Optimization of oil yield of Phaleria macrocarpa seed using response surface methodology and its fatty acids constituents

Industrial Crops and Products 52 (2014) 405–412

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Optimization of oil yield of Phaleria macrocarpa seed using response surface methodology and its fatty acids constituents J. Azmir a , I.S.M. Zaidul a,∗ , M.M. Rahman a , K.M. Sharif a , F. Sahena b , M.H.A. Jahurul b , A. Mohamed c a

Faculty of Pharmacy, International Islamic University Malaysia, Kuantan Campus, 25200 Kuantan, Pahang, Malaysia School of Industrial Technology, Universiti Sains Malaysia, Minden, 11800 Penang, Malaysia c Faculty of Pharmacy, Cyberjaya University College of Medical Sciences, 63000 Cyberjaya, Malaysia b

a r t i c l e

i n f o

Article history: Received 9 August 2013 Received in revised form 27 October 2013 Accepted 5 November 2013 Keywords: Phaleria macrocarpa seed oil Optimization by RSM CCD Fatty acids composition GC–MS analysis

a b s t r a c t Phaleria macrocarpa (Mahkota dewa) seed was examined to determine the optimal conditions of oil yield by solvent extraction method using n-hexane as extracting solvent. Response surface methodology (RSM) was employed to describe explicitly the influence of extraction time, temperature and solvent-tofeed ratio on the yield of oil using central composite design (CCD). The linear, quadratic and interaction terms of the studied variables have significant (P < 0.05) effect on the oil yield. The temperature of 72 ◦ C, extraction time of 8.4 h and solvent-to-feed ratio of 10.9 ml/g were the optimal conditions for seed oil extraction. The maximum oil yield was 55.32 g/100 g dry weight under these optimal conditions. Main chemical constituents of oil were determined by Gas chromatography–mass spectroscopy (GC–MS) and Fourier transform infrared spectroscopy (FTIR). Twelve components were identified by GC–MS analysis after formation of fatty acid methyl ester (FAME). Total saturated fatty acids were 19.38% whereas monounsaturated fatty acids and polyunsaturated fatty acids were 44.23% and 36.38%, respectively. Oleic acid, 18:1 (43.56%) and linoleic acid, 18:2 (36.25%) were the main fatty acid constituents of Mahkota dewa seed oil. The quantity of unsaturated fatty acids was higher than saturated fatty acids in P. macrocarpa seed oil. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Fatty acids are main components of fat or oil and they have importance in multidimensional functions such as energetic, metabolic, and structural activities (Elias, 1983). Some oils are used for nutritional or food purposes, some for cosmetic productions while others are necessary for producing energy like biofuel. The proper applications of particular oil depend on the characteristics of that oil which is determined by the fatty acid composition (Ramadan and Mörsel, 2003). For example, the vegetable oils are used for food preparation and production of biodiesel depending on their oil characteristics. Many polyunsaturated fatty acids like linoleic acid and arachidonic acid that are being used for human nutrition (Carvalho et al., 2006) are essential for growth and development. There are innumerable sources of fat or oil available and oily seeds are one of the important sources among them. There are many studies on the seed oil from different wild plants which indicate that those wild seeds can be promising oil sources for many purposes like nutritional, medicinal and industrial purposes

∗ Corresponding author. Tel.: +6 09 570 4841; fax: +6 09 571 6755. E-mail addresses: [email protected], [email protected] (I.S.M. Zaidul). 0926-6690/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.indcrop.2013.11.009

(Berchmans and Hirata, 2008; Tang et al., 2013; Okieimen and Eromosele, 1999; Eromosele, 1997). Phaleria macrocarpa (Mahkota dewa) belongs to Thymelaeaceae family has been traditionally treated as an important medicinal plant for centuries in Malaysia and Indonesia. Locally, the name of this plant means “God’s Crown” which implies that it descends from heaven, as a benediction from divinity to help mankind. Different parts (leaves, stem, fruit and seed) of this plant have been examined for its biological and pharmacological potencies by several researchers (Ali et al., 2012; Katrin et al., 2011; Yosie et al., 2011; Rahmawati et al., 2006; Sugiwati et al., 2006; Susilawati et al., 2011; Hendra et al., 2011). Alkaloid, lignin, saponin, terpenoid and polyphenol are the major types of bioactive compounds in this plant. The essential oils of P. macrocarpa fruit flesh are octadecana tricosan, octacosan, diocthylester and tributylacetylcitrate (Wijayani, 2005). Three active compounds namely mahkoside A, mangiferin (xanthone glycosides), kaempferol 3-O-␤-d-glucoside have been isolated from the seed part (Zhang et al., 2006). In addition, with these active compounds the seed also contains oil. There is no data in the literature about the amount of oil content of this seed as well as their composition. There are several techniques available to extract oil from plant matrix. Among these techniques, solvent extraction method is most

