modeling and optimization of jojoba oil extraction yield...

© 2019 Journal of Pharmacy & Pharmacognosy Research, 7 (5), 367-380, 2019 ISSN 0719-4250

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Original Article | Artículo Original

_____________________________________

Modeling and optimization of jojoba oil extraction yield using Response Surface Methodology

[Modelado y optimización del rendimiento de extracción de aceite de jojoba mediante la metodología de superficie de respuesta]

Iyad Al-Sheikh1, Jehad A.A. Yamin2*

1Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman 11733, Jordan. 2Mechanical Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan.

*E-mail: [email protected]

Abstract Resumen

Context: Jordan is looking for a cheap and locally affordable source for its cosmetics, fuel and medical applications. Jojoba offers one solution to such problems. It is considered as a good medicinal plant that can be used for several applications.

Aims: To evaluate the effect of parameters as mixing speed, temperature, feedstock grain size, mixture ratio and mixing time on the jojoba yield.

Methods: The mathematical model combining the effect of all the above variables was then used to find the optimum combination for maximum yield. Response Surface Methodology (RSM) technique was used for modeling and optimization. Based on the Pareto chart of parameters effect, the seed size was the most significant followed with temperature effect.

Results: It was found that the optimum values obtained for best yield were seed size of about 0.48 mm, the temperature of about 65°C and mixing time of 2.8 hours maximum yield of about 56% wt can be obtained.

Conclusions: A mathematical model was successfully built and tested for the Jojoba oil yield under different conditions. The optimum parameters that produced the highest yield were also found.

Contexto: Jordania está buscando una fuente barata y localmente asequible para sus cosméticos, combustible y aplicaciones médicas. Jojoba ofrece una solución a tales problemas. Esta se considera una buena planta medicinal que se puede utilizar para varias aplicaciones.

Objetivos: Evaluar el efecto de parámetros como la velocidad de mezcla, la temperatura, el tamaño de grano de la materia prima, la relación de mezcla y el tiempo de mezcla en el rendimiento de jojoba.

Métodos: El modelo matemático que combina el efecto de todas las variables anteriores se utilizó para encontrar la combinación óptima para obtener el máximo rendimiento. Se utilizó la técnica de metodología de superficie de respuesta (RSM) para el modelado y la optimización. Basado en la gráfico de Pareto del efecto de los parámetros, el tamaño de semilla fue el más significativo seguido del efecto de la temperatura.

Resultados: Se encontró que los valores óptimos para el mejor rendimien-to fueron el tamaño de semilla de aproximadamente 0,48 mm, la tem-peratura 65°C y el tiempo de mezcla de 2,8 horas, y se obtiene un rendimiento máximo de aproximadamente 56% en peso.

Conclusiones: Se construyó y probó con éxito un modelo matemático para el rendimiento del aceite de jojoba en diferentes condiciones. También se encontraron los parámetros óptimos que produjeron el mayor rendimiento.

Keywords: ANOVA; DOE; experimental model; optimization; response surface methodology; yield.

Palabras Clave: ANOVA, DOE; método de superficie de respuesta; modelo experimental; optimización; rendimiento.

ARTICLE INFO Received: January 19, 2019. Received in revised form: August 23, 2019. Accepted: August 24, 2019. Available Online: August 28, 2019. Declaration of interests: The authors declare no conflict of interest. Funding: The authors confirm that the project has not funding or grants.


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Al-Sheikh and Yamin Jojoba oil extraction yield modeling and optimization

http://jppres.com/jppres J Pharm Pharmacogn Res (2019) 7(5): 368

INTRODUCTION

Simmondsia chinensis (Link) C.K. Schneid. (jojo-ba) plant (Fig. 1) is a desert-like perennial shrub. It is linked to the Buxaceae family (Wisniak, 1994) and belongs to the family Simmondsiaceae and it is the raw material necessary to obtain one-of-a-kind mixture of high molecular weight monounsaturat-ed esters (Sánchez et al., 2016) in the range of C34 to C50.

A

B

Figure 1. Tree (A) and seeds (B) of Simmondsia chinensis (jojoba).

