• Identification of Fusarium sam...

Treffer: Identification of Fusarium sambucinum species complex by surface-enhanced Raman spectroscopy and XGBoost algorithm.

Title:
Identification of Fusarium sambucinum species complex by surface-enhanced Raman spectroscopy and XGBoost algorithm.
Authors:
Caramês ETS; Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 05508-900 São Paulo, Brazil., de Moraes-Neto VF; Department of Food Science, School of Food Engineering, State University of Campinas, 13083-862 Campinas, São Paulo, Brazil., Bertozzi BG; Department of Food Science, School of Food Engineering, State University of Campinas, 13083-862 Campinas, São Paulo, Brazil., da Silva LP; Institute of Chemistry, State University of Campinas, 13083-970 Campinas, São Paulo, Brazil., Villa JEL; Institute of Chemistry, State University of Campinas, 13083-970 Campinas, São Paulo, Brazil. Electronic address: jelv@unicamp.br., Pallone JAL; Department of Food Science, School of Food Engineering, State University of Campinas, 13083-862 Campinas, São Paulo, Brazil., Rocha LO; Department of Food Science, School of Food Engineering, State University of Campinas, 13083-862 Campinas, São Paulo, Brazil., Correa B; Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 05508-900 São Paulo, Brazil. Electronic address: correabe@usp.br.
Source:
Food chemistry [Food Chem] 2025 Jul 15; Vol. 480, pp. 143848. Date of Electronic Publication: 2025 Mar 12.
Publication Type:
Journal Article; Evaluation Study
Language:
English
Journal Info:
Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 7702639 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7072 (Electronic) Linking ISSN: 03088146 NLM ISO Abbreviation: Food Chem Subsets: MEDLINE
Imprint Name(s):
Publication: Barking : Elsevier Applied Science Publishers
Original Publication: Barking, Eng., Applied Science Publishers.
MeSH Terms:
Boosting Machine Learning Algorithms* , Fusarium*/chemistry , Fusarium*/classification , Fusarium*/isolation & purification , Spectrum Analysis, Raman*/methods, Hordeum/microbiology
Contributed Indexing:
Keywords: Explainability; Gold nanoparticles; Machine learning; Python; SERS
Entry Date(s):
Date Created: 20250321 Date Completed: 20250415 Latest Revision: 20250415
Update Code:
20250415
DOI:
10.1016/j.foodchem.2025.143848
PMID:
40117817
Database:
MEDLINE

Weitere Informationen

Rapid and reliable identification of Fusarium fungi is crucial, due to their role in food spoilage and potential toxicity. Traditional identification methods are often time-consuming and resource-intensive. This study explores the use of surface-enhanced Raman spectroscopy (SERS) to identify four species from the Fusarium sambucinum species complex isolated from barley. SERS spectra from 60 samples was acquired using gold nanoparticles for signal enhancement and the eXtreme Gradient Boosting (XGBoost) algorithm was applied for classification. The method achieved 100 % precision, recall, accuracy, and F1-score, thereby demonstrating excellent performance. Regarding the chemical interpretability, key spectral features at 495, 546, 764, 1228, 1274, and 1605 cm <sup>-1</sup> were revealed by XGBoost and correlated to the differences in chemical composition of fungi; particularly related to chitin, metabolites, and protein content. Therefore, SERS and XGBoost have great potential to classify a wide variety of fungi and other microorganisms.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.