- Email: [email protected]
Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology
Applied Acoustics xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Applied Acoustics journal homepage: www.elsevier.com/locate/apacoust
Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology W. Setyaningsih a,b, E. Duros c, M. Palma a,⇑, C.G. Barroso a a Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510 Puerto Real, Cadiz, Spain b Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Jalan Flora, Bulaksumur, 55281 Yogyakarta, Indonesia c Department of Physical Measurements, Institute of Technology of Lannion, 22302 Lannion Cedex, France
a r t i c l e
i n f o
Article history: Received 28 January 2015 Received in revised form 21 March 2015 Accepted 1 April 2015 Available online xxxx Keywords: Ultrasound-assisted extraction Melatonin UPLC-FD Rice grains
a b s t r a c t An analytical ultrasound-assisted extraction (UAE) technique has been optimized and validated for the extraction of melatonin from rice grains. A Box–Behnken design in conjunction with a response surface methodology based on six factors and three levels was used to evaluate the effects of the studied factors prior to optimizing the UAE conditions. The significant (p < 0.05) response surface models with high coefficients of determination were fitted to the experimental data. Solvent composition and extraction temperature were found to have very significant effects on the response value (p < 0.005). The optimal UAE conditions were as follows: extraction time of 10 min, ultrasound amplitude of 30%, cycle of 0.2 s1, extraction temperature of 40 °C, 50% methanol in water as the extraction solvent at pH 3.5 and a solvent/solid ratio 2.5:1. The method validation ensured right values for linearity, LOD, LOQ, precision and recovery. Furthermore, the method was successfully applied in the analysis of a number of rice samples throughout the rice production process. Hence, it was demonstrated that this particular UAE method is an interesting tool for the determination of melatonin in rice grain samples. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Since the beginning of this decade melatonin has been considered to have potent antioxidant properties and anti-inflammatory effects [1]. Melatonin mitigates neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases [2] and it also acts as an anticancer agent [3]. However, research into the role that melatonin plays in biological systems has been limited by a number of factors, including the very low levels present in samples, the dearth of analytical methods and the complexity of the biological matrices. It is therefore particularly important to be able to extract and quantify accurately the levels of melatonin present in food in the human diet. This goal is challenging given the complex chemistry of plant tissues, which contain a diverse range of primary and secondary metabolites. Cultivated rice (Oryza sativa L.) is one of the most important cereal crops in the world since more than half of the world’s population subsists wholly or partially on this grain [4]. Besides the contribution of rice to the total human calorie intake, rice contains
⇑ Corresponding author. Tel.: +34 956 016 360; fax: +34 956 016 460. E-mail address: [email protected] (M. Palma).
some specific compounds that have demonstrated benefits for human health, including melatonin and phenolic compounds [5] . The development of an optimal procedure for the extraction of melatonin from food presents some difficulties due to its potent antioxidant activity, which leads to rapid reaction with other constituents in the matrix. Ultrasound-Assisted Extraction (UAE) appears to offer a solution to this problem as it is a technology that can accelerate mass transfer and enhance the extraction kinetics [6]. The ultrasound method is cheaper and easier to operate than other novel extraction techniques such as pressurized liquid extraction (PLE) and microwave-assisted extraction (MAE) [7]. Additionally, like Soxhlet extraction, UAE is not restricted by the solvent and type of matrix used. The UAE technique is therefore suitable for the extraction of a wide variety of natural compounds including melatonin in a complex matrix of a biological system, e.g., rice. The UAE method is a very interesting technique to extract natural compounds from food matrices due to the cavitation effect, which enhances mass transport by disrupting the plant cell walls [8]. Consequently, ultrasonic power is considered to be one of the factors that leads to enhancement of the extraction [9]. In addition, several factors govern the efficiency of ultrasound and these include frequency, temperature, type of solvent, and sonication time.
http://dx.doi.org/10.1016/j.apacoust.2015.04.001 0003-682X/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
2
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
Various factors can affect the extraction process and these have to be optimized in order to extract quantitatively the analytes of interest. The chemometric approach based on the advantages of the Box–Behnken design (BBD) have been successfully applied in the optimization of UAE [10]. The BBD is compatible with the response surface methodology (RSM) because it allows an estimation of the parameters of the quadratic model, the building of sequential designs, the detection of lack of fit of the model and the use of blocks [11]. The particular focus of the study described here was the optimization and validation of the UAE method for the extraction of melatonin in rice grains by BBD in conjunction with RSM. 2. Materials and methods 2.1. Materials and chemicals HPLC-grade methanol, acetic acid and acetonitrile were purchased from Merck (Darmstadt, Germany). Melatonin standard M-5250 was obtained from Sigma Aldrich (St. Louis, MO, USA). Water was purified with a Milli-Q purification system (Millipore, Billerica, MA, USA).
pigmented rice samples from Indonesia and pigmented rice from Thailand. 2.3. Extraction of melatonin UAE was carried out using a 200 watts and 24 kHz UP200S ultrasonic system (Hielscher Ultrasonics GmbH, Teltow, Germany). A 7 mm diameter probe was used for the experiments. This compact ultrasonic system is designed to be mounted on a stand and is equipped with a water bath coupled to a temperature controller (Frigiterm, J.P. Selecta, Barcelona, Spain) to maintain the desired extraction temperature in the range from –10 °C to 100 °C. Rice powder (2 g) was accurately weighed and then placed in an extraction tube. Based on the experimental design, a set volume and type of solvent was added into the extraction vessel and the extraction was performed under controlled UAE conditions. After extraction, the solid material in the extract was removed using a centrifuge (J.P. Selecta, Barcelona, Spain) at 8000 rpm at 4 °C for 5 min. The centrifuge cake was subsequently washed using fresh solvent and the liquids were collected with the extract and adjusted to a certain volume based on the design of experiment (DOE). The extract was filtered through a nylon filter (0.22 lm) prior to injection into a UPLC-FD system.
2.2. Rice sample preparation 2.4. Determination of melatonin Ò
Analyses were carried out on an ACQUITY UPLC H-Class system coupled to an ACQUITY UPLCÒ Fluorescence Detector (FD) and controlled by Empower™ 3 Chromatography Data Software (Waters Corporation, Milford, MA, USA). Separations were performed at a temperature of 47 °C on a reverse phase RP 18 Acquity UPLCÒ BEH Column (Acquity UPLCÒ BEH 100 2.1 (1.7 lm) from Waters Corporation, Ireland). The mobile phase was a binary solvent system consisting of phase A (water with 0.01% acetic acid) and phase B (acetonitrile with 2% acetic acid). The flow rate was 0.7 mL min1. The 4.0 min programmed gradient was as follows (%B): 0–1 min, 0%; 1– 1.1 min, 0–10%; 1.1–2 min, 10%; 2–3 min, 10–20%; 3–3.5 min, 20–60%; 3.5–4 min, 60–100%. The column was subsequently
–Melatonin – 3.379
–Melatonin – 3.360
Red rice samples from Thailand were obtained from a regular market. Each rice sample (20 g) was placed in a plastic cylinder and the rice grains were milled with an Ultraturrax homogenizer (IKAÒ T25 Digital, Germany) for 10 min prior to extraction. The milling process was stopped every 1 min in order to avoid excessive heating of the sample. The fine powder of rice grain was then homogenized by stirring and the sample was stored in a closed container. The final extraction method was applied to two Indonesian rice varieties (umbul-umbul and IR-64) taken at different stages of the production process, i.e. drying (dried paddy), hulling (whole grain rice) and polishing (polished rice), and these were obtained randomly from various smallholder rice mills in Central Java (Indonesia). Additionally, the suitability of the developed method was also evaluated by analyzing a number of organic
Fig. 1. Chromatogram of melatonin in standard solution (A) and in rice extract (B).
