  Recently, the combination of robust one-dimensional convolutional neural
networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid
identification of unknown substances with good accuracy. Using this technique,
researchers can recognize a pure compound and distinguish it from unknown
substances in a mixture. The novelty of this approach is that the trained
neural network operates automatically without any pre- or post-processing of
data. Some studies have attempted to extend this technique to the
classification of pure compounds in an unknown mixture. However, the
application of 1-D CNNs has typically been restricted to binary classifications
of pure compounds. Here we will highlight a new approach in spectral
recognition and quantification of chemical components in a multicomponent
mixture. Two 1-D CNN models, RaMixNet I and II, have been developed for this
purpose. The former is for rapid classification of components in a mixture
while the latter is for quantitative determination of those constituents. In
the proposed method, there is no limit to the number of compounds in a mixture.
A data augmentation method is also introduced by adding random baselines to the
Raman spectra. The experimental results revealed that the classification
accuracy of RaMixNet I and II is 100% for analysis of unknown test mixtures; at
the same time, the RaMixNet II model may achieve a regression accuracy of 88%
for the quantification of each component.
