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J. of Supercritical Fluids 62 (2012) 88– 95

Contents lists available at SciVerse ScienceDirect

The Journal of Supercritical Fluids

j ourna l ho me p ag e: www.elsev ier .com/ lo cate /supf lu

ressurized liquid extraction of Orthosiphon stamineus oil: Experimental andodeling studies

arzad Pouralinazara, Mohd Aziz Che Yunusa, Gholamreza Zahedib,∗

Centre of Lipids Engineering Applied Research, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor Bahru, Johor, MalaysiaProcess Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor Bahru, Johor, Malaysia

r t i c l e i n f o

rticle history:eceived 12 April 2011eceived in revised form3 December 2011ccepted 13 December 2011

eywords:

a b s t r a c t

Extraction of Orthosiphon stamineus oil has been the subject of current study. In this case first basedon Box–Behnken experimental design method, experimental work was carried out to find the effect oftemperature, extraction time and the number of extraction cycles on extraction yield. Seventeen differentexperimental data were obtained and response surface modeling (RSM) was employed to find relationbetween extraction yield and process variables. A second order polynomial based on statistical analysiswith 95% confidence limits was found as the best estimator of extraction yields.

rthosiphon stamineusesponse surface modelingccelerated solvent extractionrtificial neural networks

In the next step of the study, artificial neural network (ANN) as a soft computing method was appliedto predict the oil yield. A multilayer perceptron (MLP) was used in this study. In order to implement anANN, temperature, extraction time and the number of extraction cycles were selected as input variablesand oil yield was considered as target variable. 70% of data were utilized for training and 30% of theremaining data were used for testing the best obtained network. The results illustrated that ANN methodis more reliable than RSM method for extraction prediction and optimization. The optimum operating

100 ◦

conditions were found at

. Introduction

Orthosiphon stamineus (OS), Benth (Lamiaceae) or Cat’s Whiskers known as ‘Misai kucing’ by the locals in Malaysia. This multi-urpose herb can be found in South East Asia such as Malaysia,

ndonesia, Thailand and Philippines. Traditionally this plant is usedy the medicine practitioners for the treatment of joint inflam-ation. Apart from that, OS is also used for the treatment of

out, arthritis, rheumatism and remedy for kidney stones. Com-ercially, OS is famous because of its slimming property. OS is

nown as Java Tea in the market for its safe and effective milderbal diuretic that throws out excess fluids, nitrogen substancesnd sodium chloride. Moreover, OS contains huge amount of potas-ium that replaces what may be lost from the body in the normaliuretic process [1]. A medicinal benefit of OS has made it onef the well-known plants in the region; for instance, it has beensed for the treatment of eruptive fever, epilepsy, gallstone, hep-titis, rheumatism, hypertension, syphilis and renal calculus [2]. OS

ontains several chemically active components such as terpenoidsditerpenes and triterpenes), polyphenols (lipophilic flavonoidsnd phenolic acids), and sterols [3]. The therapeutic effect of OSs attributed to its polyphenols which lead to decrease of oxidative

∗ Corresponding author. Fax: +60 7 5581463.E-mail addresses: grzahedi@cheme.utm.my, grzahedi@yahoo.com (G. Zahedi).

896-8446/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.supflu.2011.12.009

C, 10 min and 2 cycles.© 2011 Elsevier B.V. All rights reserved.

stress by avoiding the formation of lipid peroxidation products inbiological systems [4]. Sinensetin, eupatorin, 30-hydroxy-5,6,7,40-tetramethoxyflavone, lipophilic flavonoids, the phenolic acid androsmarinic acid are dominant in OS leaves [2]. In the present study,isolation of OS oil is one of the objectives. There have been con-ventional methods for concentrating and isolating of constituents,but they have some drawbacks such as time consuming, labori-ous and low selectivity and extraction yields. Furthermore, theyutilize the large amount of organic solvents. Therefore scientiststried to develop better techniques for lowering the demerits of con-ventional extraction methods; hence, systems such as solid phasemicroextraction (SPME), solvent free solid injection, supercriticalfluid extraction (SFE) and pressurized liquid extraction or acceler-ated solvent extraction (ASE) were introduced. At the present studySPME method is easy to implement but the purity of extraction isnot high. SFE provides almost 100% pure samples but it is energyconsuming because of high pressure and temperatures which areinvolved. ASE technique is an intermediate solution which wasselected with ethanol as an environmental friendly solvent in thisstudy [5].

