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Characteristic fingerprinting based on macamides for discrimination of maca ( Lepidium meyenii ) by LC/MS/MS and multivariate statistical analysis
Author(s) -
Pan Yu,
Zhang Ji,
Li Hong,
Wang YuanZhong,
Li WanYi
Publication year - 2016
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.7660
Subject(s) - traditional medicine , multivariate analysis , statistical analysis , biology , chemistry , mathematics , medicine , statistics
BACKGROUND Macamides with a benzylalkylamide nucleus are characteristic and major bioactive compounds in the functional food maca ( Lepidium meyenii Walp). The aim of this study was to explore variations in macamide content among maca from China and Peru. Twenty‐seven batches of maca hypocotyls with different phenotypes, sampled from different geographical origins, were extracted and profiled by liquid chromatography with ultraviolet detection/tandem mass spectrometry (LC‐UV/MS/MS). RESULTS Twelve macamides were identified by MS operated in multiple scanning modes. Similarity analysis showed that maca samples differed significantly in their macamide fingerprinting. Partial least squares discriminant analysis (PLS‐DA) was used to differentiate samples according to their geographical origin and to identify the most relevant variables in the classification model. The prediction accuracy for raw maca was 91% and five macamides were selected and considered as chemical markers for sample classification. CONCLUSION When combined with a PLS‐DA model, characteristic fingerprinting based on macamides could be recommended for labelling for the authentication of maca from different geographical origins. The results provided potential evidence for the relationships between environmental or other factors and distribution of macamides. © 2016 Society of Chemical Industry