Utah State University, Logan, UT.
J Biophotonics. 2019 Nov;12(11):e201900150. doi: 10.1002/jbio.201900150. Epub 2019 Aug 5.
When developing a Raman spectral library to identify bacteria, differences between laboratory and real world conditions must be considered. For example, culturing bacteria in laboratory settings is performed under conditions for ideal bacteria growth. In contrast, culture conditions in the human body may differ and may not support optimized bacterial growth. To address these differences, researchers have studied the effect of conditions such as growth media and phase on Raman spectra. However, the majority of these studies focused on Gram-positive or Gram-negative bacteria. This article focuses on the influence of growth media and phase on Raman spectra and discrimination of mycobacteria, an acid-fast genus. Results showed that spectral differences from growth phase and media can be distinguished by spectral observation and multivariate analysis. Results were comparable to those found for other types of bacteria, such as Gram-positive and Gram-negative. In addition, the influence of growth phase and media had a significant impact on machine learning models and their resulting classification accuracy. This study highlights the need for machine learning models and their associated spectral libraries to account for various growth parameters and stages to further the transition of Raman spectral analysis of bacteria from laboratory to clinical settings.
在开发用于识别细菌的拉曼光谱库时，必须考虑实验室和实际条件之间的差异。例如，在实验室环境中培养细菌是在理想的细菌生长条件下进行的。相比之下，人体中的培养条件可能不同，并且可能不支持细菌的最佳生长。为了解决这些差异，研究人员研究了生长介质和相态等条件对拉曼光谱的影响。然而，这些研究大多数都集中在革兰氏阳性菌或革兰氏阴性菌上。本文重点研究了生长介质和相态对分枝杆菌（一种抗酸菌）的拉曼光谱和区分的影响。结果表明，通过光谱观察和多元分析可以区分生长阶段和介质的光谱差异。结果与其他类型的细菌（如革兰氏阳性菌和革兰氏阴性菌）的结果相当。此外，生长阶段和介质的影响对机器学习模型及其分类准确性有重大影响。这项研究强调了需要机器学习模型及其相关的光谱库来考虑各种生长参数和阶段，以进一步将细菌的拉曼光谱分析从实验室转移到临床环境。
