Raman spectra-based deep learning: A tool to identify microbial contamination.

ABSTRACT: Deep learning has the potential to enhance the output of in-line, on-line, and at-line instrumentation used for process analytical technology in the pharmaceutical industry. Here, we used Raman spectroscopy-based deep learning strategies to develop a tool for detecting microbial contamination. We built a Raman dataset for microorganisms that are common contaminants in the pharmaceutical industry for Chinese Hamster Ovary (CHO) cells, which are often used in the production of biologics. Using a convolution neural network (CNN), we classified the different samples comprising individual microbes and microbes mixed with CHO cells with an accuracy of 95%-100%. The set of 12 microbes spans across Gram-positive and Gram-negative bacteria as well as fungi. We also created an attention map for diff

SUBMITTER: Maruthamuthu MK 

PROVIDER: S-EPMC7658449 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature