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A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence.
Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network
The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT.
Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network
The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectrums (10-30 dB noise power).
Noise Reduction Technique for Raman Spectrum using Deep Learning Network
It is shown that output SNR of the proposed noise reduction technology is 10. 24 dB greater than that of the wavelet noise reduction method while the RMSE and the MAPE are 292. 63 and 10. 09, which are much better than the proposed technique.