Jojoba plant is known to have utmost tolerance to various environments. It is known for the fact that it has 45-55% of the yield as long-chain esters of fatty acids lipids (wax esters) (Kumar and Sharma, 2011).

Non-edible oilseed crops as feed-stocks have several advantages for example (Ahmad et al., 2011; No, 2011; Atabani et al., 2013): (1) they can be grown in both high and low rainfall zones; (2) huge potential to restore degraded lands, create rural employment generation and fixing of CO2

emissions; (3) they do not compete with lands re-served for food and feed; (4) environmentally friendly than the first generation feedstock; (5) less farmland is required and a mixture of crops can be used; and (6) highly resistant pest and disease.

There are some problems encountered with non-edible oils of forest origin (Syers et al., 2008) as (1) collection from scattered locations, high dormancy, and problems in picking and harvest-ing in a venue and forest plantations; (2) low-quality planting seed; and (3) lack of proper tech-nology for post-harvest processing.

On the other hand, there are three main meth-ods that are used for oil extraction: mechanical, solvent and enzymatic extractions. In this study, solvent extraction (chemical extraction) method was used. It consists of extracting certain constitu-ent from a solid material by means of a liquid sol-vent. It is also called leaching (Khasawneh, 2017). Several factors affect the rate and output of this method e.g., particle size, the solvent used, tem-perature and agitation of the solvent. It is general-ly noticed that smaller particles are preferable as they have a higher surface-to-volume ratio hence more surface area of interaction with the solvent. The solvent should have good viscosity to circulate and interact with the solid materials. Temperature influences (usually increases) the solubility of the materials.

Stirring or agitation helps in enhancing the dif-fusion or transfer of the materials out of the parti-cle surface. Solvent extraction is only economical at large-scale production (Anthony and Stuart, 2015).

Solvent extraction can be done using either of the following three methods (Mahanta and Shrivastava, 2006; Achten et al., 2008; Atabani et al., 2012): (1) Hot water extraction, (2) Soxhlet ex-traction and (3) Ultrasonication technique. Mahan-ta and Shrivastava (2006) and Achten et al. (2008) indicated that solvent extraction with n-hexane can be used to extract the oil from jatropha seed and pmgamiapinnata. They reported 41% oil yield with Jatropha and 95-99% with Pmgamiapinnata. Moreover, several other studies conducted by




Kansedo et al. (2009), Moser and Vaughn (2010), Rashid et al. (2008) and Sarin et al. (2009) extracted the oil from Cerbera odollam Gaertn. (sea mango), Coriandrum sativum L. (coriander), Moringa oleifera and Guizotia abyssinica L. using Soxhlet extractor with n-hexane as the solvent.

Several mathematical methods were imple-mented in analyzing and optimizing oil extraction from seeds (leaching process). In this research work, the Response Surface Method (RSM) has been used.

MATERIAL AND METHODS

The analysis was performed based on the ex-perimentally measured jojoba oil yield reported in Abu-Arabi et al. (2000) and Allawzi et al. (2005).

Preparation of jojoba seeds

Jordanian jojoba seeds were obtained from the Center for Agricultural Research (32°29'14.5"N 35°59'01.0"E) at Jordan University of Science and Technology (JUST) farmland. The seeds were peeled, crushed, and screened using the locally made machine in the University workshop.

Measurement of seed dimensions

A Vernier caliper model (Mitutoyo DAG-500, Japan) with an accuracy of 0.01 mm was used to measure the major dimensions of the seeds e.g. length (L), width (W) and thickness (T) in millime-ters and the average values of 100 seeds were con-sidered. Other calculations e.g. area, volume were calculated using the procedure laid down in (Mohsenin, 1984; Deshpande et al., 1993; El Raie et al., 1996).

Different particles sizes were obtained by means of roll mill. The average particle size was calculated using the screen analysis procedure and found to be 1.85 mm. To obtain a smaller particle size used a home seed grinding machine, there-fore, a uniform particle size of 0.45 mm resulted from this machine.

