J. of Supercritical Fluids 55 (2011) 944–948

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Optimization of process variables for essential oil components from

Satureja hortensis by supercritical fluid extraction using Box­Behnken

experimental design

Mostafa Khajeh ∗

Department of Chemistry, University of Zabol, P.O. Box 98615­538, Mofateh Street, Zabol, Iran

a r t i c l e i n f o

Article history:

Received 23 August 2010

Received in revised form 10 October 2010

Accepted 11 October 2010

Keywords:

Satureja hortensis

Supercritical carbon dioxide

Box­Behnken design

Hydrodistillation

a b s t r a c t

The oil and extracts of Satureja hortensis cultivated in Iran were extracted using supercritical carbon

dioxide and hydrodistillation method. The oil and extracts were analyzed by GC­FID and GC/MS. The

compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects

of various parameters such as pressure, temperature, percent of modifier (methanol) and extraction time,

were investigated by a fractional factorial design (24­1) to determine the significant parameters and their

interactions. The results showed that the pressure, temperature and percent of modifier are significant

(p < 0.05), but the extraction time was found to be insignificant. The response surface methodology (RSM),

based on Box­Behnken design was employed to obtain the optimum conditions of the significant param­

eters (pressure, temperature and percent of modifier). The optimal conditions could be obtained at a

pressure of 35.0 MPa, temperature of 72.6 ◦C, and 8.6% (v/v) for methanol. The main extracted components

using SFE were g­Terpinene (35.5%), Thymol (18.2%) and Carvacrol (29.7%).

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Satureja hortensis is an annual, herbaceous plant belonging to the

Labiatae of family. It is known as summer savory, native to southern

Europe and naturalized in parts of North America. The main com­

ponents of the essential oils of this plant are the phenols, carvacrol

and thymol, plus p­cymene, b­caryophyllene, linalool and other

terpenoids [1]. The green leaves and herbaceous parts of stems of

S. hortensis were used fresh and dried as flavoring agents in sea­

soning, stews, meat dishes, poultry, sausages and vegetables. The

essential oils and oleoresin were used in the food industry. More­

over, the essential oils that have been used in the perfume industry,

either alone or with other essential oils [1,2]. As a medicinal plant,

S. hortensis has been traditionally used as a stimulant, stomachic,

carminative, expectorant and aphrodisiac. The essential oils of this

plant have demonstrated antimicrobial and antidiarrheic activity

due to of the phenols in the oil [1–5].

The traditional procedures for the extraction of plant materials

are include hydrodistillation and organic solvent extraction using

percolation, maceration or soxhlet techniques. These methods have

distinct drawbacks such as time­consuming and labour intensive

operations, handling of large volumes of hazardous solvents. More­

over, there is increasing interest in alternative extraction methods

∗ Tel.: +98 542 2232961; fax: +98 542 2226765.

E­mail address: m khajeh@uoz.ac.ir

that consume smaller quantities of organic solvent due to of the

rising solvent acquisition and disposal costs and regulatory restric­

tions [6,7].

Supercritical fluids have been shown to exhibit several advan­

tages in the extraction of essential oils from plants. The combined

liquid­like solvating capabilities and gas­like transport proper­

ties of supercritical fluids make them particularly suitable for the

extraction of diffusion­controlled matrices such as plant tissues

[7]. Moreover, the solvent strength of a supercritical fluid can

be easily turned by simply changing the applied pressure and

temperature [7]. Carbon dioxide (CO2), the most commonly used

supercritical fluid, has the advantages of being non­flammable,

fairly non­toxic, cost effective and easily removed from the extract

following decompression [7,8].

The Box­Behnken is a second­order multivariate technique

based on three­level incomplete factorial designs that received a

wide application for assessment of critical experimental condi­

tions, that is, maximum or minimum of response function. The

number of experiments (N) needed for the development of Box­

Behnken matrix is defined as N = 2k (k − 1) + C0, where (k) is the

factor number and (C0) is the replicate number of the central point

[9–14].