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commonly used because of its simplicity and cost-effectiveness. The oil yield in solvent extraction depends on the type of solvent, particle size of the sample, extraction temperature, agitation intensity (Azmir et al., 2013; Atabani et al., 2013). Other important factors are solvent-to-feed ratio and time of total extraction (Stanisavljevic´ et al., 2007; Stroescu et al., 2013). Optimization of significant influencing factors of solvent extraction has been reported by several researchers for different seed oils (Kostic´ et al., 2013; Stroescu et al., 2013; Sayyar et al., 2009). RSM is a very effective mathematical and statistical technique to identify the optimal conditions as well as to determine the effect of process parameters and the interaction of input variables (Wang et al., 2012). It is considered as the faster and less laborious technique as it requires minimum experimental runs for optimizing multiple independent variables (Sharif et al., 2013). For measuring the impact of various input parameters on the extraction of fats or oils, CCD with RSM is often used (Mani et al., 2007; Shao et al., 2008; Wei et al., 2009; Sirisompong et al., 2011). To the best of our knowledge, there is no detailed information that has been reported concerning the oil content and chemical constituents of P. macrocarpa seed. The aim of this study was to optimize the conditions for fats or oil extraction from P. macrocarpa seed using RSM with three independent variables that were examined at three levels for this purpose. The composition of the extracted oil was evaluated by GC–MS and FTIR. Identification of its fatty acids contents may become useful for determining the industrial applications in the future. 2. Materials and methods 2.1. Materials P. macrocarpa fruits were collected from a local supplier in Kuantan, Pahang, Malaysia. All reagents (analytical and HPLC grade); n-hexane, methanol, toluene and concentrated HCl were purchased from Merck Ltd. (Darmstadt, Germany). 2.2. Preparation of P. macrocarpa seeds for extraction The fruits were separated into their fiber and seed parts. The seeds were dried at 40 ◦ C using a dryer and ground into powder using a grinder, thereafter weighed and stored in air tight containers until use. The ground seeds were then sieved to get ≤1 mm particle size. The moisture content of the ground sample was determined using a halogen moisture analyzer (Mettler Toledo- HB43) in percentage of dry weight basis. 2.3. Extraction of P. macrocarpa seed oil using n-hexane Oil from P. macrocarpa seed was extracted by n-hexane and 20 g sample (dried and ground) was used for every combination of input variables. After extraction, a rotary vacuum evaporator (Buchi, Rotavapor-R210) at 40 ◦ C was used to remove the solvent (n-hexane). All extractions were carried out in triplicates. The oils were transferred into a glass vial and kept under refrigeration (0–4 ◦ C) for further processing and analysis. The total oil yields were determined in percentage of 100 g of ground seed sample on dry weight basis as described in the following equation: Oil yield (%) =

Massextracted oil × 100 Massseed powder

(1)

2.4. Derivatization of fatty acids Derivatization of fatty acids was done according to the method of Ichihara and Fukubayashi (2010) with some modifications. The

Table 1 Experimental ranges of the independent variables used in the central composite design (CCD) for the oil yield. Factors

Codes

◦

Temperature ( C) Time (h) Solvent-to-feed ratio (ml/ g)