Leaching procedure

A Soxhlet extractor (Fig. 2) was employed for the leaching experiment. For each test, the Soxhlet extractor was charged with 30 g of crushed jojoba seeds and 150 mL of hexane was used. Leaching process was done at the boiling point of the sol-vent and continued till there was until a clear liq-uid obtained from the jojoba, which indicated complete leaching of the leachable oil. The dura-tion of the process lasted for 18 hours. Oil and sol-vent were then separated into two stages of distil-lation.

Figure 2. Schematic of the experimental setup.

The first stage uses a simple distillation while rotavapor was used for the second stage. A vacu-um pump was attached to the rotavapor apparatus to ensure complete removal of the solvent. The oil produced by this method was compared with the pure pressed oil by measuring their properties.

The data under investigation were as follows:

1. The mixing speed was varied between 200 to 1000 rpm with an increment of 400 rpm.

2. The mixture temperature was varied from 35 to 65 at 10°C increment.

3. The mixing time vas varied between 0.5 to 4.0 at an increment of 0.5 hours.

4. The jojoba oil seed size was taken as 0.48, 0.86, and 1.5 mm.

The solvent-to-seed ratio 5 to 15 at an increment of 5 mL/g.




The data was then modeled using the RSM technique using Minitab 18 software with the main aim of finding the best factors for maximum yield in wt%. The factorial table as used in Minitab is shown below in Annex 1.

After the best-fit equation was found, optimiza-tion was done with the main objective of maximiz-ing the yield.

Statistical analysis

The effect of each one of the above variables was done using 814133 design of experiments anal-ysis using Minitab 18 software. RSM consists of a number of techniques (mathematical and statisti-cal) to fit empirical models to the real data ob-tained by experiment in the form of experimental design.

The simplest model, which can be used in RSM is based on a linear function, however, if the re-sponse shows any curvature (as in the case of this research paper), a higher-order model must be used. A central point in two-level factorial designs can be used for evaluating curvature.

The next level of the polynomial model should contain additional terms, which describe the inter-action between the different experimental varia-bles. If the objective is extended further towards finding the points of maximum or minimum, hence, the equation [1] must contain a quadratic term as shown below.

𝑦 = 𝛽𝑜 + 𝛽𝑖𝑥𝑖 + 𝛽𝑖𝑗𝑘

𝑖=1𝑥𝑖

2 + 𝛽𝑖𝑗𝑥𝑖𝑥𝑗𝑖

1≤𝑖≤𝑗+ 𝜖

𝑘

𝑖=1 1

[1]

where ii represents the coefficients of the quadrat-ic parameter.

RESULTS AND DISCUSSION

The data used for the modelling was originally published by Allawzi et al. (Allawzi et al., 1998; 2005; Abu-Arabi et al., 2000). The results obtained for this study are shown in Tables 1-4.

Effect of extraction factors

First, a test of correlation was conducted to check the strength of the relationship between the variables. The result is shown in Table 5.

The study clearly shows that the strongest effect among the variables studied (within the range studied) on yield was that for the seed size with -0.8636. This means that the effect of size is nega-tive i.e., as the size increases, the yield decreases. Further, it shows no interaction between the size and other variables.

On the other hand, the effect of the rest of the variables was all positive with weakest for solvent-to-seed ratio. Mixing speed, temperature and time were all significant. The most significant being for mixing speed. The study also showed that there was a moderate relationship between mixing speed with time, and, to a lesser extent, between temperature and mixing speed and seed size.

The results of the ANOVA test for this experi-ment was conducted for each variable alone and for the whole variables in one table. This is shown in Tables 6 and 7.

As shown in Tables 6 and 7, the mean square (MSA) between treatments, 10119239.74, was much larger than the mean square within treat-ments (MSB), 10865.78863. That ratio, between-groups mean square over within-groups mean square, was called an F statistic (F = MSA/MSB = 931.2936) in this example. It shows that there is significant variability between variables than with-in variables. The larger that ratio, the more confi-dent the researcher can be in rejecting the null hy-pothesis (Ho), which was that all means are equal and there is no treatment effect. But this analysis should be accompanied with the calculation of the p-value (in this case was found to be equal to 6.2E-285), obtained from the F distribution. The p-value has the usual interpretation: the probability of the between-treatments (MSA) being ≥931.2936 times the within-treatments (MSB), if the null hypothesis is true, is p = 6.2E-285.