In this work, we have used SFE for the extraction of oil and

extracts of S. hortensis. The aim of the work is to analyze the

influence of different parameters such as pressure, temperature,

and percent of modifier and extraction time on the supercritical

fluid extraction of S. hortensis. A Box­Behnken experimental design

0896­8446/$ – see front matter © 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.supflu.2010.10.017

M. Khajeh / J. of Supercritical Fluids 55 (2011) 944–948 945

method was used to investigate the effects of various parameters

on the SFE performance.

2. Experimental

2.1. Plant material

The plant materials were collected from Zabol, Iran in April 2010.

A voucher specimen has been deposited in the national herbar­

ium of Iran (TARI). The aerial parts of the plants were air­dried

at the ambient temperature (about 40 ◦C) and then were stored

at 4 ◦C prior to extractions. The moisture content of the plant was

6.4 ± 0.05% (Karl Fischer). Immediately prior to SFE, the sample was

grilled in a blender to get particles powder with diameters in the

range of about 700–800 mm.

2.2. Reagents

HPLC grade dichloromethane and methanol were purchased

from Merck (Darmstadt, Germany). Carbon dioxide (99.99% purity),

contained in a cylinder with an eductor tube, was obtained from

Sabalan Co. (Tehran, Iran).

2.3. Hydrodistillation

The plant (40 g of dried material) was submitted to hydrodistil­

lation for 4 h, using a Clevenger­type apparatus, according to the

European Pharmacopoeia [15]. The volatile distillate was collected

over anhydrous sodium sulfate and refrigerated prior to analysis.

The yield of the oil was 1.5% (w/w), based on dry plant weight.

2.4. Supercritical fluid extraction

A Suprex MPS/225 system (Pittsburgh, PA) in the SFE mode was

used for all of the extractions. The extraction vessel was a 10 ml

stainless steel vessel. The SFEs were carried out at pressures of

30.3–40.5 MPa, temperatures of 55–75 ◦C and percent of modifier

(methanol) of 5–10% (v/v). A Duraflow manual variable restric­

tor (Suprex) was used in the SFE system to collect the extracted

analytes. In order to prevent sample plugging, the restrict point

was electrically warmed. The SFE flow rate through the Duraflow

restrictor was about 0.3–0.4 ml min−1 (compressed). Plant powder

(1.0 g) was mixed well with 2 mm diameter glass beads, and was

then charged into the 10 ml extraction vessel. The extracted oil and

extracts were collected in dichloromethane in a 5.0 ml volumetric

flask. The extraction vessel was a 10 ml stainless steel vessel. For all

the modifier studies, methanol was spiked directly into the extrac­

tion vessel with charged sample before extraction. To determine

the percent recovery, bubbling of the sample solution was done

using argon gas in order to evaporate the solution. Then the weight

of oil and extracts was measured. Finally the oil and extracts yield

(Y) was calculated from the following equation:

Y% =

(

g1

g2

)

× 100 (1)

where g1 is weight of oil and extracts and g2 is weight of dry plant.

2.5. GC and GC/MS analysis

GC analyses were carrying out using a Hewlett­Packard 7890A

series II gas chromatograph equipped with a FID and a DB­5

fused silica column (30 m × 0.53 mm i.d., film thickness 1.5 mm).

Oven temperature was programmed at 50 ◦C for 1 min, and then

increased to 260 ◦C at a rate of 4 ◦C/min. Injector and detector tem­

peratures were 260 ◦C and 270 ◦C, respectively. The carrier gas,

Table 1

Experimental design the results obtained in function of the oil and extracts yield

(Y%).

