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ORIGINAL ARTICLE
Year : 2020  |  Volume : 16  |  Issue : 68  |  Page : 39-46  

Computer-aided Box–Behnken outlook towards optimization of extraction of lawsone from mehendi leaves


1 Phytomedicine Laboratory, Department of Pharmacognosy and Phytochemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
2 Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
3 Department of Pharmacology, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
4 Department of Health Information Technology, Jeddah Community College, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

Date of Submission02-Aug-2019
Date of Decision31-Oct-2019
Date of Web Publication31-Mar-2020

Correspondence Address:
Mohd Mujeeb
Phytomedicine Laboratory, Department of Pharmacognosy and Phytochemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi - 110 062
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/pm.pm_345_19

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   Abstract 


Background: Since ages lawsone, an orange-colored dye from leaves of Lawsonia inermis is being used by women for self-adornment and is also used in textile industries for dyeing reasons. Objective: Comparing traditional and modern extraction modes for extraction of lawsone from L. inermis leaves and simultaneous use of response surface methodology for optimizing its extraction procedure. Materials and Methods: Design-Expert software was exploited for optimization task. Quantification of lawsone in different extracts was done by high-performance liquid chromatography using aqueous acetic acid and methanol in the mobile phase. Results: Ultrasound-assisted extraction stood out to be a supreme technique among all. Moreover, of all solvents (ethylene glycol, dimethylformamide, and methanol) examined methanol stood out to be the most effective solvent for lawsone extraction. By the inverse matrix of the regression model and point prediction, optimal conditions for lawsone extraction were laid down as - extraction temperature - 50.24°C, extraction time - 15.70 min, liquor to material ratio - 24.16 mL/g, and methanol concentration - 75.15%v/v which yielded 17.129 g of lawsone. Under similar conditions (extraction temperature - 50°C, extraction time - 16 min, liquor to material ratio - 24 mL/g, and methanol concentration - 75%v/v), 16.98 g of lawsone was yielded which was close to predicted value. Conclusion: We conclude that a non-thermal method proved to a supreme technique for lawsone extraction which has an additional benefit which of avoiding thermal degradation of compound. Furthermore, by model fitting and analysis of regression coefficients, it was confirmed that all the factors studied significantly affected lawsone yield.

Keywords: Box–Behnken design, lawsone, Lawsonia inermis, naphthoquinone, response surface methodology, ultrasonication


How to cite this article:
Abidin L, Mujeeb M, Aqil M, Najmi AK, Ahmad A. Computer-aided Box–Behnken outlook towards optimization of extraction of lawsone from mehendi leaves. Phcog Mag 2020;16, Suppl S1:39-46

How to cite this URL:
Abidin L, Mujeeb M, Aqil M, Najmi AK, Ahmad A. Computer-aided Box–Behnken outlook towards optimization of extraction of lawsone from mehendi leaves. Phcog Mag [serial online] 2020 [cited 2020 May 27];16, Suppl S1:39-46. Available from: http://www.phcog.com/text.asp?2020/16/68/39/281700



SUMMARY

  • Lawsonia inermis, commonly known as mehendi is an herb that has been used since ancient times by people for dyeing reasons as well as curing certain ailments. It is a treasurer of a wide range of phytochemical profile. One such phytocompound found in major portion is lawsone, which is an orange dye. Since lawsone is pharmacologically active compound optimization of its extraction process becomes a crucial task so that the maximum amount of the compound can be isolated in a single go. Box–Behnken design (response surface methodology) was opted for optimization to study the effect of four variables – extraction temperature, extraction time, liquor to material ratio, and solvent concentration on lawsone extraction. Of all the extraction modes, ultrasound-assisted extraction stood out to be a supreme technique among all. Moreover, among all solvents (ethylene glycol, dimethylformamide, and methanol), examined methanol stood out to be the most effective solvent for lawsone extraction. By the inverse matrix of the regression model and point prediction, optimal conditions for lawsone extraction were laid down as - extraction temperature - 50.24°C, extraction time - 15.70 min, liquor to material ratio - 24.16 mL/g, and methanol concentration - 75.15%v/v which yielded 17.129 g of lawsone. Under similar conditions (extraction temperature - 50°C, extraction time - 16 min, liquor to material ratio - 24 mL/g, and methanol concentration - 75%v/v), 16.98 g of lawsone was yielded which was close to predicted value. Thereby, it was seen that a non-thermal modern extraction mode provided better yields of lawsone.




