitively; and (iii) experimental design, used to optimise chemical procedures [135]. Within forensic ballistics, pattern recognition and regression methods have already been explored to analyse SG and GSR chemical data. These methods include hierarchical clustering analysis (HCA), principal components analysis (PCA), partial least squares (PLS), PLS associated with discriminant analysis (PLS-DA), k-nearest neighbour (KNN), multivariate adaptive regression splines (MARS), random forests (RF), artificial neural networks (ANN), and support vector machines (SVM) [13; 50; 54; 140; 141; 142; 143; 144; 145; 146; 147; 148]. HCA is a clustering method that groups objects iteratively, agglomerating or dividing clusters in each cycle based on objects' similarity for a determined distance measure [135; 143]. PCA is an unsupervised data reduction methodology that transforms data linearly, creating a new set of orthogonal variables that successively maximise the variance present in the data [135; 142; 149]. PLS is a supervised regression methodology that, similarly to PCA, transforms data linearly by projecting the variables into a new space, after which a linear regression model is computed [140]. PLS-DA is a supervised method that extends PLS to classification tasks [144; 145]. KNN is a supervised pattern recognition based on the distance between known and unknown objects, trying to group the unknown objects with their closest k-neighbours to attribute a class to the object [150]. ANN are predictive models suitable for classification and regression formed by multiple layers of interconnected perceptrons, similar to biological neurons, weighing each input and producing an output [54; 135; 148]. MARS is a non-parametric regression analysis technique that adapts linear regression methods to non-linear interactions between variables [146; 147]. RF are an ensemble of independent decision trees, in which each tree comprises groups of nodes representing a test on a particular object attribute [135]. SVM are supervised method for classification that separates classes based on the maximum distance between them in a hyperplane, used when known classes cannot be linearly separated [135; 151]. Making use of some of these statistical analysis tools, [PERSON] et al. [152] developed a non-targeted approach for characterising unburnt SG and OGSR. The authors applied their method to various ammunition from different manufacturers, calibres, and ages based on the chemical profile obtained.