Table 1. Summary for the test (for the effect of temperature at different mixing time).

Mixing time (h) Mixing speed (RPM)

Seed size (mm)

Solvent-to-seed ratio

Yield (%)

T = 35°C T = 45°C T = 55°C T = 65°C

0.50 600.00 1.50 10.00 20.00 21.00 24.00 25.00

1.00 600.00 1.50 10.00 26.00 27.00 29.00 33.00

1.50 600.00 1.50 10.00 27.00 28.00 32.00 35.00

2.00 600.00 1.50 10.00 29.00 30.00 33.00 36.00

2.50 600.00 1.50 10.00 30.00 32.00 35.00 37.00

3.00 600.00 1.50 10.00 31.00 33.00 35.00 40.00

3.50 600.00 1.50 10.00 31.00 33.00 35.00 40.00

4.00 600.00 1.50 10.00 31.00 33.00 35.00 40.00

This table shows that as the temperature of the mixture increases, the oil yield also increases.

The data have MSA = 388926.6 and MSB = 4.801 with F = 81009.15 Fcrit = 2.29 and p< 0.00005.

Table 2. Summary for the test (for the effect of mixing speed at different mixing time).

Mixing time (h) Mixing temp (°C)

Seed size (mm)


Yield (%)

Speed = 200 RPM

Speed = 600 RPM

Speed = 1000 RPM

0.50 65.00 1.50 10.00 23.00 25.00 26.00

1.00 65.00 1.50 10.00 30.00 31.00 34.00

1.50 65.00 1.50 10.00 31.00 33.00 35.00

2.00 65.00 1.50 10.00 32.00 35.00 37.00

2.50 65.00 1.50 10.00 33.00 38.00 40.00

3.00 65.00 1.50 10.00 34.00 40.00 41.00

3.50 65.00 1.50 10.00 34.00 40.00 42.00

4.00 65.00 1.50 10.00 34.00 40.00 42.00

This table shows that as the mixing speed increases, the oil yield also increases. The data has MSA = 4239.327 and MSB = 10.586 with F = 400.4377, Fcrit = 2.29 and p< 0.00005. temp: Temperature.

Based on the above results, there is clear proof that the data obtained by this experiment is signif-icant and hence can be used for further statistical and modeling analysis.

Fig. 2 shows the effect of all variables on the yield. The figure consists of contour plots of all variables with the yield. First, it is noticed that for all parameters, mixing time above 2.7 hours was most significant except for smaller size seeds.

Uĝur et al. (2004) who also found similar results believes that the inter-particle diffusion seems to gain importance in more massive particles, caus-ing an appreciable decrease in the extraction yield.

Further, it was noticed that regardless of all other factors, mixing temperature near to 60°C showed the best yield values. This is expected and has been reported in the findings of other re-searchers (Al-Hamamre and Yamin, 2014; Khasawneh, 2017; Sheet, 2018).

It is attributed to the fact that higher tempera-tures result in higher solubility of the solute in the solvent, higher ultimate concentration in the leach liquor are possible. The viscosity of the liquids decreases and diffusivities increase with tempera-ture leading to increased rates of leaching, so, the temperature has a positive influence on the leach-ing process.




The best mixing speed for maximum yield was found to be above 600 RPM, regardless of other parameters except for seed size. A smaller seed

sizes, lower mixing speeds can be used for a good yield.

Table 3. Summary for the test (for the effect of seed size at different mixing time).