Abbreviations used: %v/v: Percent volume by volume; °C: Degree Celsius; 3D: Three dimensional; ANN: Artificial neural network; ANOVA: Analysis of variance; BBD: Box–Behnken design; G: Gram; HPLC: High-performance liquid chromatography; mL: Milli liter; RSM: Response surface methodology; UAE: Ultrasound-assisted extraction.


   Introduction Top


Quinones are a class of organic compounds that are not aromatic but are conjugated cyclic diketones. Harbourne classifies this class of phytocompounds into four categories, namely benzoquinones, naphthoquinones, anthraquinones, and isoprenoid, quinones.[1] The benzoquinone moiety in quinones is responsible for imparting color to them. Over 200 species are reported to produce naphthoquinones. Of 65 orders, only 16 reports their occurrence, which suggests that phylogenetically more advanced orders namely Caryophyllales, Ericales, and Lamiales have a higher percentage of naphthoquinones producing families.[2] However, they make little contribution to color in higher plants.[1] In lower plants such as bacteria and fungi they contribute to color like their presence in basidiomycetes.[2] Although anthraquinones have been widely studied because of their purgative actions, naphthoquinones are also treasurer of many other pharmacological activities. In plants, they are generated through acetate malonate, the mevalonic and chorismic acids and the p-hydroxybenzoic acid pathways [3],[4] and are usually colored and so attract pollinators.[5] Their colors may vary between yellow, orange, and brown and is attributed to the double bond.[5]

Lawsone is a napththoquinone well known for its coloring properties. Chemically lawsone is 2-hydroxy-1,4 naphthoquinone and is also referred to as hennotannic acid [Figure 1] In major portions lawsone is found in leaves of Lawsonia inermis (Lythraceae), commonly called “Henna.” Its occurrence has also been reported in Eichhornia crassipes (Pontederiaceae).[6] Lawsone has also been produced through hairy root cultures.[7] Henna leaves paste is very popularly used for coloring hair because of the natural burgundy color it provides. It is also used for body art where designs are made on hand and feet from henna paste mostly to beautify the bride to be. Apart from this, lawsone is also used in perfume industry as well as the textile industry for staining fabrics as well as leather.[8] Besides coloring properties, this phytocompound is valued because of a wide spectrum of biological activity it serves. Naphthoquinones including lawsone acts through two mechanisms. First through redox cycling thereby generating reactive oxyen species and second by acting as electrophiles, i.e., accepting an electron pair.[3],[9] This class of compounds has proven their actions against parasites such as Leishmania spp., Schistosoma spp. Tripanosoma cruzi, apicomplexans parasites, as Plasmodium falciparum, Babesia equi, Toxoplasma gondii, and Theileria spp.[3]
Figure 1: Chemical structure for different classes of quinones

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The portion in which any compound constitutes a plant depends on various factors such as the type of soil, topography of the geographical area, rainfall, manure used, and many more factors. The extraction of a phytocompound simply means separating it from plant matrix and certain factors such as extraction process, polarity of solvent, extraction temperature, and others play a very crucial role in the extraction process. Whenever any phytocompound is possessed with a wide range of pharmacological activities, its extraction becomes a priority so as to gain maximum benefits from it. Optimization of the extraction process comes into play in such cases. Originally, optimization was done by “one– factor-at– a-time” method where one factor was studied at a time. However, this method fails to study the interactions of different variables being studied. Some computer-aided techniques such as artificial neural network (ANN) and response surface methodology (RSM) are available which helps to provide the interactive effects of the input variables. Both ANN and RSM are mathematical-statistical technique that is void of negative aspects of traditional optimization methods. ANN is a logic programming technique which imitates the human brain features such as learning, remembering, deciding and inference, without receiving any aid. RSM, on the other hand, is statistical method developed by Box and Wilson in 1951 for modeling and analyzing a process in which the response of interest is affected by various variables.[10],[11]

