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NMR- AND GCMS -BASED METABOLOMICS APPROACH OF Morinda citrifolia

Morinda citrifolia or Noni is known as an important dietary supplement, food functional ingredient, or as natural health enhancer that is used throughout the world. Different parts of the plant have been proven to carry compounds that have high medicinal values such as terpenoids, alkaloids and anthraquinones. The stem bark of Morinda citrifolia collected from different types of soil were studied. It aimed to investigate the influence of different geographical factor on its metabolite profile by multivariate statistical analyses of spectral data deduced by NMR methods. A metabolomic approach employing concatenated GCMS and NMR data was utilized to study the relationships between environment and plant metabolism. Morinda citrifolia stem barks were pulverized into fine powder. They were extracted using chloroform and were taken to dryness in a rotary evaporator under reduced pressure. The Morinda citrifolia crudes were proceeded for Nuclear Magnetic Resonance (NMR) analysis. Visual inspection on the 1H-NMR Morinda citrifolia spectra showed that the different geographical areas have similar profile, but they have different concentration of various constituents. Major constituents identified were aromatics (δH 7.25-7.50), sugars and glycosides (δH 2.25-5.0), fatty acids/aliphatics (δH 0.5-2.0) and aldehydic compound (δH 9.75). However, since the difference among the geographical areas were not clear from visual analysis, the analysis was proceeded using multivariate analysis, Principal Component Analysis (PCA) model. Thus, further analysis was done to examine the consistency of geographical area for each batch of Morinda citrifolia. This is to identify which type of soil is suitable as class model.

Morinda citrifolia or Noni is known as an important dietary supplement, food functional ingredient, or as natural health enhancer that is used throughout the world. Different parts of the plant have been proven to carry compounds that have high medicinal values such as terpenoids, alkaloids and anthraquinones. The stem bark of Morinda citrifolia collected from different types of soil were studied. It aimed to investigate the influence of different geographical factor on its metabolite profile by multivariate statistical analyses of spectral data deduced by NMR methods. A metabolomic approach employing concatenated GCMS and NMR data was utilized to study the relationships between environment and plant metabolism. Morinda citrifolia stem barks were pulverized into fine powder. They were extracted using chloroform and were taken to dryness in a rotary evaporator under reduced pressure. The Morinda citrifolia crudes were proceeded for Nuclear Magnetic Resonance (NMR) analysis. Visual inspection on the 1 H-NMR Morinda citrifolia spectra showed that the different geographical areas have similar profile, but they have different concentration of various constituents. Major constituents identified were aromatics (δH 7.25-7.50), sugars and glycosides (δH 2.25-5.0), fatty acids/aliphatics (δH 0.5-2.0) and aldehydic compound (δH 9.75). However, since the difference among the geographical areas were not clear from visual analysis, the analysis was proceeded using multivariate analysis, Principal Component Analysis (PCA) model. Thus, further analysis was done to examine the consistency of geographical area for each batch of Morinda citrifolia. This is to identify which type of soil is suitable as class model.

INTRODUCTION
Morinda citrifolia is the scientific name for the Noni tree. The Morinda citrifolia plant is commercially known as 'noni', where Noni is the Hawaiian name for the fruit of Morinda citrifolia. It is also called 'Indian mulberry', 'nuna' or 'arch' in Indian subcontinent; 'mengkudu' in Malaysia; 'nhau' in Southeast Asia; 'painkiller bush' in the Caribbean or 'cheese fruit' in Australia [1][2][3][4][5]. Noni is originally native from Southeast Asia and was spread to Australia, Hawaii, French Polynesia Islands and other tropical areas through possible water-dispersal of buoyant seeds or by being transported by early migrants or voyagers.
Noni plant has been around for nearly 2000 to 3000 years. Traditionally, noni plant has been used as food, dye as well as medicinal purposes to cure several diseases [6]. Different parts of the plants which include the fruits,