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
3
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
washed with 100% B for 3 min and equilibrated with 0% B for 3 min. The excitation wavelength was set at 290 nm and the emission wavelength was set in the range of 300–400 nm for the 3D
Table 1 Selected factors and their levels. Factors
1
0
+1
Unit
X1, X2, X3, X4, X5, X6,
0 10 30 2 3 2.5
25 30 40 4.5 4 3.75
50 50 70 7 7 5
% °C % s1 – mL solvent/g sample
methanol in water temperature amplitude cycle pH solvent-solid ratio
Table 2 Box–Behnken design for six factors with their observed responses. Run
Extraction variables X1
X2
X3
X4
X5
X6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 –1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0
Relative values to the maximum response (%) 35.10 4.08 18.54 33.59 16.81 18.21 30.32 43.91 52.97 7.61 59.75 14.05 51.52 10.15 50.08 9.46 13.67 47.99 30.33 62.80 18.39 59.24 35.23 67.46 40.90 1.77 33.40 2.55 52.40 24.10 38.40 9.13 19.19 32.51 3.84 47.43 24.33 23.69 0.44 41.98 32.92 100.00 13.35 26.94 6.60 66.74 8.46 60.84 33.02 48.23 50.25 41.51 23.29 21.93
scan and at 334.9 nm for the 2D scan. The values were selected based on the maximum absorbance of melatonin. The FD sensitivity for the 2D scan was set at PMT gain 1, the data rate at 40 pts s1 and the time constant at 0.1 s. The injection volume was set at 3.0 lL. Fig. 1 shows the chromatogram of with melatonin retention time of 3.4 min. 2.5. Box–Behnken design (BBD) and statistical analysis RSM was employed to explore the factors that affect the UAE and this approach enables the overall number of experiments and possible interaction effects between factors to be considered. A BBD with six independent factors (X1, solvent; X2, temperature; X3, amplitude; X4, cycle; X5, pH; X6, solvent-solid ratio) at three levels was performed (1, 0, 1). The range of independent factors and their levels are listed in Table 1 and the whole design, which consisted of 54 experimental points carried out in random order, is presented in Table 2. This approach was used to obtain the surface response by fitting the data to a polynomial model and also to evaluate the effects of each factor and the interaction effects between factors. If all factors are considered to be evaluated, the RSM can be expressed as follows:
y ¼ f ðx1 ; x2 ; x3 ; . . . ; xk Þ
ð1Þ
where y is the response of the system and xi are the factors. It is supposed that the independent factors are continuous and controllable during the experiments. Since the objective was to optimize the response y, it was necessary to find the best estimation for the correlation between independent factors and the response surface. Generally, a second-order model is applied in RSM:
y ¼ b0 þ
k k k1 X k X X X bi xi þ bii x2i þ bijxi xj þ e i¼1
i¼1
ð2Þ
i¼1 j¼2
where x1, x2, . . . , xk are the factors that influence the response y; b0, bii (i = 1, 2, . . ., k), bij (i = 1, 2, . . ., k; j = 1, 2, . . ., k) are unknown parameters and e is a random error. The b coefficients are obtained by the least square method. The design of experiment (DOE) matrix was obtained using the trial version of STATGRAPHICS Centurion XVI (Statpoint Technologies, Inc., USA). The experimental results in single factor experiments were analyzed using Gnumeric 1.12.17. Analysis of Variance (ANOVA) and the Least Significant Difference (LSD) test were used to determine the significance of differences between the means. 3. Results and discussion The level of melatonin was the response used during preliminary and optimization studies thus the determination method was validated for quality assurance. The linearity, range, detection and quantification limits of the method were evaluated. Linear ranges were obtained from 0.5 to 20 lg L1 and 20 to 300 lg L1 with the regression coefficients (R2) were both equal to 0.999 using a five-point calibration curve. The detection and quantification limits were 0.93 and 3.09 lg L1 respectively. This demonstrates a good performance of the determination method for melatonin. 3.1. Stability of melatonin A preliminary study to examine the stability of melatonin under UAE conditions was carried out prior to method optimization. Previous studies indicated that extraction temperature was the
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
4
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
foremost factor to be considered as influencing the stability of melatonin [5]. The amount of melatonin in a methanol solution was assessed after applying various extraction temperatures for 15 min. Seven extraction temperature points were studied and these ranged between 5 and 60 °C. The extraction temperature was maintained by circulating a constant temperature liquid that contacted and transferred heat to water that controlled the temperature of the extraction vessel. The levels of melatonin recovered after the application of different extraction temperatures are presented in Fig. 2. The effect of the UAE temperature on the stability of melatonin was analyzed using ANOVA. The retained melatonin was taken as the signal (EU) of the melatonin peak. The ANOVA results showed that the levels of melatonin after the application of different UAE temperatures was steady. The applied UAE temperatures did not have a significant effect (p = 0.05) on the stability of melatonin since Fcalculated (1.35) was lower than Fcritical (4.28). Thus, melatonin is considered to be stable at UAE temperatures of 5, 15, 25, 35, 45, 55 and 60 °C. 3.2. Development of the UAE method Having set the working range for the extraction temperature by considering the stability of melatonin, the next step was to study
the factors that were likely to influence the recovery of melatonin from the rice sample. An experimental design was constructed in order to evaluate the influence on the extraction yield of six factors related to UAE conditions. The factors considered were the composition of the extraction solvent (X1; 0–50% methanol in water), temperature (X2; 10–70 °C), amplitude (X3; 30–70%), cycle (X4; 0.2–0.7 s1), solvent pH (X5; 3–7) and solvent-solid ratio (X6; 2.5:1–5:1). Values for the extraction variables were established based on previous information found in the revised literatures about extraction of melatonin [5,12–14]. Since the variables have different units and ranges, each of the variables was first normalized and forced to fall in the range from 1 to +1 in order to obtain a more even response [15]. Relative values with respect to the maximum response (%) of melatonin concentration were used as the responses and these are shown in Table 2. A Box–Behnken design with 54 runs that included six centre points was conducted and a mathematical model for the experimental data was then obtained. The ANOVA method was used to determine the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. The standardized effects (p = 0.05) in decreasing order of importance are plotted in the Pareto chart (Fig. 3). A bar crossing a vertical line corresponds to a factor or combination of factors that have a significant effect on the response. In this case, two effects
Fig. 2. Stability of melatonin in different UAE temperatures. The means followed by the same letter are indicated as not significant different (p = 0.05).
