THz-ATR Spectroscopy Integrated with Species Recognition Based on Multi-Classifier Voting for Automated Clinical Microbial Identification

• Published in: Biosensors (Basel) Vol. 12; no. 6; p. 378
• Main Authors: Yu, Wenjing, Shi, Jia, Huang, Guorong, Zhou, Jie, Zhan, Xinyu, Guo, Zekang, Tian, Huiyan, Xie, Fengxin, Yang, Xiang, Fu, Weiling
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 31.05.2022; MDPI
• Subjects: Algorithms; Automation; Bacteria; Biological products; Cancer; Classifiers; data analysis; Diagnostic systems; Discriminant analysis; Gram-negative bacteria; Gram-positive bacteria; Heterogeneity; Hydration; Identification; Identification methods; Learning algorithms; Machine learning; microbial identification; Microorganisms; Public health; Spectral analysis; Spectroscopy; Spectrum analysis; Sputum; Strains (organisms); Streptococcus infections; terahertz spectroscopy; Tissues; Voting; China
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios12060378

The demand for rapid and accurate identification of microorganisms is growing due to considerable importance in all areas related to public health and safety. Here, we demonstrate a rapid and label-free strategy for the identification of microorganisms by integrating terahertz-attenuated total reflection (THz-ATR) spectroscopy with an automated recognition method based on multi-classifier voting. Our results show that 13 standard microbial strains can be classified into three different groups of microorganisms (Gram-positive bacteria, Gram-negative bacteria, and fungi) by THz-ATR spectroscopy. To detect clinical microbial strains with better differentiation that accounts for their greater sample heterogeneity, an automated recognition algorithm is proposed based on multi-classifier voting. It uses three types of machine learning classifiers to identify five different groups of clinical microbial strains. The results demonstrate that common microorganisms, once time-consuming to distinguish by traditional microbial identification methods, can be rapidly and accurately recognized using THz-ATR spectra in minutes. The proposed automatic recognition method is optimized by a spectroscopic feature selection algorithm designed to identify the optimal diagnostic indicator, and the combination of different machine learning classifiers with a voting scheme. The total diagnostic accuracy reaches 80.77% (as high as 99.6% for Enterococcus faecalis) for 1123 isolates from clinical samples of sputum, blood, urine, and feces. This strategy demonstrates that THz spectroscopy integrated with an automatic recognition method based on multi-classifier voting significantly improves the accuracy of spectral analysis, thereby presenting a new method for true label-free identification of clinical microorganisms with high efficiency.