The application of word vector convolutional time series network in traceability of the origin of pickled cabbage based on E-nose

This study proposes the word vector convolutional time-series Network (WVCTS-Net) for extracting gas feature signatures from pickled vegetables to distinguish geographical origins. First, treating electronic nose sensor responses as "word sequences" akin to NLP inputs, we introduce a word vector convolutional (WVC) block to enhance interpretability. Second, a time-domain analysis module based on gated recurrent units was designed to capture long-sequence contextual dependencies. The final WVCTS-Net integrates these components into a low-complexity gas classification framework. Compared to both generic and gas-specific models, WVCTS-Net achieves optimal performance with minimal parameters while maintaining cross-gas applicability. Structural optimizations yield a lightweight model that substantially reduces computational complexity and memory usage, demonstrating exceptional efficiency on edge devices. This work provides a robust theoretical foundation for real-time intelligent gas recognition and analysis at the terminal level.