Fig. 3. Pareto chart for the standardized effects.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
have p-values below 0.05, indicating that they are significantly different from zero at the 95.0% confidence level. Based on the results shown in the standardized Pareto chart, the significant effects and the course for optimizing the extraction can be identified. The factors that gave rise to the main effects, i.e., the extraction solvent (X1) and temperature (X2), had a significant influence on the extraction yield. The extraction solvent showed a positive effect in that a higher recovery was achieved on increasing the percentage of methanol in the solvent. In contrast, the temperature had a negative effect as the recovery decreased on increasing this factor. In contrast, combination effects between extraction factors were not found to be significant. Only significant variables were studied during later optimization steps, however contribution by all variables were used to evaluate the fitting properties for the model. The equation for the fitted model is:
Y ¼ 0:180X 1 0:128X 2 þ 0:008X 3 0:025X 4 þ 0:011X 5 þ 0:014X 6 0:004X 1 X 1 þ 0:091X 1 X 2 0:013X 1 X 3 0:034X 1 X 4 0:020X 1 X 5 þ 0:040X 1 X 6 0:058X 2 X 2 þ 0:009X 2 X 3 þ 0:001X 2 X 4 þ 0:002X 2 X 5 þ 0:016X 2 X 6 0:014X 3 X 3 0:008X 3 X 4 þ 0:073X 3 X 5 þ 0:005X 3 X 6 þ 0:074X 4 X 4 0:019X 4 X 5 þ 0:11073X 4 X 6 0:060X 5 X 5 þ 0:033X 5 X 6 0:038X 6 X 6 þ 0:363705
ð3Þ
where y is the melatonin yield and Xi are the extraction variables (X1, solvent; X2, temperature; X3, amplitude; X4, cycle; X5, pH; X6, solvent-solid ratio). A lack-of-fit test was carried out in order to ascertain whether the selected model was satisfactory to describe the observed data or whether a more complex model was required. The test was performed by comparing the variability of the current model residuals to the variability between observations at replicate settings for the factors. Since the p-value for the lack-of-fit (0.2636) obtained by ANOVA is greater than 0.05, the model appears to be satisfactory for the observed data at the 95.0% confidence level.
5
The R-squared statistic indicates that the model as fitted explains 72.45% of the variability in the extraction yield. The standard error of the predicted value shows that the standard deviation of the residuals is 0.1225. Therefore, the model can be used to estimate the response for the purposes of optimization. 3.3. Response optimization Significant independent factors are essential to achieve the best extraction yield when optimizing the method. On the basis of the predicted model, three-dimensional surface plots were constructed to predict the relationships between independent factors and the response. The DOE results enabled the construction of the surface response and the factors extraction solvent (X1) and temperature (X2) were evaluated (Fig. 4). As can be observed, a high point was found at which the optimum melatonin yield (72.67%) was obtained at coordinates for an extraction solvent of 0.999786 and an extraction temperature of 0.578623. Based on RSM, the optimized melatonin extraction of the rice samples using UAE was achieved on applying an extraction temperature of 18.5 °C, an amplitude of 30%, a cycle of 0.2 s1, a pH of 3.5 and a solvent-to-sample ratio of 2.5:1, with the highest percentage of methanol in water, in the range studied, as the extraction solvent. The value for the solvent was in the corner of the range investigated for this extraction variable and it was therefore decided to study values above the highest assayed level for the percentage of methanol in the solvent. 3.4. Solvent optimization Single-factor ANOVA was used to evaluate the significance of solvent composition in the melatonin extraction. It was found that changes in solvent composition (50%, 75% and 100% methanol in water) did not have a significant effect on the melatonin extraction because the Fcalculated value for the extraction solvent (0.61) was lower than Fcritical (9.55). As a result, 50% methanol in water was defined as the optimum extraction solvent.
Fig. 4. Response surface plots showing effects of variables (X1, solvent; X2, temperature) on the extraction yield of melatonin.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
6
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
3.5. Extraction kinetics The extraction kinetics were studied by analyzing the rice sample under the UAE conditions identified as providing the optimum recovery of melatonin. The results of this particular study are shown in Fig. 5. The results of the kinetic study show that recovery for melatonin using UAE increased until a plateau starting at 10 min. 3.6. Method validation A certified reference material (CRM) was not available for melatonin in rice matrices, neither reference method has been previously established, and, as a consequence, definitive statements cannot be made with regard to accuracy. Nonetheless, the extraction recovery (% R) was determined by comparing the absolute response of the analytes spiked in control samples both before and after the extraction procedure. The standard solution of melatonin was spiked in rice sample. The spiked and non-spiked rice samples were extracted and melatonin was quantified on them, with recovery evaluated according to ICH guidelines. The extraction recovery obtained on using the developed UAE ranged from 90% to 109%. The results are therefore satisfactory for the melatonin extraction method using UAE system. The precision of the method was considered at two levels, namely repeatability and intermediate precision. The repeatability and intermediate precision were evaluated according to ICH guidelines for melatonin extraction. The precision, expressed as CV, of the developed method is as follows: 9.78% for repeatability (n = 9) and 1.88% for intermediate precision (n = 3 3). The CV values for precision were below the acceptable limit (±10% refer to AOAC) and this demonstrates the good precision of the extraction method. The method was applied in the extraction of a number of real samples in order to evaluate the applicability. Two typical Indonesian rice varieties in the form of semi-finished and finished products and some pigmented rice grains from Indonesia and Thailand were processed using the working conditions of the developed UAE. The results are shown in Table 3. It was demonstrated that the profile of melatonin during rice production could be evaluated along with the level of melatonin in some pigmented rice grains, thus demonstrating the usefulness of the developed method. The levels of melatonin remaining after several treatments, i.e. drying (dried raw rice), de-husking (whole grain rice) and polishing (polished rice), during rice production decreased in both the local variety (umbul-umbul) and the high-yielding variety of
Fig. 5. Amount of extracted melatonin versus extraction time.
