Junfan Chen, Jiaqi Hu, Chenlong Xue, Qian Zhang, Jingyan Li, Ziyue Wang, Jinqian Lv, Aoyan Zhang, Hong Dang, Dan Lu, Defeng Zou, Longqing Cong, Yuchao Li, Gina Jinna Chen, Perry Ping Shum
Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5...
April 4, 2024: Analytical Chemistry