Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC–MS and NIR spectroscopy
Abstract Amomum tsao-ko, as an edible and medicinal variety, has been cultivated for more than 600 years in China. Recently, two cultivars, A. tsao-ko and Amomum paratsao-ko, were found in A. tsao-ko planting area. The two cultivars are often confused because of the similar phenotype and difficult t...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/027b60459f7b4db48586d62a05c68f6a |
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| Sumario: | Abstract Amomum tsao-ko, as an edible and medicinal variety, has been cultivated for more than 600 years in China. Recently, two cultivars, A. tsao-ko and Amomum paratsao-ko, were found in A. tsao-ko planting area. The two cultivars are often confused because of the similar phenotype and difficult to distinguish through sensory judgment. In this study, the non-targeted gas chromatography-mass spectrometry (GC–MS) metabolomics combined with near-infrared spectroscopy (NIRS) were used for dissecting the two cultivars with phenotypic differences. According to principal component analysis (PCA) loading diagram and orthogonal partial least squares discriminant analysis (OPLS-DA) S-plot of the metabolites, the accumulation of major components including 1,8-cineole, α-phellandrene, (E)-2-decenal, (−)-β-pinene, (E)-2-octenal, 1-octanal, D-limonene, and decanal, were present differences between the two cultivars. Seven metabolites potential differentiated biomarkers as β-selinene, decamethylcyclopentasiloxane, (E,Z)-2,6-dodecadienal, (E)-2-hexenal, (E)-2-decenal, isogeranial, 1,8-cineole and β-cubebene were determined. Although A. tsao-ko and A. paratsao-ko belong to the same genera and are similar in plant and fruit morphology, the composition and content of the main components were exposed significant discrepancy, so it is necessary to distinguish them. In this study, the discriminant model established by GC–MS or NIRS combined with multivariate analysis has achieved a good classification effect. NIRS has the advantages of simple, fast and nondestructive and can be used for rapid identification of varieties and fruit tissues. |
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