ORIGINAL PAPER

Cooking and Sun Drying Effects on Properties of Allanblackiastanerana Kernels Oil

Guy Bertrand Noumi Martin Pengou

Emmanuel Ngameni

Received: 22 February 2013 / Revised: 8 May 2014 / Accepted: 11 May 2014 / Published online: 28 May 2014

AOCS 2014

Abstract Previous studies have evaluated the nutritive

potential of Allanblackia oils. Oil extraction from Allanb-

lackia is done after a pretreatment of the kernels which has

an influence on oil quality. In Cameroon, the pretreatment

consists of cooking, followed by drying of the almonds in

the sun. The oil is either edible or used as a body cream.

Because of these important applications, it is necessary to

determine treatment conditions that maximize extraction

yields and preserve its quality. This study was aimed at

finding the mathematical models that simulate the best pre-

treatment conditions. The use of multiple linear regression

analysis allowed developing satisfactory models and sur-

face response plots that predict the evolution of the

extraction rate as well as the quality of the extracted oil,

depending on cooking and sun drying times. The coeffi-

cients of correlation obtained were 72.03 % for water

content; 53.06 % for extraction yield; 71.06 % for acid;

76.48 and 83.29 % for iodine and refractive values

respectively, indicating a suitable model of the experiment

according to the studied variables. The response surface

curves were superimposed to obtain a single optimal range

that satisfies all response factors. The average cooking time

of 12.5 min and the mean residence time of 8.5 days

drying gave the following optimal values for the different

response factors studied: moisture content 21.60 %; oil

yield 70.69 %; refractive index 1.4546; iodine value 34.72;

and acid value 0.38 mg KOH/g oil. The conditions to

obtain a maximum extraction yield and low acidity were

those that gave a residual water content of about 1015 %.

The quality indicators measured in this work generally

remained within the threshold.

Keywords Allanblackia stanerana Extraction yield Low acidity Quality indicators Sun drying and cookingconditions

Introduction

The Allanblackia genus is a medium sized, evergreen and

dioecious tree with a height of 30 m. Its trunk is relatively

short, straight, and cylindrical, without buttresses and

sometimes showing a thickened base [1]. The Allanblackia

species grow only in wet Western, Eastern and Central

African forests and are found either in rich biodiversity

areas or in agricultural areas [2]. The Allanblackia genus

produces seed containing a fat which is solid at room

temperature. Its chemical composition [3] and its high

melting point (35 C) makes the fat a valuable raw materialthat can be used without transformation to improve the

consistency of margarines, cocoa butter substitutes and

related products [4]. Previous works on the Allanblackia

genus are on the phytochemicals order [5]. The production

of Allanblackia oil in Africa remains traditional.

In 2002, the Novella partnership on Allanblackia oil

program was created to stimulate its production in Ghana,

Tanzania and Nigeria [4]. This partnership aims at

developing a sustainable (economic and social

G. B. Noumi

Department of Chemistry, Faculty of Science, University of

Ngaoundere, Ngaoundere, Cameroon

M. Pengou (&)Department of Chemistry, Higher Teachers Training College,

University of Maroua, Maroua, Cameroon

e-mail: mapesu@yahoo.fr; pengou.mapesu@gmail.com

M. Pengou E. NgameniLaboratory of Analytical Chemistry, Department of Inorganic

Chemistry, Faculty of Science, University of Yaounde I,

Yaounde, Cameroon

123

J Am Oil Chem Soc (2014) 91:13031312

DOI 10.1007/s11746-014-2483-5

environment) supply chain that contributes to the devel-

opment of companies operating in Africa Allanblackia.

Novella is an international publicprivate partnership of a

wide range of stakeholders. Its biggest investor Unilever

buys the output of crude oil to be refined in Rotterdam in

the Netherlands. The World Agroforestry Centre (ICRAF)

conducts scientific research on the domestication of

Allanblackia so as to bring the harvest to a level of

commercial viability [4].