No. P (MPa) M% T (◦C) t (min) Y%

1 10.1 0.0 35 5 3.5

2 30.3 0.0 35 15 4.6

3 10.1 5.0 35 15 4.7

4 30.3 5.0 35 5 4.9

5 10.1 0.0 55 15 4.1

6 30.3 0.0 55 5 4.7

7 10.1 5.0 55 5 4.9

8 30.3 5.0 55 15 5.51

9 20.2 2.5 45 10 4.6

10 20.2 2.5 45 10 4.5

helium, was adjusted to a linear velocity of 30 cm s−1. The SFE sam­

ple (1 ml) was injected into the GC (without any further dilution)

using the splitless mode. The GC/MS analysis was carried out on

a Varian 3800 equipped to a DB­5 column with the same char­

acteristics as the one used in GC. The transfer line temperature

was 270 ◦C. The ionization energy was 70 eV with a scan time of

1 s and mass range of 40–300 amu. The percentages of compounds

were calculated by area normalization method, without consid­

ering response factors. The components of oil and extracts were

identified by comparison of their mass spectra with those of a com­

puter library (Wiley and NIST) or with authentic compounds. Data

obtained were confirmed by comparison of their retention indices,

either with those of authentic compounds or with the data, which

published in the literature [16].

3. Results and discussion

3.1. Factorial design

The following factors were evaluated: pressure, temperature,

and time and modifier volume. A two­level fractional factorial

design (24­1) with two replicates of center point was performed

in order to determine the main factors of the extraction process.

Table 1 shows the experimental design and the results, which

derived from each run. The significance of the effects was checked

by analysis of the variance (ANOVA) and Pareto chart (Fig. 1). The

standardized effect of the independent variables and their inter­

action on the dependent variable was investigated by Pareto chart

(Fig. 1), which detects the main influence of the independent vari­

ables and interactions with their relative significance on the oil and

extracts extraction. A positive value for the estimated effect indi­

cates an increase in the extraction yield if the variable increases to

its high level. A negative value indicates that a better extraction

Fig. 1. Pareto chart of main effects obtained from 24­1 fractional factorial designs.

The vertical line defines the 95% confidence interval [A = P, B = M%, C = T and D = t].

946 M. Khajeh / J. of Supercritical Fluids 55 (2011) 944–948

Table 2

Design matrix in the Box­Behnken model, observed response and predicted values.

Trial no. T P M% Observed, Yaa (%) Predicted, Yp (%) Error (%)

1 55 30.3 7.5 5.8 5.84 −0.65

2 75 30.3 7.5 8.1 8.01 1.08

3 55 40.5 7.5 7.5 7.59 −1.17

4 75 40.5 7.5 8.7 8.66 0.43

5 55 35.4 5.0 7.4 7.3 1.35

6 75 35.4 5.0 7.8 7.83 −0.32

7 55 35.4 10.0 5.9 5.88 0.42

8 75 35.4 10.0 8.5 8.6 −1.18

9 65 30.3 5.0 7.8 7.86 −0.80

10 65 40.5 5.0 8.6 8.61 −0.15

11 65 30.3 10.0 7.1 7.09 0.18

12 65 40.5 10.0 8.8 8.74 0.71

13 65 35.4 7.5 8.5 8.47 0.39

14 65 35.4 7.5 8.7 8.47 2.68

15 65 35.4 7.5 8.2 8.47 −3.25

a Average of triplicate extraction.

yield is obtained at low levels of the variables [17]. The inter­

pretation of this chart demonstrates that the factors pressure and

modifier volume are highly significant. An increase in pressure and

modifier volume increases the oil and extracts yield. The tempera­

ture is a less significant factor. The factor time has an insignificant

effect. The positive value for the effect of the pressure, temperature

and modifier volume indicated that in the studied levels, the extrac­

tion efficiency increased with increase of these factors [17]. Results

of the fractional factorial design demonstrated that the variables

(pressure, temperature and modifier volume) in the studied levels

required a final optimization. Therefore, a Box­Behnken design was

carried out. These results are similar to previous work [18].

3.2. Box­Behnken design

The significant variables like pressure (P), temperature (T) and

percent of modifier (M%) were selected as the critical variables and

designated as A, B and C, respectively. The low, middle, and high lev­

els of each variable were designated as −1, 0, and +1, respectively.

The design of real experiments is given in Table 2.

