Metabolic characterization of Brassica rapa leaves by NMR spectroscopy

The Brassica has been intensively studied due to the nutritional and beneficial effects. However, many species, varieties, and cultivars of this genus and the resulting large metabolic variation have been obstacles for systematic research of the plant. In order to overcome the problems posed by the biological variation, the metabolomic analysis of various cultivars of Brassica rapa was performed by NMR spectroscopy combined with multivariate data analysis. Discriminating metabolites in different cultivars and development stages were elucidated by diverse 2D-NMR techniques after sorting out different significant signals using (1)H NMR measurements and principal component analysis. Among the elucidated metabolites, several organic and amino acids, carbohydrates, adenine, indole acetic acid (IAA), phenylpropanoids, flavonoids, and glucosinolates were found to be the metabolites contributing to the differentiation between cultivars and age of Brassica rapa. On the basis of these results, the distribution of plant metabolites among different cultivars and development stages of B. rapa is discussed.     

Metabolite profiling of Angelica gigas from different geographical origins using 1H NMR and UPLC-MS analyses

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.     

Investigation of different apple cultivars by high resolution magic angle spinning NMR. A feasibility study

(1)H HR-MAS NMR spectroscopy was applied to apple tissue samples deriving from 3 different cultivars. The NMR data were statistically evaluated by analysis of variance (ANOVA), principal component analysis (PCA), and partial least-squares-discriminant analysis (PLS-DA). The intra-apple variability of the compounds was found to be significantly lower than the inter-apple variability within one cultivar. A clear separation of the three different apple cultivars could be obtained by multivariate analysis. Direct comparison of the NMR spectra obtained from apple tissue (with HR-MAS) and juice (with liquid-state HR NMR) showed distinct differences in some metabolites, which are probably due to changes induced by juice preparation. This preliminary study demonstrates the feasibility of (1)H HR-MAS NMR in combination with multivariate analysis as a tool for future chemometric studies applied to intact fruit tissues, e.g. for investigating compositional changes due to physiological disorders, specific growth or storage conditions.     

Classification of Ilex species based on metabolomic fingerprinting using nuclear magnetic resonance and multivariate data analysis

The metabolomic analysis of 11 Ilex species, I. argentina, I. brasiliensis, I. brevicuspis, I. dumosavar. dumosa, I. dumosa var. guaranina, I. integerrima, I. microdonta, I. paraguariensis var. paraguariensis, I. pseudobuxus, I. taubertiana, and I. theezans, was carried out by NMR spectroscopy and multivariate data analysis. The analysis using principal component analysis and classification of the (1)H NMR spectra showed a clear discrimination of those samples based on the metabolites present in the organic and aqueous fractions. The major metabolites that contribute to the discrimination are arbutin, caffeine, phenylpropanoids, and theobromine. Among those metabolites, arbutin, which has not been reported yet as a constituent of Ilex species, was found to be a biomarker for I. argentina,I. brasiliensis, I. brevicuspis, I. integerrima, I. microdonta, I. pseudobuxus, I. taubertiana, and I. theezans. This reliable method based on the determination of a large number of metabolites makes the chemotaxonomical analysis of Ilex species possible.     

Roasting process of coffee beans as studied by nuclear magnetic resonance: time course of changes in composition

In this paper, we report a (1)H and (13)C nuclear magnetic resonance (NMR)-based comprehensive analysis of coffee bean extracts of different degrees of roast. The roasting process of coffee bean extracts was chemically characterized using detailed signal assignment information coupled with multivariate data analysis. A total of 30 NMR-visible components of coffee bean extracts were monitored simultaneously as a function of the roasting duration. During roasting, components such as sucrose and chlorogenic acids were degraded and components such as quinic acids, N-methylpyridinium, and water-soluble polysaccharides were formed. Caffeine and myo-inositol were relatively thermally stable. Multivariate data analysis indicated that some components such as sucrose, chlorogenic acids, quinic acids, and polysaccharides could serve as chemical markers during coffee bean roasting. The present composition-based quality analysis provides an excellent holistic method and suggests useful chemical markers to control and characterize the coffee-roasting process.     