X1 X2 X3

Levels −1

0

+1

40 6 10

60 7.5 12.5

80 9 15

oil sample was melted and homogenized thoroughly before taking for experiment. Commercial concentrated HCl (35%, w/w) was diluted with 41.5 ml methanol to make 50 ml 8.0% (w/v) HCl reagent. The HCl reagent was stored in a refrigerator before using. 100 ␮l of oil sample was placed in a black screw cap glass test tube and dissolved in 0.2 ml toluene. After that, 1.5 ml methanol and 0.3 ml of HCl reagent were added with the oil solution. The tube was vortexed and then heated at 95 ◦ C for 90 min for rapid methylation. After cooling the oil solution to room temperature, 1 ml hexane and 1 ml water were added for the extraction of fatty acid methyl esters (FAMEs). The tube was vortexed again and the hexane layer with FAMEs was separated and analyzed by GC–MS. 2.5. Fatty acid composition analysis by GC–MS Agilent 6890 N gas chromatography coupled with Agilent MS5973 mass selective detector (Agilent Technologies, USA, serial no. US14113031) was used for the GC–MS analysis of FAMEs. The sample of FAMEs was injected through an Agilent autosamplier 7683 series injector onto the HP-5MS column (30 m dimension, 0.25 mm i.d., 0.25 ␮m film thickness). Initially the oven temperature was maintained 150 ◦ C for 2 min and increased 4 ◦ C/min up to 230 ◦ C, then kept at 230 ◦ C for 5 min. The injector and detector temperatures were held at 240 ◦ C and 260 ◦ C, respectively. Helium was used as a carrier gas at a flow rate of 0.8 ml/min and 1:50 was the split ratio. A 70 eV electron impact (EI) in 50–550m/z scan range was applied for the operation of mass spectrometer (Kandhro et al., 2010). For the analysis, ChemStation integrated software (Agilent Technologies) was used and mass spectra were collected by AMDIS software. 2.6. FTIR analysis The infrared spectra were acquired using PerkinElmer frontier FTIR with UATR Diamond/ZnSe, 1 Reflection top-plate and pressure arm (PerkinElmer Singapore Pte Ltd). Measurements were obtained between 4000 and -400 cm−1 . PerkinElmer Spectrum ES Version 10.03.03 software was used to analyze the spectrum. 2.7. Experimental design P. macrocarpa seed oil extraction using n-hexane was optimized by employing CCD and RSM techniques. The independent variables studied to optimize oil yield were temperature (X1 ), extraction time (X2 ) and solvent-to-feed ratio (X3 ). On the basis of our preliminary findings, the input variables and their levels were set (data not shown). A temperature from 40 to 80 ◦ C, extraction time from 6 to 8 h and solvent-to-feed ratio at a range of 10–15 ml/g were selected. The coded and uncoded variables used for this experiment were shown in Table 1. The formulated experimental design was based on CCD having three parameters and 20 experiments. For estimation of “pure error” six replicates were performed at the central point (0,0,0) of design. A randomized experimental order was followed to lower the overall impact of unexplained variability of extraneous variable in the observed response. Table 2 shows the run order,

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Table 2 Central composite design matrix of factors and the responses of oil yield for P. macrocarpa seed oil. Run order

Temperature, X1 (◦ C)

Time, X2 (h)

Solvent-to-feed ratio, X3 (ml/g)

Oil yield (%)

Predicted yield (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

40.0 60.0 60.0 60.0 60.0 60.0 60.0 80.0 60.0 72.2 47.7 60.0 60.0 47.7 72.2 47.7 72.2 60.0 47.7 72.2

7.50 7.50 6.00 7.50 9.00 7.50 7.50 7.50 7.50 8.41 8.41 7.50 7.50 6.58 6.58 8.41 8.41 7.50 6.58 6.58

12.5 15.0 12.5 10.0 12.5 12.5 12.5 12.5 12.5 14.0 10.9 12.5 12.5 10.9 10.9 14.0 10.9 12.5 14.0 14.0

48.10 54.97 50.79 52.54 55.73 54.61 54.60 55.17 54.58 55.00 50.59 54.65 54.68 45.93 54.61 51.03 55.32 54.60 47.94 53.81

47.23 54.26 50.94 52.96 55.29 55.02 55.02 55.75 54.44 54.89 50.61 54.44 54.44 46.22 53.79 52.03 55.26 54.44 48.18 53.97

3. Result and discussion

determination (R2 ) was 0.97, meaning that the regression model for the oil yield was satisfactory, which adequately fits with the experimental results. Moreover, the predicted oil yields were obtained from the regression model using the data of the experimental oil yields. The experimental and predicted yields were compared with each other where the predicted oil yields were seen close to the experimental oil yields (Table 3). The linear and quadratic values of extraction temperature and time were highly significant (P < 0.01), and solvent-to-feed ratio was also significant (0.01 < P < 0.05), when it was examined for coefficients by the t-test. The interaction between temperature and extraction time was statistically significant, P < 0.05 for the oil yield. But the interaction between extraction temperature and solvent-to-feed ratio, and extraction time and solvent-to-feed ratio within the experimental range were not significant (P > 0.05). Linear, quadratic and interaction effects of the studied variables were thus the important impacting factors on the oil yield. The second order polynomial equation in Eq. (2) can be written as follow with coefficient:

