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Optimization of the ultrasound-assisted extraction of tryptophan and its derivatives from rice (Oryza sativa) grains through a response surface methodology
Journal of Cereal Science 75 (2017) 192e197
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Journal of Cereal Science journal homepage: www.elsevier.com/locate/jcs
Optimization of the ultrasound-assisted extraction of tryptophan and its derivatives from rice (Oryza sativa) grains through a response surface methodology Widiastuti Setyaningsih a, b, Irfan E. Saputro b, Miguel Palma b, *, Carmelo G. Barroso b a
Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Jalan Flora, Bulaksumur, 55281, Yogyakarta, Indonesia Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), diz, Spain Campus del Rio San Pedro, 11510, Puerto Real, Ca b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 August 2016 Received in revised form 30 March 2017 Accepted 12 April 2017 Available online 21 April 2017
An analytical ultrasound-assisted extraction (UAE) technique has been optimized and validated for the extraction of tryptophan and its derivatives from rice grains. A BoxeBehnken 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. The most significant (p < 0.0001) effect is the solvent-to-sample ratio while quadratic effects caused by temperature and solvent-to-sample ratio were of moderate importance (p < 0.05). The optimal UAE conditions were as follows: extraction time of 5 min, ultrasound amplitude of 30%, cycle of 0.7 s1, extraction temperature of 30 C, 8% methanol in water as the extraction solvent at pH 3 and a solvent/solid ratio 5:1. The method validation ensured that appropriate values were obtained for the LOD, LOQ, precision and recovery. Furthermore, the method was successfully applied to the analysis of a number of rice samples of different varieties. It was demonstrated that this particular UAE method is an interesting tool for the determination of tryptophan and tryptophan derivatives in rice grain samples. © 2017 Elsevier Ltd. All rights reserved.
Chemical compounds studied in this article: L-tryptophan (PubChem CID: 6305) Tryptamine (PubChem CID: 1150) 5-hydroxy-L-tryptophan (PubChem CID: 439280) Serotonin (PubChem CID: 5202) Indole-3-acetic acid (PubChem CID: 802) Keywords: Ultrasound-assisted extraction Tryptophan UPLC-FD Rice grains
1. Introduction Since the beginning of the 20th century, tryptophan (TRP) has been extensively reviewed over the years due to the diversity of its biological functions (Barker, 1906; Polyzos and Ketelhuth, 2015). It has been acknowledged that tryptophan is converted into numerous compounds of biological significance such as vitamins, neurotransmitters and auxins (Fukuwatari and Shibata, 2013). Among these biogenetically tryptophan-related compounds, indole-3-acetic acid (IAA) and serotonin (5-hydroxytryptamine, 5HTAM) inspired in-depth research on plant morphogenesis and growth regulators as they contribute to the possible coordinated regulation in plants (Pelagio-Flores et al., 2011; Ramakrishna et al.,
* Corresponding author. E-mail address: [email protected] (M. Palma). http://dx.doi.org/10.1016/j.jcs.2017.04.006 0733-5210/© 2017 Elsevier Ltd. All rights reserved.
2011). The relative ratio between IAA and indoleamines, including 5HTAM, is further information that describes the regulation of plant morphogenesis upon the induction of regeneration along with the auxin-to-cytokinin ratio, although the former indicator has proven to be elusive (Murch et al., 2001). In addition to the levels of IAA and 5HTAM, the biosynthesis pathways of these phytohormones have been proposed (Di et al., 2015; Murch et al., 2000) and this provides tools to manipulate their levels in plants with temporal and spatial precision (Zhao, 2010). Hence, the intermediate metabolites, i.e., oxitriptan (5hydroxytryptophan, 5HTRP) and tryptamine (TAM), could potentially interact with the aforementioned primary metabolites of TRP. The ongoing analysis of TRP and compounds derived from it has led to acceptance of the idea that the assessment of an individual substance may not be successful to explain the diverse physiological responses of complex matrices, e.g., rice samples. Furthermore,
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the results of a number of studies seem to suggest that the pathways of IAA and 5HTAM biosynthesis in rice (Ishihara et al., 2008; Kang et al., 2007; Yamamoto et al., 2007), as illustrated in Fig. 1, include TRP, TAM, 5HTAM, 5HTRP and IAA. As a consequence, an accurate and selective method for the simultaneous determination of TRP and its derivatives is required to unscramble the limited physiological data available and the possible role that these compounds play in vegetation. In an effort to achieve this goal, an analytical Ultrasound-Assisted Extraction (UAE) method has been developed to extract TRP and its derivatives from rice (Oryza sativa) grains. The new extraction method is applied prior to ultrahigh performance liquid chromatography (UPLC) coupled with a fluorescence detector (FD). This goal is challenging given the complex chemistry of vegetable tissue, which contains a diverse range of primary and secondary metabolites. The development of an optimal method for the extraction of multiple analytes from a complex matrix presents some difficulties due to the possible rapid interaction of target analytes with other constituents in the matrix. Ultrasound-Assisted Extraction (UAE) is a good method to solve this problem as it can speed-up mass transfer and increase the extraction kinetics (Wu et al., 2001). Furthermore, the ultrasound method is inexpensive and relatively simple to operate in comparison to other novel extraction techniques such as pressurized liquid extraction (PLE) and microwaveassisted extraction (MAE) (Wang and Weller, 2006). Along with Soxhlet extraction, UAE is not limited by the type of matrix and solvent used. The UAE technique is therefore appropriate for the extraction of a wide variety of natural compounds, including tryptophan and its derivatives, from a complex matrix of a biological system, e.g., rice grains. 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 (Pringet et al., 2013; Vinatoru, 2001). Consequently, ultrasonic power is considered to be one of the factors that leads to an
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enhancement of the extraction (Mason, 1996). In addition, several factors govern the efficiency of ultrasound and these include frequency, temperature, type of solvent, and sonication time. Various factors can affect the extraction process and these have to be optimized in order to extract quantitatively the compounds of interest. The chemometric approach based on the advantages of the BoxeBehnken design (BBD) have been successfully applied in the optimization of UAE (Setyaningsih et al., 2016; Tabaraki and Nateghi, 2011). The BBD is compatible with the response surface methodology (RSM) because it allows an estimation of the parameters of the model, the building of sequential designs, the detection of lack of fit of the model and the use of blocks (Ferreira et al., 2007). The particular focus of the study described here was the optimization and validation of the UAE method for the extraction of TRP and its derivatives 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). Standard compounds of the highest available purity were used. Tryptophan (TRP), tryptamine (TAM), oxitriptan or 5-hydroxytryptophan (5HTRP), serotonin or 5-hydroxytryptamine (5HTAM), and indole-3-acetic acid (IAA) were obtained from Sigma Aldrich (St. Louis, MO, USA). Water was purified with a Milli-Q purification system (Billerica, MA, USA). Stock standard solutions of studied compounds were prepared in aqueous methanol 50:50 (v/v) and stored in a freezer at 32 C. 2.2. Rice sample preparation 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 (IKA® 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 from the rice grain was then homogenized by stirring and the sample was stored in a closed container. The final extraction method was applied to basmati and sushi rice varieties taken from different production processes (polished and un-polished grain). 2.3. Ultrasound-assisted extraction (UAE)
Fig. 1. The pathways of IAA and SER biosynthesis in rice plant (Yamamoto et al., 2007; Kang et al., 2007; Ishihara et al., 2008; Kang et al., 2009). (a) tryptophan 5-hydroxylase, (b) tryptophan decarboxylase, (c) tryptamine deaminase, (d) aromatic L-amino acid decarboxylase, (e) tryptamine 5-hydroxylase.
