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2000
Volume 21, Issue 9
  • ISSN: 1573-4110
  • E-ISSN: 1875-6727

Abstract

Background

Different C-3 substituted flavonoids have different biological activities and applications in food pharmacology, toxicology, and medicine. Thus, the rapid identification and classification of substitution patterns at C-3 of flavonoids can benefit the processing of flavonoid-related food and medicine.

Objective

This study aimed to classify flavonoids with different C3 substituents using Raman spectroscopy, providing a feasible approach for identifying flavonoids in plants.

Methods

Eighteen flavonoid samples were selected and dissolved in different solvents. The corresponding Raman spectra were collected by a portable Raman spectrograph. After preprocessing, feature reduction and machine learning were used for the accurate classification of three flavonoids based on 66 Raman spectra.

Results

The signals of flavone at 1002, 1245, 1590, and 1609 cm-1 were identified as the characteristic peaks. Peaks at 1298, 1586, and 1605 cm-1 were the special features observed of flavonol. The fingerprint features of isoflavone appeared at 894, 1227, 1321, and 1620 cm-1. All combinations achieved a good classification accuracy of 85%, and the accuracy of the neural network reached 93.3%.

Conclusion

The results have demonstrated machine learning to be applicable for the detection and classification of C-3 substituted flavonoids and that feature reduction can aid in the discrimination of Raman spectra variations among diverse C-3 substituted flavonoids, thereby facilitating their further application.

© 2025 Bentham Science Publishers
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Raman Spectra-based Structural Classification Analysis of Flavones, Flavonols, and Isoflavones Using Machine Learning
Current Analytical Chemistry 21, 1252 (2025); https://doi.org/10.2174/0115734110301113240528102507
/content/journals/cac/10.2174/0115734110301113240528102507
/content/journals/cac/10.2174/0115734110301113240528102507
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  • Article Type:     Research Article
Keyword(s): classification; Flavonoids; K-nearest neighbor (KNN); machine learning; quadratic discriminant analysis (QDA); raman spectroscopy; spectral analysis

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