Several studies have been conducted on extraction and iso-

lation of bioactive compounds and antioxidants. The extractionof the carotenoid from micro algae Haematococcus pluvialis andDunaliella salina using ethanol as a solvent was studied by Den-ery et al. [6] and Herrero et al. [5] demonstrated optimization ofantioxidant from Spirulina platensis micro alga. In addition, their

percritical Fluids 62 (2012) 88– 95 89

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container was weighed and subtracted from the empty weight sothat the real weight of extract was calculated. The obtained dataare given in Table 1.

F. Pouralinazar et al. / J. of Su

esults showed the possibility of fast and easy isolation of nat-ral antioxidants from natural sources such as micro algae [6].ho et al. optimized conditions for the extraction of secondaryolatile metabolites in Angelica’s roots by accelerated solventxtraction [7]. Akowuah et al. studied extraction of OS using soxh-et extraction. Since dynamic characteristics of extraction are quiteomplicated, experimental-based optimization is a methodologyllowing better understanding of relationship between indepen-ent and dependant variables; in other words, the effect of

ndependent variables on response or dependent variables can benderstood easily. However, some investigators have mentionedhat in case there are a large amount of experiments, it will becomempractical to conduct all tests [8]. Therefore, response surface

odeling (RSM) as an effective statistical tool can be used for inves-igating the influences of various variables, affecting the responses9]. The beauty of novel optimization techniques is that the interac-ions of variables affecting the dependent variables are consideredccurately [10]. Similarly, Guo et al. stated that the methods oftatistics are influential and powerful [11]. Many parameters mayffect the response; therefore, it is important to choose the besttatistical model in order to minimize the number of experiments,s well as evaluating the effects of important variables and inter-ctions among them in multivariable system, as stated by Bhuniand Ghangrekar [12]. Recently, modeling, optimization and pro-ess characterization have been conducted by statistical designf experiments in many processes [11,13–18]. Another approachhich will be employed for prediction of oil yield is artificial neuraletworks (ANNs). In fact, ANN is able to make a non-linear map-ing between input and output [19,20]. ANNs have been used forifferent kinds of processes such as fermentation [21], crossflowicrofiltration [22] drying trend of various food and agricultural

egetables such as carrot [23], tomato [24], ginseng [25], cassavand mango [26] and osmotic dehydration [27], but applicationf artificial neural networks in simulation of accelerated solventxtraction process (in particular for OS) is a unique study that hasot been investigated by other researchers.

In spite of the fact that many studies have been conducted byxperimental design technique and artificial network (ANN) forarious processes, but to the best of our knowledge there is notudy on RSM of accelerated solvent extraction of OS and also onNN modeling of accelerated solvent extraction of OS.

. Materials and method

.1. Sample preparation

The leaves were collected in Bumbung Lima, Penang State,alaysia from 30- to 45-day-old white-flowered plants. Specimenas labeled, numbered and annotated with the date of collection

nd deposited at Chemical Engineering Pilot Plant, CEPP, Universitieknologi Malaysia. Figs. 1 and 2 depict OS in the form of whiteowers and pretreated form, respectively.

Some samples must be mixed with drying or dispersing agentefore being loaded into the cell. Pelletized diatomaceous earthDE) is a drying agent and it is easier to work with than other dryinggent like sodium sulfate (NA2SO4). DE dries the sample quickly,rovides a cleaner transfer of the mixture to the cell, and extractsell. There is a guideline that determines which drying agent isseful and based on the guideline 5 g sample to 1 g DE is introduced.herefore, 5 g OS to 1 g DE was mixed and loaded into cell.

.2. Accelerated solvent extraction

To perform the extractions with an accelerated solvent extrac-ion system, ASE 100, from Dionex Corporation (Sunnyvale, CA,

Fig. 1. Orthosiphon stamineus (Misai kucing).