UAE was carried out using a 200 Watts/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 to 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 and 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 mm) prior to injection into a UPLC-FD system.
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2.4. Determination of TRP and TRP-derived compounds ®
Analyses were carried out on an ACQUITY UPLC H-Class system coupled to an ACQUITY UPLC® 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 CORTECS UPLC® column (silica-based solid-core particle; 100 mm length; 2.1 mm I.D.; 1.6 mm particle size, both from Waters Corporation, Ireland) (Setyaningsih et al., 2017). 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): 0e1 min, 0%; 1e1.1 min, 0e10%; 1.1e2 min, 10%; 2e3 min, 10e20%; 3e3.5 min, 20e60%; 3.5e4 min, 60e100%. The column was subsequently 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 300e400 nm for the 3D scan to identify the compound. For compound quantification the emission of the FD detector was set at fixed wavelength for 2D scan at 334.9 nm (5HTAM and 5HTRP) and 345.1 nm (TRP, TAM and IAA). The values were selected based on the maximum absorbance of each compound. 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 mL. 2.5. BoxeBehnken Design (BBD) and statistical analysis RSM was employed to evaluate the factors that affect the recovery 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 (1, 0, 1) was performed. 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. Relative values with respect to the maximum response (%) of the total concentration of the studied compounds were used as the responses. 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
(1)
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 final 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: Table 1 Selected factors and their levels. Factors
1
0
þ1
Unit
x1, x2, x3, x4, x5, x6,
0 10 30 2 3 2.5
25 30 40 4.5 5 3.75
50 50 70 7 7 5
% C % s1 e mL solvent/g sample
methanol in water temperature amplitude cycle pH solvent-solid ratio
y ¼ b0 þ
k X i¼1
bi xi þ
k X
bii x2i þ
i¼1
k1 X k X
bijxi xj þ ε
(2)
i¼1 j¼2
where x1, x2, …, xk are the factors that influence the response y; b0, bii (i ¼ 1, 2, …, 6), b ij (i ¼ 1, 2, …,6; j ¼ 1, 2, …,6) are unknown parameters and ε is a random error. The b coefficients are obtained by the least square regression 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 analysed 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 3.1. Performance of the chromatographic method The total concentrations of TRP, IAA, 5HTAM, 5HTRP and TAM were the responses used for the optimization studies and the determination method for these compounds was validated for quality assurance. The chromatographic analytical procedure used to determine the five studied compounds was carried out according to the ICH Guideline Q2 (R1) and suggestions made in ISO 17025 (ICH, 2005; ISO, 2005). The linearity, range, precision, and detection and quantification limits of the method were evaluated and the results are given in Table 2. Linear ranges were obtained for 1e100 mg L1 and 0.1e4 mg L1 with a coefficient of determination (R2) higher than 0.999 in both cases. Gnumeric 1.12.17 was used to generate the regression analysis for the calibration curves. The standard deviation estimated for the response and the slope from the regression were then used to calculate the limit of detection (LOD) and limit of quantification (LOQ). It was observed that TAM has the highest LOD (3.17 mg L1) and LOQ (9.62 mg L1) while 5HTRP had the lowest limits (2.58 mg L1 and 7.80 mg L1, respectively). These results demonstrate the effective performance of the chromatographic method. The precision of the method was evaluated by performing repeatability (intra-day) and intermediate precision (extra-day) experiments. Repeatability was assessed by nine independent analyses of the same samples on the same day while intermediate precision was determined by three independent analyses on three consecutive days. Both precision indicators are expressed as coefficient of variance (CV) with reference to the retention time and peak area. The acceptable CV limit is ±10% according to the AOAC manual for the Peer-Verified Methods program (AOAC, 2012). The CV values obtained were less than 10% and this confirmed that the method has a high precision. 3.2. Effects of the extraction variables A BoxeBehnken design with 54 runs that included six centre points was constructed to evaluate the effects that six factors related to UAE conditions had on the extraction yield. The factors measured were the composition of the extraction solvent (x1; 0e50% methanol in water), temperature (x2; 10e50 C), amplitude (x3; 30e70%), cycle (x4; 0.2e0.7 s1), solvent pH (x5; 3e7) and solvent-to-sample ratio (x6; 2.5:1e5:1). Values for the extraction variables were established based on previous information found in the revised literature on the extraction of TRP-related compounds (Alcaide-Molina et al., 2013; Cao et al., 2006; Huang and Mazza, 2011; Saracino et al., 2010). Since the variables have different units and ranges, each of the variables was first normalized and
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Table 2 Analytical characteristics for the determination of TRP and its derivatives in the chromatographic system. Compound
5HTAM 5HTRP TRP TAM IAA
Linear Range
1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg
1
L L1 L1 L1 L1 L1 L1 L1 L1 L1
Observations
12 16 12 16 12 16 12 16 12 16
Linear Equation
y y y y y y y y y y
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
2533.5x 1858.2x 2093.5x 2709.7x 884.26x 722.51x 1289.4x 2346.7x 1541.6x 1675.4x
e 388.58 þ 94516 þ 415.61 þ 227123 þ 305.03 þ 61508 e 398.32 þ 31458 e 1217.8 þ 42566
R2
0.9996 0.9991 0.9996 0.9986 0.9996 0.9991 0.9994 0.9992 0.9996 0.9993
LOD (mg L1)
LOQ (mg L1)
Intra-day, CV (%) n ¼ 9
Intra-day, CV (%) n ¼ 3 3
RT
Area
RT
Area
2.79
8.46
0.76
1.61
0.40
3.25
2.58
7.80
1.01
4.15
0.54
2.73
2.79
8.47
0.78
1.31
0.28
4.36
3.17
9.62
0.85
2.26
0.25