In a system involving three significant independent variables A,

B, and C the mathematical relationship of the response on these

variables can be approximated by the quadratic (second degree)

polynomial equation as follow:

Y = ˇ0 + ˇ1A + ˇ2B + ˇ3C + ˇ12AB + ˇ13AC + ˇ23BC

+ ˇ11A2+ ˇ22B2

+ ˇ33C2 (2)

where Y = estimate response; ˇ0 = constant; ˇ1, ˇ2 and ˇ3 = linear

coefficients; ˇ12, ˇ13 and ˇ23 = interaction coefficients between the

three factors; ˇ11, ˇ22 and ˇ33 = quadratic coefficients.

A multiple regression analysis was performed to obtain the

coefficients and the equation, which can be used to estimate the

response. A Box­Behnken experimental design was used in this

study. It is important to note that all interactions higher than sec­

ond order have been neglected in Eq. (2). A total of 15 experiments

were needed to estimate of the model.

3.3. Optimization of the experimental conditions

The optimization step of SFE method was carried out using a

Box­Behnken experimental design. Several variables that could be

potentially affected on the oil and extracts yield were chosen such

as: pressure (P), temperature (T) and percent of modifier (M%).

Other parameters implicated in the extraction of oil and extracts

were kept constant, namely the extraction time was 15 min.

The number of experiments needed to investigate the before

noted three parameters at three levels would be 27 (33). Thus, this

was reduced to 15 using a Box­Behnken experimental design. The

results of this limited number of experiments provided a statistical

model that was used to identify trends in high yield for the extrac­

tion process. The matrix, oil and extracts yield (Y%) were presented

in Table 2. Triplicate extractions were carried out for each run. The

relative standard deviation (RSD%) of the three replicate extraction

was less than 0.3%. The equations below illustrate the relationship

of the three variables (i.e. P, T, M%) and Y.

Y = −40.6 + 1.61(T) + 0.09(P) − 1.51(M%) − 0.0081(T)2

− 0.0001(P) − 0.0413(M%)2− 5.0 × 10−4(T)(P)

+ 0.022(T)(M%) + 0.0018(P)(M%) (3)

The critical points in the surface response are found by solv­

ing these equation systems for the condition of ı(Y)/ı(P) = 0,

ı(Y)/ı(T) = 0 and ı(Y)/ı(M %)= 0. The method for calculating these

critical points has been published elsewhere [19]. The calculated

values for the critical points are as follows: P = 35.0 MPa, T = 72.6 ◦C

and M% = 8.6. The summary of the analysis of variance (ANOVA) is

shown in Table 3. The ANOVA of regression model demonstrates

that the model is highly significant. The goodness of the model

can be checked by the determination coefficient (R2). The value of

adjusted R2 (0.962) indicating that only 3.8% of the total variations

was not explained by the model. The value of R2 (0.986) indicates

good relation between the experimental and predicted values of

the response. The lack­of­fit measures the failure of the model to

represent data in the experimental domain at points which are not

included in the regression. The non­significant value of lack­of­fit

(>0.05) revealed that the quadratic model is statistically significant

for the response.

The ANOVA of the regression model showed that the quadratic

model was significant, as was evident from the Fisher’s F­test

(Fmodel = 40.37) with a very low probability value (p), as sug­

gested by Yetilmezsoy et al. [20]. Moreover, the calculated F

value was founded to be higher than the tabulated F value

(Fa, df, (n−df+1) = F0.05, 9, 5 = Ftab = 4.77) at the 5% level, indicating that

the computed Fisher’s variance ratio at this level was large enough

to justify a very high degree of adequacy of the quadratic model

and also to show that the treatment combinations were highly

significant, as similarly reported by other [20].

Results were assessed with different descriptive statistic such

as chi­square test and Mallow’s Cp statistic to reflect the statistical

significance of the quadratic model. The chi­square (�2) test (Eq.

(4)) was also performed to check whether there was a significant

difference between the expected responses (Ei) and the observed

data (Oi).