3.1. Analysis of the model

Y = 2.058X1 + 19.272X2 + 8.057X3 − 0.009X12 − 0.847X22

variables conditions, experimental and predicted yields. A secondorder polynomial regression equation was used for predicting the response variable (Y) as follows: Y = ˇ0 +



ˇi Xi +



ˇii Xi2 +



ˇij Xi Xj

(2)

where, Y is the response variable, ˇ0 is a constant, ˇi , ˇii and ˇij represent the linear, quadratic and interactive coefficients, respectively. Xi and Xj represent the independent variables. The Minitab software (version 16) was used for multiple regression analysis, analysis of variance (ANOVA), correlation coefficient R, and determination of coefficient R2 which measures the goodness of fit of regression model. It also includes the t-value for the estimated coefficients and associated probabilities. The total error criteria with a confidence level of 95.0% were the basis for test of statistical significance.

The oil yield obtained based on the RSM design is shown in Table 2. The effect of temperature and extraction time were highly significant (P < 0.01) and solvent-to feed ratio also had significant effect on oil yield with P < 0.05. Table 3 summarizes the multiple regression coefficients gained by a least squares technique to predict a second-order polynomial model for the oil yield. The regression coefficients of Table 3 Estimated regression coefficients, corresponding t and p-values and analysis of variance for the final regression model of oil yield. Term

Coefficients

Standard error

t

p-Valuea

ˇ0 X1 (ˇ1 ) X2 (ˇ2 ) X3 (ˇ3 ) X12 (ˇ11 ) X22 (ˇ22 ) X32 (ˇ33 ) X1 *X2 (ˇ12 ) X1 *X3 (ˇ13 ) X2 *X3 (ˇ23 ) R2 Adj.R2

−143.096 2.058 19.272 8.057 −0.009 −0.847 −0.226 −0.065 −0.024 −0.097 97.26 94.22

29.0647 0.2781 4.2535 2.5521 0.0013 0.2267 0.0816 0.0218 0.0131 0.1747

−4.923 7.403 4.531 3.157 7.403 −3.738 −2.765 −2.977 −1.818 −0.555

0.001 0.000 0.001 0.012 0.000 0.005 0.022 0.016 0.102 0.593

a

P < 0.01 highly significant; 0.01 < P < 0.05 significant; P > 0.05 not significant.

− 0.226X32 − 0.065X1 X2 − 0.024X1 X3 − 0.097X2 X3 − 143.096 (3) In the second order polynomial model, a three-dimensional response surface graph and a two-dimensional contour plot can be generated using the linear, quadratic and interaction terms. Response surface graph is a three dimensional presentation of the impact of variables to response and it helps researchers to find the maximum, minimum and saddle points of the response. From this graph, it is not possible to determine the level of the variables responsible for the desired response. However, a two dimensional contour plot is more preferable for this job. In a contour plot, the levels of the variables are plotted in a curve with equal response. Therefore, contour plot is easier to interpret and to get the level of the variables at which the desired optimization occurred. Fig. 1 is the response surface and contour plot showing the effect of temperature and extraction time on the oil yield at a fixed solvent-to-feed ratio of 12.5 ml/g. The oil yield increased with the increment of the temperature until it reached a plateau above 70 ◦ C. The possible reason for this are; (i) the improved solubility of oil in solvent with the increment of temperature which increases the mass transfer of oil from sample matrix; (ii) higher temperature reduces the viscosity of solvent and increases the penetration of

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Fig. 2. (a) Response surface and (b) contour plot for the oil yield (Y) as a function of temperature and solvent-to-feed ratio at a fixed time of 7.5 h.

Fig. 1. (a) Response surface and (b) contour plot for the oil yield (Y) as a function of temperature and time at a fixed solvent-to-feed ratio of 12.5 ml/g.