RSM can be successfully used where different combinations of input variables (such as extraction temperature, extraction time, and pH) are given and its response (quantity of phytocompound) is studied. Besides providing interactive effects of the variables, RSM is time-saving and economical.[12] This approach has been opted by various researchers for optimizing biotechnological processes. Hafshejani et al. optimized decolorization and mineralization of triazo dye Direct Blue 71 by Pseudomonas aeruginosa using RSM.[13] A comparison of RSM and ANN to enhance the release of reducing sugars from nonedible seed cake by autoclave assisted HCl hydrolysis was done by Shet et al.[14] Similarly, optimization of extracellular fungal-mediated nanosilver green synthesis was done using RSM by Othman et al.[15] Phytocompound extraction field has also exploited this multivariate technique to a large extent [Table 1].
Table 1: The use of response surface methodology in phytocompound extraction field

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In the present study, we have utilized RSM to optimize the extraction parameters of lawsone from leaves of L. inermis and concomitant use of high-performance liquid chromatography (HPLC) for its quantification. The optimization of extraction of total phenolics from leaves of L. inermis has been reported by Uma et al. in 2010,[34] but the optimization of extraction of lawsone through RSM has not been reported yet.


   Materials and Methods Top


Plant material and chemicals

Leaves of L. inermis were procured from Herbal garden, Jamia Hamdard University, New Delhi, India. After authentication from a taxonomist, a specimen was preserved in School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India (Voucher number-PPLI/2015/20). HPLC grade solvents-methanol, water, and other analytical grade solvents were purchased from S.D. Fine Chemicals, India. Standard lawsone was bought from Sigma-Aldrich, St. Louis, Missouri, USA.

Statistical technique

Design-Expert Software (version 11, Stat-Ease, Minneapolis, USA) was used for optimization needs.

Precursory experiments

Initial examinations were conducted to determine the best mode and most effective solvent for lawsone extraction. Dried leaves were powdered in a grinder (Sujata Supermix, 900W) and subjected to extraction through maceration, hot solvent extraction through reflux and soxhlet technique, microwave-assisted extraction, and ultrasound-assisted extraction (UAE) in solvents of different polarity.

Quantification of lawsone by high-performance liquid chromatography

Preparation of standard lawsone solution

Stock solution in a concentration of 1 mg/mL of lawsone was prepared in HPLC grade methanol. Aliquots were prepared from stock solution ranging from 50 μg/ml to 0.039 μg/ml. The solutions were filtered through 0.2 μm membrane filter (Axiva) and then stored at −20°C till further use. Before subjecting the solutions to HPLC analysis, they were bought to room temperature. Peak area versus concentration graph was plotted for the same using Microsoft excel 2007 and the linear equation obtained was used to determine the concentration of lawsone in sample solutions.

Preparation of test solution

Accurately weighed 10 mg of each extract was dissolved in HPLC grade methanol, filtered through 0.2 μm membrane filter (Axiva) and then subjected to HPLC analysis.