Table 3 Melatonin levels in tested rice grain samples. Rice sample
Description
Paddy var. Umbulumbul Whole grain var. Umbulumbul Polished rice var. Umbulumbul Paddy var. IR64
Short grain; local variety of Indonesian rice; raw grain rice before milling process
121.4 ± 17.6
Short grain; local variety of Indonesian rice; semi-finished rice after de-husking process Short grain; local variety of Indonesian rice; finished rice after polishing process
33.83 ± 2.2
Whole grain var. IR64 Polished rice var. IR64 Organic black rice Non-organic black rice organic red rice Non-organic red rice
Melatonin (lg kg1)
7.13 ± 4.7
Long grain; high-yielding variety of Indonesian rice; raw grain rice before milling process Long grain; high-yielding variety of Indonesian rice; semi-finished rice after de-husking process Long grain; high-yielding variety of Indonesian rice; finished rice after polishing process
132.11 ± 12.38
Black pigmented rice obtained from organic farming, Indonesia Black pigmented rice obtained from conventional farming, Thailand Red pigmented rice obtained from organic farming, Indonesia Red pigmented rice obtained from conventional farming, Thailand
188.07 ± 12.71
50.62 ± 3.24
9.24 ± 3.82
156.72 ± 11.62 216.94 ± 7.79 101.69 ± 8.91
Indonesian rice (IR-64). Subsequently, in order to study the effect of rice production processes and rice varieties on the level of melatonin, a two-factor ANOVA was employed (p.0.05). ANOVA revealed that the tested rice varieties showed non-significant differences in melatonin losses during the rice production processes. Nevertheless, the processing steps in rice production do have a significant effect and therefore LSDs for this factor were estimated (see Fig. 6). The levels of melatonin in the two rice varieties decreased gradually during the course of processing throughout rice production, particularly after a set of milling processes, and only 30–40% of the original melatonin remained in the final samples. These data are consistent with previously reported results [16]. The decreases in the melatonin content in each step during rice production seem reasonable bearing in mind that the husk and bran have been removed during rice production. The pericarp in bran, which consists of fibrous layers of protein, is a source of
Fig. 6. Melatonin levels in a series of rice production processes. The melatonin level followed by the different letter are indicated as significant different (p = 0.05).
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
nitrogen-containing compounds [17]. These compounds, specifically the amino acid tryptophan, are essential for the biosynthesis of melatonin in plants [18]. The removal of this part of the grain may reduce the concentration of melatonin in the product. The levels of melatonin in pigmented (black and red) rice grains from both organic and conventional rice farming were markedly higher than those in the polished samples (umbul-umbul and IR-64). In pigmented rice the melatonin level was maintained in the final product due to the absence of bran removal process. Furthermore, pigmented rice contains higher levels of vitamins and antioxidants [19] and it is known that vitamins B12 and B6 are necessary for melatonin biosynthesis and also that vitamin B3 prevents the breakdown of tryptophan [20].
4. Conclusions The ultrasound assisted-extraction method developed in this work for the extraction of melatonin from rice grains was effectively optimized by Box–Benhken design in combination with the response surface methodology. The optimum extraction conditions involved the use of 50% methanol in water at 18.5 °C for 10 min with an amplitude 30%, cycle 0.2 s1, pH 3.5 and solvent-to-sample ratio of 2.5:1. The proposed method was validated and gave acceptable values for linearity, precision and recovery. The method was successfully applied to real rice grain samples. It can be concluded from the results that the ultrasound-assisted extraction method proposed in this study is a reliable, cheap and simple technique for the determination of melatonin in rice grains.
Acknowledgements W.S. is grateful to the CIMB Foundation for a Ph.D. studentship through the CIMB Regional Scholarships 2012. The authors acknowledge the support of CV. Green Health Agriculture, Indonesia, in providing the organic pigmented rice grains and Ms. Nikmatul Hidayah at the Indonesian Center of Agricultural Postharvest Research & Development, Bogor, Indonesia for providing the semi-finished and finished white rice products. Thanks are also due to STATGRAPHICS for permission to use the trial version of the Centurion XVI Trial Software.
7
References [1] Esposito E, Cuzzocrea S. Antiinflammatory activity of melatonin in central nervous system. Curr Neuropharmacol 2010;8:228–42. http://dx.doi.org/ 10.2174/157015910792246155. [2] Wang X. The antiapoptotic activity of melatonin in neurodegenerative diseases. CNS Neurosci Ther 2009;15:345–57. http://dx.doi.org/10.1111/ j.1755-5949.2009.00105.x. [3] Jung B, Ahmad N. Melatonin in cancer management: progress and promise. Cancer Res 2006;66:9789–93. [4] OECD–FAO. Agricultural Outlook, 2011. [5] Setyaningsih W, Palma M, Barroso CG. A new microwave-assisted extraction method for melatonin determination in rice grains. J Cereal Sci 2012;56:340–6. [6] Wu J, Lin L, Chau FT. Ultrasound-assisted extraction of ginseng saponins from ginseng roots and cultured ginseng cells. Ultrason Sonochem 2001;8:347–52. [7] Wang L, Weller CL. Recent advances in extraction of nutraceuticals from plants. Trends Food Sci Technol 2006;17:300–12. [8] Vinatoru M. An overview of the ultrasonically assisted extraction of bioactive principles from herbs. Ultrason Sonochem 2001;8:303–13. [9] Mason T. The uses of ultrasound in food technology. Ultrason Sonochem 1996;3:S253–60. [10] Tabaraki R, Nateghi A. Optimization of ultrasonic-assisted extraction of natural antioxidants from rice bran using response surface methodology. Ultrason Sonochem 2011;18:1279–86. [11] Ferreira SLC, Bruns RE, Ferreira HS, Matos GD, David JM, Brandão GC, et al. Box–Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta 2007;597:179–86. [12] Pothinuch P, Tongchitpakdee S. Melatonin contents in mulberry (Morus spp.) leaves: effects of sample preparation, cultivar, leaf age and tea processing. Food Chem 2011;128:415–9. http://dx.doi.org/10.1016/j.foodchem.2011. 03.045. [13] Oladi E, Mohamadi M, Shamspur T, Mostafavi A. Spectrofluorimetric determination of melatonin in kernels of four different Pistacia varieties after ultrasound-assisted solid–liquid extraction. Spectrochim Acta A Mol Biomol Spectrosc 2014;132:326–9. http://dx.doi.org/10.1016/ j.saa.2014.05.010. [14] Chen G, Huo Y, Tan D-X, Liang Z, Zhang W, Zhang Y. Melatonin in Chinese medicinal herbs. Life Sci 2003;73:19–26. http://dx.doi.org/10.1016/S00243205(03)00252-2. _ Modeling and optimization I: usability of response surface [15] Basß D, Boyacı IH. methodology. J Food Eng 2007;78:836–45. [16] Setyaningsih W, Hidayah N, Saputro IE, Lovillo MP, Barroso CG. Melatonin profile during rice (Oryza Sativa) production. J Adv Agric Technol 2014;1:60–4. [17] Arendt E, Zannini E. Rice. Cereal Grains Food Beverage Ind. Cambridge (UK): Woodhead Publishing Limited; 2013. p. 120–1. [18] Murch SJ, KrishnaRaj S, Saxena PK. Tryptophan is a precursor for melatonin and serotonin biosynthesis in in vitro regenerated St. John’s wort (Hypericum perforatum L. cv. Anthos) plants. Plant Cell Rep 2000;19:698–704. [19] Zhang M, Guo B, Zhang R, Chi J, Wei Z, Xu Z, et al. Separation, purification and identification of antioxidant compositions in black rice. Agric Sci China 2006;5:431–40. [20] USA RiceÒ Federation. Nutrition facts 2014.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
Contents lists available at ScienceDirect
Applied Acoustics journal homepage: www.elsevier.com/locate/apacoust
Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology W. Setyaningsih a,b, E. Duros c, M. Palma a,⇑, C.G. Barroso a a Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Campus del Rio San Pedro, 11510 Puerto Real, Cadiz, Spain b Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Jalan Flora, Bulaksumur, 55281 Yogyakarta, Indonesia c Department of Physical Measurements, Institute of Technology of Lannion, 22302 Lannion Cedex, France
a r t i c l e
i n f o