solvent to sample matrix and thus enhances the oil yield; (iii) at high temperature, the diffusion coefficient of oil increase that leads to better rate of oil diffusion. On the other hand, extraction time had a positive effect on the oil yield at lower extraction time. This observation suggests that in the initial period of extraction, large amount of oil was extracted from sample matrix (so called washing stage), but the rate was reduced with increasing extraction time. At high extraction time, the negative effect of this variable on the oil yield is also important. This is probably a reflection of the degradation of some oils during prolonged treatment. The effect of temperature and solvent-to-feed ratio with constant extraction time at a level of 7.5 h can be seen in Fig. 2. The oil yield increased with increase of extraction temperature as well as the solvent-to-feed ratio. At high extraction temperature, the oil yield increased until it reached a plateau and then declined slightly with increasing solvent-to-feed ratio. The impact of temperature is more prominent than solvent-to-feed ratio at high temperature because the system may has already reached equilibrium at high temperature. Fig. 3 shows the effect of extraction time and solvent-to-feed ratio on oil yield at a constant temperature of 60 ◦ C. Both linear and quadratic effects of temperature and solvent-to-feed ratio on oil yield have been described earlier under Figs. 1 and 2. Their interaction effect was found to have non-significant effect on oil yield although their linear and quadratic effects were significant (P < 0.05). This may be due to the prominent impact of temperature,

Fig. 3. (a) Response surface and (b) contour plot for the oil yield (Y) as a function of time and solvent-to-feed ratio at a fixed temperature of 60 ◦ C.

J. Azmir et al. / Industrial Crops and Products 52 (2014) 405–412 Table 4 Fatty acid composition (% as methyl ester) of Phaleria macrocarpa oil as determined by gas chromatography–mass spectroscopy. RT

Area (%)

Fatty acids

9.414 14.398 18.439 18.610 19.022 19.290 22.662 23.211 Others

0.08 15.00 36.25 43.56 4.11 0.22 0.32 0.19 0.27

Tetradecanoic acid Hexadecanoic acid 9,12-Octadecanoic acid 9-Octadecanoic acid Octadecanoic acid trans-13-Octadecanoic acid 11-Eicosenoic acid Nonadecanoic acid

which at 60 ◦ C makes the system stable and thus the impact of other two variables become non-significant on this level of temperature. The optimum conditions for maximum oil yield according to the model of second order polynomial equation were 72.2 ◦ C temperature, 8.4 h time and 10.9 ml/g solvent-to-feed ratio. At this optimized condition, the predicted yield of the oil was 55.32 g/100 g. 3.2. Fatty acid composition analysis The fatty acid composition of P. macrocarpa seed oil is very important for further analysis as there is lack of information about this seed oil. Table 4 shows the fatty acids constituents (%) in P. macrocarpa seed oil extracted using n-hexane. Twelve fatty acids were identified by GC–MS analysis and 9-octadecanoic acid or oleic acid (18:1) was the dominant fatty acid with area percentage of 43.56. The percentage of 9,12-octadecanoic acid or linoleic acid (18:2) and hexadecanoic acid or palmitic acid (16:0) were 36.25% and 15.00%, respectively. Among all the fatty acids four fatty acids were found in small amount which are tabulated as others in Table 4. There are also higher chain fatty acids like 11Eicosenoic acid (20:1) and Nonadecanoic acid (19:0) but in minor quantities (0.32% and 0.19%). GC–MS chromatogram is shown in Fig. 4. Fig. 5 represents the mass spectra of some important fatty acids identified; methyl tetradecanoate, methyl hexadecanoate, methyl 9,12-octadecanoate, methyl 9-octadecanoate, methyl octadecanoate, methyl trans 3-octadecanoate, methyl 11eicosanoate, methyl nonadecanoate. The comparison of fatty acids composition of studied oil and vegetable oils such as palm oil, sunflower oil and soybean oil are shown in Table 5. Three main types of fatty acids (saturated, monounsaturated and polyunsaturated fatty acids) were found as triglycerides. Usually vegetable oils are high in monounsaturated

409

Table 5 Comparison of Fatty acid composition (%) of P. macrocarpa seed oil with different oils. Fatty acids

P. macrocarpa seed oil

Palm oila

Sunflower oila

Soybean oila

Myristic 14:0 Palmitic 16:0 Stearic 18:0 Oleic 18:1 Linoleic 18:2 Linolenic 18:3 Nonadecanoic 19:0 Arachidic 20:0 11-Eicosenoic acid 20:1 saturated Monounsaturated Polyunsaturated

0.08 15.0 4.11 43.56 36.25 – 0.19 – 0.32 19.38 44.23 36.38

1.1 44.0 4.5 39.2 10.1 0.04 – – – 49.9 39.2 10.5

– – 4.5 21.1 66.2 – – 0.3 – 11.3 21.1 66.2

0.1 11.0 4.0 23.4 53.2 7.8 – – – 15.1 23.4 61.0

a

From Edem (2002).

