Recommended Citation
Maruthamuthu, Murali K.; Raffiee, Amir Hossein; Mendes de Oliveira, Denilson; Ardekani, Arezoo M.; and Verma, Mohit S., "Raman spectra-based deep learning: A tool to identify microbial contamination" (2020). Purdue University Libraries Open Access Publishing Fund. Paper 223.
http://dx.doi.org/10.1002/mbo3.1122
DOI
10.1002/mbo3.1122
Date of this Version
9-7-2020
Keywords
biologics, CHO cells, convolution neural network, deep learning, microbial contamination, process analytical technology
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 different microbes and CHO cells to highlight which segments of the Raman spectra contribute the most to help discriminate between different species. This dataset and algorithm provide a route for implementing Raman spectroscopy for detecting microbial contamination in the pharmaceutical industry.
Comments
This is the publisher PDF of Maruthamuthu, MK, et al. Raman spectra-based deep learning: A tool to identify microbial contamination. MicrobiologyOpen. 2020;9:e1122. This article is distributed under a CC-BY-NC license, and is available at DOI: 10.1002/mbo3.1122.