Article history: Received 28 January 2015 Received in revised form 21 March 2015 Accepted 1 April 2015 Available online xxxx Keywords: Ultrasound-assisted extraction Melatonin UPLC-FD Rice grains
a b s t r a c t An analytical ultrasound-assisted extraction (UAE) technique has been optimized and validated for the extraction of melatonin from rice grains. A Box–Behnken design in conjunction with a response surface methodology based on six factors and three levels was used to evaluate the effects of the studied factors prior to optimizing the UAE conditions. The significant (p < 0.05) response surface models with high coefficients of determination were fitted to the experimental data. Solvent composition and extraction temperature were found to have very significant effects on the response value (p < 0.005). The optimal UAE conditions were as follows: extraction time of 10 min, ultrasound amplitude of 30%, cycle of 0.2 s1, extraction temperature of 40 °C, 50% methanol in water as the extraction solvent at pH 3.5 and a solvent/solid ratio 2.5:1. The method validation ensured right values for linearity, LOD, LOQ, precision and recovery. Furthermore, the method was successfully applied in the analysis of a number of rice samples throughout the rice production process. Hence, it was demonstrated that this particular UAE method is an interesting tool for the determination of melatonin in rice grain samples. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Since the beginning of this decade melatonin has been considered to have potent antioxidant properties and anti-inflammatory effects [1]. Melatonin mitigates neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases [2] and it also acts as an anticancer agent [3]. However, research into the role that melatonin plays in biological systems has been limited by a number of factors, including the very low levels present in samples, the dearth of analytical methods and the complexity of the biological matrices. It is therefore particularly important to be able to extract and quantify accurately the levels of melatonin present in food in the human diet. This goal is challenging given the complex chemistry of plant tissues, which contain a diverse range of primary and secondary metabolites. Cultivated rice (Oryza sativa L.) is one of the most important cereal crops in the world since more than half of the world’s population subsists wholly or partially on this grain [4]. Besides the contribution of rice to the total human calorie intake, rice contains
⇑ Corresponding author. Tel.: +34 956 016 360; fax: +34 956 016 460. E-mail address: [email protected] (M. Palma).
some specific compounds that have demonstrated benefits for human health, including melatonin and phenolic compounds [5] . The development of an optimal procedure for the extraction of melatonin from food presents some difficulties due to its potent antioxidant activity, which leads to rapid reaction with other constituents in the matrix. Ultrasound-Assisted Extraction (UAE) appears to offer a solution to this problem as it is a technology that can accelerate mass transfer and enhance the extraction kinetics [6]. The ultrasound method is cheaper and easier to operate than other novel extraction techniques such as pressurized liquid extraction (PLE) and microwave-assisted extraction (MAE) [7]. Additionally, like Soxhlet extraction, UAE is not restricted by the solvent and type of matrix used. The UAE technique is therefore suitable for the extraction of a wide variety of natural compounds including melatonin in a complex matrix of a biological system, e.g., rice. The UAE method is a very interesting technique to extract natural compounds from food matrices due to the cavitation effect, which enhances mass transport by disrupting the plant cell walls [8]. Consequently, ultrasonic power is considered to be one of the factors that leads to enhancement of the extraction [9]. In addition, several factors govern the efficiency of ultrasound and these include frequency, temperature, type of solvent, and sonication time.
http://dx.doi.org/10.1016/j.apacoust.2015.04.001 0003-682X/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
2
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
Various factors can affect the extraction process and these have to be optimized in order to extract quantitatively the analytes of interest. The chemometric approach based on the advantages of the Box–Behnken design (BBD) have been successfully applied in the optimization of UAE [10]. The BBD is compatible with the response surface methodology (RSM) because it allows an estimation of the parameters of the quadratic model, the building of sequential designs, the detection of lack of fit of the model and the use of blocks [11]. The particular focus of the study described here was the optimization and validation of the UAE method for the extraction of melatonin in rice grains by BBD in conjunction with RSM. 2. Materials and methods 2.1. Materials and chemicals HPLC-grade methanol, acetic acid and acetonitrile were purchased from Merck (Darmstadt, Germany). Melatonin standard M-5250 was obtained from Sigma Aldrich (St. Louis, MO, USA). Water was purified with a Milli-Q purification system (Millipore, Billerica, MA, USA).
pigmented rice samples from Indonesia and pigmented rice from Thailand. 2.3. Extraction of melatonin UAE was carried out using a 200 watts and 24 kHz UP200S ultrasonic system (Hielscher Ultrasonics GmbH, Teltow, Germany). A 7 mm diameter probe was used for the experiments. This compact ultrasonic system is designed to be mounted on a stand and is equipped with a water bath coupled to a temperature controller (Frigiterm, J.P. Selecta, Barcelona, Spain) to maintain the desired extraction temperature in the range from –10 °C to 100 °C. Rice powder (2 g) was accurately weighed and then placed in an extraction tube. Based on the experimental design, a set volume and type of solvent was added into the extraction vessel and the extraction was performed under controlled UAE conditions. After extraction, the solid material in the extract was removed using a centrifuge (J.P. Selecta, Barcelona, Spain) at 8000 rpm at 4 °C for 5 min. The centrifuge cake was subsequently washed using fresh solvent and the liquids were collected with the extract and adjusted to a certain volume based on the design of experiment (DOE). The extract was filtered through a nylon filter (0.22 lm) prior to injection into a UPLC-FD system.
2.2. Rice sample preparation 2.4. Determination of melatonin Ò
Analyses were carried out on an ACQUITY UPLC H-Class system coupled to an ACQUITY UPLCÒ Fluorescence Detector (FD) and controlled by Empower™ 3 Chromatography Data Software (Waters Corporation, Milford, MA, USA). Separations were performed at a temperature of 47 °C on a reverse phase RP 18 Acquity UPLCÒ BEH Column (Acquity UPLCÒ BEH 100 2.1 (1.7 lm) from Waters Corporation, Ireland). The mobile phase was a binary solvent system consisting of phase A (water with 0.01% acetic acid) and phase B (acetonitrile with 2% acetic acid). The flow rate was 0.7 mL min1. The 4.0 min programmed gradient was as follows (%B): 0–1 min, 0%; 1– 1.1 min, 0–10%; 1.1–2 min, 10%; 2–3 min, 10–20%; 3–3.5 min, 20–60%; 3.5–4 min, 60–100%. The column was subsequently
–Melatonin – 3.379
–Melatonin – 3.360
Red rice samples from Thailand were obtained from a regular market. Each rice sample (20 g) was placed in a plastic cylinder and the rice grains were milled with an Ultraturrax homogenizer (IKAÒ T25 Digital, Germany) for 10 min prior to extraction. The milling process was stopped every 1 min in order to avoid excessive heating of the sample. The fine powder of rice grain was then homogenized by stirring and the sample was stored in a closed container. The final extraction method was applied to two Indonesian rice varieties (umbul-umbul and IR-64) taken at different stages of the production process, i.e. drying (dried paddy), hulling (whole grain rice) and polishing (polished rice), and these were obtained randomly from various smallholder rice mills in Central Java (Indonesia). Additionally, the suitability of the developed method was also evaluated by analyzing a number of organic
Fig. 1. Chromatogram of melatonin in standard solution (A) and in rice extract (B).