Mixing time (h)

Mixing temp (°C)

Seed speed (RPM)


Yield (%)

Seed size = 0.48 mm Seed size = 0.86 mm Seed size = 1.5 mm

0.50 65.00 1000.00 10.00 48.00 35.00 26.00

1.00 65.00 1000.00 10.00 51.00 39.00 34.00

1.50 65.00 1000.00 10.00 52.00 41.00 35.00

2.00 65.00 1000.00 10.00 53.00 42.00 37.00

2.50 65.00 1000.00 10.00 55.00 44.00 40.00

3.00 65.00 1000.00 10.00 55.00 45.00 41.00

3.50 65.00 1000.00 10.00 55.00 45.00 42.00

4.00 65.00 1000.00 10.00 55.00 45.00 42.00

This table shows that there is a significant increase in the yield with seed size reduction. The data have MSA = 1068453.6 and MSB = 7.25 with F = 147321.1 Fcrit = 2.29 and p< 0.00005. temp: Temperature.

Table 4. Summary for the test (for the effect of solvent-to-seed ratio at different mixing time).

Mixing time (h)

Mixing temp (°C)

Mixing speed (RPM)

Seed size (mm)

Yield (%)

Solvent-to-ratio = 5 Solvent-to-ratio = 10 Solvent-to-ratio = 15

0.50 65.00 1000.00 0.48 34.00 48.00 50.00

1.00 65.00 1000.00 0.48 48.00 51.00 52.00

1.50 65.00 1000.00 0.48 50.00 52.00 54.00

2.00 65.00 1000.00 0.48 51.00 53.00 55.00

2.50 65.00 1000.00 0.48 52.00 55.00 55.00

3.00 65.00 1000.00 0.48 52.00 55.00 55.00

3.50 65.00 1000.00 0.48 52.00 55.00 55.00

4.00 65.00 1000.00 0.48 52.00 55.00 55.00

This table shows that there is no significant change in the yield with solvent-to-seed ratio.

The data has MSA = 1064649.66 and MSB = 7.107 with F = 149799.95 Fcrit = 2.29 and p< 0.00005. temp: Temperature.

Table 5. Correlation results of the data.

Time Temp Speed Size Ratio Yield

Time 1

Temperature 0 1

Speed -3.8E-18 0.361303 1

Size -4E-18 -0.38477 -0.657764247 1

Ratio 0 0 0 0 1

Yield 0.345926 0.527601 0.673835851 -0.86359 0.099362 1

This correlation study shows a significant effect of seed size over other factors. It also shows that this effect is negative compared with other variables. The solvent-to-seed ratio has a very weak influence. Temp: Temperature.




Table 6. Summary of the data.

Groups Count Sum Average Variance

Mixing time 104 234 2.25 1.325243

Mixing temperature 104 6280 60.38461538 87.22928

Mixing speed 104 81600 784.6153846 65003.73

Seed size 104 118.2 1.136538462 0.222157

Ratio 104 1040 10 3.883495

Yield 104 4139 39.79807692 98.33747

Table 7. ANOVA table.

Source of variation SS df MS F P-value F crit

Between groups 50596199 5 10119239.74 931.2936 6.2E-285 2.228605

Within groups 6715057 618 10865.78863

Total 57311256 623

The table shows that the results obtained by this experiment are significant. The p-value was < 0.05 and the F-value was 931.29, which indicates the same result.

Based on the leaching process mechanism, there are two resistances, i.e., internal and external re-sistances. The internal resistance is due to the dif-fusion process. It can be reduced by reducing the particle size of seeds. The external resistance takes place in solution, so, ensuring good mixing would reduce this resistance significantly (keeping the uniform distribution of solute concentration in the solution). So, the mixing process can be improved by speeding up the leaching process by decreasing the external resistance (bulk resistance) (Allawzi et al., 2005; Khasawneh, 2017).

The solvent ratio, on the other hand, had the least effect at smaller seed size and higher mixing temperatures (Fig. 2). This can be attributed to the mass transfer phenomena.

Mass transfer process occurs due to a concen-tration gradient. So, when the solute concentration in the bulk is less than its concentration inside the solid, the diffusion process continues until the concentrations of solute in the bulk and inside the solid are in equilibrium. Therefore, when the sol-vent amount is increased relative to seed weight, the leaching role will increase. Increasing the sol-vent-to-seeds ratio will increase the oil yield up to a point where increasing the amount of solvent

will not have an effect on the leaching rate (Khasawneh, 2017).