Chromatographic conditions

HPLC Quaternary System (Shimadzu, Japan) with LC10AT VP pumps (Shimadzu, Japan), single wavelength programmable ultraviolet-visible detector and a system controller was used for HPLC analysis. Lichrosphere C18 reverse-phase column (Merck, Germany) with 25 mm × 4.6 mm, particle size 5 μm was used for separation. The sample injection was done using rheodyne injector fitted with a 20 μL fixed loop. 0.1% mol L −1 acetic acid: Methanol in ratio of 33:67%v/v at a flow rate of 1 mL min −1 and 42°C was used as mobile phase.[35]

Single factorial experiments

Ranging a particular variable over a range while keeping other variables constant helps to analyze the effect of the variable. The variables studied here included extraction temperature, extraction time, liquor to material ratio, and percentage of solvent. The extracts were quantified by HPLC as discussed above.

Statistical optimization

Box–Behnken Design (BBD) was opted since it is devoid of any embedded factorial design.[12] The experimental design consisted of 29 runs, 24 factorial experiments, and 5 replicates of the center points. Coding of the independent variables was done as per below given equation where xi is coded value of an independent variable, Xi is actual value of independent variable, Xo is actual value of independent variable at center point and ΔX is step-change value of independent variable.



The three variables were designated as A, B, C, and D and were ordered into three levels coded as + 1, 0, and −1 for high, intermediate, and low levels, respectively. [Table 2] and [Table 3] give the coded and actual values of variables and the BBD runs, respectively. All extracts were quantified by HPLC as discussed above.
Table 2: Levels of variables for Box-Behnken design

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Table 3: Design of experiments by Box-Behnken design

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   Results and Discussion Top


Precursory experiments

HPLC analysis showed a retention time of 6.2 min for standard lawsone [Figure 2]a and [Figure 2]b. Initial experiments were conducted to determine the best mode and most effective solvent for extraction of lawsone. Among the three solvents investigated, namely, methanol, ethylene glycol and dimethylformamide, methanol extracted maximum amount of lawsone. Simultaneously UAE stood out to be the best mode of extraction [Figure 2]c.
Figure 2: (a) High performance liquid chromatography chromatogram of pure lawsone (2.5 μg). (b) High performance liquid chromatography chromatogram of Lawsonia inermis ultrasound-assisted extraction methanolic extract. (c) Calibration curve of pure lawsone with respect to peak area. (d) Comparative chart of different methods and solvents for extraction of Lawsone

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Six dilutions were taken to prepare the calibration plot of standard lawsone (peak area versus concentration) which gave a good correlation coefficient (R2) of 0.994 [Figure 2]d. The corresponding linear equation (y = mx + c) obtained was used to calculate the content of lawsone in each extract.

Single factorial experiments

These trials guided to select ranges of factors for RSM, BBD. The leveling for BBD was done based on the results of these trials. Results are given in [Figure 3].
Figure 3: Results of single factorial experiments

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Optimization by response surface methodology

BBD provided 29 runs with different combinations of four variables. By multiple regression analysis, second-order polynomial models were established for the variables and the relationship between lawsone content and the variables was given as per the equation given below:

Y = β0+ β1A + β2B + β3C + β4D + β0A1+ β12 AB + β13 AC + β14 AD + β23 BC + β24 BD + β34 CD + β11A 2+ β22B 2+ β33C 2+ β44D 2

Where Y is the predicted response, i.e., lawsone content, β0 is model constant, β1, β2, β3, and β4 linear coefficients, A, B, C, and D are independent variables, β12,β13,β14,β23,β24, and β34 are cross-product coefficients, β11,β22,β33, and β44 are the quadratic coefficients.

Model fitting, analysis, and response surface curves

The analysis of the results was done through the coefficient of regression (R2), analysis of variance (ANOVA), and response surfaces. R2 value of 0.9860 implies the closeness of the data to the fitted regression. The values of Adjusted-R2 (0.9611) and Predicted-R2 (0.9018) were close enough signifying excellent fit of the model. Meantime, the coefficient of variance (%CV) of 15.27% which is small value advocated a good dependency on the experimental model. “Adequate Precision” (signal to noise ratio) which is desired to be more than 4 was 22.9270 further showing goodness of the model. The lack of Fit test gives the variation of the data around the fitted model. The F-value and P value for the lack-of-fit were 1.96 and 0.2703, respectively, implying it to be non-significant which was good for the model. By multiple regression analysis, the following second-order polynomial equation was obtained:

