Traditionally, optical microscopy is used to visualize the morphological features of pathogenic bacteria, of which the features are further used for the detection and identification of the bacteria. However, due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification, the effectiveness of this optical microscopybased method is limited. Here, we reported a pilot study on a combined use of Structured Illumination Microscopy (SIM) with machine learning for rapid bacteria identification. After applying machine learning to the SIM image datasets from three model bacteria (including Escherichia coli, Mycobacterium smegmatis, and Pseudomonas aeruginosa), we obtained a classification accuracy of up to 98%. This study points out a promising possibility for rapid bacterial identification by morphological features.

为了实时、原位和非接触测量工业生产过程中封闭管道系统内透明、半透明液体折射率，提出了一种简单的基于玻璃管壁光学特性的液体折射率测量方法。该方法通过涂覆在玻璃管壁外表面上的透射散射层，将入射激光束转换为进入玻璃管壁的大角度分布的透射散射光; 透射散射光到达玻璃管壁与液体的界面上后，符合全反射条件的散射光反射到透射散射层上，自动形成与玻璃管内液体折射率值相关的椭圆形暗斑图像。根据椭圆形暗斑长轴长度与液体折射率之间的解析关系，即可实现玻璃管内液体折射率的原位、非接触测量。对几种常见的透明、半透明液体的折射率进行了实验测量， 结果表明: 该测量方法的准确性与目前商用数字阿贝折射仪相当(±2×10-4 RIU)。该测量方法具有成本低、稳定性好、抗干扰且光源稳定等优点，而且在处理与液体折射率相关的光学图像时无需调试，光照即显，有望用于封闭管道在非常温、非常压状态下液体折射率的实时、自动和非接触在线监测。