Traceability analysis of tobacco products based on GC-MS and electronic nose detection technology

To achieve cross-regional precise identification of tobacco varieties and explore efficient, reliable, and low-cost methods for origin traceability, this study establishes a tobacco traceability framework by integrating gas chromatography-mass spectrometry (GC-MS) technology with machine learning algorithms. First, GC-MS was employed for qualitative and quantitative analysis of volatile components in tobacco, guiding the selection of high-sensitivity commercial electronic nose sensors to construct a tobacco detection system. Subsequently, the collected data underwent preprocessing and feature extraction, followed by classification and prediction using four algorithms: Support Vector Machine (SVM), Random Forest (RF), Back-Propagation Neural Network (BP), and Radial Basis Function Neural Network (RBF). Experimental results demonstrated that the STB (extracted value) feature achieved the highest classification accuracy of 98.28% with the SVM algorithm, representing an improvement of 1.73% compared to the STB-MAX feature (96.55%). The SVM algorithm significantly outperformed other algorithms, including BP (96.55%), RBF (94.83%), and RF (93.10%). Further analysis revealed that the temporal characteristics of STB features effectively suppressed data noise interference, while the STB-MAX hybrid parameter caused a 3.8% performance degradation in ensemble algorithms (e.g., RF) due to feature covariance issues. By optimizing feature selection and algorithm adaptation mechanisms, this study provides an efficient solution for tobacco origin traceability. The proposed methodological framework can be extended to quality control scenarios in the food and pharmaceutical industries.