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
3
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
washed with 100% B for 3 min and equilibrated with 0% B for 3 min. The excitation wavelength was set at 290 nm and the emission wavelength was set in the range of 300–400 nm for the 3D
Table 1 Selected factors and their levels. Factors
1
0
+1
Unit
X1, X2, X3, X4, X5, X6,
0 10 30 2 3 2.5
25 30 40 4.5 4 3.75
50 50 70 7 7 5
% °C % s1 – mL solvent/g sample
methanol in water temperature amplitude cycle pH solvent-solid ratio
Table 2 Box–Behnken design for six factors with their observed responses. Run
Extraction variables X1
X2
X3
X4
X5
X6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 –1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0
Relative values to the maximum response (%) 35.10 4.08 18.54 33.59 16.81 18.21 30.32 43.91 52.97 7.61 59.75 14.05 51.52 10.15 50.08 9.46 13.67 47.99 30.33 62.80 18.39 59.24 35.23 67.46 40.90 1.77 33.40 2.55 52.40 24.10 38.40 9.13 19.19 32.51 3.84 47.43 24.33 23.69 0.44 41.98 32.92 100.00 13.35 26.94 6.60 66.74 8.46 60.84 33.02 48.23 50.25 41.51 23.29 21.93
scan and at 334.9 nm for the 2D scan. The values were selected based on the maximum absorbance of melatonin. The FD sensitivity for the 2D scan was set at PMT gain 1, the data rate at 40 pts s1 and the time constant at 0.1 s. The injection volume was set at 3.0 lL. Fig. 1 shows the chromatogram of with melatonin retention time of 3.4 min. 2.5. Box–Behnken design (BBD) and statistical analysis RSM was employed to explore the factors that affect the UAE and this approach enables the overall number of experiments and possible interaction effects between factors to be considered. A BBD with six independent factors (X1, solvent; X2, temperature; X3, amplitude; X4, cycle; X5, pH; X6, solvent-solid ratio) at three levels was performed (1, 0, 1). The range of independent factors and their levels are listed in Table 1 and the whole design, which consisted of 54 experimental points carried out in random order, is presented in Table 2. This approach was used to obtain the surface response by fitting the data to a polynomial model and also to evaluate the effects of each factor and the interaction effects between factors. If all factors are considered to be evaluated, the RSM can be expressed as follows:
y ¼ f ðx1 ; x2 ; x3 ; . . . ; xk Þ
ð1Þ
where y is the response of the system and xi are the factors. It is supposed that the independent factors are continuous and controllable during the experiments. Since the objective was to optimize the response y, it was necessary to find the best estimation for the correlation between independent factors and the response surface. Generally, a second-order model is applied in RSM:
y ¼ b0 þ
k k k1 X k X X X bi xi þ bii x2i þ bijxi xj þ e i¼1
i¼1
ð2Þ
i¼1 j¼2
where x1, x2, . . . , xk are the factors that influence the response y; b0, bii (i = 1, 2, . . ., k), bij (i = 1, 2, . . ., k; j = 1, 2, . . ., k) are unknown parameters and e is a random error. The b coefficients are obtained by the least square method. The design of experiment (DOE) matrix was obtained using the trial version of STATGRAPHICS Centurion XVI (Statpoint Technologies, Inc., USA). The experimental results in single factor experiments were analyzed using Gnumeric 1.12.17. Analysis of Variance (ANOVA) and the Least Significant Difference (LSD) test were used to determine the significance of differences between the means. 3. Results and discussion The level of melatonin was the response used during preliminary and optimization studies thus the determination method was validated for quality assurance. The linearity, range, detection and quantification limits of the method were evaluated. Linear ranges were obtained from 0.5 to 20 lg L1 and 20 to 300 lg L1 with the regression coefficients (R2) were both equal to 0.999 using a five-point calibration curve. The detection and quantification limits were 0.93 and 3.09 lg L1 respectively. This demonstrates a good performance of the determination method for melatonin. 3.1. Stability of melatonin A preliminary study to examine the stability of melatonin under UAE conditions was carried out prior to method optimization. Previous studies indicated that extraction temperature was the
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
4
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
foremost factor to be considered as influencing the stability of melatonin [5]. The amount of melatonin in a methanol solution was assessed after applying various extraction temperatures for 15 min. Seven extraction temperature points were studied and these ranged between 5 and 60 °C. The extraction temperature was maintained by circulating a constant temperature liquid that contacted and transferred heat to water that controlled the temperature of the extraction vessel. The levels of melatonin recovered after the application of different extraction temperatures are presented in Fig. 2. The effect of the UAE temperature on the stability of melatonin was analyzed using ANOVA. The retained melatonin was taken as the signal (EU) of the melatonin peak. The ANOVA results showed that the levels of melatonin after the application of different UAE temperatures was steady. The applied UAE temperatures did not have a significant effect (p = 0.05) on the stability of melatonin since Fcalculated (1.35) was lower than Fcritical (4.28). Thus, melatonin is considered to be stable at UAE temperatures of 5, 15, 25, 35, 45, 55 and 60 °C. 3.2. Development of the UAE method Having set the working range for the extraction temperature by considering the stability of melatonin, the next step was to study
the factors that were likely to influence the recovery of melatonin from the rice sample. An experimental design was constructed in order to evaluate the influence on the extraction yield of six factors related to UAE conditions. The factors considered were the composition of the extraction solvent (X1; 0–50% methanol in water), temperature (X2; 10–70 °C), amplitude (X3; 30–70%), cycle (X4; 0.2–0.7 s1), solvent pH (X5; 3–7) and solvent-solid ratio (X6; 2.5:1–5:1). Values for the extraction variables were established based on previous information found in the revised literatures about extraction of melatonin [5,12–14]. Since the variables have different units and ranges, each of the variables was first normalized and forced to fall in the range from 1 to +1 in order to obtain a more even response [15]. Relative values with respect to the maximum response (%) of melatonin concentration were used as the responses and these are shown in Table 2. A Box–Behnken design with 54 runs that included six centre points was conducted and a mathematical model for the experimental data was then obtained. The ANOVA method was used to determine the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. The standardized effects (p = 0.05) in decreasing order of importance are plotted in the Pareto chart (Fig. 3). A bar crossing a vertical line corresponds to a factor or combination of factors that have a significant effect on the response. In this case, two effects
Fig. 2. Stability of melatonin in different UAE temperatures. The means followed by the same letter are indicated as not significant different (p = 0.05).