USA) was used. Extractions were performed at three differentextraction temperatures (80, 100 and 120 ◦C), static times (5, 10 and15 min) and number of extraction cycles (1, 2 and 3) according tothe Box–Behnken design of experiment (which will be discussed innext section). In this study, ethanol was selected as a solvent basedon its Generally Recognized as Safe (GRAS) statue [5] since ethanol’sboiling point is 78.4 ◦C and analyte solubilities are increased byASE at temperatures above the boiling point [34,35]. Temperaturerange was chosen between 80 and 120 ◦C. Different static timesbetween 5 and 15 min were chosen. In order to run the system anddo our experiments, first samples were prepared based on the ratioof 5 g OS and 1 g DE. At first, 0.5 g DE was loaded at the bottom ofthe cell and then 5 g of OS and finally 0.5 g of DE was loaded atthe top of the OS within the cell. Before placing the cell into therig, all the operation conditions were arranged in accordance toBox–Behnken design of experiment (DOE); afterwards, 17 experi-ments were carried out so that the cell is loaded into system andfinally the extract is poured in the collection cell. Next, the collec-tion cell was removed and was placed in a rotary evaporator fora while in order to evaporate ethanol from the solution extract.Accordingly ethanol was separated and stored in a container andconcentrated extract remains in it. Finally, extract was poured ina small container and it was placed in an oven with a fixed tem-perature of 75 ◦C to ensure that all ethanol is evaporated. Then the

Fig. 2. Pretreated Orthosiphon stamineus (OS).

90 F. Pouralinazar et al. / J. of Supercri

Table 1Experimental range and levels of independent variables.

Variables Range and level

Low level(−1)

Centerlevel (0)

Highlevel (+1)

�Xia

Temperature (◦C) 80 100 120 20

iTtvatdeRuefE

2

ieBoNadw

Extraction time (min) 5 10 15 5Extraction cycle 1 2 3 1

a Step change values.

First, the cell was filled with sample; then, solvent was pouredn it. The pressure of the system should reach 1500 psia (102.4 atm).hen temperature conditions were set according to the experimen-al design; thereafter, extraction was conducted when all systemalves were closed. The cell was washed with extraction solventnd 60% of the cell volume was used to rinse it. N2 gas was usedo discharge solvent from the cell and finally the system wasepressurized. In order to avoid any extract carry-over betweenxtractions, system was rinsed completely after each extraction.otavapor R-200 (Büchi Labortechnik AG, Flawil, Switzerland) wassed for evaporation purpose. All the extracts one by one, werevaporated and were placed in an oven at a temperature of 75 ◦Cor 24 h. Dry extracts were weighed. Fig. 3 depicts schematic ofxperimental rig (ASE100).

.3. Experimental design

In this study, for obtaining a relationship between the responses,.e., oil recovery, and three process parameters (temperature,xtraction time, and the number of extraction cycles), theox–Behnken experimental design was employed [28]. Numberf runs in Box–Behnken design is calculated in accordance with

= 2k*(k − 1) + cp, where k is the number of independent variablesnd cp is the number of central point repeats [28,29]. Box–Behnkenesign based on RSM provides a design of experiments throughhich not only optimum conditions are achieved, but also the

Fig. 3. Schematic accelerate

tical Fluids 62 (2012) 88– 95

number of experiments is reduced. RSM constitutes an experi-mental methods category for the determination of a relationshipbetween independent and dependant variables [30,31]. In RSMdevelopment, at the beginning, a first order model was developedto find a relationship between input and output variables. It wasfound that the model is not accurate and in this case a second-order model was developed in the next attempt [30]. The secondorder model has the following form:

Y = ˇ +k∑

i=1

ˇixi +k∑

i=1

ˇiix2i +

k∑i=1

k∑j=1

ˇijxixj + ε (1)

where Y is the process response or output (dependent variable), kis the representative of the number of independent variables; iscalled intercept term and j are indexes for independent variables;x1, x2, . . ., xk are the coded independent variables, i, ii, and ij arecalled linear, quadratic, and interaction effects, respectively, and εis the random error, which shows discrepancies between observedand predicted values. The uncoded independent variables (X1, X2,. . ., Xk) are coded in accordance with the transfer equation givenbelow [9,30–32]:

xi = Xi − X0

�Xi(2)

where xi, Xi, X0, and �Xi are dimensionless coded value for the ithindependent variable, uncoded value for the ith independent vari-able, uncoded value for the ith independent variable at the centerpoint, and �Xi is value of the step change, respectively. In orderto find the fitted function, as well as contour and surface plots aStatistica 6 software was employed.

2.4. Statistical analysis

In this study, each experiment repeated three times to observethe reproducibility. STATISTICA software package version 6.0 wasused for regression and graphical analysis of obtained data [33].Analysis of variance known as ANOVA was used in order to find

d solvent extraction.

percritical Fluids 62 (2012) 88– 95 91

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Table 2Box–Behnken design with coded/actual values and results.

