1D CNN-LSTM Based Electronic Nose Algorithm for Disinfectant Concentration Detection

Proper use of disinfectants in the cold chain environment is critical to prevent the cold chain transmission of the new coronavirus and other viruses. To address this issue, this paper presents a model for predicting the disinfectant concentrations based on 1D CNN-LSTM to quantify the concentration of four types of disinfectants in cold chain. An electronic nose platform developed in the laboratory is used and combined with the algorithm. The electronic nose signal incorporates information such as disinfectant concentration, and combining 1D CNN and LSTM models can achieve multiple advantages. 1D CNN is able to capture local patterns and features in sequences, while LSTM is able to model long-term dependencies, enabling the model to efficiently process sequence data and reduce the computational complexity and memory consumption of the model, effectively preventing overfitting. Experimental results demonstrate that the algorithm can effectively quantify the concentration of disinfectants in the cold chain environment, with a reduction in error.