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 OD₆₀₀. 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 OD₆₀₀ 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.

中文翻译：

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一种高通量和机器学习的耐药监测系统，用于确定大肠杆菌与四环素的耐药点：结合紫外-可见分光光度法与主成分分析

紫外-可见光谱 (UV-Vis) 常用于微生物学中，作为检查与微生物培养物生长阶段相关的光密度 (OD) 的工具，波长为 600 nm，更广为人知的是 OD ₆₀₀. 通常，现代 UV-Vis 分光光度计可以在大约 200-1000 nm 的电磁光谱范围内进行扫描，用户不会在实验室中扩展使用仪器的全部功能。在这项研究中，UV-Vis 分光光度法（多波长收集）的全部潜力被用于检查用抗生素处理时的细菌生长阶段，展示了在将抗生素引入培养基时了解耐药点并因此了解生化变化的能力的传染性病原体。多板阅读器演示了一项高通量实验（96 个样品），以了解大肠杆菌的生长情况当将不同浓度的抗生素四环素添加到孔板中时。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 分光光度计读板器生成机器学习“原理证明”。这项研究的结果表明，PCA 分析提供了准确而简单的视觉分类以及对属于每种处理的大肠杆菌样本的识别。_{与传统 OD 600}相比，这些数据显示出显着优势我们现在可以了解系统中的生化变化而不仅仅是光密度测量的方法。由于涉及间接使用抗生素的独特实验设置和程序，相同的测试可用于获得有关抗生素类型、耐药性和剂量的实用信息，以建立最佳诊断、治疗和去污策略，以用于致病性和抗生素耐药物种。