Pharmacognosy Magazine

ORIGINAL ARTICLE
Year
: 2018  |  Volume : 14  |  Issue : 54  |  Page : 155--161

Optimization of extraction conditions for phenolic acids from the leaves of Melissa officinalis L. using response surface methodology


Guijae Yoo1, Il Kyun Lee2, Seonju Park1, Nanyoung Kim1, Jun Hyung Park1, Seung Hyun Kim1,  
1 College of Pharmacy, Yonsei Institute of Pharmaceutical Science, Yonsei University, Incheon, Korea
2 Research Center Natural Medicine Research Team, Richwood Pharmaceutical Company Ltd., Seoul, Korea

Correspondence Address:
Seung Hyun Kim
College of Pharmacy, Yonsei University, 162-1, Songdo-dong, Yeonsu-gu, Incheon 406-840
Korea

Abstract

Background: Melissa officinalis L. is a well-known medicinal plant from the family Lamiaceae, which is distributed throughout Eastern Mediterranean region and Western Asia. Objective: In this study, response surface methodology (RSM) was utilized to optimize the extraction conditions for bioactive compounds from the leaves of M. officinalis L. Materials and Methods: A Box–Behnken design (BBD) was utilized to evaluate the effects of three independent variables, namely extraction temperature (°C), methanol concentration (%), and solvent-to-material ratio (mL/g) on the responses of the contents of caffeic acid and rosmarinic acid. Results: Regression analysis showed a good fit of the experimental data. The optimal condition was obtained at extraction temperature 80.53°C, methanol concentration 29.89%, and solvent-to-material ratio 30 mL/g. Conclusion: These results indicate the suitability of the model employed and the successful application of RSM in optimizing the extraction conditions. This study may be useful for standardizing production quality, including improving the efficiency of large-scale extraction systems. Abbreviations used: RSM: Response surface methodology, BBD: Box–Behnken design, CA: Caffeic acid, RA: Rosmarinic acid, HPLC: High-performance liquid chromatography.



How to cite this article:
Yoo G, Lee IK, Park S, Kim N, Park JH, Kim SH. Optimization of extraction conditions for phenolic acids from the leaves of Melissa officinalis L. using response surface methodology.Phcog Mag 2018;14:155-161


How to cite this URL:
Yoo G, Lee IK, Park S, Kim N, Park JH, Kim SH. Optimization of extraction conditions for phenolic acids from the leaves of Melissa officinalis L. using response surface methodology. Phcog Mag [serial online] 2018 [cited 2019 Aug 22 ];14:155-161
Available from: http://www.phcog.com/text.asp?2018/14/54/155/229695


Full Text



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SUMMARY

The optimum conditions for the extraction of major phenolic acids from the leaves of Melissa officinalis L. were determined using response surface methodologyBox–Behnken design was utilized to evaluate the effects of three independent variablesQuadratic polynomial model provided a satisfactory description of the experimental dataThe optimized condition for simultaneous maximum contents of caffeic acid and rosmarinic acid was determined.

 Introduction



Melissa officinalis L. (lemon balm) is a perennial herb that belongs to the family Lamiaceae and is found throughout the Eastern Mediterranean region and Western Asia.[1],[2] Therapeutic properties of M. officinalis L. are anti-inflammatory,[3] hepatoprotective,[4],[5] antioxidant,[6],[7],[8],[9] sedative,[10] anxiolytic,[11] antifungal, anti-bacterial,[12],[13] antiviral,[14] antilipidemic, antihistaminic,[15] and antispasmolytic [16] activities, mainly due to the content of essential oils and phenolic acids.[17] In particular, leaves of M. officinalis L. have been used as a medicinal plant for the treatment of headaches, rheumatism, and gastrointestinal disease.[18] In the leaves of M. officinalis L., caffeic acid (CA) and rosmarinic acid (RA) are the representative phenolic acids.[19] CA has antitumor [20],[21] and antifungal activities,[22] while RA has antioxidant,[23],[24] anti-allergic, and immunosuppressive effects.[25],[26] Therefore, the biological activities of phenolic acids from M. officinalis L. have recently been the subject of many studies.[27],[28],[29]

Extraction is the first important step to recover bioactive compounds from plants for natural product research.[30] Many factors such as extraction temperature,[31] solvent-to-material ratio,[32] extraction time, solvent composition, and extraction pressure,[33] among others, may significantly influence the extraction efficacy. In general, optimization of a process can be determined by an empirical or statistical method. However, empirical methods have the limitations in perfect optimization.[34] Response surface methodology (RSM) is a powerful tool for improving and optimizing extraction process variables.[34],[35],[36]

 Materials and Methods



Plant materials

The dried leaves of M. officinalis L. were kindly supplied by Richwood Pharmaceutical Co. Inc. (Seoul, Korea) that also provided a certificate of identity and quality. A voucher specimen (YIPS-MO-160518) was deposited at the Herbarium of College of Pharmacy, Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Korea.