Based on the above, it can be concluded that all the factors studied are effective in producing jojo-ba oil. However, it is desired to know which one is most effective amongst the factors and within the range studied.

For this purpose, the RSM technique was used. The level of effect for each factor is shown in Fig. 3. This is called the interaction plot. Fig. 3 clearly show that the most significant factor in the study was the seed size. It is the greatest effect on the amount of jojoba yield. As the size decreases, the surface area increases and hence the leaching pro-cess increases resulting in good yield.

The second most influential factor was the mix-ing temperature, then mixing speed.

Mathematical model

The final stage of the study was to find a math-ematical model linking all variables to the yield. This is shown in equation [2] below.

Yield (wt%) = 44.00 + 6.09 Time - 0.250 Temp + 0.01274 RPM - 61.55 Size + 2.336 Ratio – 1.353 Time2 + 0.00434 Temp2 - 0.000008 RPM2 20.91 Size2 - 0.0650 Ratio2 + 0.0278 Time*Temp + 0.001740 Time*RPM + 2.035 Time*Size - 0.2381 Time*Ratio

[2]




The model accuracy is represented in the re-gression coefficient R and its R2. This is shown in Table 8. The model had excellent accuracy in pre-

dicting the output of the study with 98.35% accu-racy.

Time 2.25

Temp 50

RPM 600

Size 0.99

Ratio 10

Hold Values

Temp*Time

3.92.71.5

60

50

40

RPM*Time

3.92.71.5

900

600

300

Size*Time

3.92.71.5

1.5

1.0

0.5

Ratio*Time

3.92.71.5

15

10

5

RPM*Temp

605040

900

600

300

Size*Temp

605040

1.5

1.0

0.5

Ratio*Temp

605040

15

10

5

Size*RPM

900600300

1.5

1.0

0.5

Ratio*RPM

900600300

15

10

5

Ratio*Size

1.51.00.5

15

10

5

>

–

–

–

–

–

< 25

25 30

30 35

35 40

40 45

45 50

50

Yield

Contour Plots of Yield

Figure 2. Contour plots of yield of all parameters.

The figures show that for the mixing time within 2.7 h, seed size within 0.5 mm, higher temperatures and mixing speeds, the yield was at its best. Solvent-to-seed ratio beyond 10 did not produce a significant effect on yield.

Figure 3. Main effect plot for the studied variables.

These interaction plots show that there is peak value for time for best yield and same for seed-to-solvent ratio. It also shows that the yield increases with mixing temperature and speed, and with smaller seed sizes.

Table 8. Model summary.

S R2 R2 (adj) R2 (pred)

1.27259 98.58% 98.35% 97.49%

The model has significant accuracy in representing the data with an error of less than 2%.




Figure 4. Comparison between predicted and experimental values.

The figure shows how accurate the model is in predicting the yield values.

Based on Table 8, this model can explain 98.35% of the data, which is good accuracy. This gives confidence in doing further analysis. Fig. 4 shows the output of the model compared with the exper-imental data of (Allawzi et al., 1998; 2005; Abu-Arabi et al, 2000). The model is clearly accurate and can be used for further analysis.

The level of effectiveness of each term of the pa-rameters in the equation is shown in the Pareto chart of Fig. 5. Again, it shows that the most signif-icant factor in the seed size followed by mixing temperature. However, it also shows a strong cor-relation and effect between size and time ad to less extent between time and solvent ratio.

Further noticed is that the seed size and time have an effect of power 2 on the yield, which makes them important in the study of oil extrac-tion.

The results for the optimum parameters for maximum yield is shown in Fig. 6. Based on the RSM technique, the optimum values are shown in Fig. 6 (shown in red). With values of size of about 0.48 mm, the temperature of about 65°C and a mixing time of 2.8 h maximum yield of about 56%

wt can be obtained. Similar results were found by Al Hamamre and Yamin (2014).