2.20
2.65
8.03
0.56
0.69
0.27
0.29
forced to fall in the range from 1 to þ1 in order to obtain a more even response (Bas¸ and Boyacı, 2007). Relative values with respect to the maximum response (%) for the total concentration of the studied compounds were used as the responses. The response for each extraction in the experimental design generated by BBD was calculated 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 ¼ 0.05) in decreasing order of importance are plotted in a Pareto chart (Fig. 2). 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, three effects have p-values below 0.05, indicating that they are significantly different from zero at the 95.0% confidence level. It can also be seen that the most significant (p < 0.0001) main effect is the ratio of solvent to sample (x6) while quadratic effects caused by temperature (x2 x2) and ratio of solvent to sample (x6 x6) were also moderately important (p < 0.05). The ratio of solvent to sample showed a positive effect in that a higher recovery was achieved on increasing the amount of solvent. In contrast, the quadratic temperature had a negative effect as the recovery decreased on increasing this factor. 3.3. Prediction capability of the regression model A regression model was developed and then used to produce a final predictive equation for the studied compounds in rice grain extracts by using significant variables. In order to avoid a high variability it is advisable to keep the number of factors for an optimized design low by disregarding the non-significant factors
Fig. 2. Regression obtained using the relative recoveries from experiments in the BBD (X axis) and predicted relative recoveries from the model (Y axis).
(p > 0.05). The resulting equation for the fitted model is as follows:
y ¼ 0:837 þ 0:153x6 0:151x2 x2 0:059x6 x6
(3)
where y is the extraction yield and xi are the extraction variables (x2, extraction temperature; 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 (0.6212) obtained by ANOVA is greater than 0.05, the model appears to be satisfactory for the observed data at the 95.0% confidence level. Additionally, the R-Squared statistic indicates that the model as fitted explains 88.49% of the variability in the extraction yield. The standard error of the predicted value shows that the standard deviation of the residuals is 0.071. A comparison between the experimental and the predicted values by the model is shown in Fig. 3. It can be seen that a very good agreement between experimental and predicted values was obtained, the resulting slope was for the regression curve was 0.89. Therefore, the model can be used to estimate the response for the purposes of optimization.
3.4. 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
Fig. 3. Pareto chart for the standardized effects.
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surface response and the factors of extraction temperature (x2) and ratio of solvent to sample (x6) were plotted (Fig. 4). As can be observed, a high point was found within the design domain at which the optimum extraction yield (105%) was obtained at coordinates 0.0133286 for extraction temperature and 0.949789 for ratio of solvent to sample. Based on RSM, the optimized extraction of tryptophan and its derived compounds from rice samples using UAE was achieved on applying an extraction temperature of 30 C, an amplitude of 30%, a cycle of 0.7 s1, a pH of 3 and a solvent-to-sample ratio of 5:1, with 8% MeOH in water as the extraction solvent. 3.5. Extraction kinetics The extraction kinetics were studied by analysing the rice sample under the UAE conditions identified as providing the optimum recovery of the target compounds. Extraction time between 5 and 30 min were applied. The results of the kinetic assessment show that recovery of the total target compounds using UAE increased until a plateau was reached at 5 min. 3.6. Method validation The precision of the UAE method was considered at two levels, namely repeatability and intermediate precision. Both levels were evaluated according to ICH guidelines for the UAE. The precision, expressed as CV, of the developed method was in the range from 0.69% (IAA) to 4.15% (5HTRP) for repeatability (n ¼ 9) and 0.29% (IAA) to 4.36% (TRP) for intermediate precision (n ¼ 3 3). These results confirm the reliability of the extraction method, i.e., even with recoveries below 90% the method is able to produce the same extraction yields. 3.7. Application to real samples The optimized and validated UAE method was applied in the extraction of a number of real samples in order to evaluate the applicability of the technique. Three polished rice products (glutinous, sushi and basmati rice) with different amylose contents (0e2%, 10e20% and 15e25%, respectively) were processed using the working conditions of the developed UAE. The levels of TRP and its derivatives in unpolished rice products (whole grain of sushi, basmati and short-grain rice) were also evaluated along with the levels in some pigmented (black and red) rice grains. The results obtained on applying the method to real samples
revealed that the contents of TRP and TRP-derived compounds vary markedly between rice clusters, i.e., polished, whole-grain and pigmented rice. This variation in the studied compounds is reasonable bearing in mind the physical and chemical characteristics of the different types of rice grains and this demonstrates the utility of the developed method for the assessment of rice grains. The results obtained in this study confirmed that the levels of TRP and TRP-derived compounds in whole-grain or unpolished rice samples (11.34e14.18 mg g1) were higher than those in the polished grains (2.27e4.38 mg g1). However, these levels mainly corresponded to the amount of TRP, which contributes 70% of the total TRP and TRP-derived compounds in the grains. The second most abundant compounds after TRP were 5HTAM and IAA, while the levels of their intermediates (TAM and 5HTRP) were the lowest. The contents of TAM and 5HTRP are closely related to 5HTAM synthesis (Fig. 