Fig. 3. Pareto chart for the standardized effects.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
have p-values below 0.05, indicating that they are significantly different from zero at the 95.0% confidence level. Based on the results shown in the standardized Pareto chart, the significant effects and the course for optimizing the extraction can be identified. The factors that gave rise to the main effects, i.e., the extraction solvent (X1) and temperature (X2), had a significant influence on the extraction yield. The extraction solvent showed a positive effect in that a higher recovery was achieved on increasing the percentage of methanol in the solvent. In contrast, the temperature had a negative effect as the recovery decreased on increasing this factor. In contrast, combination effects between extraction factors were not found to be significant. Only significant variables were studied during later optimization steps, however contribution by all variables were used to evaluate the fitting properties for the model. The equation for the fitted model is:
Y ¼ 0:180X 1 0:128X 2 þ 0:008X 3 0:025X 4 þ 0:011X 5 þ 0:014X 6 0:004X 1 X 1 þ 0:091X 1 X 2 0:013X 1 X 3 0:034X 1 X 4 0:020X 1 X 5 þ 0:040X 1 X 6 0:058X 2 X 2 þ 0:009X 2 X 3 þ 0:001X 2 X 4 þ 0:002X 2 X 5 þ 0:016X 2 X 6 0:014X 3 X 3 0:008X 3 X 4 þ 0:073X 3 X 5 þ 0:005X 3 X 6 þ 0:074X 4 X 4 0:019X 4 X 5 þ 0:11073X 4 X 6 0:060X 5 X 5 þ 0:033X 5 X 6 0:038X 6 X 6 þ 0:363705
ð3Þ
where y is the melatonin yield and Xi are the extraction variables (X1, solvent; X2, temperature; X3, amplitude; X4, cycle; X5, pH; X6, solvent-solid ratio). A lack-of-fit test was carried out in order to ascertain whether the selected model was satisfactory to describe the observed data or whether a more complex model was required. The test was performed by comparing the variability of the current model residuals to the variability between observations at replicate settings for the factors. Since the p-value for the lack-of-fit (0.2636) obtained by ANOVA is greater than 0.05, the model appears to be satisfactory for the observed data at the 95.0% confidence level.
5
The R-squared statistic indicates that the model as fitted explains 72.45% of the variability in the extraction yield. The standard error of the predicted value shows that the standard deviation of the residuals is 0.1225. Therefore, the model can be used to estimate the response for the purposes of optimization. 3.3. Response optimization Significant independent factors are essential to achieve the best extraction yield when optimizing the method. On the basis of the predicted model, three-dimensional surface plots were constructed to predict the relationships between independent factors and the response. The DOE results enabled the construction of the surface response and the factors extraction solvent (X1) and temperature (X2) were evaluated (Fig. 4). As can be observed, a high point was found at which the optimum melatonin yield (72.67%) was obtained at coordinates for an extraction solvent of 0.999786 and an extraction temperature of 0.578623. Based on RSM, the optimized melatonin extraction of the rice samples using UAE was achieved on applying an extraction temperature of 18.5 °C, an amplitude of 30%, a cycle of 0.2 s1, a pH of 3.5 and a solvent-to-sample ratio of 2.5:1, with the highest percentage of methanol in water, in the range studied, as the extraction solvent. The value for the solvent was in the corner of the range investigated for this extraction variable and it was therefore decided to study values above the highest assayed level for the percentage of methanol in the solvent. 3.4. Solvent optimization Single-factor ANOVA was used to evaluate the significance of solvent composition in the melatonin extraction. It was found that changes in solvent composition (50%, 75% and 100% methanol in water) did not have a significant effect on the melatonin extraction because the Fcalculated value for the extraction solvent (0.61) was lower than Fcritical (9.55). As a result, 50% methanol in water was defined as the optimum extraction solvent.
Fig. 4. Response surface plots showing effects of variables (X1, solvent; X2, temperature) on the extraction yield of melatonin.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
6
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
3.5. Extraction kinetics The extraction kinetics were studied by analyzing the rice sample under the UAE conditions identified as providing the optimum recovery of melatonin. The results of this particular study are shown in Fig. 5. The results of the kinetic study show that recovery for melatonin using UAE increased until a plateau starting at 10 min. 3.6. Method validation A certified reference material (CRM) was not available for melatonin in rice matrices, neither reference method has been previously established, and, as a consequence, definitive statements cannot be made with regard to accuracy. Nonetheless, the extraction recovery (% R) was determined by comparing the absolute response of the analytes spiked in control samples both before and after the extraction procedure. The standard solution of melatonin was spiked in rice sample. The spiked and non-spiked rice samples were extracted and melatonin was quantified on them, with recovery evaluated according to ICH guidelines. The extraction recovery obtained on using the developed UAE ranged from 90% to 109%. The results are therefore satisfactory for the melatonin extraction method using UAE system. The precision of the method was considered at two levels, namely repeatability and intermediate precision. The repeatability and intermediate precision were evaluated according to ICH guidelines for melatonin extraction. The precision, expressed as CV, of the developed method is as follows: 9.78% for repeatability (n = 9) and 1.88% for intermediate precision (n = 3 3). The CV values for precision were below the acceptable limit (±10% refer to AOAC) and this demonstrates the good precision of the extraction method. The method was applied in the extraction of a number of real samples in order to evaluate the applicability. Two typical Indonesian rice varieties in the form of semi-finished and finished products and some pigmented rice grains from Indonesia and Thailand were processed using the working conditions of the developed UAE. The results are shown in Table 3. It was demonstrated that the profile of melatonin during rice production could be evaluated along with the level of melatonin in some pigmented rice grains, thus demonstrating the usefulness of the developed method. The levels of melatonin remaining after several treatments, i.e. drying (dried raw rice), de-husking (whole grain rice) and polishing (polished rice), during rice production decreased in both the local variety (umbul-umbul) and the high-yielding variety of
Fig. 5. Amount of extracted melatonin versus extraction time.
