Rapid detection and identification of fungi in grain crops using colloidal Au nanoparticles based on surface-enhanced Raman scattering and multivariate statistical analysis

被引：6

作者：

Wang, Huiqin ^{[1
]}

Liu, Mengjia ^{[1
]}

Zhao, Huimin ^{[1
]}

Ren, Xiaofeng ^{[1
]}

Lin, Taifeng ^{[1
]}

Zhang, Ping ^{[1
]}

Zheng, Dawei ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Environm & Life, Beijing 100124, Peoples R China

来源：

WORLD JOURNAL OF MICROBIOLOGY & BIOTECHNOLOGY | 2023年 / 39卷 / 01期

关键词：

Fungi; Colloidal Au nanoparticles; Surface-enhanced Raman scattering; Multivariate statistical analysis; SPECTROSCOPY; ZEARALENONE; ASSAY;

D O I：

10.1007/s11274-022-03467-2

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Grain crops are easily contaminated by fungi due to the existence of various microorganisms in the storage process, especially in humid and warm storage conditions. Compared with conventional methods, surface-enhanced Raman scattering (SERS) has paved the way for the detection of fungi in grain crops as it is a rapid, nondestructive, and sensitive analytical method. In this work, Aspergillus niger, Saccharomyces cerevisiae, Fusarium moniliforme and Trichoderma viride in grain crops were detected using colloidal Au nanoparticles and SERS. The results indicated that different fungi showed different Raman phenotypes, which could be easily characterized by SERS. Combined with multivariate statistical analysis, identification of a variety of fungi could be accomplished rapidly and accurately. This research can be applied for the rapid detection of fungi in the food and biomedical industries.

引用

页数：10

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