Birck Nanotechnology Center, Purdue University, West Lafayette, IN, USA.
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA.
Microbiologyopen. 2020 Nov;9(11):e1122. doi: 10.1002/mbo3.1122. Epub 2020 Oct 16.
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.
深度学习有可能增强制药行业中用于过程分析技术的在线、离线和在线仪器的输出。在这里，我们使用基于拉曼光谱的深度学习策略来开发一种用于检测微生物污染的工具。我们为中国仓鼠卵巢 (CHO) 细胞中常见的微生物污染物建立了一个拉曼数据集，CHO 细胞常用于生物制品的生产。使用卷积神经网络 (CNN)，我们对包含单个微生物和与 CHO 细胞混合的微生物的不同样本进行了分类，准确率达到 95%-100%。这 12 种微生物组跨越了革兰氏阳性和革兰氏阴性细菌以及真菌。我们还为不同的微生物和 CHO 细胞创建了一个注意力图，以突出显示拉曼光谱中对帮助区分不同物种贡献最大的部分。该数据集和算法为在制药行业中实施拉曼光谱检测微生物污染提供了一条途径。