fatty acid but low in saturated and polyunsaturated fatty acids (Gunstone, 2004). The highest amount of monounsaturated fat was found in P. macrocarpa seed oil (44.23%) compared to palm oil (39.2%), sunflower oil (21.1%) and soybean oil (23.4%). This is because the P. macrocarpa seed oil has the highest content of oleic acid than others. Soybean and sunflower oils are rich in polyunsaturated (61.0% and 66.2%) such as linoleic and linolenic acids. The main polyunsaturated fatty acid found in the P. macrocarpa seed oil is linoleic acid in the amount of 36.25% where the palm, soybean and sunflower oils have 10.1%, 53.2% and 66.2% linoleic acid, respectively (Edem, 2002). The P. macrocarpa seed oil consists of 19.38% saturated fat which is lower than palm oil (49.9%) but slight higher than soybean (15.1%) and sunflower (11.3%) oil. Among the four oil discussed, Nonadecanoic acids and 11-Eicosenoic acid were found only in the studied oil. 3.3. FTIR spectra analysis The spectral features of P. macrocarpa seed oil are displayed in Fig. 6. The absorption peak at 3006.26 cm−1 is due to C H asymmetric stretch. The 2923.78 cm−1 and 2855.25 are due to the C H symmetric and antisymmetric stretch of methyl group from lipids (Lu and Rasco, 2010). Peaks appearing at 1460.55 cm−1 and 1376.06 cm−1 are assigned to the C H antisymmetric and symmetric deformation vibrations, respectively (Günzler and Gremlich, 2002). The band at 1095.86 cm−1 , 1236.83 cm−1 , 1160.38 and 1236.83 cm−1 correspond to the C O stretching vibration (Socrates and Socrates, 2001; Silverstein et al., 1981). The absorption at

Fig. 4. The chromatogram of GC–MS analysis of P. macrocarpa seed oil.

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Fig. 5. Mass spectra of methyl tetradecanoate (a), methyl hexadecanoate (b), methyl 9,12-octadecanoate (c), methyl 9-octadecanoate (d), methyl octadecanoate (e), methyl trans 3-octadecanoate (f), methyl 11-eicosanoate (g), methyl nonadecanoate (h).

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Fig. 6. FTIR spectra of P. macrocarpa seed oil at frequency 4000–400 cm−1 .

1743.66 cm−1 is due C O stretch (Silverstein et al., 1981). The peak at 720.79 cm−1 is due to C H bond from long chain alkane (Nabi et al., 2009). 4. Conclusion Optimization by RSM based on CCD was successfully employed for P. macrocarpa seed oil extraction. Three parameters; extraction temperature, time and solvent-to-feed ratio were found to have significant effect on oil yield. On the optimum extraction condition the oil yield was 55.32 g/100 g dry weight which is reported for the first time. The major fatty acids were oleic acid, linoleic acid and palmitic acid. This seed oil contains high unsaturated to saturated fatty acids ratio and unsaturated fatty acids were also in good amount compared with the other vegetable oils. Identification of fatty acids and accurate quantification of individual fatty acids were achieved by GC–MS and FTIR analysis which will be helpful for further analysis of P. macrocarpa seed oil. Acknowledgement This work was supported by the Endowment type-B grand from International Islamic University Malaysia (Grant No.: EDW B 13063-0948). References Ali, R.B., Atangwho, I.J., Kaur, N., Abraika, O.S., Ahmad, M., Mahmud, R., Asmawi, M.Z., 2012. Bioassay-guided antidiabetic study of Phaleria macrocarpa fruit extract. Molecules 17, 4986–5002, http://dx.doi.org/10.3390/molecules17054986. Azmir, J., Zaidul, I.S.M., Rahman, M.M., Sharif, K.M., Mohamed, A., Sahena, F., Omar, A.K.M., 2013. Techniques for extraction of bioactive compounds from plant materials: a review. J. Food Eng. 117, 426–436, http://dx.doi.org/10.1016/j.jfoodeng.2013.01.014. Atabani, A.E., Silitonga, A.S., Ong, H.C., Mahlia, T.M.I., Masjuki, H.H., Badruddin, I.A., Fayaz, H., 2013. Non-edible vegetable oils: a critical evaluation of oil extraction, fatty acid compositions, biodiesel production, characteristics, engine performance and emissions production. Renew. Sustain. Energy Rev. 18, 211–245, http://dx.doi.org/10.1016/j.rser.2012.10.013. Berchmans, H.J., Hirata, S., 2008. Biodiesel production from crude Jatropha curcas L. seed oil with a high content of free fatty acids. Bioresour. Technol. 99, 1716–1721.

