Classification of two species of Gram-positive bacteria through hyperspectral microscopy coupled with machine learning.

Gram stain is one of the most common techniques used to visualize bacteria under microscopy and classify bacteria into two large groups (Gram-positive and Gram-negative). However, such an inaccurate classification is unfavorable for bacterial research. For instance, soil-rhizosphere bacteria, Bacillus megaterium (B. megaterium) and Bacillus cereus (B. cereus) have different effects on plants, nonetheless, they are both Gram-positive and difficult to be differentiated. Here, we present a method to precisely classify Gram-positive bacteria via hyperspectral microscopy. The pH-value differences in the intracellular environment of various types of bacteria can lead to different ionization of the auxochrome of crystal violet (CV) molecules during the Gram stain process. Consequently, there is a subtle difference in the absorption peak of Gram-stained bacteria. Harnessing hyperspectral microscopy can capture this subtle difference and enable precise classification. Besides the spectral features, the spatial features were also used to improve the quality of bacterial identification. The results show that the classification accuracy of two species of Gram-positive bacteria, B. megaterium and B. cereus, is up to 98.06%. We believe this method can be used for other Gram-positive bacteria and Gram-negative bacteria, realizing a more elaborate classification for Gram-stained bacteria.