1). The levels of metabolite intermediates were depleted in all types of rice samples tested. The combined levels of TAM and 5HTRP only contributed 2e4.5% of the total studied compounds regardless of the rice varieties. The results of a previous study suggest that TAM is a bottleneck intermediate substrate for 5HTAM synthesis that occurs in rice seedlings (Kang et al., 2007). Therefore, the content of this intermediate was limited by its existence (<2% of total TRP and TRP-derived compounds) in the grain. Nevertheless, in general the concentration of each compound evidently decreased when the grains were subjected to the polishing process. This change is understandable as the rice bran is removed during polishing steps. A relevant study on the nutritional quality of rice bran protein (Han et al., 2015) confirmed that the highest amount of TRP was found in the protein extract of rice bran rather than in the endosperm. Therefore, the bran removal process in polishing may reduce the concentration of TRP in the grain. Apart from TRP, a marked reduction in the concentration (up to 90%) of 5HTAM was observed. The whole grains of sushi and basmati rice were found to contain significant amounts of 5HTAM, at 4.11 ± 0.28 mg g1 and 3.12 ± 0.30 mg g1, respectively, whereas the levels of 5HTAM in polished grains were only 0.28 ± 0.03 mg g1 (sushi) and 0.29 ± 0.01 mg g1 (basmati). Hence, it is suggested that 5HTAM accumulates in the outer part of the rice caryopses, i.e., the bran layers. This result is attributable to the protective role of 5HTAM in plants. This finding is consistent with the results of a previous study, which indicated that the initiation of TRP metabolism is involved in the establishment of effective physical defences by producing 5HTAM (Ishihara et al., 2008) as well as a protective role in place of an antioxidant (Wojtaszek, 2003). 4. Conclusions The ultrasound-assisted extraction method developed in this work for the extraction of tryptophan and compounds derived from it from rice grains was effectively optimized by a BoxeBehnken design in combination with the response surface methodology. The optimum extraction conditions involved the use of 8% MeOH in water at 30 C for 5 min with an amplitude 30%, cycle 0.7 s1, pH 3 and solvent-to-sample ratio of 5:1. The proposed method was validated and it gave satisfactory values for linearity and precision. 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 rapid technique for the determination of tryptophan and compounds derived from it in rice grains. Acknowledgements
Fig. 4. Response surface plot showing the effects of variables (x2, extraction temperature; x6, ratio of solvent to sample) on the extraction yield.
I.E. Saputro thanks the Education, Audiovisual and Culture
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Executive Agency (EACEA) for a fellowship. Thanks are also due to STATGRAPHICS for permission to use the trial version of the Centurion XVI Trial Software. References Alcaide-Molina, M., Priego-Capote, F., De Castro, M.D.L., 2013. Sequential determination of metabolites involved in the biosynthesis of aromatic amino acids after ultrasound-assisted extraction from plants and reverse LC separation. Talanta 105, 429e434. http://dx.doi.org/10.1016/j.talanta.2012.10.077. AOAC, 2012. AOAC Peer Verified Methods Program, Manual on Policies and Procedures. AOAC International, Maryland, USA, USA. Barker, L.F., 1906. Amino-Acids and metabolism. Br. Med. J. 2, 1093e1100. _ Bas¸, D., Boyacı, I.H., 2007. Modeling and optimization I: usability of response surface methodology. J. Food Eng. 78, 836e845. Cao, J., Murch, S.J., O’Brien, R., Saxena, P.K., 2006. Rapid method for accurate analysis of melatonin, serotonin and auxin in plant samples using liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 1134, 333e337. http://dx.doi.org/10.1016/j.chroma.2006.09.079. Di, D.-W., Zhang, C., Luo, P., An, C.-W., Guo, G.-Q., 2015. The biosynthesis of auxin: how many paths truly lead to IAA? Plant Growth Regul. http://dx.doi.org/ 10.1007/s10725-015-0103-5. ~o, G.C., da Ferreira, S.L.C., Bruns, R.E., Ferreira, H.S., Matos, G.D., David, J.M., Branda Silva, E.G.P., Portugal, L. a, dos Reis, P.S., Souza, a S., dos Santos, W.N.L., 2007. Box-Behnken design: an alternative for the optimization of analytical methods. Anal. Chim. Acta 597, 179e186. Fukuwatari, T., Shibata, K., 2013. Nutritional aspect of tryptophan metabolism. Int. J. Tryptophan Res. 6, 3e8. http://dx.doi.org/10.4137/IJTR.S11588. Han, S.-W., Chee, K.-M., Cho, S.-J., 2015. Nutritional quality of rice bran protein in comparison to animal and vegetable protein. Food Chem. 172, 766e769. http:// dx.doi.org/10.1016/j.foodchem.2014.09.127. Huang, X., Mazza, G., 2011. Simultaneous analysis of serotonin, melatonin, piceid and resveratrol in fruits using liquid chromatography tandem mass spectrometry. J. Chromatogr. A 1218, 3890e3899. http://dx.doi.org/10.1016/ j.chroma.2011.04.049. ICH, 2005. ICH Topic Q2 (R1) Validation of Analytical Procedures. Text and Methodology, Geneva. Ishihara, A., Hashimoto, Y., Tanaka, C., Dubouzet, J.G., Nakao, T., Matsuda, F., Nishioka, T., Miyagawa, H., Wakasa, K., 2008. The tryptophan pathway is involved in the defense responses of rice against pathogenic infection via serotonin production. Plant J. 54, 481e495. http://dx.doi.org/10.1111/j.1365313X.2008.03441.x. ISO, 2005. ISO/IEC 17025:2005 General Requirements for the Competence of Testing and Calibration Laboratories. Switzerland. Kang, S., Kang, K., Lee, K., Back, K., 2007. Characterization of tryptamine 5hydroxylase and serotonin synthesis in rice plants. Plant Cell Rep. 26, 2009e2015. http://dx.doi.org/10.1007/s00299-007-0405-9. Kang, K., Kim, Y.-S., Park, S., Back, K., 2009. Senescence-induced serotonin biosynthesis and its role in delaying senescence in rice leaves. Plant Physiol. 150, 1380e1393. http://dx.doi.org/10.1104/pp.109.138552. Mason, T., 1996. The uses of ultrasound in food technology. Ultrason. Sonochem. 3, S253eS260. http://dx.doi.org/10.1016/S1350-4177(96)00034-X.
