Signal Processing for Miniature Mass Spectrometer Based on LSTM-EEMD Feature Digging

Miniature mass spectrometers exhibit immense application potential in on-site detection due to their small size and low cost. However, their detection accuracy is severely affected by factors such as sample pre-processing and environmental conditions. In this study, we propose a data processing method based on long short-term memory-ensemble empirical mode decomposition (LSTM-EEMD) to improve the quality of on-site detection data from miniature mass spectrometers. The EEMD method can clearly decompose the different physical feature components in the small-scale spectrometer signals, while the LSTM method can adaptively learn the internal feature relationships of the signals. Thus, by combining the two, the parameters for the EEMD signal reconstruction can be optimized in an adaptive manner, obtaining the optimized coefficients. Compared to the previous EEMD feature enhancement approach, the LSTM-EEMD method not only significantly improves the coefficient of determination (R