Having a high quality and readily available food supply

is a vital necessity. Nowadays, the need for lipids remains a

fundamental problem in developing countries. In Camer-

oon for instance, the coverage rate of dietary lipids is about

49 % [6]. Inadequate treatment of food can lead to nutri-

tional losses as well as some causal diseases. Food products

can be preserved in many ways. People have always sought

to develop preservation techniques and these have been

recently improved. These conservation techniques included

either the prevention of food contamination by microor-

ganisms or the preservation of the organoleptic and nutri-

tional properties [7]. To meet the increasing demand for

oil, both for human consumption or for industrial purposes,

improvements have been made and several studies have

been done accordingly [814]. As a result, a large amount

of oils and fats have been obtained from plant sources that

have the ability to produce the desired quality. In recent

years, there has been an increasing interest in new oil

sources such as plant seeds that are important oil sources of

nutritional, industrial and pharmaceutical quality [15].

Given the increasing scientific and public awareness about

the nutritional and functional properties of these oils, the

evaluation of the quality and composition of nonconven-

tional seed oils have become the concern of researchers.

Humans first used elements available in nature to keep their

foods. Sun drying is one of the oldest used processes. This

process aims at limiting or avoiding the development of

micro-organisms. However, sun-drying is not always

without consequences on the food nutritional value.

Cooking is considered to be a critical operation in the

extraction process [16], blocking seed germination [17].

This is because germination does not only reduce the

extraction yield, but also makes the oil bitter [18]. The

present work aims at evaluating the impact of cooking and

sun drying times on some properties (extraction yield,

residual water content, acid value, iodine value and

refraction value) of oil extracted from Allanblackia sta-

nerana seeds as well as to establish the optimum conditions

for quality preservation of the extracted oil. Through this,

mathematical models that can simulate the experimental

phenomenon will be developed. The interest in modeling

the response by a polynomial is to enable the calculation of

all the responses of the study area without necessarily

repeating the experiments [19].

Materials and Methods

Plant Material and Pretreatment

The seeds from A. stanerana fruit were collected at Nkole-

nyeng and Ndjantom, small villages situated about 65 km

from Sangmelima in the South Region of Cameroon. Each test

consisted of cooking 200 g of A. stanerana seeds in 5 L water

in a 20-L electric pot. The cooking temperature ranged

between 90 and 100 C and is considered as suitable in termsof oil degradation [7]. The cooking time ranged from 0 to

30 min. The sun drying time varied from 0 to 14 days until the

achievement of a relative humidity suitable for dehulling and

for oil extraction. These intervals were chosen with reference

to the treatment of other kernels such as Shea [16]. The

diameters of Allanblackia seeds ranged between 18 and

40 mm. Seeds, cooked and dried at different times were de-

hulled and the recovered kernels crushed in a cutter mill

(Moulinex, France). The average outdoor temperatures and

humidity during drying ranged from 25 to 30 C and 75 to85 % respectively. The oil extraction process was performed

by maceration for 48 h in 200 mL hexane and after filtration

the filtrate was evaporated on a rotary evaporator to recover the

oil. The oil samples obtained were kept in small dark bottles.

The acid values, iodine and refractive indices were determined

using standard methods [20, 21]. The residual water content

was calculated after cooking and drying. The extraction yield

was determined from the dry matter. The experiment was

conducted in duplicate and the average value was determined.

Response Surface Methodology and Experimental

Design

The principle is to model the surface of experimental

responses, that is, the evolution of the criterion on a uni-

verse of discourse of bounded variables and find the opti-

mum of the estimated area [2225]. Among the many types

of methods for constructing response surfaces, the center

composite experimental design (ECCP) was used. It allows

one to study and compare the effects of factors on different

responses. It has an advantage of facilitating the con-

struction as it is built by adding measurement points to a

full factorial design [26]. Since the method for studying a

response surface is often used after the effects of the fac-

tors, it is sufficient to carry out a few additional experi-

ments to estimate the response surface of the studied

criteria. However, the number of trials is large compared to

other methods [23], but this number would be reasonable

when the number of the studied factors is between 2 and 4

parameters [22]. Another disadvantage is that, and this type

requires five levels per factor that physically can be diffi-

cult to achieve [22]. A comprehensive presentation of the

method is given in the literature [23]. A central composite

1304 J Am Oil Chem Soc (2014) 91:13031312

123

design is defined by: a full factorial design 2 k, n0 repli-

cations at the center of the experimental domain, dedicated

to statistical analysis - two points per parameter and setting

out on the axes of each of them to a distance of a (a kp )from the domain center. These points contribute to the

evaluation of quadratic terms of the polynomial model,

giving information about the curvature of the response

surface. The number of tests to be carried out N will

depend on the number of k-factors studied and on the

number of replications in the center of the domain, n0:

N = 2k ? 2k ? n0, k is the number of factors. In this

study, k = 2 (cooking and drying times). The choice is

based on the fact that this model is well known and easy

to operate and has a particular form, based on second-

degree polynomials and is applicable to many problems.

The experimental matrix results that emerge from the

study of the effect of cooking and drying of the seeds, are

given in Table 1. The coded values were converted into

real values by the relation Xi X0i xiDu [27], where Xiis the value of natural variable (or real value) X0i is the

central value of natural variable i, xi is the coded value

of variable i, Du is the increment that can be calculated

from the following equation: Du real value maxX0ia

p with

X0i real value max real value min2 .The model chosen is that of a second degree polynomial

with interaction between the factors and governed by the

following equation:

y a0 X

n

i1aixi

X

ji

X

n1

i1aijxixj

X

n

i1aiix

2i

with ai linear coefficients of the equation, xi, xj, coded

values and y the expected response.

Table 1 indicates that the seeds of A. stanerana were

cooked between 0 and 30 min and dried for between 0 and

14 days.

Validation of the Model

Criteria for assessing the reliability of the simulations were

the regression coefficients (R2) and/or the relative error (RE)

observed between the experimental and theoretical results.

The RE was determined by the following equation:

RE % 100N

PNi1

yexp ytheoyexp

where, yexp and ytheo are

respectively the experimental and the theoretical values, the

latter is calculated from the equation generated by the model

and using the coded value, N is the number of trials. The

model is considered valid for a given result, if at least one of

the following criteria is met (R2 C 70 % and/or RE B10 %).

Statistical Processing of Data

The results obtained were statistically analyzed through the

analysis of variance in order to assess the influence of the

factors on the observed responses. Multiple regression tests

and the plotting of surface response curves led to the

development of mathematical models to simulate the

experimental phenomenon. The STAGRAPHICS plus 3.0

and SIGMA PLOT 9.0 Software were used for this

purpose.

Results and Discussion

The coefficients of the regression equation (CR), coeffi-

cients of determination (R2) and P values of ANOVA for

expression models of water content, extraction yield, acid

value, iodine value and refractive index of A. stanerana

oils obtained from cooked and dried seeds by composite

experimental design center, are shown in Table 2.

Data in Table 2 show that the conditions for validation

of the results (R2 C 70 % and/or ER \10 %) were met byall the results obtained. This suggests that our model

(ECCP) is therefore valid for these variable responses.

Residual Water Content (RW)

The residual water content of seeds (in g/100 g dry matter)

or any other material from which oil can be extracted is

very important. In fact, the water content in the raw

material influences both the extraction rate and the quality

of oil extracted from it [28]. The water content is a quan-

titative indicator of the existence of water in a food prod-

uct. Its value is crucial in the food industry since it

determines the intensity of enzymatic and chemical

Table 1 Centre composite experimental design of the study of theeffect of cooking and sun drying of A. stanerana seeds on the quality

of the extracted oil, with, a 2p 1:4142Tests Coded values Real values

Cooking

time (x1)

Drying

time (x3)

Cooking time

(min) (X1)

Drying time

(day)

(X3)

1. 0 0 15 7

2. 0 0 15 7

3. 1 1 25.6 11.95

4. 1 -1 26.6 2.05

5. -1 1 4.4 11.95

6. -1 -1 4.4 2.05

7. ?a 0 30 7

8. -a 0 0 7

9. 0 ?a 15 14

10. 0 -a 15 0

J Am Oil Chem Soc (2014) 91:13031312 1305

123

reactions, and it also controls microbial growth; according

to the nature of the products, the critical threshold of the

water content is very variable [29]. Statistical analysis of

data (Table 2) on the water content showed that only the

first and second order terms of the drying time has a sig-

nificant influence on the water content (P \ 0.05). Theprocessing of experimental data by multiple regressions led

to the development of a mathematical model to simulate

the evolution of water content accordingly as a function of

cooking (x1) and drying time (x3):

RW 23:025 0:8908x1 7:9757x3 1:3888x21 0:375x1x3 9; 0862x23

The model has a determination coefficient higher than

70 % (see Table 2), suitable with the experimental design.

