One-dimensional convolutional neural network-based analysis of dielectric spectral abundance in liquid-phase media.

Aiming at the difficult problem of component information analysis of mixed dielectric spectra, the component information characteristics of mixed dielectric spectra are investigated by one-dimensional convolutional neural network, and the component analysis of mixed media is realised. First, the mixed dielectric spectra of water, ethanol and isopropoxyethano (iso) with different volume ratios were obtained by experimental measurements, and the singular spectrum analysis (SSA) method was applied to denoise the raw data, which provides a new data processing method for the effective analysis of dielectric spectra. Then, Utilizing the linear mixing model, we systematically obtained the dielectric spectra of binary mixtures of pure water and isopropoxyethanol with diverse ratios, along with those of multi-component mixtures integrating water, ethanol, and isopropoxyethanol at various proportion settings.