Microclimate influence on mineral and metabolic profiles of grape berries

The grape berry microclimate is known to influence berry quality. The effects of the light exposure of grape berry clusters on the composition of berry tissues were studied on the "Merlot" variety grown in a vineyard in Bordeaux, France. The light exposure of the fruiting zone was modified using different intensities of leaf removal, cluster position relative to azimuth, and berry position in the cluster. Light exposures were identified and classified by in situ measurements of berry temperatures. Berries were sampled at maturity (>19 Brix) for determination of skin and/or pulp chemical and metabolic profiles based on (1) chemical and physicochemical measurement of minerals (N, P, K, Ca, Mg), (2) untargeted 1H NMR metabolic fingerprints, and HPLC targeted analyses of (3) amino acids and (4) phenolics. Each profile defined by partial least-square discriminant analysis allowed us to discriminate berries from different light exposure. Discriminant compounds between shaded and light-exposed berries were quercetin-3-glucoside, kaempferol-3-glucoside, myricetin-3-glucoside, and isorhamnetin-3-glucoside for the phenolics, histidine, valine, GABA, alanine, and arginine for the amino acids, and malate for the organic acids. Capacities of the different profiling techniques to discriminate berries were compared. Although the proportion of explained variance from the 1H NMR fingerprint was lower compared to that of chemical measurements, NMR spectroscopy allowed us to identify lit and shaded berries. Light exposure of berries increased the skin and pulp flavonols, histidine and valine contents, and reduced the organic acids, GABA, and alanine contents. All the targeted and nontargeted analytical data sets used made it possible to discriminate sun-exposed and shaded berries. The skin phenolics pattern was the most discriminating and allowed us to sort sun from shade berries. These metabolite classes can be used to qualify berries collected in an undetermined environment. The physiological significance of light and temperature effects on berry composition is discussed.     

Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans

Epidemiological studies indicate that a high intake of flavonoids is associated with an improved health status. Tea is one of the most abundant sources of flavonoids in the human diet. The bioavailability and biotransformation of tea flavonoids are, however, not clearly understood. The aim of the present study was to investigate the metabolism of black tea via a nonspecific screening method. (1)H nuclear magnetic resonance (NMR) spectroscopy was used to obtain nonselective profiles of urine samples collected from three human volunteers before and after a single dose of black tea. The complex spectroscopic profiles were interpreted with the use of pattern recognition techniques. Hippuric acid was confirmed as the major urinary black tea metabolite. One previously unknown metabolite was detected and identified as 1,3-dihydroxyphenyl-2-O-sulfate (sulfate conjugate of pyrogallol) using HPLC directly coupled to mass spectrometry and (1)H NMR spectroscopy. This study shows that NMR-pattern recognition studies can be used for the discovery of unknown flavonoid metabolites in humans.     

1H NMR metabolite fingerprint and pattern recognition of mullet (Mugil cephalus) bottarga

Nuclear magnetic resonance (NMR) spectroscopy combined with multivariate data analysis (MVA) was used to investigate the molecular components of the aqueous extract of samples of bottarga, that is, salted and dried mullet (Mugil cephalus) roe, manufactured in Sardinia (Italy) from mullets of known and unknown geographical provenience. Principal component analysis (PCA) applied to the processed (1)H NMR spectra indicated that samples tend to cluster according to their geographical origin and also on the basis of storage and manufacturing procedures. The most important metabolites that characterized grouping of samples are the free amino acids methionine (Met), glutamate (Glu), histidine (His), phenylalanine (Phe), tyrosine (Tyr), and isoleucine (Ile); trimethylamine (TMA) and dimethylamine (DMA), both biomarkers of degradation; nucleotides and derivatives; choline (Cho) and phosphorylcholine (P-cho); and lactate (Lac).     

