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Research Article

Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification

Yongqiang Zhang1, Kunxing Liu2, Jingkun Yu1, Haifeng Chen4, Rui Fu1, Siqi Zhu2( ), Zhenqiang Chen2, Shuangpeng Wang3( ), Siyu Lu1( )
Green Catalysis Center, and College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
Institute of Applied Physics and Materials Engineering, University of Macau, Taipa, Macao 999078, China
School of electronic information and electrical engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China
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Graphical Abstract

Concentration-dependent wavelength-tunable carbon dots (CWT-CDs) were developed as the only stain for microorganism identification. By establishing the characteristic databases with spatial hyperspectral information through the hyperspectral microscopy system, using the support vector machine classifier to learn and train these databases, the identification accuracy of pathogenic microorganisms could be close to 100%, and the relative proportions and spatial distributions of different types of microorganisms in mixed populations could also be indexed.

Abstract

The most widely used method of identification of microbial morphology and structure is microscopy, but it can be difficult to distinguish between pathogens with a similar appearance. Existing fluorescent staining methods require a combination of a variety of fluorescent materials to meet this demand. In this study, unique concentration-dependent fluorescent carbon dots (CDs) were synthesized for the identification and quantification of pathogens. The emission wavelength of the CDs could be tuned spanning the full visible region by virtue of aggregation-induced narrowing of bandgaps. This tunable emission wavelength of the specific concentration response to diverse microbes can be used to distinguish microorganisms with a similar appearance, even in a same genus. A hyperspectral microscopy system was demonstrated to distinguish Aspergillus flavus and A. fumigatus based on the results above. The identification accuracy of the two similar-looking pathogens can be close to 100%, and the relative proportions and spatial distributions can also be profiled from the mixture of the pathogens. This technique can provide a solution to the fast detection of microorganisms and is potentially applicable to a wide range of problems in areas such as healthcare, food preparation, biotechnology, and health emergency.

Keywords

carbon dotsconcentration-dependent wavelength-tunablehyperspectral imagingpathogens rapid detectionmachine-learning

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Nano Research
Volume 15 Issue 7,
July 2022
Pages 6399-6406
DOI: 10.1007/s12274-021-3776-2
Cite this article:
Zhang Y, Liu K, Yu J, et al. Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification. Nano Research, 2022, 15(7): 6399-6406. https://doi.org/10.1007/s12274-021-3776-2
Topics:
Carbon quantum dots

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Received: 06 May 2021
Revised: 22 July 2021
Accepted: 26 July 2021
Published: 06 September 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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