Semiquantitative
Fingerprinting Based on Pseudotargeted
Metabolomics and Deep Learning for the Identification of Listeria monocytogenes and Its Major Serotypes
The rapid identification of pathogenic microorganism
serotypes
is still a bottleneck problem to be solved urgently. Compared with
proteomics technology, metabolomics technology is directly related
to phenotypes and has higher specificity in identifying pathogenic
microorganism serotypes. Our study combines pseudotargeted metabolomics
with deep learning techniques to obtain a new deep semiquantitative
fingerprinting method for Listeria monocytogenes identification at the serotype levels. We prescreened 396 features
with orthogonal partial least-squares discrimination analysis (OPLS-DA),
and 200 features were selected for deep learning model building. A
residual learning framework for L. monocytogenes identification was established. There were 256 convolutional filters
in the initial convolution layer, and each hidden layer contained
128 filters. The total depth included seven layers, consisting of
an initial convolution layer, a residual layer, and two final fully
connected classification layers, with each residual layer containing
four convolutional layers. In addition, transfer learning was used
to predict new isolates that did not participate in model training
to verify the method’s feasibility. Finally, we achieved prediction
accuracies of L. monocytogenes at the
serotype level exceeding 99%. The prediction accuracy of the new strain
validation set was greater than 97%, further demonstrating the feasibility
of this method. Therefore, this technology will be a powerful tool
for the rapid and accurate identification of pathogens.