The difference of about 28 % would be related to factors

such as temperature of the water used for cooking, the

diameter of the kernel and the relative humidity of air in

the studied area, which were not taken into account. The

influence of these factors had already been found by some

researchers [30] and others have taken these into account to

improve the extraction process [6, 31]. Changes in water

content depending on the factors studied are shown in

Fig. 1a and b.

It can be seen, after an analysis of the curves in Fig. 1

that, the drying time influences the water content. When the

drying time increased, the water content decreased and

after about 10 days of drying (Fig. 1a) it started to

increase. This water content varied from 55 to 15 % and

the observed increase is probably due to absorption of

Table 2 Coefficients of the regression equation (CR), coefficients ofdetermination (R2) and P values of ANOVA for expression models of

residual water content, extraction yield, acid, iodine and refractive

values of A. stanerana oils obtained from cooked and dried seeds by

center composite experimental design

Residual water content Extraction yield Acid value Iodine value Refractive value

CR P value CR P value CR P value CR P value CR P value

x1 (cooking time) -0.8908 0.6601 0.9661 0.5645 -0.0540 0.1158 1.2153 0.3471 0.00066 0.7345

x3 (drying time) -7.9757 0.0014* -0.1079 0.9483 -0.0730 0.0402* 0.1072 0.9327 0.00889 0.0005*

x12 -1.3888 0.6048 -7.2609 0.0051* -0.0290 0.5053 -3.3712 0.0616 -0.00056 0.8284

x1x3 -0.3750 0.8955 -3.7450 0.1291 0.1062 0.0357* -7.6925 0.0008* 0.00237 0.3981

x32 9.0862 0.0041* -4.0697 0.0823 0.0609 0.1745 -3.3187 0.0652 -0.0149 0.0001*

Constant 23.0250 71.4675 0.3875 34.9700 1.4582

R2 (%) 72.03 53.06 71.06 76.48 83.29

ER (%) 14.47 7.50 25.42 12.08 0.19

* P value less than 0.05 indicates the significant effect of 095 % level of confidence

15

20

25

30

35

40

45

50

55

0

510

1520

2530

02

468

1012

wate

r con

tent

(% d.

m.)

cook

ing tim

e (min)

drying time (dy)

15 20 25 30 35 40 45 50 55

25

20

20

20

2525 25

25

3030 30

30

3535 35

35

40 40 40 4045 45 45 45

50 50 50 50

25 2525

30

cooking time (min)0 5 10 15 20 25 30

dryin

g tim

e (dy

)

0

2

4

6

8

10

12

14a b

Fig. 1 Surface plots of the residual water content of Allanblackia kernels as affected by the variable processes of cooking and drying times

1306 J Am Oil Chem Soc (2014) 91:13031312

123

moisture by the product from the surrounding air. We also

noted that the cooking time had practically no influence on

the water content. However, it must be cooked for at least

25 min to have an effect on residual water content or some

other parameter (Fig. 1b). A cooking time longer than

30 min predisposes the kernels to release water easily.

Afoakwa Ohene et al. [32] previously reported that a

decrease in product moisture causes a decrease in the

enzymatic degradation and a reduction in microbial and

chemical activities of the product, extending its life. A

water content of 20 % is obtained after a cooking time of at

least 30 min and a drying time of about 12 days (Fig. 1b).

Extraction yield (EY)

The change in the extraction yield as a function of cooking

and drying times is shown in Fig. 2a and b.