Lawsone content = 8.88 + 0.5633 A + 0.7167 B + 0.3117 C + 0.1017 D − 0.1100 AB + 0.1300 AC − 0.200 AD + 0.0950 BC + 0.1900 BD + 0.0050 CD − 5.69 A 2 − 2.57 B 2 − 02.00 C 2 − 1.94 D 2

The results for ANOVA have been tabulated in [Table 4]. F and P values give the significance of coefficient terms. The larger the F-value and the smaller the P value, more significant is the model.[36] Here, A, B, A 2, B 2, C 2, and D 2 had P < 0.0500 proposing that the model could be used to predict these responses.
Table 4: Analysis of variance for quadratic model

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Three dimensional response surface plots help to understand the interactive effects of variables more clearly. [Figure 4]a helps to understand the interactions between A and B. As the extraction temperature is increased from 45°C to 52°C, an increase in lawsone content is noted. However, further increment in temperature brings a drop in lawsone content. Similarly, increasing the extraction time from 10 min to 17 min increases the lawsone content, beyond which a dip is observed. As shown in [Figure 4]b, as the liquor to material ratio is varied from 22 mL/g to 24.9 mL/g lawsone extracted also increases after which a decrement in seen till 26 mL/g. Extraction temperature had the same effect as seen with extraction time, i.e., increment in lawsone content till 52°C. [Figure 4]c shows the interactive effects of A and D which shows that as methanol concentration rises from 70%v/v to 76.8%v/v lawsone content also increases. Further increasing the methanol concentration do not further increase lawsone content. Furthermore, extraction temperature till 52.5°C shows an uphill trend toward lawsone but beyond that a downhill trend is observed. [Figure 4]d shows that maximum lawsone content can be achieved at extraction time of 19 min and liquor to the material ratio of 25.8 mL/g. Interactive effects of B and D from [Figure 4]e shows lawsone content to increase as the extraction time increases till 19.41 min and methanol concentration till 79.41%v/v after which both factors shows a dip in lawsone concentration. In case of C and D [Figure 4]f, both the factors shows maximum lawsone extracted at 25.6 mL/g liquor to material ratio and 79.28%v/v of methanol concentration. Further enhancement in both factors decreased the lawsone content in the extracts.
Figure 4: (a) 3D surface plot for A (extraction temperature) and B (extraction time). (b) 3D surface plot for A (extraction temperature) and C (liquor to material ratio). (c) 3D surface plot for A (extraction temperature) and D (methanol concentration).(d)- 3D surface plot for B (extraction time) and C (liquor to material ratio). (e) 3D surface plot for B (extraction time) and D (methanol concentration). (f) 3D surface plot for C (liquor to material ratio) and D (methanol concentration)

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Validation of the model

Through point prediction analysis and inverse matrix of regression polynomial equation, the optimal levels of variables (A - 50.24°C, B -15.70 min, C - 24.16 mL/g, and D - 75.16%v/v) as well as predicted value of lawsone content (17.12 g/25 g raw material) were obtained. Under similar conditions (A - 50°C, B - 16 min, C - 24 mL/g, and D - 75%v/v) experiments in triplicate (n = 3) were conducted as recheck run. Lawsone content in these experiments was found to be 16.98 g/25 g of raw material which was slightly lower than predicted value [Figure 5]. However, no significant difference was observed between the predicted yield and experimental one when the Student's t-test was conducted, indicating that the model was satisfactory and adequate for reflecting the expected optimization.
Figure 5: The illustration of predicted and experimental conditions for lawsone extraction