Multivariate analysis of CPMAS 13C-NMR spectra of soils and humic matter as a tool to evaluate organic carbon quality in natural systems

A series of humic and fulvic acids isolated from different sources, size‐fractions separated from a humic acid, and three soils of different origin were subjected to CPMAS ¹³C‐NMR spectroscopy to obtain the distribution of their carbon contents. The relative areas of chemical shift regions in NMR spectra were used to apply a principal component analysis (PCA) to the three sets of samples. The multivariate analysis was successful in efficiently differentiating samples on the basis of the quality of their organic carbon content. The PC biplots based on two principal components distinguished objectively among samples as accurately as it was possible to do by subjective qualitative evaluation of the original spectra. In the case of the soils, a discriminant analysis (DA) was applied to build a classification model that allowed the validation of the three soils according to their origin. Percentage of validation in the classification model is expected to increase when a large number of NMR spectra are accumulated and/or the concentration of organic carbon in samples is enhanced. The multivariate analyses described are likely to become a useful tool to increase the importance of CPMAS ¹³C‐NMR spectra in the appraisal of natural organic matter variations in heterogeneous natural systems.     

Effect of fertilizers on galanthamine and metabolite profiles in Narcissus bulbs by 1H NMR

Narcissus bulbs contain the biologically active alkaloid galanthamine, and Narcissus is being developed as a natural source of the molecule for the pharmaceutical industry. The effect of fertilizer on galanthamine production was investigated in a field study using a (1)H nuclear magnetic resonance (NMR) metabolite profiling approach. Galanthamine was quantitated and major metabolites in the bulbs were identified. The application of standard fertilization levels of nitrogen and potassium caused a significant increase in galanthamine as compared to a control. Multivariate data analysis of the (1)H NMR data revealed that applying double the standard level of nitrogen fertilizer resulted in production of more amino acids and citric acid cycle intermediates, but not more galanthamine. The results indicated that standard levels of fertilizer currently applied in The Netherlands are sufficient for optimal galanthamine accumulation in the bulbs. This study shows how (1)H NMR-based metabolic profiling can provide insight into the response of plant metabolism to agricultural practices.     

A multianalytical approach for determining the geographical origin of ginseng using strontium isotopes, multielements, and 1H NMR analysis

Asian ginseng (Panax ginseng C.A. Meyer) is widely used as an Oriental medicine in the East Asian regions, particularly Korea and China. In the study, the strontium isotope ratios ((87)Sr/(86)Sr), multielements, and metabolite profiles of 35 ginseng samples collected from Korea and China were examined in an attempt to develop a method to distinguish the origin of ginsengs from the two countries. A multivariate statistical approach was performed to analyze the multielements and the (1)H nuclear magnetic resonance (NMR) data. Results of a t-test for Mg, Fe, Al, and Sc showed significant variation between Korean and Chinese ginsengs, indicating potential tracers for discriminating them. Discriminating between the ginsengs from the two countries was generally successful when both the (87)Sr/(86)Sr ratios and rare earth element (REE) contents were used together. Moreover, principal component analysis (PCA) derived from the (1)H NMR data revealed a significant separation between the ginsengs originating from the two countries. The major metabolites responsible for differentiation were sugars such as glucose, xylose, and sucrose. The results suggest that this multiplatform approach offers a comprehensive method to distinguish the origin of ginsengs.     