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Murch, S.J., Campbell, S.S.B., Saxena, P.K., 2001. The role of serotonin and melatonin in plant morphogenesis: regulation of auxin-induced root organogenesis in in vitro-cultured explants of st. John’s Wort (Hypericum perforatum L.). Vitr. Cell. Dev. Biol. Plant 37, 786e793. http://dx.doi.org/10.1007/s11627-001-0130-y. Murch, S.J., KrishnaRaj, S., Saxena, P.K., 2000. 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. 19, 698e704. http:// dx.doi.org/10.1007/s002990000206. ndez-Bravo, A., MacIas-Rodríguez, L., Lo pezPelagio-Flores, R., Ortíz-Castro, R., Me Bucio, J., 2011. Serotonin, a tryptophan-derived signal conserved in plants and animals, regulates root system architecture probably acting as a natural auxin inhibitor in arabidopsis thaliana. Plant Cell Physiol. 52, 490e508. http:// dx.doi.org/10.1093/pcp/pcr006. Polyzos, K.A., Ketelhuth, D.F.J., 2015. The role of the kynurenine pathway of tryptophan metabolism in cardiovascular disease. An emerging field. Hamostaseologie 35, 128e136. http://dx.doi.org/10.5482/HAMO-14-10-0052. Pringet, D., Fabiano-Tixier, A.-S., Chemat, F., 2013. Ultrasound-assisted extraction. In: Rostagno, M.A., Prado, J.M. (Eds.), Natural Product Extraction: Principles and Applications. Royal Society of Chemistry, Cambridge,UK, pp. 89e112. Ramakrishna, A., Giridhar, P., Ravishankar, G.A., 2011. Phytoserotonin: a review. Plant Signal. Behav. 6, 800e809. Saracino, M.A., Gerra, G., Somaini, L., Colombati, M., Raggi, M.A., 2010. Chromatographic analysis of serotonin, 5-hydroxyindolacetic acid and homovanillic acid in dried blood spots and platelet poor and rich plasma samples. J. Chromatogr. A 1217, 4808e4814. http://dx.doi.org/10.1016/j.chroma.2010.05.031. Setyaningsih, W., Duros, E., Palma, M., Barroso, C.G., 2016. Optimization of the ultrasound-assisted extraction of melatonin from red rice (Oryza sativa) grains through a response surface methodology. Appl. Acoust. 103, 129e135. http:// dx.doi.org/10.1016/j.apacoust.2015.04.001. Setyaningsih, W., Saputro, I.E., Carrera, C.A., Palma, M., Barroso, C.G., 2017. Multiresponse optimization of a UPLC method for the simultaneous determination of tryptophan and 15 tryptophan-derived compounds using a Box-Behnken design with a desirability function. Food Chem. 225, 1e9. http://dx.doi.org/ 10.1016/j.foodchem.2016.12.034. Tabaraki, R., Nateghi, A., 2011. Optimization of ultrasonic-assisted extraction of natural antioxidants from rice bran using response surface methodology. Ultrason. Sonochem. 18, 1279e1286. http://dx.doi.org/10.1016/j.ultsonch. 2011.05.004. Vinatoru, M., 2001. An overview of the ultrasonically assisted extraction of bioactive principles from herbs. Ultrason. Sonochem. 8, 303e313. Wang, L., Weller, C.L., 2006. Recent advances in extraction of nutraceuticals from plants. Trends Food Sci. Technol. 17, 300e312. Wojtaszek, P., 2003. Roshchina, V.V. Neurotransmitters in plant life. Ann. Bot. 92 http://dx.doi.org/10.1093/aob/mcg113, 166e166. Wu, J., Lin, L., Chau, F.T., 2001. Ultrasound-assisted extraction of ginseng saponins from ginseng roots and cultured ginseng cells. Ultrason. Sonochem. 8, 347e352. Yamamoto, Y., Kamiya, N., Morinaka, Y., Matsuoka, M., Sazuka, T., 2007. Auxin biosynthesis by the YUCCA genes in rice. Plant Physiol. 143, 1362e1371. http:// dx.doi.org/10.1104/pp.106.091561. Zhao, Y., 2010. Auxin biosynthesis and its role in plant development. Annu. Rev. Plant Biol. 61, 49e64. http://dx.doi.org/10.1146/annurev-arplant-042809112308.
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Optimization of the ultrasound-assisted extraction of tryptophan and its derivatives from rice (Oryza sativa) grains through a response surface methodology Widiastuti Setyaningsih a, b, Irfan E. Saputro b, Miguel Palma b, *, Carmelo G. Barroso b a
Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Jalan Flora, Bulaksumur, 55281, Yogyakarta, Indonesia Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), diz, Spain Campus del Rio San Pedro, 11510, Puerto Real, Ca b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 26 August 2016 Received in revised form 30 March 2017 Accepted 12 April 2017 Available online 21 April 2017
An analytical ultrasound-assisted extraction (UAE) technique has been optimized and validated for the extraction of tryptophan and its derivatives from rice grains. A BoxeBehnken 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. The most significant (p < 0.0001) effect is the solvent-to-sample ratio while quadratic effects caused by temperature and solvent-to-sample ratio were of moderate importance (p < 0.05). The optimal UAE conditions were as follows: extraction time of 5 min, ultrasound amplitude of 30%, cycle of 0.7 s1, extraction temperature of 30 C, 8% methanol in water as the extraction solvent at pH 3 and a solvent/solid ratio 5:1. The method validation ensured that appropriate values were obtained for the LOD, LOQ, precision and recovery. Furthermore, the method was successfully applied to the analysis of a number of rice samples of different varieties. It was demonstrated that this particular UAE method is an interesting tool for the determination of tryptophan and tryptophan derivatives in rice grain samples. © 2017 Elsevier Ltd. All rights reserved.