Figure 2a shows that the combination of cooking and

drying times varied with the extraction yield within the

range of 3575 %. It clearly shows that the extraction yield

increases with cooking time up to a certain percentage

(around 65 % for a cooking time of about 20 min, Fig. 2a)

and then decreases. In fact, cooking coagulates proteins,

freeing more space for the release of oil during the

extraction process [31], hence the increase in extraction

yield with increased cooking time is the most influential

parameter. Prolonged cooking resulted in a release of oil

with time. This could explain the low extraction yield

observed. Moreover, it was observed that oil floated during

cooking. In addition, the water absorbed during the

cooking of the kernels reduces the affinity of the oil with

the kernels solid particles and the opening of vacuoles due

to the oil suction, resulting in oil release [33]. Conversely,

the extraction rate increases with the drying time and

becomes constant after about 9 days (Fig. 2a). Figure 2b

shows that maximum extraction is obtained for a cooking

time up to 20 min and a drying time up to 10 days.

Table 2 summarizes the results of the analyses of vari-

ance. It shows that, only the quadratic term of the cooking

time has a significant influence (P \ 0.05) on the extrac-tion yield. The analysis of data by multiple regressions led

to the development of a model allowing one to simulate the

experimental phenomenon. The equation is as follows:

EY 71:4675 0:9661x1 0:1079x3 7:2609x21 3:745x1x3 4:0697x23

with R2 = 53 %. Thus, the model explains only 53.06 % of

the results obtained, unless the experiment is carefully

conducted (ER \10) (see Table 2). The 47 % differencemight be due to factors that have not been taken into

account in this study. These include the water temperature

during cooking, the relative humidity of the air, the powder

size and the extraction method.

Refractive Value (RV)

The refractive indexes of oils were determined and the treat-

ment of data by analysis of variance showed a highly signifi-

cant influence of linear and quadratic terms for the drying time

(P \ 0.001). Conversely, the cooking time and the interaction

35

40

45

50

55

60

65

70

75

0

510

1520

2530

02

468

1012

ext

ract

ion

yield

(% d.

m))

cook

ing tim

e (min)

drying time (dy.)

35 40 45 50 55 60 65 70 75

45

50

55

55

60

60

60

60

65

65

65

65

65

65

65

65

70

70

70

70

70

65

60

60

60

60

55

55

60

50

45

cooking time (min)0 5 10 15 20 25 30

dryin

g tim

e (dy

.)

0

2

4

6

8

10

12

14a b

Fig. 2 Surface plots of the extraction yield oil of Allanblackia kernels as affected by the variable processes of cooking and drying times

J Am Oil Chem Soc (2014) 91:13031312 1307

123

between the two factors had no influence (P [ 0.05)(Table 2). The equation of the model is determined through

the treatment of results by multiple regressions:

RV 1:4582 0:00066x1 0:00889x3 0:00056x21 0:00237x1x3 0:0149x23

With R2 = 83.2969 % [ 70 %, the experimental phe-nomenon is well fitted to the developed model. The dif-

ference of about 17 % might be due to factors that have not

been taken into account in this study. Table 2 summarizes

the results of the analysis of variance. The curves shown in

Fig. 3a and b allow one to visualize changes in the

refractive index as a function of cooking and drying times.

An analysis of these curves shows that the refractive index

varies from 1.40 to 1.46, independent of the cooking time.

The refractive index increases with drying time up to a

value of 1.46 (see Fig. 3a) and starts to decrease on the 8th

day of drying. This index is almost constant and suggests

the slightly saturated character of A. stanerana oil. In fact,

the refractive index varies in an interesting manner

depending on how unsaturated the oil is. It is lower in the

oils with high levels of unsaturation. A low refractive index

would be better for health. Whatever the cooking time, A.

stanerana seeds must be dried beyond 14 days to maintain

the refractive index at around 1.40 (see Fig. 3b).

Iodine Value (IV)

Results obtained for the iodine index showed good oil

quality and were comparable to those of commercial oils

such as palm oil and olive oil [34]. In fact, the iodine value

(35 mg of iodine/100 g oil) was significantly lower than

those of palm oil (60.27) and olive oil (83.1). In this study,

the low iodine value could be of benefit since it is always

associated with good quality and guarantees the length of

the oil conservation [35]. Falade et al. [36] reported that a

low iodine content of oil prevents oxidative degradation of

food and predisposes this oil to be used as biodiesel fuel.

