In recent years, deep learning has been widely used in the field of Raman spectral classification. However, the majority of the training and test sets are generated by the same...

Raman spectroscopy is widely used in the identification of substances. Raman spectra contain molecular information from various components and interference from noise and instruments. Therefore, using Raman spectroscopy to identify components is still challenging, especially for substances with high similarity. In this study, a class of highly similar products (methanol and ethanol) and a multicomponent mixture of propanol and water were tested using a portable Raman spectrometer. They are divided into 11 categories according to the volume fraction ratio. A total of 5,060 groups of Raman spectrum data were obtained, constituting the data set of this study. The deep neural network structure adopted in this study is based on ResNet architecture, on which the SE module in the attention mechanism is added, which increases the weight of some spectral features and achieves significant performance improvement. Finally, SE-ResNet achieves a recall of 0.95, a precision rate of 0.95, a Micro-F1 of 0.95, and an accuracy rate of 99.20%.

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