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   Conclusion Top


Extraction and isolation of natural dyes is an indispensible act for textile industries as well as pharmaceutical firms, especially where the dye is credited with biological activities too. Stereotypically extraction was done by traditional means which includes solid–liquid extraction which are still most commonly used methods for extraction because of ease of use, efficiency, and wide-ranging applicability.[36] However, modern extraction techniques provide a upper hand over conventional methods of being ecofriendly. Our study explored both ways and found out that UAE which is a modern and a greener approach of extraction provides better yields of lawsone. The solvent also plays a very vital role in extraction. A solvent apart from being according to the polarity of the compound being extracted should also be able to withstand the interfering substances. In this piece of work, we optimized that extraction process of lawsone, which is natural dye from henna leaves through UAE. RSM, a multivariate statistical approach was utilized in this regard to reduce the number of experimental trials and simultaneously studying the interactive effects of various independent variables being studied. The extraction optimization of dye from Butea monosperma by RSM has been done earlier by Sinha et al. (2012)[37] but for lawsone has not been reported yet. The optimized conditions for lawsone extraction were found to be as extraction temperature - 50.24°C, extraction time - 15.70 min, liquor to material ratio - 24.16 mL/g, and methanol concentration - 75.16%v/v. Under such conditions 16.98 g/25 g of raw material lawsone was yielded which was slightly lower than the value predicted by BBD.

The outcome of our research will help the other researchers to take advantage of the conditions given by RSM, BBD to isolate maximum amount of lawsone from L. inermis leaves by UAE. UAE which is also a non-thermal method provides additional benefit of avoiding thermal degradation of the phytocompound as well as being environmentally friendly.

Acknowledgements

The authors would like to thank Head, Department of Pharmacognosy and Phytochemistry, Jamia Hamdard.

Financial support and sponsorship

The authors also acknowledge the UGC-MANF, Government of India, for providing financial support to carry out this research.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Harbourne JB. Phytochemical Methods: A Guide to Modern Techniques of Plant Analysis. London, United Kingdom: Chapman and Hall; 1980.  Back to cited text no. 1
    
2.
Kuete V. Medical Plant Research in Africa. Amsterdam, Netherlands: Elsevier; 2013.  Back to cited text no. 2
    
3.
Pinho BR, Sousa C, Oliveira JM, Valentão P, Andrade PB. Bioactive Compounds: Type, Biological Activities and Health Effects. New York: Nova Science Publishers; 2012.  Back to cited text no. 3
    
4.
Bruneton J. Pharmacognosy: Phytochemistry, Medicinal Plants. 2nd ed. Hampshire, United Kingdom: Intercept Ltd.; 1999.  Back to cited text no. 4
    
5.
Babula P, Adam V, Havel L, Kizek R. Noteworthy secondary metabolites naphthoquinones – Their occurrence, pharmacological properties and analysis. Curr Pharm Anal 2009;5:47-67.  Back to cited text no. 5
    
6.
Ashnagar A, Shiri A. Isolation and characterization of 2-hydroxy1, 4-naphthoquinone (lawsone) from the powdered leaves of henna plant marketed in Ahwaz city of Iran. Int J Chem Tech Res 2011;3:1941-4.  Back to cited text no. 6
    
7.
Bakkali AT, Jaziri M, Foriers A, Vander Heyden Y, Vanhaelen M, Homes J. Lawsone accumulation in normal and transformed culture of henna, Lawsonia inermis. Plant Cell Tissue Organ Cult 1997;51:83.  Back to cited text no. 7
    
8.
Hasan MM, Nayem KA, Azim AY, Ghosh NC. Application of purified lawsone as natural dye on cotton and silk fabric. J Text 2015.  Back to cited text no. 8
    
9.
Ramos-Peralta L, López-López LI, Silva-Belmares SY, Zugasti-Cruz A, Rodríguez-Herrera R, Aguilar-González CN. Naphthoquinone: Bioactivity and green synthesis. In: Méndez-Vilas A, editor. The Battle against Microbial Pathogens: Basic Science, Technological Advances and Educational Programs. 1st ed. Badajoz, Spain: Formatex; 2015. p. 42-55.  Back to cited text no. 9
    
