Feasibility study on identifying the source of cigarette ash based on infrared spectroscopy and chemometrics

Crime scene investigation is a key step in collecting and identifying physical evidence that may be closely related to the crime. The size of physical evidence can range from macro to micro. Cigarettes are a type of popular consumables, and their burned ashes are valuable resources of physical evidence since they contain important information such as brand preferences. This work explores the feasibility of using attenuated total reflection mid-infrared (ATR-MIR) spectroscopy and chemometrics to achieve cigarette brand recognition from burned ash. A total of 600 cigarette samples from ten brands were collected for experiments, and the samples were divided into a training set and a testing set in a 2:1 ratio. The Relief-F algorithm was used to sort variables and the forward search was used to further optimize variables to obtain the optimal subset of variables. Based on this, a partial least-squares discriminant analysis (PLS-DA) model was established, achieving a total accuracy of 97% on the test set. As a reference, the maximum correlation coefficient method was also used for classification, with an accuracy of only 73%. It seems that using the variable selection and modeling scheme proposed in this article is feasible for identifying cigarette brands from burned ash.