�2cal =

n∑

i=1

(Oi − Ei)2

Ei(4)

The calculated chi­square (�2cal

= 0.021) was found to be less

than the tabulated chi­square value , represent that there was no

Table 3

ANOVA analysis for oil and extracts extraction.

Source Sum of

squares (SS)

Degree of

freedom

Mean square

(MSS)

F­value P

Regression 12.655 9 1.406 40.37 <0.0001

Linear 3.048 3 1.016 29.17 0.001

Square 2.568 3 0.856 24.57 0.002

Interaction 1.715 3 0.572 16.41 0.005

Residual 0.174 5 0.035

Lack­of­fit 0.048 3 0.016 0.25 0.858

Pure error 0.127 2 0.063

Adjust­R2 0.962

R2 0.986

M. Khajeh / J. of Supercritical Fluids 55 (2011) 944–948 947

Fig. 2. Response surface diagrams showing the effects of the mutual interactions

between two independent variables (a) pressure and percent of modifier, (b) tem­

perature and percent of modifier, (c) pressure and temperature.

statistically significant difference between the observed data and

the expected responses. The �2 test concluded with 95% certainly

that the quadratic model provided a satisfactory fit to the experi­

mental data.

Dawson and Martinez­Dawson [21] have reported that Mallow’s

Cp statistic (Eq. (5)) can be used to determine terms which can be

omitted from the response surface model. For a response surface

model including all terms, Cp = p, where p is the number of variables

in the regression model including the intercept term. For response

surface models with omitted terms Cp ∼ p shows a good model with

little bias, and Cp ≤ p shows a very good prediction model. The goal

is to remove terms from the response surface model until a min­

imum Cp value near p is obtained. If Cp > p, this shows that too

many terms have been removed or some remaining terms are not

necessary [20,21].

Cp =

(

SSe

MSSe

)

+ 2p − n (5)

where SSe is error sum of squares, MSSe is error variance of the esti­

mate, p is the number of variables in the regression model including

the intercept term and n is number of experiment in Table 2. In

this study Mallow’s Cp statistic (Cp = 9.97) showed the third condi­

tion (Cp ≤ p and p = 10), indicating a very good prediction model, as

similarly reported by other [20].

Yetilmezsoy et al. [20] have reported that three dimensional

(3D) response surface plots as a function of two factors, maintain­

ing all other factors at fixed levels are helpful in understanding

both the main and their interaction effects of these two factors.

Thus, in order to gain a better understanding of the influences of

the independent variables and their interactions on the dependent

variables, 3D response surface plots for the measured responses

were formed based on the model equation (Eq. (3)) in this study.

Since the regression model has three independent variables, one

variable was held at constant at the central level for each plot. Fig. 2

shows the 3D response surfaces as the functions of two variables

at the center level of other variables, respectively.

The common belief among analytical chemists is that the max­

imum solubility of a solute in the SFE is achieved at the maximum

fluid density. The influence of pressure on the composition of oil and

extracts shown; the recovery was increased with an increase in the

pressure. This result was predictable due to raising the extraction

pressure, leads to a higher fluid density, which increases the solu­

bility of the oil and extracts, as similar to previous work [22]. The

variation of temperature during the SFE affects the density and the

volatility of the oil and extracts from the plant matrix. By increas­

ing the temperature, the volatilities of the oil and extracts increases

but the SFE density decreases. In this study, raising temperature

drastically increases the concentrations of content in the oil and

extracts composition due to of increasing the volatilities of the oil

and extracts [22].

Due to the limited solubility of polar organic compounds in

the SFE, quantitative extraction of these compounds with pure

supercritical carbon dioxide is impossible. Modifier is added to an

extraction process mainly for two reasons [23]: (i) to increase the

polarity of the SFE in order to improve the solubility of the oil and

extracts; and (ii) to facilitate desorption of oil and extracts from

the plant matrix. The polar modifier molecules accelerate desorp­

tion processes by competing with the oil and extracts for the active

binding sites; as well as by disrupting matrix structures. In the

present work the methanol was used as modifier, and it enhanced

the solubility of solutes in SFE and thus, the efficiency of extraction

increased.

