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Differentiation of Lepidium meyenii (Maca) from Different Origins by Electrospray Ionization Mass Spectrometry with Principal Component Analysis.


ABSTRACT: In this paper, we report a high-speed, high-sensitivity, and economic method to authenticate Maca. After being extracted by ethanol, nutritional components of a dozen kinds of Maca were detected by electrospray ionization mass spectrometry. Combined with principal component analysis (PCA), these samples can be rapidly differentiated after selecting the origins and principal components in the projection of components 1 and 2. The result suggests that sample 2 from Lijiang gets the highest comprehensive score among the samples and sample 1 from Huize gets the lowest comprehensive score among the samples in positive-ion mass spectra. However, sample 2 from Peru gets the highest comprehensive score among the samples and sample 3 from Lijiang gets the lowest comprehensive score among the samples in negative-ion mass spectra. Compared with the PCA results, the data of negative-ion mass spectra can better differentiate these samples than those of positive-ion mass spectra. This method has the advantages of easy operation and high efficiency, which make it a differential tool in the fields of food safety, medicinal chemistry, and materials science.

SUBMITTER: Yang S 

PROVIDER: S-EPMC6787903 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Differentiation of <i>Lepidium meyenii</i> (Maca) from Different Origins by Electrospray Ionization Mass Spectrometry with Principal Component Analysis.

Yang Sihou S   Sun Xiaochun X   Gao Yumei Y   Chen Rui R  

ACS omega 20190927 15


In this paper, we report a high-speed, high-sensitivity, and economic method to authenticate Maca. After being extracted by ethanol, nutritional components of a dozen kinds of Maca were detected by electrospray ionization mass spectrometry. Combined with principal component analysis (PCA), these samples can be rapidly differentiated after selecting the origins and principal components in the projection of components 1 and 2. The result suggests that sample 2 from Lijiang gets the highest compreh  ...[more]

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