Department of Applied Chemistry and Environmental Science, School of Science, RMIT University, Melbourne, Victoria, Australia.
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA.
Biotechnol Bioeng. 2021 Apr;118(4):1511-1519. doi: 10.1002/bit.27664. Epub 2021 Jan 21.
UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD . Typically, modern UV-Vis spectrophotometers can scan in the region of approximately 200-1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV-Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV-Vis spectral data and generate machine learning "proof of principle" for the UV-Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.
紫外-可见光谱（UV-Vis）在微生物学中通常被用作一种工具，用于检查微生物培养物在 600nm 单波长处的光学密度（OD），该波长通常被称为 OD 。通常，现代紫外-可见分光光度计可以在电磁光谱的大约 200-1000nm 范围内进行扫描，而在实验室中，用户并未扩展仪器的全部功能。在这项研究中，使用了紫外-可见分光光度法（多波长采集）的全部潜力来检查抗生素处理下的细菌生长阶段，展示了在引入抗生素时理解耐药点的能力，从而理解传染病原体的生化变化。多板读数器进行了高通量实验（96 个样本），以了解加入四环素抗生素时大肠杆菌的生长情况。然后，使用主成分分析（PCA）和偏最小二乘判别分析作为数据挖掘技术来解释 UV-Vis 光谱数据，并为 UV-Vis 分光光度计板读数器生成机器学习“原理证明”。该研究的结果表明，PCA 分析提供了准确而简单的可视化分类和对属于每种处理的大肠杆菌样本的识别。与传统的 OD 方法相比，这些数据具有显著的优势，因为我们现在可以了解系统中的生化变化，而不仅仅是光学密度测量。由于涉及间接使用抗生素的独特实验设置和程序，因此相同的测试可以用于获得有关建立最佳诊断，治疗和消毒策略所需的抗生素类型，抗性和剂量的实用信息。