3.4. GC/MS results

The results of GC/MS analyses of oil and extracts obtained by

SFE and hydrodistillation were given in Table 4. The table shows the

composition of volatile constituents in the extract, which are minor

Table 4

Satureja hortensis oils and extracts obtained by SFE and hydrodistillation.

No. Name of compound R.I.a SFEb (%) Hydrodistillation (%)

1 a­Thujene 931 0.7 1.9

2 a­Pinene 939 0.6 0.8

3 Camphene 952 0.1 0.6

4 1­Octene­3­ol 959 – 2.5

5 b­Pinene 984 0.4 0.9

6 Myrecene 999 1.5 1.7

7 a­Phellandrene 1006 0.5 0.3

8 d­3­Carene 1017 – 1.6

9 a­Terpinene 1021 3.5 4.7

10 p­Cymene 1027 4.1 3.8

11 Limonene 1033 0.8 1.1

12 (Z)­b­Ocimene 1035 – 2.1

13 Cis­Sabinene hydrate 1055 – 1.4

14 g­Terpinene 1060 35.5 22.5

15 Terpinolene 1080 – 4.5

16 Trans­Sabinene hydrate 1083 – 1.9

17 Linalool 1104 1.2 1.7

18 Trans­z­Caren­4­ol 1143 – 1.5

19 Terpinene­4­ol 1176 0.2 1.8

20 Thymol 1290 18.2 15.5

21 Carvacrol 1299 29.7 21.5

22 Carvacrol acetate 1374 – 2.5

23 b­Caryophyllene 1413 0.4 0.6

24 b­Bisabolene 1502 0.5 0.8

a Kovats retention indices on DB­5 column.b Percent of component based on the area normalization (P%).

948 M. Khajeh / J. of Supercritical Fluids 55 (2011) 944–948

constituents of the products. The SFE procedure was compared with

the traditional hydrodistillation method. Table 4 reports the oil and

extracts of SFE and hydrodistillation method. This results show that

different compounds can be extracted from S. hortensis using SFE

and hydrodistillation procedure. The percent of g­Terpinene, Thy­

mol and Carvacrol in the SFE was higher than hydrodistillation. The

SFE is more selective than hydrodistillation. Furthermore, the SFE

is less tedious and minimizes the risk of compound degradation

due to heat. In addition, the SFE presents some advantages over

hydrodistillation such as extraction rate.

4. Conclusion

In this study, the SFE of S. hortensis was studied. The response

surface methodology (RSM) has several advantages compared to

the classical methods in which one variable at a time technique

is used. Firstly, RSM gives a large amount of information from a

small number of experiments. Indeed, classical methods are time

consuming and a large number of experiments are required to

explain the behavior of a system. Secondly, in RSM it is possible

to observe the interaction effect of the independent parameters

on the response. The model equation easily clarifies effects for

binary combination of the independent parameters [24]. This study

showed that RSM was a convenient approach to optimize the best

culture conditions for obtained maximum oil and extracts of the

plant. The application of a Box­Behnken design became possible,

fast, economical and efficient way of an optimization strategy of the

proposed method. The SFE procedure developed for the extraction

of oil and extracts required to P = 35.0 MPa, T = 72.6 ◦C and M% = 8.6.

The SFE of S. hortensis was studied, and the results were

compared with essential oil composition obtained by hydrodistil­

lation. The SFE procedure offers many important advantages over

hydrodistillation. The SFE required shorter extraction time (15 min

vs. 4 h for hydrodistillation). Energy cost was higher for perform­

ing hydrodistillation than SFE. The flexibility in the manipulation of

the variables involved in the SFE process allows one to optimize the

experimental conditions considering the selectivity of a substance

or classes of substances of interest. The selectivity of SFE allowed

one to maximize the concentration of the selected compounds.

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