10.
Aydar AY. Utilization of response surface methodology in optimization of plant materials. In: Silva V, editor. Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes. 1st ed. London, United Kingdom: InTechOpen; 2018.  Back to cited text no. 10
    
11.
Sarker S, Nahar L. Computational Phytochemistry. 1st ed. Netherlands: Elsevier; 2018.  Back to cited text no. 11
    
12.
Jibril S, Basar N, Sirat HM, Wahab RA, Mahat NA, Nahar L, et al. Application of box-Behnken design for ultrasound-assisted extraction and recycling preparative HPLC for isolation of anthraquinones from Cassia singueana. Phytochem Anal 2019;30:101-9.  Back to cited text no. 12
    
13.
Hafshejani MK, Ogugbue CJ, Morad N. Application of response surface methodology for optimization of decolorization and mineralization of triazo dye Direct Blue 71 by Pseudomonas aeruginosa. Biotech 2014;4:605-19.  Back to cited text no. 13
    
14.
Shet VB, Palan AM, Rao SU, Varun C, Aishwarya U, Raja S, et al. Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non-edible seed cake by autoclave assisted HCl hydrolysis. 3 Biotech 2018;8:127.  Back to cited text no. 14
    
15.
Othman AM, Elsayed MA, Elshafei AM, Hassan MM. Application of response surface methodology to optimize the extracellular fungal mediated nanosilver green synthesis. J Genet Eng Biotechnol 2017;15:497-504.  Back to cited text no. 15
    
16.
Abidin L, Mujeeb M, Mir SR. Maximized extraction of flavonoid luteolin from V. negundo L. Leaves: Optimization using box-Behnken design. Curr Bioact Compd 2018;15:343-50.  Back to cited text no. 16
    
17.
Alam MS, Damanhouri ZA, Ahmad A, Abidin L, Amir M, Aqil M, et al. Development of response surface methodology for optimization of extraction parameters and quantitative estimation of embelin from Embelia ribes burm by high performance liquid chromatography. Pharmacogn Mag 2015;11:S166-72.  Back to cited text no. 17
    
18.
Siddiqui N, Aeri V. Optimization of betulinic acid extraction from Tecomella undulata bark using a box-Behnken design and its densitometric validation. Molecules 2016;21:393.  Back to cited text no. 18
    
19.
Anuar N, Mohd Adnan AF, Saat N, Aziz N, Mat Taha R. Optimization of extraction parameters by using response surface methodology, purification, and identification of anthocyanin pigments in Melastoma malabathricum fruit. Scientific World Journal 2013.  Back to cited text no. 19
    
20.
Pandey A, Bewal T, Sekar KC, Bhatt ID, Rawal RS. Optimization of ultrasonic-assisted extraction (UAE) of phenolics and antioxidant compounds from rhizomes of Rheum moorcroftianum using response surface methodology (RSM). Ind Crops Pro 2018;119:218-25.  Back to cited text no. 20
    
21.
Liu YK, Yan E, Zhan HY, Zhang ZQ. Response surface optimization of microwave-assisted extraction for HPLC-fluorescence determination of puerarin and daidzein in RadixPuerariae thomsonii. J Pharm Anal 2011;1:13-9.  Back to cited text no. 21
    
22.
Ahamad J, Amin S, Ahmad J, Mir SR. Response surface methodology for optimization of ultrasound assisted extraction of swertiamarin from Enicostema littorale blume. Curr Bioact Compd 2016;12:87-92.  Back to cited text no. 22
    
23.
Zaghdoudi K, Framboisier X, Frochot C, Vanderesse R, Barth D, Kalthoum-Cherif J, et al. Response surface methodology applied to Supercritical Fluid Extraction (SFE) of carotenoids from Persimmon (Diospyros kaki L.). Food Chem 2016;208:209-19.  Back to cited text no. 23
    