Carvalho, I.S.D., Miranda, I., Pereira, H., 2006. Evaluation of oil composition of some crops suitable for human nutrition. Ind. Crops Prod. 24, 75–78, http://dx.doi.org/10.1016/j.indcrop.2006.03.005. Elias, P.M., 1983. Epidermal lipids, barrier function, and desquamation. J. Invest. Dermatol. 80, 44–49. Eromosele, I.C., 1997. Biochemical and nutritional characteristics of seed oils from wild plants. In: Proceedings of the Second International Workshop on African Pear Improvement and Other New Sources of Vegetable Oils, Ngaoundere, pp. 203–208. Edem, D.O., 2002. Palm oil: biochemical, physiological, nutritional, hematological and toxicological aspects: a review. Plant Foods Hum. Nutr. 57, 319–341. Gunstone, F.D., 2004. Rapeseed and Canola Oil: Production, Processing, Properties and Uses. CRC Press/Blackwell Publishing Ltd, London. Günzler, H., Gremlich, H.U., 2002. IR Spectroscopy: An Introduction, vol. 69469. Wiley-VCH, Weinheim. Hendra, R., Ahmad, S., Sukari, A., Shukor, M.Y., Oskoueian, E., 2011. Flavonoid analyses and antimicrobial activity of various parts of Phaleria macrocarpa (Scheff.) boerl fruit. Int. J. Mol. Sci. 12, 3422–3431, http://dx.doi.org/10.3390/ijms12063422. Ichihara, K.I., Fukubayashi, Y., 2010. Preparation of fatty acid methyl esters for gas–liquid chromatography. J. Lipid Res. 51, 635–640, http://dx.doi.org/10.1194/jlr.D001065. Katrin, E., Selvie, S., Winarno, H., 2011. Chromatogram profiles and cytotoxic activity of irradiated Mahkota dewa (Phaleria macrocarpa (Scheff.) Boerl) leaves. Atom Indones. J. 37, 17. ´ M.D., Jokovic, ´ M.N., Stamenkovic, ´ O.S., Rajkovic, ´ K.M., Milic, ´ P.S., Veljkovic, ´ Kostic, V.B., 2013. Optimization of hempseed oil extraction by n-hexane. Ind. Crops Prod. 48, 133–143, http://dx.doi.org/10.1016/j.indcrop.2013.04.028. Kandhro, A., Sherazi, S.T.H., Mahesar, S.A., Bhanger, M.I., Talpur, M.Y., Latif, Y., 2010. Variation in fatty acids composition including trans fat in different brands of potato chips by GC–MS. Pak. J. Anal. Environ. Chem. 11, 36–41. Lu, X., Rasco, B., 2010. Rapid determination of antioxidant content and activity in foods using infrared spectroscopy and chemometrics: a review. Crit. Rev. Food Sci. Nutr. 52, 853–885, http://dx.doi.org/10.1080/10408398.2010.511322. Mani, S., Jaya, S., Vadivambal, R., 2007. Optimization of solvent extraction of moringa (Moringa oleifera) seed kernel oil using response surface methodology. Trans. Inst. Chem. Eng. 85, 328–335. Nabi, M.N., Hoque, S.M., Akhter, M.S., 2009. Karanja (Pongamia pinnata) biodiesel production in Bangladesh, characterization of karanja biodiesel and its effect on diesel emissions. Fuel Process. Technol. 90, 1080–1086, http://dx.doi.org/10.1016/j.fuproc.2009.04.014. Okieimen, F.E., Eromosele, C.O., 1999. Fatty acid composition of the seed oil of Khaya senegalensis. Bioresour. Technol. 69, 279–280, http://dx.doi.org/10.1016/S0960-8524(98)00190-4. Ramadan, M.F., Mörsel, J.T., 2003. Determination of the lipid classes and fatty acid profile of Niger (Guizotia abyssinica Cass.) seed oil. Phytochem. Anal. 14, 366–370. Rahmawati, E., Dewoto, H.R., Wuyung, P.E., 2006. Anticancer activity study of ethanol extract of Mahkota dewa fruit pulp (Phaleria macrocarpa (Scheff.) Boerl.) in C3H mouse mammary tumor induced by transplantation. Med. J. Indones. 15 (4), doi:10.13181/mji.v15i4.237.