This low value of iodine is probably due to its high content

of saturated fatty acids. In fact, the fatty acid profile of this

oil shows that it contains 59.17 % saturated fatty acid,

confirming the low iodine index. Interaction between

cooking and drying times had a significant influence on the

iodine value (see Table 2, P \ 0.05). The analysis of databy multiple regressions allows the development of a sim-

ulation model based on the cooking time (x1) and drying

time (x3):

IV 34:97 1; 2153x1 0:1072x3 3:3712x21 7:6925x1x3 3:3187x23

Figure 4a and b below, show the evolution of the iodine

value according to cooking and drying times.

It obviously appears that a cooking time of about

30 min is necessary to dry the seeds for at least 813 days

so as to have an iodine value not exceeding 35 mg of

iodine/100 g oil (see Fig. 4a). Allanblackia stanerana oil

could be classified as a non-drying oil because of its low

iodine value. Hao et al. [37] established a link between

the low iodine value and the non-drying character of the

oil.

1,40

1,41

1,42

1,43

1,44

1,45

1,46

1,47

0

510

1520

2530

02

468

1012

refra

ctive

inde

x

cook

ing tim

e (min)

drying time (dy.)

1,40 1,41 1,42 1,43 1,44 1,45 1,46

1,45

1,46

1,461,46

1,45 1,45 1,451,45

1,44 1,44 1,441,44

1,43 1,431,43 1,43

1,42 1,421,42 1,42

1,451,45

1,451,44

1,44

cooking time (min)0 5 10 15 20 25 30

dryin

g tim

e (dy

.)

0

2

4

6

8

10

12

14a b

Fig. 3 Surface plots of the refractive values of Allanblackia oil as affected by a cooking and drying times

1308 J Am Oil Chem Soc (2014) 91:13031312

123

Acid Value (AV)

This study presents an oil with an acid value of 0.3 mg

KOH/g oil or 0.168 % oleic acid, which is much lower than

that of refined oils, palm oil (0.84 mg KOH/g oil) and olive

oil (0.84 mg KOH/g oil) as reported by Azrin et al. [34].

Therefore, A. stanerana oil could be used without being

refined. The acid value found in this study clearly indicates

that A. stanerana oil may have low levels of oxidative

activity and a high content of natural antioxidants [37, 38].

In addition, these values prove the purity and stability of

this oil at room temperature and could be criteria regarding

stability and sensitivity for fruit rancidity during storage

[38]. Table 2 shows that the drying time and the interaction

between cooking and drying times have significant influ-

ence (P \ 0.05) on the evolution of the acid value. The

0

10

20

30

40

50

0

510

1520

2530

02

468

1012

Iodi

ne v

alue

cook

ing tim

e (min)

drying time (dy.)

0 10 20 30 40 50

1015

20

25

25

30

30

35

35

35

35

30

30

30

30

25

25

20

2015

35

10

cooking time (min)0 5 10 15 20 25 30

dryin

g tim

e (dy

.)

0

2

4

6

8

10

12

14ba

Fig. 4 Surface plots of the iodine values of Allanblackia oil as affected by a cooking and drying times

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

0

510

1520

2530

02

468

1012

aci

d va

lue

cook

ing tim

e (min)

drying time (dy.)

0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9

0,4

0,3

0,3

0,3

0,4

0,4

0,4 0,4

0,4

0,4

0,5

0,5

0,5

0,6

0,60,7

0,7

0,3

0,3

0,8

cooking time (min)0 5 10 15 20 25 30

dryin

g tim

e (dy

.)

0

2

4

6

8

10

12

14a b

Fig. 5 Surface plots of the acid value of Allanblackia oil as affected by a cooking and drying times

J Am Oil Chem Soc (2014) 91:13031312 1309

123

analysis of data by multiple regressions allows the devel-

opment of a mathematical model for simulating the evo-

lution of acid according to cooking time (x1) and drying

time (x3):

AV 0:3875 0:054x1 0:073x3 0:029x21 0:1062x1x3 0:0609x23

Factors not considered in this study have certainly

influenced the iodine and acid values. The coefficients of

determination obtained did not reach 100 %. Figure 5a and

b give an overview of the evolution of the acid value

according to cooking and drying times.

