Stain-free Gram staining classification of pathogens<i>via</i>single-cell Raman spectroscopy combined with machine learning
Huijie Hu, Jingkai Wang, Xiaofei Yi, Kaicheng Lin, Siyu Meng, Xin Zhang, Chenyu Jiang, Yuguo Tang, Minggui Wang, Jian He, Xiaogang Xu, Yizhi Song
Abstract
-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.