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J. Azmir et al. / Industrial Crops and Products 52 (2014) 405–412

Sugiwati, S., Kardono, L.B.S., Bintang, M., 2006. ␣-Glucosidase inhibitory activity and hypoglycemic effect of Phaleria macrocarpa fruit pericarp extracts by oral administration to rats. J. Appl. Sci. 6, 2312–2316. Susilawati, S., Matsjeh, S., Pranowo, H.D., Anwar, C., 2011. Antioxidant activity of 2,6,4 -trihydroxy-4-methoxy benzophenone from ethyl acetate extract of leaves of Mahkota dewa (Phaleria macrocarpa (Scheff.) Boerl.). Indones. J. Chem. 11, 180–185. ´ I., Lakicevi ´ ´ S., Veliˇckovic, ´ D., Lazic, ´ M., Veljkovic, ´ V., 2007. The extracStanisavljevic, c, tion of oil from tobacco (Nicotiana tabacum L.) seeds. Chem. Ind. Chem. Eng. Q. 13, 41–50. Stroescu, M., Stoica-Guzun, A., Ghergu, S., Chira, N., Jipa, I., 2013. Optimization of fatty acids extraction from Portulaca oleracea seed using response surface methodology. Ind. Crops Prod. 43, 405–411. Sayyar, S., Abidin, Z.Z., Yunus, R., Muhammad, A., 2009. Extraction of oil from Jatropha seeds-optimization and kinetics. Am. J. Appl. Sci. 6, 1390. Sharif, K.M., Rahman, M.M., Azmir, J., Mohamed, A., Jahurul, M.H.A., Sahena, F., Zaidul, I.S.M., 2013. Experimental design of supercritical fluid extraction – a review. J. Food Eng., http://dx.doi.org/10.1016/j.jfoodeng.2013.10.003. Shao, P., Sun, P., Ying, Y., 2008. Response surface optimization of wheat germ oil yield by supercritical carbon dioxide extraction. Food Bioprod. Process. 86, 227–231, http://dx.doi.org/10.1016/j.fbp.2007.04.001. Sirisompong, W., Jirapakkul, W., Klinkesorn, U., 2011. Response surface optimization and characteristics of rambutan (Nephelium lappaceum L.) kernel fat by hexane extraction. LWT – Food Sci. Technol. 44, 1946–1951, http://dx.doi.org/10.1016/j.lwt.2011.04.011.

Socrates, G., Socrates, G., 2001. Infrared and Raman Characteristic Group Frequencies: Tables and Charts. Wiley, Chichester, pp. 107–113. Silverstein, R.M., Bassler, G.C., Morrill, T.C., 1981. Spectrometric Identification of Organic Compounds, 4th ed. John Wiley and Sons, New York. Tang, T.F., Liu, X.M., Ling, M., Lai, F., Zhang, L., Zhou, Y.H., Sun, R.R., 2013. Constituents of the essential oil and fatty acid from Malania oleifera. Ind. Crops Prod. 43, 1–5. Wijayani, W., 2005. Sitotoksisitas Minyak Atsiri Biji dan Daging Buah Mahkota Dewa (Phaleria macrocarpa [Scheff.] Boerl.) Terhadap Myeloma Cell Line dan HeLa Cell Line Serta Identifikasi Senyawa Minyak Atsiri dengan Kromatografi Gas-Spektrometri Massa (GC–MS), Skripsi FMIPA UGM. Yogyakarta, 41–50. Wang, H., Liu, Y., Wei, S., Yan, Z., 2012. Application of response surface methodology to optimise supercritical carbon dioxide extraction of essential oil from Cyperus rotundus Linn. Food Chem. 132, 582–587, http://dx.doi.org/10.1016/j.foodchem.2011.10.075. Wei, Z.-J., Liao, A.-M., Zhang, H.-X., Liu, J., Jiang, S.-T., 2009. Optimization of supercritical carbon dioxide extraction of silkworm pupal oil applying the response surface methodology. Bioresour. Technol. 100, 4214–4219, http://dx.doi.org/10.1016/j.biortech.2009.04.010. Yosie, A., Effendy, M.A.W., Sifzizul, T.M.T., Habsah, M., 2011. Antibacterial, radical- scavenging activities and cytotoxicity properties of Phaleria macrocarpa (Scheff.) Boerl leaves in HepG2 cell lines. Int. J. Pharm. Sci. Res. (IJPSR) 2, 1700–1706. Zhang, Y.B., Xu, X.J., Liu, H.M., 2006. Chemical constituents from Mahkota dewa. J. Asian Nat. Prod. Res. 8, 119–123.