Chemical compounds studied in this article: L-tryptophan (PubChem CID: 6305) Tryptamine (PubChem CID: 1150) 5-hydroxy-L-tryptophan (PubChem CID: 439280) Serotonin (PubChem CID: 5202) Indole-3-acetic acid (PubChem CID: 802) Keywords: Ultrasound-assisted extraction Tryptophan UPLC-FD Rice grains
1. Introduction Since the beginning of the 20th century, tryptophan (TRP) has been extensively reviewed over the years due to the diversity of its biological functions (Barker, 1906; Polyzos and Ketelhuth, 2015). It has been acknowledged that tryptophan is converted into numerous compounds of biological significance such as vitamins, neurotransmitters and auxins (Fukuwatari and Shibata, 2013). Among these biogenetically tryptophan-related compounds, indole-3-acetic acid (IAA) and serotonin (5-hydroxytryptamine, 5HTAM) inspired in-depth research on plant morphogenesis and growth regulators as they contribute to the possible coordinated regulation in plants (Pelagio-Flores et al., 2011; Ramakrishna et al.,
* Corresponding author. E-mail address: [email protected] (M. Palma). http://dx.doi.org/10.1016/j.jcs.2017.04.006 0733-5210/© 2017 Elsevier Ltd. All rights reserved.
2011). The relative ratio between IAA and indoleamines, including 5HTAM, is further information that describes the regulation of plant morphogenesis upon the induction of regeneration along with the auxin-to-cytokinin ratio, although the former indicator has proven to be elusive (Murch et al., 2001). In addition to the levels of IAA and 5HTAM, the biosynthesis pathways of these phytohormones have been proposed (Di et al., 2015; Murch et al., 2000) and this provides tools to manipulate their levels in plants with temporal and spatial precision (Zhao, 2010). Hence, the intermediate metabolites, i.e., oxitriptan (5hydroxytryptophan, 5HTRP) and tryptamine (TAM), could potentially interact with the aforementioned primary metabolites of TRP. The ongoing analysis of TRP and compounds derived from it has led to acceptance of the idea that the assessment of an individual substance may not be successful to explain the diverse physiological responses of complex matrices, e.g., rice samples. Furthermore,
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the results of a number of studies seem to suggest that the pathways of IAA and 5HTAM biosynthesis in rice (Ishihara et al., 2008; Kang et al., 2007; Yamamoto et al., 2007), as illustrated in Fig. 1, include TRP, TAM, 5HTAM, 5HTRP and IAA. As a consequence, an accurate and selective method for the simultaneous determination of TRP and its derivatives is required to unscramble the limited physiological data available and the possible role that these compounds play in vegetation. In an effort to achieve this goal, an analytical Ultrasound-Assisted Extraction (UAE) method has been developed to extract TRP and its derivatives from rice (Oryza sativa) grains. The new extraction method is applied prior to ultrahigh performance liquid chromatography (UPLC) coupled with a fluorescence detector (FD). This goal is challenging given the complex chemistry of vegetable tissue, which contains a diverse range of primary and secondary metabolites. The development of an optimal method for the extraction of multiple analytes from a complex matrix presents some difficulties due to the possible rapid interaction of target analytes with other constituents in the matrix. Ultrasound-Assisted Extraction (UAE) is a good method to solve this problem as it can speed-up mass transfer and increase the extraction kinetics (Wu et al., 2001). Furthermore, the ultrasound method is inexpensive and relatively simple to operate in comparison to other novel extraction techniques such as pressurized liquid extraction (PLE) and microwaveassisted extraction (MAE) (Wang and Weller, 2006). Along with Soxhlet extraction, UAE is not limited by the type of matrix and solvent used. The UAE technique is therefore appropriate for the extraction of a wide variety of natural compounds, including tryptophan and its derivatives, from a complex matrix of a biological system, e.g., rice grains. 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 (Pringet et al., 2013; Vinatoru, 2001). Consequently, ultrasonic power is considered to be one of the factors that leads to an
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enhancement of the extraction (Mason, 1996). In addition, several factors govern the efficiency of ultrasound and these include frequency, temperature, type of solvent, and sonication time. Various factors can affect the extraction process and these have to be optimized in order to extract quantitatively the compounds of interest. The chemometric approach based on the advantages of the BoxeBehnken design (BBD) have been successfully applied in the optimization of UAE (Setyaningsih et al., 2016; Tabaraki and Nateghi, 2011). The BBD is compatible with the response surface methodology (RSM) because it allows an estimation of the parameters of the model, the building of sequential designs, the detection of lack of fit of the model and the use of blocks (Ferreira et al., 2007). The particular focus of the study described here was the optimization and validation of the UAE method for the extraction of TRP and its derivatives 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). Standard compounds of the highest available purity were used. Tryptophan (TRP), tryptamine (TAM), oxitriptan or 5-hydroxytryptophan (5HTRP), serotonin or 5-hydroxytryptamine (5HTAM), and indole-3-acetic acid (IAA) were obtained from Sigma Aldrich (St. Louis, MO, USA). Water was purified with a Milli-Q purification system (Billerica, MA, USA). Stock standard solutions of studied compounds were prepared in aqueous methanol 50:50 (v/v) and stored in a freezer at 32 C. 2.2. Rice sample preparation 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 (IKA® 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 from the rice grain was then homogenized by stirring and the sample was stored in a closed container. The final extraction method was applied to basmati and sushi rice varieties taken from different production processes (polished and un-polished grain). 2.3. Ultrasound-assisted extraction (UAE)
Fig. 1. The pathways of IAA and SER biosynthesis in rice plant (Yamamoto et al., 2007; Kang et al., 2007; Ishihara et al., 2008; Kang et al., 2009). (a) tryptophan 5-hydroxylase, (b) tryptophan decarboxylase, (c) tryptamine deaminase, (d) aromatic L-amino acid decarboxylase, (e) tryptamine 5-hydroxylase.
UAE was carried out using a 200 Watts/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 to 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 and 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 mm) prior to injection into a UPLC-FD system.
