Metabolite Profiling of Angelica gigas from Different Geographical Origins Using ¹H NMR and UPLC-MS Analyses

Abstract

Angelica gigas obtained from different geographical regions was characterized using (1)H nuclear magnetic resonance (NMR) spectroscopy and ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) followed by multivariate data analyses. Principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) score plots from (1)H NMR and UPLC-MS data sets showed a clear distinction among A. gigas from three different regions in Korea. The major metabolites that contributed to the discrimination factor were primary metabolites including acetate, choline, citrate, 1,3-dimethylurate, fumarate, glucose, histamine, lactose, malate, N-acetylglutamate, succinate, and valine and secondary metabolites including decursin, decursinol, nodakenin, marmesin, 7-hydroxy-6-(2R-hydroxy-3-methylbut-3-ethyl)coumarin in A. gigas roots. The results demonstrate that (1)H NMR and UPLC-MS-based metabolic profiling coupled with chemometric analysis can be used to discriminate the geographical origins of various herbal medicines and to identify primary and secondary metabolites responsible for discrimination.

Metabolic profiling and identification of the genetic varieties and agricultural origin of Cnidium officinale and Ligusticum chuanxiong

“…We previously showed that primary metabolite profiling enabled us to evaluate the species and cultivation area of Angelica acutiloba (23,24). Recently, primary metabolite profiling contributed to the discrimination of different geographical regions for Salvia miltiorrhiza Bunge (25) and Angelica gigas (26). Primary metabolites, such as sugars and amino acids, could be used as markers to assess species and production area.…”

Metabolite Profiling of Justicia gendarussa Burm. f. Leaves Using UPLC-UHR-QTOF-MS

“…Environmental factors, such as the site of cultivation, altitude, temperature, sun exposure time, rainfall, climate, and soil can influence the primary and secondary metabolites of plants [20][21][22]. These factors may affect secondary metabolites qualitatively and quantitatively, so their bioactivities could be varied [21][22][23].…”

“…These factors may affect secondary metabolites qualitatively and quantitatively, so their bioactivities could be varied [21][22][23]. Therefore, metabolite profiling studies of herbal plants is very important for ensuring their safety and efficacy.…”

Gas chromatography–mass spectrometry based metabolic profiling for the identification of discrimination markers of Angelicae Radix and its application to gas chromatography–flame ionization detector system

“…Whether or not a prediction model is good can be judged in terms of the Q 2 value, and a model is considered to be good if Q 2 > 0.5; if Q 2 > 0.9, a model is considered to have an excellent predictive ability (24). Additionally, permutations test were performed in the PLS-DA model to validate each OPLS-DA model according to validation methods in previous work of A. gigas (17). All Q 2 and R 2 values were higher in the permutation test than in the real model, revealing great predictability and goodness of fit.…”

“…However, the discrimination was performed only by qualitative analysis, and the statistical significance of candidate markers was not mentioned in the reports. One recent report that used statistic analysis for the evaluation of markers by NMR and UPLCeMS is the study on A. gigas from three different regions in Korea (17). The samples analyzed only represented cultivation area as quality factor.…”