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Fusion data from FT-IR and MALDI-TOF MS result in more accurate classification of specific microbiota.
Gao, Wenjing; Han, Ying; Chen, Liangqiang; Tan, Xue; Liu, Jieyou; Xie, Jinghang; Li, Bin; Zhao, Huilin; Yu, Shaoning; Tu, Huabin; Feng, Bin; Yang, Fan.
Afiliação
  • Gao W; Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China. fengbin@nbu.edu.cn.
  • Han Y; Kweichow Moutai Group, Renhuai, Guizhou 564501, China. yangfanmt@189.cn.
  • Chen L; Kweichow Moutai Group, Renhuai, Guizhou 564501, China. yangfanmt@189.cn.
  • Tan X; Kweichow Moutai Group, Renhuai, Guizhou 564501, China. yangfanmt@189.cn.
  • Liu J; Zhuhai DL Biotech Co., Ltd, Zhuhai, Guangdong 519041, China.
  • Xie J; Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China. fengbin@nbu.edu.cn.
  • Li B; Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China. fengbin@nbu.edu.cn.
  • Zhao H; Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China. fengbin@nbu.edu.cn.
  • Yu S; Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China. fengbin@nbu.edu.cn.
  • Tu H; Kweichow Moutai Group, Renhuai, Guizhou 564501, China. yangfanmt@189.cn.
  • Feng B; Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China. fengbin@nbu.edu.cn.
  • Yang F; Kweichow Moutai Group, Renhuai, Guizhou 564501, China. yangfanmt@189.cn.
Analyst ; 148(22): 5650-5657, 2023 Nov 06.
Article em En | MEDLINE | ID: mdl-37800908
Microbes are usually present as a specific microbiota, and their classification remains a challenge. MALDI-TOF MS is particularly successful in library-based microbial identification at the species level as it analyzes the molecular weight of peptides and ribosomal proteins. FT-IR allows more accurate classification of bacteria at the subspecies level due to the high sensitivity, specificity and repeatability of FT-IR signals from bacteria, which is not achievable with MALDI-TOF MS. Previous studies have shown that more accurate identification results can be obtained by the fusion of FT-IR and MALDI-TOF MS spectral data. Here, we constructed 20 groups of model microbiota samples and used FT-IR, MALDI-TOF MS, and their fusion data to classify them. Hierarchical clustering analysis (HCA) showed that the classification accuracy of FT-IR, MALDI-TOF MS, and the fusion data was 85%, 90%, and 100%, respectively. These results indicate that both FT-IR and MALDI-TOF MS can effectively classify specific microbiota, and the fusion of their spectral data could improve the classification accuracy. The FT-IR and MALDI-TOF MS data fusion strategy may be a promising technology for specific microbiota classification.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Microbiota Idioma: En Revista: Analyst Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Microbiota Idioma: En Revista: Analyst Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China