Dataset on effect of decolourisation on metabolomic profile of Moringa oleifera leaf powder

Moringa leaf has been widely used in the enrichment of staple foods due to its high nutritional value and hypoglycaemic, immune boosting, antiviral, antioxidant and antimicrobial activities. However, the acceptability of these products is generally low due to the green colour imparted by the colour of Moringa leaf. Decolourisation of the leaves may improve the acceptability of the food products. The decolorisation process may not only change the chlorophyll concentration of the Moringa leaves but also its other chemical components. The data set describes the effect of decolourisation on the metabolites present in Moringa leaf powder. The raw and decolourised samples were extracted with methanol/water (80:20 v/v) and analysed using a gas chromatography-high resolution time of flight-mass spectrometer (GC-HRTOF-MS). The metabolites identified were classified based on their functional group into acids, alcohols, aldehydes, amides hydrocarbons, phenols, phytosterols, vitamins and others. The data presented can be useful in identifying functional compounds available in Moringa-based foods and understanding the effect of decolourisation on the metabolite profile.


a b s t r a c t
Moringa leaf has been widely used in the enrichment of staple foods due to its high nutritional value and hypoglycaemic, immune boosting, antiviral, antioxidant and antimicrobial activities. However, the acceptability of these products is generally low due to the green colour imparted by the colour of Moringa leaf. Decolourisation of the leaves may improve the acceptability of the food products. The decolorisation process may not only change the chlorophyll concentration of the Moringa leaves but also its other chemical components. The data set describes the effect of decolourisation on the metabolites present in Moringa leaf powder. The raw and decolourised samples were extracted with methanol/water (80:20 v/v) and analysed using a gas chromatography-high resolution time of flight-mass spectrometer (GC-HRTOF-MS). The metabolites identified were classified based on their functional group into acids, alcohols, aldehydes, amides hydrocarbons, phenols, phytosterols, vitamins and others. The data presented can be useful in identifying functional compounds available in Moringa-based foods and understanding the effect of decolourisation on the metabolite profile.

Value of the Data
• The data present the effect of decolourisation on the metabolite profile of Moringa leaf powder samples and give an insight to metabolite modifications after decolourisation. • The data reported would be useful to food processors for supplementing staple foods with decolourised moringa leaf powder for improved nutrition. • The data could be useful for comparative analysis of metabolite composition of moringa leaf powder grown in different locations and may be useful for selecting moringa leaf species for various food and non-food applications.

Data Description
The metabolite data obtained from raw and decolourised MOLP are presented. Table 1 represents the metabolites obtained from MOLP. The data in the table includes the following: retention time, observed mass, metabolite name, molecular formula and average peak area for each metabolite identified in the different samples. These were obtained from the peaks generated   Values are reported as mean ± ± SD; a Samples differ significantly ( p < 0.5); b No significant difference ( p ≥ 0.5); RT-retention time; ND -not detected  from GC-HRTOF-MS analysis and comparison of spectra obtained with NIST, Mainlib and Feihn metabolite databases. The raw and analysed data together with the spectra obtained are available as supplementary documents ( https://data.mendeley.com/datasets/7mrhxrt9kr/1 ) [1] . Figs. 1 and 2 summarises the percentage distribution of the compound groups in the raw and decolourised samples, respectively.

Sample Preparation
MOLP was obtained from the ARC, Pretoria, South Africa. The MOLP was decolourised by homogenising the leaf powder with ethanol (95%) at a powder to solvent ratio of 1:20, placed on an orbital shaker (Stuart SSL1, Keison Products, Essex, UK) at 150 rpm for 30 min. The samples were later centrifuged, supernatant discarded, and the slurry dried at 40 °C. Further analysis was conducted on both the raw and the decolourised samples

Extraction of Metabolites and GC-HRTOF-MS Analysis
Metabolites were extracted as previously described by Oyedeji, Chinma, Green and Adebo [2] . Ten millilitres of the extraction solvent (methanol/water at 80:20 v/v) together with 1 g each of MOLP samples were thoroughly agitated and sonicated in an ultrasonic bath (Scientech 704, Labotech, South Africa) for 1 h at 4 °C. The mixture was then centrifuged at 3500 rpm at 4 °C for 5 min (Eppendorf 5702R, Merck, South Africa). Supernatant obtained after centrifuging was concentrated in a vacuum concentrator (Eppendorf Plus, Merck, South Africa) and made into solution with 1 ml of chromatographic grade methanol (Merck, South Africa). The solution was vortexed and filtered through 0.22 μm microfilters into an amber vial and solvent blanks were also prepared. The extracts were analysed using a GC-HRTOF-MS (LECO Corporation, St Joseph, MI, USA) with a multipurpose sample (Gerstel Inc., Mülheim an der Ruhr Germany) and Rxi ®-5 ms column (30 m × 0.25 mm ID × 0.25 μm) (Restek, Bellefonte, USA). Injection of 1 μl extract was done a splitless mode at a flowrate of 1 ml/min and helium used as the carrier gas. The ion source temperature was at 250 °C while the transfer line and inlet temperatures were set at 225 and 250 °C, respectively. The oven temperature cycle used was initial temperature of 70 °C for 0.5 min; then an increase of 10 °C/min to 150 °C held for 2 min; then ramped at 10 °C/min to 330 °C and held for 3 min for the column to 'bake-out'. Data obtained were processed using DataPrep Solutions and metabolites were identified by matching the spectra with NIST, Mainlib and Feihn reference library databases, and their identities determined. Table 1 represents the mean of values obtained from triplicate runs of samples after prior processing of raw data.

Ethics Statements
None.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.