Journal of Asian Scientific Research
countries where Noni juice is the usual form of consumption. Therefore, most Noni is consumed as juice even though the leaves, flowers, bark, and roots can also be used. In fact, the pulp of this fruit has incredible healing and pain-relieving properties, whether to be eaten or applied to the skin.
Noni plant contained about 160 biologically active compounds or phytochemical compounds that have been identified, where the major micronutrients are phenolic compounds, organic acids and alkaloids [11]. The most important phenolic compounds found in Noni are anthraquinones (morindone, morindin, etc.), and aucubin, asperuloside, and scopoletin. The main organic acids are caproic and caprylic acids, whereas the main found alkaloid is xeronine. Noni fruit also contains proxeronine, which is converted to xeronine in the body by enzyme proxeroninase. Apart from that, there are also several metabolites that have been described including polysaccharides, fatty acid, glycosides, iridoids, triterpenoid, coumarins, flavonoids, lignans, phytosterols, carotenoids and a range of volatile constituents including monoterpenes, short chain fatty acids and fatty acid esters [12]. However, chemical composition differs greatly according to the part of the plant. Different parts of Noni plant have been the proposed subject of medicinal research to investigate its effects on health.
The commercialization potential of Noni fruits as a medicinal food and dietary supplement has tremendously facilitated its availability worldwide, encouraged its use as well as brought its benefits to more people. Countless of Noni products have emerged in the global market such as dried Noni fruit, Noni fruit juice, Noni tea, and Noni capsules. Nevertheless, the quality of commercial Noni fruit products may differ significantly, attributing to different geographical conditions such as soil, sunlight, precipitation, and air and post-growth factors which include harvesting, storage, transportation, and manufacturing processes. Thus, there are concerns on the consistency of phytochemical profiles of Noni fruits from different locations. To address these concerns, analytical NMR-and GCMS-based metabolomics approach can be developed to conduct an extensive quantitative comparison of characteristics components in Noni stem barks from different geographical areas which are three different types of soil, namely sandy, silty and clay soil. As such, this study aimed to study the relationships between environment and plant metabolism because different environmental changes influence the phytochemicals in Morinda citrifolia.

Sample Preparation
At least 500 g of stem bark of Morinda citrifolia (Noni) was collected from three different types of soil; sandy, silty and clay soil. The stem bark was cut into tiny little pieces and then dried in an oven for four days at 39°C. The

Nuclear Magnetic Resonance (NMR) Spectrometer
CDCl3 deuterated solvent was used to obtain proton NMR 1D spectra (zg30, zgpr, NOESY and CPMG) and 2D spectra (JRES and HSQC) by using Bruker 400 MHz NMR spectrometer with tetramethylsilane (TMS) as the internal standard. 1 H NMR enable to identify the chemical constituents of the samples.

Chromatography Mass Spectrometer (GCMS)
The purity of the compound and its molecular weight was obtained from Perkin Elmer Clarus 680 Mass Spectrometer. The gas chromatography was used to detect the secondary metabolites of the samples. The samples were injected with helium as the carrier gas. The initial temperature was set at 50°C and hold for two minutes. The temperature was then ramped to 280°C at a rate of 6.5°C/min, and it was hold for 10 minutes. The temperature for injector and detector of the gas chromatography were set at 250°C. The compound was identified by comparing it with the mass spectrum data.

NMR Sample Preparation
Samples for NMR measurements were prepared by resuspending 20 mg of each sample extract in 700 μL chloroform-d (CDCl3) to which 5% tetramethylsilane (TMS) was added as reference standard. The sample-solvent mixtures were transferred to 5 mm NMR tubes for NMR data acquisition.

Data Acquisition
1 H NMR spectra were recorded at 25°C on a 400 MHz NMR (Bruker) spectrometer, using 128 scans over a proton frequency range of 15 ppm. The water suppression program was used to suppress undesirable signals caused by residual water.

