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

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 sp...

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Vydané v:Food chemistry Ročník 480; s. 143848
Hlavní autori: Caramês, Elem T.S., de Moraes-Neto, Venancio F., Bertozzi, Bruno G., da Silva, Leandro P., Villa, Javier E.L., Pallone, Juliana A.L., Rocha, Liliana O., Correa, Benedito
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 15.07.2025
Predmet:
algorithms
barley
Boosting Machine Learning Algorithms
chitin
Explainability
food chemistry
food spoilage
Fusarium - chemistry
Fusarium - classification
Fusarium - isolation & purification
Fusarium sambucinum
Gold nanoparticles
Hordeum - microbiology
Machine learning
metabolites
nanogold
protein content
Python
Raman spectroscopy
SERS
species
Spectrum Analysis, Raman - methods
toxicity
SERS
Explainability
Gold nanoparticles
Machine learning
Python
ISSN:0308-8146, 1873-7072, 1873-7072
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Shrnutí: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−1 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. [Display omitted] •Machine learning method achieves 100 % accuracy in fungal identification.•XGBoost applied to classify Fusarium species effectively.•Spectral bands highlight chitin and proteins in XGBoost model.•Data science and XGBoost: a promising approach in food science.
Bibliografia:ObjectType-Article-1
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ISSN:0308-8146
1873-7072
1873-7072
DOI:10.1016/j.foodchem.2025.143848