Table 3 Melatonin levels in tested rice grain samples. Rice sample
Description
Paddy var. Umbulumbul Whole grain var. Umbulumbul Polished rice var. Umbulumbul Paddy var. IR64
Short grain; local variety of Indonesian rice; raw grain rice before milling process
121.4 ± 17.6
Short grain; local variety of Indonesian rice; semi-finished rice after de-husking process Short grain; local variety of Indonesian rice; finished rice after polishing process
33.83 ± 2.2
Whole grain var. IR64 Polished rice var. IR64 Organic black rice Non-organic black rice organic red rice Non-organic red rice
Melatonin (lg kg1)
7.13 ± 4.7
Long grain; high-yielding variety of Indonesian rice; raw grain rice before milling process Long grain; high-yielding variety of Indonesian rice; semi-finished rice after de-husking process Long grain; high-yielding variety of Indonesian rice; finished rice after polishing process
132.11 ± 12.38
Black pigmented rice obtained from organic farming, Indonesia Black pigmented rice obtained from conventional farming, Thailand Red pigmented rice obtained from organic farming, Indonesia Red pigmented rice obtained from conventional farming, Thailand
188.07 ± 12.71
50.62 ± 3.24
9.24 ± 3.82
156.72 ± 11.62 216.94 ± 7.79 101.69 ± 8.91
Indonesian rice (IR-64). Subsequently, in order to study the effect of rice production processes and rice varieties on the level of melatonin, a two-factor ANOVA was employed (p.0.05). ANOVA revealed that the tested rice varieties showed non-significant differences in melatonin losses during the rice production processes. Nevertheless, the processing steps in rice production do have a significant effect and therefore LSDs for this factor were estimated (see Fig. 6). The levels of melatonin in the two rice varieties decreased gradually during the course of processing throughout rice production, particularly after a set of milling processes, and only 30–40% of the original melatonin remained in the final samples. These data are consistent with previously reported results [16]. The decreases in the melatonin content in each step during rice production seem reasonable bearing in mind that the husk and bran have been removed during rice production. The pericarp in bran, which consists of fibrous layers of protein, is a source of
Fig. 6. Melatonin levels in a series of rice production processes. The melatonin level followed by the different letter are indicated as significant different (p = 0.05).
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001
W. Setyaningsih et al. / Applied Acoustics xxx (2015) xxx–xxx
nitrogen-containing compounds [17]. These compounds, specifically the amino acid tryptophan, are essential for the biosynthesis of melatonin in plants [18]. The removal of this part of the grain may reduce the concentration of melatonin in the product. The levels of melatonin in pigmented (black and red) rice grains from both organic and conventional rice farming were markedly higher than those in the polished samples (umbul-umbul and IR-64). In pigmented rice the melatonin level was maintained in the final product due to the absence of bran removal process. Furthermore, pigmented rice contains higher levels of vitamins and antioxidants [19] and it is known that vitamins B12 and B6 are necessary for melatonin biosynthesis and also that vitamin B3 prevents the breakdown of tryptophan [20].
4. Conclusions The ultrasound assisted-extraction method developed in this work for the extraction of melatonin from rice grains was effectively optimized by Box–Benhken design in combination with the response surface methodology. The optimum extraction conditions involved the use of 50% methanol in water at 18.5 °C for 10 min with an amplitude 30%, cycle 0.2 s1, pH 3.5 and solvent-to-sample ratio of 2.5:1. The proposed method was validated and gave acceptable values for linearity, precision and recovery. The method was successfully applied to real rice grain samples. It can be concluded from the results that the ultrasound-assisted extraction method proposed in this study is a reliable, cheap and simple technique for the determination of melatonin in rice grains.
Acknowledgements W.S. is grateful to the CIMB Foundation for a Ph.D. studentship through the CIMB Regional Scholarships 2012. The authors acknowledge the support of CV. Green Health Agriculture, Indonesia, in providing the organic pigmented rice grains and Ms. Nikmatul Hidayah at the Indonesian Center of Agricultural Postharvest Research & Development, Bogor, Indonesia for providing the semi-finished and finished white rice products. Thanks are also due to STATGRAPHICS for permission to use the trial version of the Centurion XVI Trial Software.
7
References [1] Esposito E, Cuzzocrea S. Antiinflammatory activity of melatonin in central nervous system. Curr Neuropharmacol 2010;8:228–42. http://dx.doi.org/ 10.2174/157015910792246155. [2] Wang X. The antiapoptotic activity of melatonin in neurodegenerative diseases. CNS Neurosci Ther 2009;15:345–57. http://dx.doi.org/10.1111/ j.1755-5949.2009.00105.x. [3] Jung B, Ahmad N. Melatonin in cancer management: progress and promise. Cancer Res 2006;66:9789–93. [4] OECD–FAO. Agricultural Outlook, 2011. [5] Setyaningsih W, Palma M, Barroso CG. A new microwave-assisted extraction method for melatonin determination in rice grains. J Cereal Sci 2012;56:340–6. [6] Wu J, Lin L, Chau FT. Ultrasound-assisted extraction of ginseng saponins from ginseng roots and cultured ginseng cells. Ultrason Sonochem 2001;8:347–52. [7] Wang L, Weller CL. Recent advances in extraction of nutraceuticals from plants. Trends Food Sci Technol 2006;17:300–12. [8] Vinatoru M. An overview of the ultrasonically assisted extraction of bioactive principles from herbs. Ultrason Sonochem 2001;8:303–13. [9] Mason T. The uses of ultrasound in food technology. Ultrason Sonochem 1996;3:S253–60. [10] Tabaraki R, Nateghi A. Optimization of ultrasonic-assisted extraction of natural antioxidants from rice bran using response surface methodology. Ultrason Sonochem 2011;18:1279–86. [11] Ferreira SLC, Bruns RE, Ferreira HS, Matos GD, David JM, Brandão GC, et al. Box–Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta 2007;597:179–86. [12] Pothinuch P, Tongchitpakdee S. Melatonin contents in mulberry (Morus spp.) leaves: effects of sample preparation, cultivar, leaf age and tea processing. Food Chem 2011;128:415–9. http://dx.doi.org/10.1016/j.foodchem.2011. 03.045. [13] Oladi E, Mohamadi M, Shamspur T, Mostafavi A. Spectrofluorimetric determination of melatonin in kernels of four different Pistacia varieties after ultrasound-assisted solid–liquid extraction. Spectrochim Acta A Mol Biomol Spectrosc 2014;132:326–9. http://dx.doi.org/10.1016/ j.saa.2014.05.010. [14] Chen G, Huo Y, Tan D-X, Liang Z, Zhang W, Zhang Y. Melatonin in Chinese medicinal herbs. Life Sci 2003;73:19–26. http://dx.doi.org/10.1016/S00243205(03)00252-2. _ Modeling and optimization I: usability of response surface [15] Basß D, Boyacı IH. methodology. J Food Eng 2007;78:836–45. [16] Setyaningsih W, Hidayah N, Saputro IE, Lovillo MP, Barroso CG. Melatonin profile during rice (Oryza Sativa) production. J Adv Agric Technol 2014;1:60–4. [17] Arendt E, Zannini E. Rice. Cereal Grains Food Beverage Ind. Cambridge (UK): Woodhead Publishing Limited; 2013. p. 120–1. [18] Murch SJ, KrishnaRaj S, Saxena PK. Tryptophan is a precursor for melatonin and serotonin biosynthesis in in vitro regenerated St. John’s wort (Hypericum perforatum L. cv. Anthos) plants. Plant Cell Rep 2000;19:698–704. [19] Zhang M, Guo B, Zhang R, Chi J, Wei Z, Xu Z, et al. Separation, purification and identification of antioxidant compositions in black rice. Agric Sci China 2006;5:431–40. [20] USA RiceÒ Federation. Nutrition facts 2014.
Please cite this article in press as: Setyaningsih W et al. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl Acoust (2015), http://dx.doi.org/10.1016/j.apacoust.2015.04.001