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2.4. Determination of TRP and TRP-derived compounds ®
Analyses were carried out on an ACQUITY UPLC H-Class system coupled to an ACQUITY UPLC® 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 CORTECS UPLC® column (silica-based solid-core particle; 100 mm length; 2.1 mm I.D.; 1.6 mm particle size, both from Waters Corporation, Ireland) (Setyaningsih et al., 2017). 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): 0e1 min, 0%; 1e1.1 min, 0e10%; 1.1e2 min, 10%; 2e3 min, 10e20%; 3e3.5 min, 20e60%; 3.5e4 min, 60e100%. The column was subsequently 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 300e400 nm for the 3D scan to identify the compound. For compound quantification the emission of the FD detector was set at fixed wavelength for 2D scan at 334.9 nm (5HTAM and 5HTRP) and 345.1 nm (TRP, TAM and IAA). The values were selected based on the maximum absorbance of each compound. 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 mL. 2.5. BoxeBehnken Design (BBD) and statistical analysis RSM was employed to evaluate the factors that affect the recovery 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 (1, 0, 1) was performed. 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. Relative values with respect to the maximum response (%) of the total concentration of the studied compounds were used as the responses. 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
(1)
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 final 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: Table 1 Selected factors and their levels. Factors
1
0
þ1
Unit
x1, x2, x3, x4, x5, x6,
0 10 30 2 3 2.5
25 30 40 4.5 5 3.75
50 50 70 7 7 5
% C % s1 e mL solvent/g sample
methanol in water temperature amplitude cycle pH solvent-solid ratio
y ¼ b0 þ
k X i¼1
bi xi þ
k X
bii x2i þ
i¼1
k1 X k X
bijxi xj þ ε
(2)
i¼1 j¼2
where x1, x2, …, xk are the factors that influence the response y; b0, bii (i ¼ 1, 2, …, 6), b ij (i ¼ 1, 2, …,6; j ¼ 1, 2, …,6) are unknown parameters and ε is a random error. The b coefficients are obtained by the least square regression 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 analysed 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 3.1. Performance of the chromatographic method The total concentrations of TRP, IAA, 5HTAM, 5HTRP and TAM were the responses used for the optimization studies and the determination method for these compounds was validated for quality assurance. The chromatographic analytical procedure used to determine the five studied compounds was carried out according to the ICH Guideline Q2 (R1) and suggestions made in ISO 17025 (ICH, 2005; ISO, 2005). The linearity, range, precision, and detection and quantification limits of the method were evaluated and the results are given in Table 2. Linear ranges were obtained for 1e100 mg L1 and 0.1e4 mg L1 with a coefficient of determination (R2) higher than 0.999 in both cases. Gnumeric 1.12.17 was used to generate the regression analysis for the calibration curves. The standard deviation estimated for the response and the slope from the regression were then used to calculate the limit of detection (LOD) and limit of quantification (LOQ). It was observed that TAM has the highest LOD (3.17 mg L1) and LOQ (9.62 mg L1) while 5HTRP had the lowest limits (2.58 mg L1 and 7.80 mg L1, respectively). These results demonstrate the effective performance of the chromatographic method. The precision of the method was evaluated by performing repeatability (intra-day) and intermediate precision (extra-day) experiments. Repeatability was assessed by nine independent analyses of the same samples on the same day while intermediate precision was determined by three independent analyses on three consecutive days. Both precision indicators are expressed as coefficient of variance (CV) with reference to the retention time and peak area. The acceptable CV limit is ±10% according to the AOAC manual for the Peer-Verified Methods program (AOAC, 2012). The CV values obtained were less than 10% and this confirmed that the method has a high precision. 3.2. Effects of the extraction variables A BoxeBehnken design with 54 runs that included six centre points was constructed to evaluate the effects that six factors related to UAE conditions had on the extraction yield. The factors measured were the composition of the extraction solvent (x1; 0e50% methanol in water), temperature (x2; 10e50 C), amplitude (x3; 30e70%), cycle (x4; 0.2e0.7 s1), solvent pH (x5; 3e7) and solvent-to-sample ratio (x6; 2.5:1e5:1). Values for the extraction variables were established based on previous information found in the revised literature on the extraction of TRP-related compounds (Alcaide-Molina et al., 2013; Cao et al., 2006; Huang and Mazza, 2011; Saracino et al., 2010). Since the variables have different units and ranges, each of the variables was first normalized and
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Table 2 Analytical characteristics for the determination of TRP and its derivatives in the chromatographic system. Compound
5HTAM 5HTRP TRP TAM IAA
Linear Range
1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg 1e100 mg 0.1e4 mg
1
L L1 L1 L1 L1 L1 L1 L1 L1 L1
Observations
12 16 12 16 12 16 12 16 12 16
Linear Equation
y y y y y y y y y y
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
2533.5x 1858.2x 2093.5x 2709.7x 884.26x 722.51x 1289.4x 2346.7x 1541.6x 1675.4x
e 388.58 þ 94516 þ 415.61 þ 227123 þ 305.03 þ 61508 e 398.32 þ 31458 e 1217.8 þ 42566
R2
0.9996 0.9991 0.9996 0.9986 0.9996 0.9991 0.9994 0.9992 0.9996 0.9993
LOD (mg L1)
LOQ (mg L1)
Intra-day, CV (%) n ¼ 9
Intra-day, CV (%) n ¼ 3 3
RT
Area
RT
Area
2.79
8.46
0.76
1.61
0.40
3.25
2.58
7.80
1.01
4.15
0.54
2.73
2.79
8.47
0.78
1.31
0.28
4.36
3.17
9.62
0.85
2.26
0.25
2.20
2.65
8.03
0.56
0.69
0.27
0.29
forced to fall in the range from 1 to þ1 in order to obtain a more even response (Bas¸ and Boyacı, 2007). Relative values with respect to the maximum response (%) for the total concentration of the studied compounds were used as the responses. The response for each extraction in the experimental design generated by BBD was calculated 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 ¼ 0.05) in decreasing order of importance are plotted in a Pareto chart (Fig. 2). 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, three effects have p-values below 0.05, indicating that they are significantly different from zero at the 95.0% confidence level. It can also be seen that the most significant (p < 0.0001) main effect is the ratio of solvent to sample (x6) while quadratic effects caused by temperature (x2 x2) and ratio of solvent to sample (x6 x6) were also moderately important (p < 0.05). The ratio of solvent to sample showed a positive effect in that a higher recovery was achieved on increasing the amount of solvent. In contrast, the quadratic temperature had a negative effect as the recovery decreased on increasing this factor. 3.3. Prediction capability of the regression model A regression model was developed and then used to produce a final predictive equation for the studied compounds in rice grain extracts by using significant variables. In order to avoid a high variability it is advisable to keep the number of factors for an optimized design low by disregarding the non-significant factors
Fig. 2. Regression obtained using the relative recoveries from experiments in the BBD (X axis) and predicted relative recoveries from the model (Y axis).