Reagents and apparatus

All organic solvents, such as hexane, chloroform, ethyl acetate, methanol, and n-butanol, used for extraction and chromatography were of analytical grade and were purchased from Daesan Chemical (Gyeonggi, Korea). Thin layer chromatography was performed on a precoated silica gel 60 F254 (Merck, NJ, USA). high-performance liquid chromatography (HPLC) was carried out using an Agilent 1260 HPLC system.

High-performance liquid chromatography analysis

Chromatographic separation was carried out on an YMC hydrosphere C18 column (250 mm × 4.6 mm, 5 μm) equipped with a C18 guard column. The mobile phase consisted of acetonitrile (A) and 0.1% phosphoric acid (v/v) (B) at a flow rate of 0.1 mL/min. Analysis was performed using a linear gradient program at the following conditions: 0 min, 10% A and 90% B and 25 min, 90% A and 10% B. Then, the column was reconditioned with 90% B for 5 min. The column temperature was set at 40°C, and the analysis was monitored at 320 nm. Aliquots of 10 μL were injected into the HPLC. Standard working solutions were prepared by serial dilutions of 1.0, 0.5, 0.25, 0.125, and 0.0625 mg/mL and used for calibration curve. Good linearity of calibration curve for CA and RA was achieved with correlation coefficient of 0.999. The powdered leaves of M. officinalis L. (1.00 g) were precisely weighed and extracted with methanol solvent as indicated. Each sample solution was filtered through 0.2 mm membrane filter before being subject to HPLC analysis.

Experimental design

An optimization of extraction conditions for the extraction of CA and RA from the leaves of M. officinalis L. was conducted using RSM. Before conducting the RSM, the levels of RSM-independent variables for phenolic acid extraction were determined based on the preliminary experiments. In brief, 15 experimental runs were conducted with three independent variables, and three levels were developed according to the Box–Behnken design (BBD) as shown in [Table 1]. BBD has been widely used to experiment with the designing that evaluates nonlinear relationships between response values and factors. Compared with other designs, it has the advantage of reducing the number of experiments.[37] The independent variables were extraction temperature (X1, °C), methanol concentration (X2, %, v/v, methanol/water), and solvent-to-material ratio (X3, mL/g), while the response variable was the amount of phenolic acids from the leaves of M. officinalis L.{Table 1}

Statistical analysis

Data were analyzed using the Design Expert 7.0 (Stat-Ease Inc., MN, USA) statistical software. The experimental data were applied to the quadratic polynomial model through which the regression coefficients were obtained. The generalized quadratic polynomial model used for the response surface analysis is shown by the following equation (1):

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where X i and X j values are independent variables which influence the responses Y; β 0, β i, β ii, and β ij values are the coefficients of regression model for intercept, linear, quadratic, and interaction terms, respectively; and k is the number of variables.

Verification of model

To verify the predictive value of the model, the optimum conditions with the maximum yield for phenolic acid were determined and used in the extraction test. The precision of the fitted model was verified by comparing the predicted value of the experimental value obtained from the three replicates.

 Results and Discussion



Model fitting

Optimization of the extraction condition was performed by BBD. According to the BBD design, 15 experiments were performed. Extraction temperature (°C), methanol concentration (%), and solvent-to-material ratio (mL/g) were selected as three variables that could potentially affect the contents of CA and RA. Quantitation of CA and RA was performed by HPLC analysis [Figure 1]. As shown in [Table 1], the CA and RA contents were varied notably depending on extraction condition [Figure S1]. The analysis of variance (ANOVA) was statistically significant (P < 0.05) and suggested that the variables in the model can explain the experimental variation of CA and RA contents. Coefficient of determination (R2), adjusted R2 (adj R2), and lack-of-fit values are shown in [Table 2]. The results suggested that the model was suitable for the experimental data.{Figure 1}{Table 2}

Effect of extraction parameters on the yield of caffeic acid

The relationship between the CA contents and the extraction parameters is explained in the mathematical equation (2) as follows:

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In the models, the linear term of methanol concentration (X2) had the most significant effect (P < 0.01) on CA Contents, followed by the linear term of extraction temperature (X1) [Table 3]. The linear term of solvent-to-material ratio (X3) and quadratic term of X12 and X32 also showed a significant effect (P < 0.05) whereas interaction terms of X1X 2, X1X 3, and X2X 3 were not significant. [Table 2] shows the ANOVA of the fitted quadratic polynomial model for CA contents. The fitness of the predicted model for CA contents was verified by f-value of 24.8411 and P = 0.0012. In this response, the R2 was 0.978 and the adj. R2 was 0.939, indicating a high correlation between the observed and predicted values. In addition, Pvalue for lack-of-fit was 0.3297, which is not significant relative to the pure errors. In general, lack-of-fit test for the model is used to verify the adequacy of the model.[38] If the model does not fit well with the data, the lack-of-fit value will be significant, and the incorrect response surface values can be induced.[39] In this study, statistical analysis demonstrated the suitability of predictive and experimental values and the usefulness of a quadratic polynomial model for optimization. To visualize the relationship between the CA contents, which are dependent variables, and the extraction conditions, which are independent variables, we applied a quadratic polynomial model equation to construct three-dimensional (3D) surface plots [Figure 2]. As shown in [Figure 2]a, when solvent-to-material ratio was fixed at center point (20 mL/g), CA contents was increased slightly by decreasing methanol concentration from 75% to 0% and reached the maximum value at the fixed extraction temperature of 80°C. [Figure 2]b shows the correlation between the extraction temperature and the solvent-to-material ratio to the CA content at a fixed center point (50%) of the methanol concentration. Maximum CA contents were obtained at the highest solvent-to-material ratio (30 mL/g) and then increased slightly by increasing extraction temperature to 80°C. [Figure 2]c shows the correlation between methanol concentration and solvent-to-material ratio on the CA contents at a fixed extraction temperature of 60°C. Maximum CA contents were obtained at the lowest methanol concentration at a fixed solvent-to-material ratio of 10 mL/g. As a result, the response surface analysis and statistical analysis showed that the CA contents were significantly increased when the methanol concentration decreased.{Table 3}{Figure 2}

Effect of extraction parameters on the yield of rosmarinic acid

The relationship between the RA contents and the extraction parameters is explained in mathematical equation (3) as follows:

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In the models, the quadratic term of methanol concentration (X22) had the most significant effect (P < 0.05) on RA content [Table 3]. The linear term of extraction temperature (X1) also showed a significant effect (P < 0.05). However, the interaction terms X1X 2, X1X 3, and X2X 3 with the other linear terms X 2 and X 3 and the quadratic terms X12 and X32 did not show a significant effect on RA contents. The fitness of the predicted model for RA contents was verified by f-value of 4.3963 and a Pvalue of 0.0588. In this response, the R2 and the adj. R2 were 0.973 and 0.954, respectively, and Pvalue for lack-of-fit was 0.0726 [Table 2]. These values represent a good match between the experimental and predicted values. To visualize the relationship between the RA contents, which are dependent variables, and the extraction conditions, which are independent variables, we applied a quadratic polynomial model equation to construct 3D surface plots [Figure 3]. As shown in [Figure 3]a, when solvent-to-material ratio was fixed at the center point (20 mL/g), RA contents were increased by increasing the extracted temperature from 20°C to 78°C and reached the maximum value by increasing methanol concentration from 0% to 49%. [Figure 3]b shows the correlation between extraction temperature and solvent-to-material ratio on the RA contents at a fixed methanol concentration of 50%. RA contents increased steadily when extraction temperature increased from 20°C to 87°C; however, the RA contents decreased when temperature exceeded 90°C. RA contents also increased when solvent-to-material ratio increased from 10 to 30 mL/g. [Figure 3]c shows the correlation between methanol concentration and solvent-to-material ratio on the RA contents at a fixed extraction temperature of 60°C. The maximum RA content was obtained at a methanol concentration of 25%–75% and a solvent-to-material ratio of 29.52 mL/g. As a result, the response surface analysis and statistical analysis showed that the RA contents were significantly affected by the methanol concentration, extraction temperature, and solvent-to-material ratio.{Figure 3}

Experimental validation of the optimum conditions

Verification experiments were performed using the recommended optimal conditions derived from RSM [Table 4]. The optimal conditions for both the maximum CA and RA contents were determined at a temperature of 80.53°C, methanol concentration of 29.89%, and solvent-to-material ratio of 30 mL/g. This predicted an extraction of 0.33 mg of CA content and 5.48 mg of RA content. Under the temperature of 80°C, methanol concentration of 30%, and solvent-to-material ratio of 30 mL/g, the observed values were 0.37 ± 0.2 mg of CA content and 5.62 ± 0.4 mg of RA content. These results show that the actual experimental values are in good agreement with the predicted values in the regression model. Hence, the response surface modeling can be applied effectively to the prediction of extraction of major phenolic acids from M. officinalis L. leaves.{Table 4}

 Conclusion



In this study, RSM was successfully employed to optimize the major phenolic acids extraction from M. officinalis L. leaves. The quadratic polynomial model provided a satisfactory description of the experimental data. These results showed that the optimized conditions with the simultaneous maximum content of CA and RA were determined, and methanol concentration was the most important factor for phenolic acid extraction from M. officinalis L. This study may be useful for standardizing production quality, including improving the efficiency of large-scale extraction systems.

Acknowledgement

This research was supported by the Ministry of Food and Drug Safety (15172MFDS191), the Republic of Korea.

.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

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