Data Processing
The generated 1 H NMR FIDs were manually processed, phased, and referenced to the internal standard, TMS (δ0.00 ppm). Water and solvent peaks which displayed in the regions of δ4.70 to δ4.96 and δ3.28 to δ3.33 ppm, respectively were excluded from the multivariate analysis.

Multivariate Data Analysis
Principle Component Analysis (PCA) was carried out using AMIX Bruker program software. Bruker's AMIX program enabled unsurpassed integration of spectroscopy and metabolomics where it offered a range of powerful tools that provide several integrated routines for spectroscopic and statistical analyses. AMIX then completely integrated the statistical and spectroscopic analysis of NMR by using PCA to better find valid metabolomic results.
PCA is a technique that automatically extract and display any natural variation in a data matrix.

Visual Analysis of 1 H NMR Spectra
The representative of 1 H NMR spectrum of the chloroform extract for each type of Morinda citrifolia (Noni), named Sample A (silty soil), Sample B (sandy soil) and Sample C (clay soil) is shown in Figure 1. A visual inspection of the spectra indicated that each type has very similar profile with the expected differences in concentration of the various constituents. The major chemical constituents of the extract consisted of aromatics (δH 7.25-7.50), sugars and glycosides (δH 2.25-5.0), and fatty acids/aliphatics (δH 0.5-2.0). Closer examination of the spectra showed that the Noni extracts also contain small proportion of aldehydic compounds as can be seen in Figure 2 which shows the expanded aldehydic region. The aldehyde peaks (δH 9.7 ppm) were markedly higher in Noni sample C. Other differences between the spectra were not very distinct by visual inspection. Thus, it required the multivariate analysis to discriminate between the different soil types.

Multivariate Analysis of 1 H NMR Spectra of Various Soil Types of Noni
Since the differences among the soil types were not clear from the visual analysis, multivariate analysis needs to be applied to the NMR data to prevent bias or misinterpretation. The unsupervised method of Principal Component Analysis (PCA) was applied to build a model since PCA is a technique which can automatically extract and display any natural variation in a data matrix without any external intervention or supervision [13]. A spectrum with n peaks can be plotted in a n-dimensional space, and these two are equivalent. The PCA transforms the original coordinate system in which the new coordinates are called principal components. The values that the spectra have in the PC-coordinate system are called scores. As seen in Figure 3, PCA was not able to properly discriminate between the types of soil. The left side of the PCA plot represents the scores plot and each spectrum is assigned to different colors, where each spectrum indicates the different type of soil. The PCA plot below consists of three different types of soil spectrum of different batches which are represented by the color code black for silty soil; color code blue for sandy soil; and color code green for clay soil. From the PC-coordinate system, there seem to be scattered and the PCA does not tell which spectra belong together. Although the different colors for different classes (types of soil) were seen in the scores plot, but PCA is not a classification, instead it can be used as the basis of a classification, where the different colors that come from each spectrum represents its class.
From the loadings plot Figure 3, the PCA is a transformation of the old coordinate system (peaks) into the new coordinate system (PC). It estimated how much each of the old coordinates (peaks) contribute to each of the new ones (PCs), where these values are called loadings. The higher the loading of a peak onto a PC, the more it contributes to that PC. Each point (dot) in the corresponding loadings plot below represents a peak and every dot also represents a bucket. Thus, loadings plot indicating buckets at various chemical shifts. From the PCA plot for all types of soil of Noni shown below, these three types of soil gave almost the same chemical constituents as most of the buckets clustered in the middle. The initial PCA plot was not able to provide any useful information about the different types of Noni samples, but the Noni samples were differentiated according to subjective criteria, where it was decided to analyse the consistency of the individual soil types for Noni samples. Thus, class models were constructed by creating PCA plot data matrix according to different types of Noni's soil. This PCA plot by type is meant to see which types are more consistent in order to create a class model.