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

Abstract

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.

Urinary Metabolic Biomarker Profiling for Cancer Diagnosis by Terahertz Spectroscopy: Review and Perspective

“…In this field, THz spectroscopy has shown its unique advantages as a new way to detect bacteria [158][159][160][161]. Yu et al presented THz-ATR as one of the methods that allow rapid and label-free identification of microorganisms by an automated recognition method based on voting with multiple classifiers [51]. Using THz-ATR spectroscopy, they were able to classify microbial strains into three different groups of microorganisms (Gram-positive bacteria, Gram-negative bacteria, and fungi), as shown in Figure 10.…”

“…Furthermore, glucose content in urine linked to diabetes [45] was also determined by THz radiation as well as nitriles in urine vapours during chemotherapy [50]. Moreover, THz spectroscopy has been used to detect bacteria species and other substances in urine samples [51,52].…”

“…THz-attenuated total reflection (THz-ATR) spectroscopy [51]: A method that acquires the THz signal of a sample attached to an ATR prism using evanescent waves that are concentrated within a few tens of microns of the prism surface. The signal is derived from THz radiation focused on the prism at its critical angle.…”

Developing a Novel Terahertz Fabry–Perot Microcavity Biosensor by Incorporating Porous Film for Yeast Sensing

“…Terahertz (THz) spectroscopy has shown great potential as a label-free, non-contact, and non-destructive technique for sensing chemical and biological substances [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Metamaterials operating in the THz range are particularly useful for detecting biological substances, including microorganisms and viruses [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Detection of the minimum concentrations of α-lactose solution using high-power THz-ATR spectroscopy

“…The second approach is the use of THz detection devices to enhance biological THz signals. Existing works focus on methods that utilize evanescent waves or enhance local THz field strength, such as the use of microfluidic chips, metamaterial sensors, waveguides, and THz-ATR ( Liu et al, 2019 ; Hou et al, 2021 ; Yang et al, 2021 ; Yu et al, 2022 ). THz-ATR has several advantages, including high detection sensitivity, low sample requirements, and the ability to achieve qualitative and quantitative detection of samples simultaneously.…”