Run Coded level of variables Actual level of variables Observed result

x1 x2 x3 x1 x2 x3 Recovery (%)

1 −1 −1 0 80 5 2 7.1292 1 0 −1 120 10 1 10.8003 0 0 0 100 10 2 22.8004 0 −1 −1 100 5 1 11.0005 −1 1 0 80 15 2 10.6016 −1 2 1 80 10 3 12.6007 0 0 0 100 10 2 22.0408 1 0 1 120 10 3 13.0609 0 1 1 100 15 3 17.604

10 0 1 −1 100 15 1 11.55011 1 −1 1 120 5 2 13.01012 0 −1 1 100 5 3 14.69013 1 1 0 120 15 2 10.32414 −1 0 −1 80 10 1 9.0401

F. Pouralinazar et al. / J. of Su

tatistical significance in all analyses. Pareto chart was used in ordero determine the influence of independent variables on extractionield. On the whole, results were assessed with various descriptivetatistics such as, F value, degrees of freedom (df), determinationoefficient (R2), adjusted determination coefficient (R2

a), sum ofquares (SS), mean sum of squares (MSS). F value is an importantarameter to ensure that the function or model is selected appro-riately. The F tabulated was obtained by nN (numerator = df) andD (denominator = n − df + 1) at the desired probability level (i.e.,

= 0.05 or 95% confidence).

. Artificial neural network modeling

.1. Overview of neural networks

Neural network is a deductive model, including complex units,hich are similar to the neurons of the body. The units are in the

hape of linked loop structure, operating like axon and dendrites36]. In other words ANN attempts to understand and model brainehavior. The back propagation in ANN is commonly used networkspecially in chemical engineering because of simplicity, compactesign and flexibility [37]. Multilayer perceptron (MLP) is famouseural network structure which is employed to categorize and esti-ate neural problems. A neural network consists of an input layer

or receiving data from an external source, one or more hiddenayers for processing and an output layer for displaying the outputalues (Fig. 4). In each layer there are neurons, which are connectedo previous and next layers. A neuron has input, output, weight andias and transfer function. In back propagation, first feed forward isade and net parameters known as weight and bias are adjusted so

hat a particular input leads to a specific target value. The networks adjusted based on a comparison of the output and the target.his trend continues until the network output matches target [38].n fact, input neurons receive the data values and pass them on firstidden layer neurons; then the data are multiplied to weight factornd bias is added to the value; afterwards, result passes through aon-linear transformation function. A result from transformationart forms input for either next hidden layer or output layer. Finally,utput values are checked with target and back propagation con-inues until the global error approaches a small, determined error.omponent of output error from the nth neuron on the output layernally is defined as the following function:

m = dm − cm (3)

here dm is the desired output value and cm is the calculated value;oreover, the total squared error function E is defined by the fol-

owing equation:

e2m =

∑(dm − cm)2. (4)

eights are adjusted to minimize the total error function [37]. Asrror reaches specified minimum limit the teaching process ter-inates. In this study, error function is Mean Square Error (MSE)

nstead of total error function, which can be shown by the followingormula:

j = 1n

n∑i=1

(Ci − Cir)2. (5)

In this formula Cir is real output and Ci is an estimated outputor j in the input [36]. In Fig. 4 input layer, hidden layer and outputayer have been shown nicely. In order to find output neuron j, the

ollowing formula is introduced for f:

j = f

(l∑

i=1

vijaj

)(6)

15 0 0 0 100 10 2 22.20316 0 0 0 100 10 2 22.43017 0 0 0 100 10 2 21.800

where i is an index representing the hidden layer, j is an index forinput layer, vij are weights and aj is output from first layer. Sum-mation in this formula is started from 1 to L in which L representsL input layers m hidden layers.

3.2. Development of a neural network model

In this study, a feed-forward neural network was used to repre-sent non-linear relationships among variables [39]. In order to trainANN, the data were categorized into two parts: training data andtest data. Training is a process in which ANN modifies weights basedon input data [40–42]. Test is a procedure through which accuracyof the network is checked. 70% of data were utilized for trainingof the network and 30% were dedicated to test procedure [43]. Oilyield data resulted from ASE were used for ANN model develop-ment. The network was trained with well-known algorithms suchas Conjugate gradient, Quasi-Newton and Levenberg–Marquardtin MATLAB environment. “tansig” as a non-linear transfer func-tion and “purline” as a linear transfer function were utilized inthis study. Temperature, extraction time and the number of extrac-tion cycles were employed as input variables where oil yield wasconsidered as an output variable.

