  With noisy environment caused by fluoresence and additive white noise as well
as complicated spectrum fingerprints, the identification of complex mixture
materials remains a major challenge in Raman spectroscopy application. In this
paper, we propose a new scheme based on a constant wavelet transform (CWT) and
a deep network for classifying complex mixture. The scheme first transforms the
noisy Raman spectrum to a two-dimensional scale map using CWT. A multi-label
deep neural network model (MDNN) is then applied for classifying material. The
proposed model accelerates the feature extraction and expands the feature graph
using the global averaging pooling layer. The Sigmoid function is implemented
in the last layer of the model. The MDNN model was trained, validated and
tested with data collected from the samples prepared from substances in palm
oil. During training and validating process, data augmentation is applied to
overcome the imbalance of data and enrich the diversity of Raman spectra. From
the test results, it is found that the MDNN model outperforms previously
proposed deep neural network models in terms of Hamming loss, one error,
coverage, ranking loss, average precision, F1 macro averaging and F1 micro
averaging, respectively. The average detection time obtained from our model is
5.31 s, which is much faster than the detection time of the previously proposed
models.