CONCLUSIONS

A mathematical model that includes the effect of all the jojoba oil extraction variables was suc-cessfully derived and tested for accuracy. Jojoba yield was found to be more sensitive to particle size and mixing time and least sensitive to solvent-to-seed ratio. The optimum speed and temperature were agreed upon by both methods to be 1000 rpm and 65°C.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

ACKNOWLEDGMENTS

The authors would like to extend their thanks to Dr. Allawzi and Abu-Arabi for their permission to use the data in this article. The authors declare that the present research study was performed without any funding from any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.




Figure 5. Pareto chart for main effects.

This Pareto chart shows the most significant effect is for the seed size. It also shows an interaction between mixing time and seed size and mixing temperature.

Figure 6. Optimum values of the extraction parameters.

This result of optimization shows that for best yield, mixing time should be within 2.8 h, at 65°C, 0.48 mm size, 12.88 seed-to-solvent ratio and mixing speed of 1000 RPM. At these conditions, maximum yield is expected to reach 56%.

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AUTHOR CONTRIBUTION:

Contribution Al-Sheikh I Yamin JAA

Concepts or ideas x x

Design x

Definition of intellectual content x

Literature search x

Experimental studies x

Data acquisition x

Data analysis x

Statistical analysis x

Manuscript preparation x

Manuscript editing x x

Manuscript review x x

Citation Format: Al-Sheikh I, Yamin JAA (2019) Modelling and optimization of jojoba oil extraction yield using Response Surface Methodology. J Pharm Pharmacogn Res 7(5): 367–380.




Annex 1. Factorial design for RSM method used in Minitab software.

Time Temp RPM Size Ratio Yield StdOrder RunOrder Blocks PtType

0.5 35 600 1.5 10 20 1 1 1 1

1.0 35 600 1.5 10 26 2 2 1 1

1.5 35 600 1.5 10 27 3 3 1 1

2.0 35 600 1.5 10 29 4 4 1 1

2.5 35 600 1.5 10 30 5 5 1 1

3.0 35 600 1.5 10 31 6 6 1 1

3.5 35 600 1.5 10 31 7 7 1 1

4.0 35 600 1.5 10 31 8 8 1 1

0.5 45 600 1.5 10 21 9 9 1 1

1.0 45 600 1.5 10 27 10 10 1 1

1.5 45 600 1.5 10 28 11 11 1 1

2.0 45 600 1.5 10 30 12 12 1 1

2.5 45 600 1.5 10 32 13 13 1 1

3.0 45 600 1.5 10 33 14 14 1 1

3.5 45 600 1.5 10 33 15 15 1 1

4.0 45 600 1.5 10 33 16 16 1 1

0.5 55 600 1.5 10 24 17 17 1 1

1.0 55 600 1.5 10 29 18 18 1 1

1.5 55 600 1.5 10 32 19 19 1 1

2.0 55 600 1.5 10 33 20 20 1 1

2.5 55 600 1.5 10 35 21 21 1 1

3.0 55 600 1.5 10 35 22 22 1 1

3.5 55 600 1.5 10 35 23 23 1 1

4.0 55 600 1.5 10 35 24 24 1 1

0.5 65 600 1.5 10 25 25 25 1 1

1.0 65 600 1.5 10 33 26 26 1 1

1.5 65 600 1.5 10 35 27 27 1 1

2.0 65 600 1.5 10 36 28 28 1 1

2.5 65 600 1.5 10 37 29 29 1 1

3.0 65 600 1.5 10 40 30 30 1 1

3.5 65 600 1.5 10 40 31 31 1 1

4.0 65 600 1.5 10 40 32 32 1 1

0.5 65 200 1.5 10 23 33 33 1 1

1.0 65 200 1.5 10 30 34 34 1 1

1.5 65 200 1.5 10 31 35 35 1 1

2.0 65 200 1.5 10 32 36 36 1 1

2.5 65 200 1.5 10 33 37 37 1 1

3.0 65 200 1.5 10 34 38 38 1 1

3.5 65 200 1.5 10 34 39 39 1 1




Annex 1. Factorial design for RSM method used in Minitab software (continued…).