4. Results and discussion

4.1. Statistical model

Results of Box–Behnken design with coded, uncoded variablesand oil recovery is shown in Table 2. In this study, temperature inthe quadratic form had the most dominant effect on the oil yield.The model equation representing the response (z) was expressedas function of temperature (x1), extraction time (x2), the numberof extraction cycles the number of the number of extraction cycles(x3) for coded unit as the following:

Z = −217.085 + 3.816x1 + 5.2703x2 + 17.26x3

− 0.0179x21 − 0.193x2

2 − 3.717x23 − 0.01625x1x3

− 0.01539x1x2 + 0.118x2x3 (7)

The obtained polynomial equation needs to be analyzed by anal-ysis of variance (ANOVA). In other words, significance of the modelshould be tested; therefore, ANOVA was carried out and the resultsare given in Table 3 [32].

92 F. Pouralinazar et al. / J. of Supercritical Fluids 62 (2012) 88– 95

Fig. 4. Structure of a n

Table 3Analysis of variance (ANOVA) of the response surface model to predict the response.

Factors (coded) SSb dfc MSSd F value, Fcal Probability (p) > F

Model 423.7579 9 47.0842 68.4462 0.000000e

X1 7.6245 1 7.6245 11.0834 0.012605X2

1 216.0512 1 216.0512 314.0640 0.000000e

X2 2.2578 1 2.2578 3.2821 0.112941X2

2 98.1558 1 98.1558 142.6847 0.000007e

X3 30.2642 1 30.2642 43.9937 0.000295e

X23 58.1339 1 58.1339 84.5067 0.000037e

X1X2 9.4556 1 9.4556 13.7452 0.007580e

X1X3 0.4225 1 0.4225 0.6142 0.458913X2X3 1.3924 1 1.3924 2.0241 0.197822Ea 4.8154 7 0.6879Total SS 468.0075 16

a E is indicating the error.

fit(l

dnoltrtp

b

nant effect on the oil yield so that response increased with the riseof the number of extraction cycles.

Figs. 8 and 9 illustrate the effect of temperature and static timeon the oil yield at the fixed effect of two the number of extrac-tion cycles. Static time has a positive linear effect on the oil yield,

b Sum of squares.c Degrees of freedom.d Mean sum of squares.e p values <0.05 were considered to be significant.

It is obvious from Table 3 that the obtained equation is quitetted. Higher Fisher’s F-test, which is a criteria for proving how fit-ed the function is, shows better fit of the model. Since Fcal > Ftab68.4462 > 3.685), it can be concluded that with 95% confidenceevel Eq. (7) is reliable.

In this study R2 was found as 0.9897, indicating that only 1.03%ifference was observed. In addition, the value of adjusted determi-ation coefficient (R2

a) was found as 0.9764, meaning high reliabilityf statistical model [8,9,31]. Pareto chart is introduced (Fig. 5). Theength of bars shows the standardized effects of independent fac-ors and their interactions on the oil yield recovery [44]. The main

eason that the factors such as X2, X2X3, and X1X3 remained insidehe reference line in Fig. 5 is that they have least contribution inrediction of the oil yield.

In order to understand the effect of variables on the responseetter, three-dimensional (3D) plots for the measured response

eural network.

were formed based on the obtained statistical quadratic model. Inthis case one variable kept in constant state (center level) and thenthe surface and contour plots were generated.

Figs. 7 and 6 represent the response surface and contour plots,respectively. They show the effect of temperature and the numberof extraction cycles on the oil yield at the static time of 10 min. Pres-sure was equal to 1500 psia in all experiments. At low temperaturesthe oil yield increases with temperature; however, at higher tem-peratures the oil yield decreased with increment of temperature.The reason might be due to reduced selectivity of constituents athigh temperature [7]. The number of extraction cycles has a domi-

Fig. 5. Pareto chart showing the standardized effect of independent variables andtheir interaction on the oil yield extraction.

F. Pouralinazar et al. / J. of Supercritical Fluids 62 (2012) 88– 95 93

Fig. 6. Effect of temperature and the number of extraction cycles on the oil yield(contour plot).

Fig. 7. Effect of the number of extraction cycles and temperature on the oil yield(response surface plot).

