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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Bibliographic Details
Published in:	Food chemistry Vol. 480; p. 143848
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 15.07.2025
Subjects:	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
Online Access:	Get full text
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