24.
Siddiqui N, Aeri V. Ultrasonic extraction optimization of stigmasterol using response surface methodology and quantification by high-performance thin-layer chromatography from Tecomella undulata bark. J Planar Chrom 2017;30:1.5.  Back to cited text no. 24
    
25.
Sheoran S, Panda BP, Admane PS, Panda AK, Wajid S. Ultrasound-assisted extraction of gymnemic acids from Gymnema sylvestre leaves and its effect on insulin-producing RINm-5 F ß cell lines. Phytochem Anal 2015;26:97-104.  Back to cited text no. 25
    
26.
Arvindekar AU, Laddha KS. An efficient microwave-assisted extraction of anthraquinones from Rheum emodi: Optimisation using RSM, UV and HPLC analysis and antioxidant studies. Indus Crops Pro 2016;83:587-95.  Back to cited text no. 26
    
27.
Živkovic J, Šavikin K, Jankovic J, Cujic Menkovic N. Optimization of ultrasound-assisted extraction of polyphenolic compounds from pomegranate peel using response surface methodology. Sep Purif Techno 2018;194:40-7.  Back to cited text no. 27
    
28.
Caleja C, Barros L, Prieto MA, Barreiro MF, Oliveira MB, Ferreira IC. Extraction of rosmarinic acid from Melissa officinalis L. Sep Purif Technol 2017;186:297-308.  Back to cited text no. 28
    
29.
Cai M, Luo Y, Chen J, Liang H, Sun P. Optimization and comparison of ultrasound-assisted extraction and microwave-assisted extraction of shikmic acid from Chinese star anise. Sep Purif Technol 2014;133:375-9.  Back to cited text no. 29
    
30.
Simic VM, Rajkovic KM, Stojicevic S, Velickovic DT, Nikolic N, Lazic ML, Stanisavljevic ITK. Optimization of microwave-assisted extraction of total polyphenolic compounds from chokeberries by response surface methodology and artificial neural network. Sep Purif Technol 2016;160:89-97.  Back to cited text no. 30
    
31.
Pan Y, Zhang J, Shen T, Zuo ZT, Jin H, Wang YZ, et al. Optimization of ultrasonic extraction by response surface methodology combined with ultrafast liquid chromatography-ultraviolet method for determination of four iridoids in Gentiana rigescens. J Food Drug Anal 2015;23:529-37.  Back to cited text no. 31
    
32.
Tian S, Hao C, Xu G, Yang J, Sun R. Optimization conditions for extracting polysaccharide from Angelica sinensis and its antioxidant activities. J Food Drug Anal 2017;25:766-75.  Back to cited text no. 32
    
33.
Pandey DK, Kaur P. Optimization of extraction parameters of pentacyclic triterpenoids from Swertia chiratastem stem using response surface methodology. 3 Biotech 2018;8:152.  Back to cited text no. 33
    
34.
Uma DB, Ho CW, Wan Aida WM. Optimization of extraction parameters of Total phenolic compounds from henna (Lawsonia inermis) Leaves. Sains Malaysiana 2010;39:119-28.  Back to cited text no. 34
    
35.
Babula P, Mikelova R, Adam V, Kizek R, Havel L, Sladky Z. Using of liquid chromatography coupled with diode array detector for determination of naphthoquinones in plants and for investigation of influence of pH of cultivation medium on content of plumbagin in Dionaea muscipula. J Chromatogr B Analyt Technol Biomed Life Sci 2006;842:28-35.  Back to cited text no. 35
    
36.
Stalikas CD. Extraction, separation, and detection methods for phenolic acids and flavonoids. J Sep Sci 2007;30:3268-95.  Back to cited text no. 36
    
37.
Sinha K, Das P, Datta S. Extraction of natural dye from petals of flame of forest (Butea monosperma) flower: Process optimization using response surface methodology (RSM). Dyes Pigm 2012;94:212-6.  Back to cited text no. 37
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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