‘Forensic-DataFusion-Tool’: A Python-based application for exploratory forensic data analysis using merged datasets from analytical sensors

Portable sensors for on-site forensic analysis have advanced significantly, enabling reliable methods for crime scene investigation. Non-destructive analytical instruments are especially useful for providing chemical information from the same specimen. Combining data from these instruments through data fusion enhances analytical responses. Data fusion merges data from different sources to improve exploratory and predictive models. No current application supports multi-dataset fusion on a single platform. To address this, we developed a Python-based ‘Forensic-DataFusion-Tool’ to merge raw and preprocessed data from multiple sensors, speeding up data fusion and enabling future machine learning updates, including classification algorithms. • Developed Python-based Forensic-DataFusion-Tool for integrating multi-sensor data. • Enables low-level data fusion and data exploration through PCA. • Supports multiple preprocessing techniques: autoscaling, mean centering, SNV. • Enhances forensic analysis by merging raw data for faster, more accurate insights. • Future updates will include machine learning functionalities for classification.