(p > 0.05). The resulting equation for the fitted model is as follows:
y ¼ 0:837 þ 0:153x6 0:151x2 x2 0:059x6 x6
(3)
where y is the extraction yield and xi are the extraction variables (x2, extraction temperature; 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 (0.6212) obtained by ANOVA is greater than 0.05, the model appears to be satisfactory for the observed data at the 95.0% confidence level. Additionally, the R-Squared statistic indicates that the model as fitted explains 88.49% of the variability in the extraction yield. The standard error of the predicted value shows that the standard deviation of the residuals is 0.071. A comparison between the experimental and the predicted values by the model is shown in Fig. 3. It can be seen that a very good agreement between experimental and predicted values was obtained, the resulting slope was for the regression curve was 0.89. Therefore, the model can be used to estimate the response for the purposes of optimization.
3.4. 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
Fig. 3. Pareto chart for the standardized effects.
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surface response and the factors of extraction temperature (x2) and ratio of solvent to sample (x6) were plotted (Fig. 4). As can be observed, a high point was found within the design domain at which the optimum extraction yield (105%) was obtained at coordinates 0.0133286 for extraction temperature and 0.949789 for ratio of solvent to sample. Based on RSM, the optimized extraction of tryptophan and its derived compounds from rice samples using UAE was achieved on applying an extraction temperature of 30 C, an amplitude of 30%, a cycle of 0.7 s1, a pH of 3 and a solvent-to-sample ratio of 5:1, with 8% MeOH in water as the extraction solvent. 3.5. Extraction kinetics The extraction kinetics were studied by analysing the rice sample under the UAE conditions identified as providing the optimum recovery of the target compounds. Extraction time between 5 and 30 min were applied. The results of the kinetic assessment show that recovery of the total target compounds using UAE increased until a plateau was reached at 5 min. 3.6. Method validation The precision of the UAE method was considered at two levels, namely repeatability and intermediate precision. Both levels were evaluated according to ICH guidelines for the UAE. The precision, expressed as CV, of the developed method was in the range from 0.69% (IAA) to 4.15% (5HTRP) for repeatability (n ¼ 9) and 0.29% (IAA) to 4.36% (TRP) for intermediate precision (n ¼ 3 3). These results confirm the reliability of the extraction method, i.e., even with recoveries below 90% the method is able to produce the same extraction yields. 3.7. Application to real samples The optimized and validated UAE method was applied in the extraction of a number of real samples in order to evaluate the applicability of the technique. Three polished rice products (glutinous, sushi and basmati rice) with different amylose contents (0e2%, 10e20% and 15e25%, respectively) were processed using the working conditions of the developed UAE. The levels of TRP and its derivatives in unpolished rice products (whole grain of sushi, basmati and short-grain rice) were also evaluated along with the levels in some pigmented (black and red) rice grains. The results obtained on applying the method to real samples
revealed that the contents of TRP and TRP-derived compounds vary markedly between rice clusters, i.e., polished, whole-grain and pigmented rice. This variation in the studied compounds is reasonable bearing in mind the physical and chemical characteristics of the different types of rice grains and this demonstrates the utility of the developed method for the assessment of rice grains. The results obtained in this study confirmed that the levels of TRP and TRP-derived compounds in whole-grain or unpolished rice samples (11.34e14.18 mg g1) were higher than those in the polished grains (2.27e4.38 mg g1). However, these levels mainly corresponded to the amount of TRP, which contributes 70% of the total TRP and TRP-derived compounds in the grains. The second most abundant compounds after TRP were 5HTAM and IAA, while the levels of their intermediates (TAM and 5HTRP) were the lowest. The contents of TAM and 5HTRP are closely related to 5HTAM synthesis (Fig. 1). The levels of metabolite intermediates were depleted in all types of rice samples tested. The combined levels of TAM and 5HTRP only contributed 2e4.5% of the total studied compounds regardless of the rice varieties. The results of a previous study suggest that TAM is a bottleneck intermediate substrate for 5HTAM synthesis that occurs in rice seedlings (Kang et al., 2007). Therefore, the content of this intermediate was limited by its existence (<2% of total TRP and TRP-derived compounds) in the grain. Nevertheless, in general the concentration of each compound evidently decreased when the grains were subjected to the polishing process. This change is understandable as the rice bran is removed during polishing steps. A relevant study on the nutritional quality of rice bran protein (Han et al., 2015) confirmed that the highest amount of TRP was found in the protein extract of rice bran rather than in the endosperm. Therefore, the bran removal process in polishing may reduce the concentration of TRP in the grain. Apart from TRP, a marked reduction in the concentration (up to 90%) of 5HTAM was observed. The whole grains of sushi and basmati rice were found to contain significant amounts of 5HTAM, at 4.11 ± 0.28 mg g1 and 3.12 ± 0.30 mg g1, respectively, whereas the levels of 5HTAM in polished grains were only 0.28 ± 0.03 mg g1 (sushi) and 0.29 ± 0.01 mg g1 (basmati). Hence, it is suggested that 5HTAM accumulates in the outer part of the rice caryopses, i.e., the bran layers. This result is attributable to the protective role of 5HTAM in plants. This finding is consistent with the results of a previous study, which indicated that the initiation of TRP metabolism is involved in the establishment of effective physical defences by producing 5HTAM (Ishihara et al., 2008) as well as a protective role in place of an antioxidant (Wojtaszek, 2003). 4. Conclusions The ultrasound-assisted extraction method developed in this work for the extraction of tryptophan and compounds derived from it from rice grains was effectively optimized by a BoxeBehnken design in combination with the response surface methodology. The optimum extraction conditions involved the use of 8% MeOH in water at 30 C for 5 min with an amplitude 30%, cycle 0.7 s1, pH 3 and solvent-to-sample ratio of 5:1. The proposed method was validated and it gave satisfactory values for linearity and precision. 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 rapid technique for the determination of tryptophan and compounds derived from it in rice grains. Acknowledgements
Fig. 4. Response surface plot showing the effects of variables (x2, extraction temperature; x6, ratio of solvent to sample) on the extraction yield.
I.E. Saputro thanks the Education, Audiovisual and Culture
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