The analysis of those figures shows that the acid value

decreases with the drying time. As the drying time

increases, the acid number decreases (see Fig. 5a, b).

Cooking time of 30 min would require a drying time of

10 days for a maximum acid value of about 0.3. The oil

which is of low acidity is edible and may be used for a long

period of time [39]. The influence of methods for extract-

ing oils [31] on the physicochemical properties of the

extracted oils has already been reported.

Optimization

To optimize the process, the partial first derivatives of the

validated equations were found and equated to zero and

then resolved for optimum values x1 and x3 in coded val-

ues. These coded values were then transformed into real

values of the optimum point. As observed from Table 3 the

optimization process did not give a unique optimum for all

responses. Contour plots (Fig. 6) of the responses were

therefore superimposed in order to define a unique opti-

mum range (shaded region in Fig. 6) that satisfies all

responses. These ranges were: cooking time 1015 min

and drying time 710 days. Substituting middle values

within these ranges (cooking time 12.5 min and drying

time 8.5 days) gave optimum responses of moisture con-

tent 21.60 %, oil yield 70.69 %, refractive index 1.4546,

iodine value 34.72 and acid value 0.38 mg/g KOH.

Conclusion

The surface response methodology using central composite

experimental design was used to estimate the optimum

conditions of cooking and drying of A. stanerana kernels.

All the results obtained fitted the required conditions. The

Table 3 Optimum values obtained mathematically for the responsesstudied

Cooking time (min) Drying time (day)(d)

Moisture content (%) 12.22 4.8

Yield extraction (%) 15.84 6.75

Refractive index 30.56 9.05

Iodine value 9.674 9.96

Acid value 21.78 9.69

25

20

20

20

2525

25

25

3030 30

30

40 40 40 40

3535 35

35

45 45 45 45

50 50 50 50

25 2525

30

Cooking time (min)

Dryin

g tim

e (h)

(d)

Moisture content

45

50

55

55

60

60

60

60

65

65

65

65

65

65

65

65

70

70

70

70

70

65

60

60

60

60

55

55

60

50

45

Yield

1.42 1.42 1.42 1.421.43 1.43 1.43 1.43

1.44 1.44 1.44 1.44

1.45

1.46

1.46

1.46

1.451.45

1.451.45

1.45 1.45 1.45

1.441.44

Refractive index

1015

20

20

25

25

30

30

35

35

35

35

30

30

30

30

25

25

20

2015

10

Iodine value

-0.1

0.0

0.1

0.1

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.3

0.30.2

0 5 10 15 20 25 300

2

4

6

8

10

12

Acid value

Fig. 6 Superimposed contourcurves for the various responses

1310 J Am Oil Chem Soc (2014) 91:13031312

123

model yielded results that were validated by the conditions

set out. The optimum treatment conditions were found to

be; cooking time (1015 min), drying time (710 days).

The average values of these time intervals were; 12.5 min

for the cooking time and 8.5 days for the drying time.

These gave the following optimum responses: water con-

tent 21.60 %; yield extraction 70.69 %; refractive index

1.4546; iodine value 34.72; and acid value 0.38 mg/KOH.

For acid and iodine values, the model depicted interesting

values for the oil quality. These values for the quality of the

Allanblackia oil have been reported by Pengou et al. [3].

Acknowledgments This research was supported by the Interna-tional Foundation for Science (Sweden), through the Research Grant

No. F/4448-1 awarded to Dr. Guy Bertrand Noumi.

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Cooking and Sun Drying Effects on Properties of Allanblackia stanerana Kernels OilAbstractIntroductionMaterials and MethodsPlant Material and PretreatmentResponse Surface Methodology and Experimental DesignValidation of the ModelStatistical Processing of Data

Results and DiscussionResidual Water Content (RW)Extraction yield (EY)Refractive Value (RV)Iodine Value (IV)Acid Value (AV)Optimization

ConclusionAcknowledgmentsReferences