Isolation and structure elucidation of antioxidant polyphenols from quince (Cydonia vulgaris) peels

Thirteen new compounds, as well as 16 already known, have been isolated from organic extracts of peels of Cydonia vulgaris, a fruit of a shrub belonging to the same tribe as the apple. All of the structures were elucidated by EI- or ESI-MS and (1)H and (13)C NMR after purification of individual compounds by HPLC. Thirteen fatty acid esters of cinnamyl alcohols, three fatty acid esters of hydroxybenzoic acid, three fatty acid esters of hydroxybenzaldehyde, three glucosides of aromatic acids, four chlorogenic acids, two flavonols, and a benzylamine have been identified. The fatty acid moieties have been identified by GC-MS analysis of the methanolysis products. All of the compounds were tested for their radical scavenging and antioxidant activities by measuring their capacity to scavenge the 2,2'-diphenyl-1-picrylhydrazyl (DPPH) radical and anion superoxide radical and to induce the reduction of Mo(VI) to Mo(V). The chlorogenic acids and the flavonols exhibited more antioxidant and radical scavenger capacity than the positive standards alpha-tocopherol and ascorbic acid. The results of the tests were analyzed by cluster analysis that grouped all of the compounds on the basis of the substituents on the aromatic ring.     

Extraction and identification of antioxidants in the roots of yacon (Smallanthus sonchifolius).

Yacon, Smallanthus sonchifolius (Poepp. & Endl.) H. Robinson, Asteraceae, an important economic species grown for its juicy tuberous root, is potentially beneficial in the diet to diabetics. The antioxidative activity of yacon root was studied by 1, 1-diphenyl-2-picrylhydrazyl (DPPH) assay. Antioxidants were extracted by methanol and isolated and purified by gel permeation chromatography and preparative reverse-phase HPLC. Two of the major antioxidants were identified as chlorogenic acid and tryptophan by NMR and mass spectrometry.     

Online identification of chlorogenic acids, sesquiterpene lactones, and flavonoids in the Brazilian arnica Lychnophora ericoides Mart. (Asteraceae) leaves by HPLC-DAD-MS and HPLC-DAD-MS/MS and a validated HPLC-DAD method for their simultaneous analysis

Lychnophora ericoides Mart. (Asteraceae, Vernonieae) is a plant, endemic to Brazil, with occurrence restricted to the "cerrado" biome. Traditional medicine employs alcoholic and aqueous-alcoholic preparations of leaves from this species for the treatment of wounds, inflammation, and pain. Furthermore, leaves of L. ericoides are also widely used as flavorings for the Brazilian traditional spirit "cacha?a". A method has been developed for the extraction and HPLC-DAD analysis of the secondary metabolites of L. ericoides leaves. This analytical method was validated with 11 secondary metabolites chosen to represent the different classes and polarities of secondary metabolites occurring in L. ericoides leaves, and good responses were obtained for each validation parameter analyzed. The same HPLC analytical method was also employed for online secondary metabolite identification by HPLC-DAD-MS and HPLC-DAD-MS/MS, leading to the identification of di- C-glucosylflavones, coumaroylglucosylflavonols, flavone, flavanones, flavonols, chalcones, goyazensolide, and eremantholide-type sesquiterpene lactones and positional isomeric series of chlorogenic acids possessing caffeic and/or ferulic moieties. Among the 52 chromatographic peaks observed, 36 were fully identified and 8 were attributed to compounds belonging to series of caffeoylferuloylquinic and diferuloylquinic acids that could not be individualized from each other.     

Two-dimensional 1H-13C nuclear magnetic resonance (NMR)-based comprehensive analysis of roasted coffee bean extract

Coffee was characterized by proton and carbon nuclear magnetic resonance (NMR) spectroscopy. To identify the coffee components, a detailed and approximately 90% signal assignment was carried out using various two-dimensional NMR spectra and a spiking method, in which authentic compounds were added to the roasted coffee bean extract (RCBE) sample. A total of 24 coffee components, including 5 polysaccharide units, 3 stereoisomers of chlorogenic acids, and 2 stereoisomers of quinic acids, were identified with the NMR spectra of RCBE. On the basis of the signal assignment, state analyses were further launched for the metal ion-citrate complexes and caffeine-chlorogenate complexes. On the basis of the signal integration, the coffee components were successfully quantified. This NMR methodology yielded detailed information on RCBE using only a single observation and provides a systemic approach for the analysis of other complex mixtures.     

