Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics
Artemisia argyi Folium (AAF), the leaf of the perennial plant Artemisia argyi H. Léveillé & Vaniot, has a long history of medicinal and edible use in East Asia. This study developed a rapid method, which integrated near-infrared spectroscopy (NIRS) and chemometrics to discriminate the geogra...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154325006660 |
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| Summary: | Artemisia argyi Folium (AAF), the leaf of the perennial plant Artemisia argyi H. Léveillé & Vaniot, has a long history of medicinal and edible use in East Asia. This study developed a rapid method, which integrated near-infrared spectroscopy (NIRS) and chemometrics to discriminate the geographic origin of AAF and quantify its phenolic components and moisture content. Phenolic-based analysis of adjacent-origin samples with varying storage times demonstrated that storage duration minimally affected geographical origin discrimination of AAF, and it provided a chemical basis for using cross-year samples in NIRS analysis. Multivariate statistical analysis (four methods) and six machine learning algorithms were employed for origin discrimination. The results showed that partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) outperformed unsupervised methods, with key wavenumbers in high and low-frequency regions showing similarities, but exhibiting differences mainly in the 7783–6773 cm−1 range. Spectral preprocessing methods (Savitzky-Golay smoothing, normalization, standard normal variate, and multiplicative scatter correction) enhanced machine learning performance, with support vector machine (SVM), radial basis function (RBF), and convolutional neural network (CNN) models achieving scores of 1.0000 across performance metrics, indicating strong generalization and robustness. Partial least squares regression (PLSR) models for 17 phenolic components and moisture content were screened using different preprocessing methods, identifying three parameters suitable for rapid quantification: eupatilin, jaceosidin, and moisture. This NIRS-based approach achieved a higher detection efficiency and lower cost compared to conventional methods and thus provides a rapid and efficient solution for the geographic traceability and quantitative evaluation of AAF. |
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| ISSN: | 2666-1543 |