A Chemometric Method to Quickly and Efficiently Guarantee the Authenticity of Commercialized Cigarettes in Brazil

Cigarette smuggling and counterfeiting undermine public health and border security. Conventional analytical methods are expensive, non-specific and often ignore multicollinearity and robust validation. No current supervised multiclass model meets forensic laboratory needs for simplicity and scalability. In this context, this study proposed a cheaper and faster novel chemometric method to confirm the origin of cigarette brands marketed in Brazil. The method employs ultrasonic extraction using dichloromethane followed by gas chromatography coupled to flame ionization detection (GC‑FID), and full chromatographic fingerprints (peak areas and retention times) aligned with the R package GCalignR. This method developed can captures and exploits a larger number of chromatographic peaks, thereby providing greater chemical information per sample than conventional targeted analytical approaches. The aligned data were used in a Hierarchical Cluster Analysis (HCA) for exploratory grouping and in a Linear Discriminant Analysis (LDA) for supervised classification. Model validation employed train/test partitioning and a one‑vs‑one receiver operating characteristic (ROC) analysis, yielding area under the curve (AUCs) > 0.95 for all classes and cross‑validated accuracy > 90%. The workflow requires under 15 min of sample preparation per batch and can be automated for high‑throughput screening in standard analytical laboratories. The method validation results demonstrated high sensitivity, accuracy, and AUC confirming the robustness of the model and strong potential for use as a practical, predictive, and scalable forensic screening tool in regulatory and enforcement settings. This cost‑effective, quick and predictive method fills a critical gap, providing agencies with a robust workflow that is both operationally practical and easily integrated into routine analytical environments.