4.0 65 200 1.5 10 34 40 40 1 1

0.5 65 600 1.5 10 25 41 41 1 1

1.0 65 600 1.5 10 31 42 42 1 1

1.5 65 600 1.5 10 33 43 43 1 1

2.0 65 600 1.5 10 35 44 44 1 1

2.5 65 600 1.5 10 38 45 45 1 1

3.0 65 600 1.5 10 40 46 46 1 1

3.5 65 600 1.5 10 40 47 47 1 1

4.0 65 600 1.5 10 40 48 48 1 1

0.5 65 1000 1.5 10 26 49 49 1 1

1.0 65 1000 1.5 10 34 50 50 1 1

1.5 65 1000 1.5 10 35 51 51 1 1

2.0 65 1000 1.5 10 37 52 52 1 1

2.5 65 1000 1.5 10 40 53 53 1 1

3.0 65 1000 1.5 10 41 54 54 1 1

3.5 65 1000 1.5 10 42 55 55 1 1

4.0 65 1000 1.5 10 42 56 56 1 1

0.5 65 1000 0.48 10 48 57 57 1 1

1.0 65 1000 0.48 10 51 58 58 1 1

1.5 65 1000 0.48 10 52 59 59 1 1

2.0 65 1000 0.48 10 53 60 60 1 1

2.5 65 1000 0.48 10 55 61 61 1 1

3.0 65 1000 0.48 10 55 62 62 1 1

3.5 65 1000 0.48 10 55 63 63 1 1

4.0 65 1000 0.48 10 55 64 64 1 1

0.5 65 1000 0.86 10 35 65 65 1 1

1.0 65 1000 0.86 10 39 66 66 1 1

1.5 65 1000 0.86 10 41 67 67 1 1

2.0 65 1000 0.86 10 42 68 68 1 1

2.5 65 1000 0.86 10 44 69 69 1 1

3.0 65 1000 0.86 10 45 70 70 1 1

3.5 65 1000 0.86 10 45 71 71 1 1

4.0 65 1000 0.86 10 45 72 72 1 1

0.5 65 1000 1.5 10 26 73 73 1 1

1.0 65 1000 1.5 10 34 74 74 1 1

1.5 65 1000 1.5 10 35 75 75 1 1

2.0 65 1000 1.5 10 37 76 76 1 1

2.5 65 1000 1.5 10 40 77 77 1 1

3.0 65 1000 1.5 10 41 78 78 1 1




Annex 1. Factorial design for RSM method used in Minitab software (continued…).


3.5 65 1000 1.5 10 42 79 79 1 1

4.0 65 1000 1.5 10 42 80 80 1 1

0.5 65 1000 0.48 5 34 81 81 1 1

1.0 65 1000 0.48 5 48 82 82 1 1

1.5 65 1000 0.48 5 50 83 83 1 1

2.0 65 1000 0.48 5 51 84 84 1 1

2.5 65 1000 0.48 5 52 85 85 1 1

3.0 65 1000 0.48 5 52 86 86 1 1

3.5 65 1000 0.48 5 52 87 87 1 1

4.0 65 1000 0.48 5 52 88 88 1 1

0.5 65 1000 0.48 10 48 89 89 1 1

1.0 65 1000 0.48 10 51 90 90 1 1

1.5 65 1000 0.48 10 52 91 91 1 1

2.0 65 1000 0.48 10 53 92 92 1 1

2.5 65 1000 0.48 10 55 93 93 1 1

3.0 65 1000 0.48 10 55 94 94 1 1

3.5 65 1000 0.48 10 55 95 95 1 1

4.0 65 1000 0.48 10 55 96 96 1 1

0.5 65 1000 0.48 15 50 97 97 1 1

1.0 65 1000 0.48 15 52 98 98 1 1

1.5 65 1000 0.48 15 54 99 99 1 1

2.0 65 1000 0.48 15 55 100 100 1 1

2.5 65 1000 0.48 15 55 101 101 1 1

3.0 65 1000 0.48 15 55 102 102 1 1

3.5 65 1000 0.48 15 55 103 103 1 1

4.0 65 1000 0.48 15 55 104 104 1 1

The table shows that there is a total of 104 experiments need to be done with 5 variables. The P-value for the test data was <0.05.


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