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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Veröffentlicht in:	Food chemistry Jg. 480; S. 143848
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 15.07.2025
Schlagworte:	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-Zugang:	Volltext
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