Biochemical diversity in the genus Coffea L.: chlorogenic acids,caffeine and mozambioside contents

Summary The extent and nature of biochemical diversity in green coffee beans was established by a large sampling of genus Coffea, representing nine species from the Guineo-Congolian region, nine species from east Africa and seven species from Madagascar. HPLC analyses were used to determine the contents of caffeine, chlorogenic acids and mozambioside. Data processed by principal component analyses showed the existence of two metabolic pathways. One leads to the synthesis of small quantities of chlorogenic acids (< 2.5% dmb) and caffeine ( < 0.3 % dmb) while the other leads to large concentrations of chlorogenic acids (> 4.5% dmb) and caffeine (> 0.4% dmb). Their distribution in the genus Coffea is discussed in relation to the biogeographic origin of the plant material.     

Effect of dietary nitrogen content on the urine metabolite profile of dairy cows assessed by nuclear magnetic resonance (NMR)-based metabolomics

NMR-based metabolomics was applied on urine samples from 32 cows that were fed four levels of crude protein (124, 135, 151, and 166 g/kg DM, respectively) in a crossover design with the aim of identifying urinary metabolites related to nitrogen intake and nitrogen efficiency. Principal component analysis (PCA) on selected regions of the obtained (1)H NMR spectra revealed an effect of crude protein intake on NMR signals in the 0.5-3.0 and 5.0-10.0 ppm regions. Partial least-squares (PLS) regressions confirmed a correlation between the NMR metabolite profile and both nitrogen intake and efficiency. The NMR signals that correlated with nitrogen intake and efficiency included urea, hippurate, phenylacetylglutamine, and p-cresol sulfate, which all contributed to the prediction of nitrogen intake and efficiency. Thus, it was not possible to identify a single metabolite that could be used as a marker to predict nitrogen efficiency, and it can be concluded that a wide-ranging urinary metabolite profile is needed to evaluate nitrogen efficiency in ruminants.     

Metabolite profiling based on lipophilic compounds for quality assessment of perilla (Perilla frutescens) cultivars

Lipophilic compounds from Korean perilla ( Perilla frutescens ) seeds were characterized to determine the diversity among their phytochemicals and to analyze relationships between their contents. Twenty-four metabolites consisting of policosanol, phytosterol, tocopherol, and fatty acids were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), partial least-squares discriminate analysis (PLS-DA), and Pearson's correlation analysis. PLS-DA could distinguish between all cultivars except between Daesil and Daeyeup cultivars. Linolenic acid contents were positively correlated with β-sitosterol (r = 0.8367, P < 0.0001) and γ-tocopherol contents (r = 0. 7201, P < 0.001) among all perilla grains. The Daesil and Daeyeup cultivars appear to be good candidates for future breeding programs because they have simultaneously high linolenic acid, phytosterol, and tocopherol levels. These results demonstrate the use of metabolite profiling as a tool for assessing the quality of food.     

Quality evaluation and prediction of Citrullus lanatus by 1H NMR-based metabolomics and multivariate analysis

(1)H NMR spectrometry in combination with multivariate analysis was considered to provide greater information on quality assessment over an ordinary sensory testing method due to its high reliability and high accuracy. The sensory quality evaluation of watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) was carried out by means of (1)H NMR-based metabolomics. Multivariate analyses by partial least-squares projections to latent structures-discrimination analysis (PLS-DA) and PLS-regression offered extensive information for quality differentiation and quality evaluation, respectively. The impact of watermelon and rootstock cultivars on the sensory qualities of watermelon was determined on the basis of (1)H NMR metabolic fingerprinting and profiling. The significant metabolites contributing to the discrimination were also identified. A multivariate calibration model was successfully constructed by PLS-regression with extremely high reliability and accuracy. Thus, (1)H NMR-based metabolomics with multivariate analysis was considered to be one of the most suitable complementary techniques that could be applied to assess and predict the sensory quality of watermelons and other horticultural plants.