Semiquantitative Fingerprinting Based on Pseudotargeted Metabolomics and Deep Learning for the Identification of Listeria monocytogenes and Its Major Serotypes.

Abstract

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.