UM
Affiliated with RCfalse
Status已發表Published
Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification
Zhang, Yongqiang1; Liu, Kunxing2; Yu, Jingkun1; Chen, Haifeng4; Fu, Rui1; Zhu, Siqi2; Chen, Zhenqiang2; Wang, Shuangpeng3; Lu, Siyu1
2021-09
Source PublicationNano Research
ISSN1998-0124
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.

KeywordCarbon Dots Concentration-dependent Wavelength-tunable Hyperspectral Imaging Machine-learning Pathogens Rapid Detection
DOI10.1007/s12274-021-3776-2
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaChemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
WOS SubjectChemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical
WOS IDWOS:000692975000003
Scopus ID2-s2.0-85114361752
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhu, Siqi; Wang, Shuangpeng; Lu, Siyu
Affiliation1.Green Catalysis Center, and College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China
2.Department of Optoelectronic Engineering, Jinan University, Guangzhou, 510632, China
3.Institute of Applied Physics and Materials Engineering, University of Macau, Taipa, 999078, Macao
4.School of electronic information and electrical engineering, Chongqing University of Arts and Sciences, Chongqing, 402160, China
Corresponding Author AffilicationINSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING
Recommended Citation
GB/T 7714
Zhang, Yongqiang,Liu, Kunxing,Yu, Jingkun,et al. Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification[J]. Nano Research,2021.
APA Zhang, Yongqiang,Liu, Kunxing,Yu, Jingkun,Chen, Haifeng,Fu, Rui,Zhu, Siqi,Chen, Zhenqiang,Wang, Shuangpeng,&Lu, Siyu.(2021).Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification.Nano Research.
MLA Zhang, Yongqiang,et al."Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification".Nano Research (2021).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Yongqiang]'s Articles
[Liu, Kunxing]'s Articles
[Yu, Jingkun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Yongqiang]'s Articles
[Liu, Kunxing]'s Articles
[Yu, Jingkun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Yongqiang]'s Articles
[Liu, Kunxing]'s Articles
[Yu, Jingkun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.