Dielectric characterization of alcoholic beverages and solutions of ethanol in water under microwave radiation in the 1–20GHz range

Characterizing the concentration of ethanol-water solutions by oblique-incidence reflectivity difference combined with deep learning algorithms

The detection of ethanol-water solution concentration plays an important role in industries, medical care, food and other aspects, which has attracted much attention. In this paper, a 632.8 nm laser combined with the oblique-incidence reflectivity difference (OIRD) method was used to obtain a signal linearly related to the solution concentration and containing the information of the dielectric constant of the solution. Combined with a variety of deep learning algorithms, ethanol-water solutions with a volume concentration of 0-95 % are detected. Among them, the prediction accuracy of the MLP, CNN, LSTM, CNN + BiLSTM + Attention models were 93.65 %, 96.54 %, 97.12 %, 99.23 %, respectively. The experimental results indicate that the OIRD method can achieve rapid, non-destructive, accurate and reliable detection of ethanol-water solutions.