Fp

fqwta

solvent during the extraction process, helping to maintain goodextraction equilibrium. Different cycles sometimes are applied in amethod, so that flush volume is divided by the number of the cycles.The trend is in a way that when the first step is complete, extract

ig. 8. Effect of temperature and extraction time cycles on the oil yield (contourlot).

or most of the times; however, for long static times, the negativeuadratic effect also becomes significant. Eleven-minute static time

as found as the optimum time needed for a maximum response. In

his case, there was a significant interaction between temperaturend static time.

Fig. 9. Effect of temperature and the number of extraction cycles on the oil yield(response surface plot).

In Figs. 10 and 11, response surface and contour plots depict theeffect of the number of extraction cycles and static time on the oilyield at the fixed temperature of 100 ◦C. There was not a significantinteraction between static time and the number of extraction cycleson the basis of Pareto charts.

Increasing the temperature from 80 to 100 ◦C at constant pres-sure of 1500 psi and constant static time of 10 min increased theamount of oil yield; however, increasing the temperature from 100to 120 ◦C reduced the amount of oil yield. In case of two constants,the number of extraction cycles and 1500 psia pressure, by increas-ing the temperature at low levels of static time, better oil yield wasobtained; in contrast, low oil yield was obtained at high levels ofstatic time. There is a significant interaction between static timeand temperature based on the Pareto chart. Temperature as a sin-gle and independent variable increases the ability of the solventto solubilize the compounds and, reduce the viscosity of the liquidsolvent which is allowing better penetration of the solvent into thesolid matrix. High temperature (above 100 ◦C) first increases sol-ubility and mass transfer, but may reduce selectivity. 100 ◦C wasfound as the optimum temperature in this study.

The use of the number of extraction cycles is to enter fresh

Fig. 10. Effect of the number of extraction cycles and static time on the oil yield(contour plot).

94 F. Pouralinazar et al. / J. of Supercritical Fluids 62 (2012) 88– 95

Fig. 11. Effect of the number of extraction cycles and static time on the oil yield(response surface plot).

itcvtpie

4

cMd

TC

Fig. 12. MSE versus number of epochs for MLP.

s directed to the collection vial along with “used” solvent. Afterhat, the second cycle starts and the procedure continues until allycles are complete. More than one cycle is used for samples with aery high concentration of analyte, as well as samples with difficulto penetrate matrices. Static time plays an important role in com-lete extraction. The effect of static time should always be explored

n conjunction with static cycles, in order to produce a completextraction in the most efficient way possible.

.2. ANN modeling

Table 4 shows comparison of different network generalizationapabilities. In order to estimate oil yield data, one network, namelyLP, was utilized. Fig. 12 depicts the error percentage for tested

ata. Different training algorithms such as Conjugate gradient and

able 4omparison of different networks.

Observed data 7.1290 10.3240 14.6900 13.0100Levenberg–Marquardt 7.1286 10.3250 14.6857 13.0104Quasi-Newton 11.3778 14.1032 16.2320 12.8394Conjugate gradient 8.2974 20.2625 11.1037 13.5550

Fig. 13. Comparison of different training algorithms.

Quasi-Newton and Levenberg–Marquardt were employed. Fig. 13depicts the comparison between training algorithms. Using Matlab2010 programming software, Levenberg–Marquardt was found asthe best training algorithms in this study. The optimum amount ofoil yield, 22.29%, was found at 100 ◦C, 10 min, and 2 cycles on thebasis of second-order polynomial model, whereas this amount wasfound 22.80% by ANN. The results show that ANN model is morereliable than RSM.

5. Conclusion

In this study obtained results form second-order polynomialmodel, Box–Behnken design, predicted the response variable of theoil yield to change in the process parameters for accelerated sol-vent extraction of O. stamineus within the experimental ranges.Quadratic temperature, static time and the number of extractioncycles affected the oil yield, respectively on the basis of Paretochart. In addition, interactions between temperature and statictime and the number of extraction cycles and static time had a sig-nificant effect on the oil yield. The results of statistical model werecompared with an ANN model. R2 of RSM was found as 0.9897;meanwhile, for this value for ANN, it was 0.9999 which shows thatANN is more reliable than RSM model for subcritical extractionmodeling. The result, which indicates that ANN is more accuratethan RSM is in agreement with our previous study [45].

Acknowledgments

The authors thank the Department of Chemical Engineering ofthe “Universiti Teknologi Malaysia”, as well as Chemical Engineer-ing Pilot Plant, CEPP, for the special support that made this researchpossible the financial support of Universiti Teknologi Malaysiaunder grant no. 4D042 is gratefully acknowledged.

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