Some physical and frictional properties of dikanut (irvingiawombolu) as a function of moisture content

Journal of Natural Sciences Research www.iiste.org
ISSN 2224-3186 (Paper) ISSN 2225-0921 (Online)
Vol.4, No.14, 2014
15
Some Physical and Frictional Properties of Dikanut
(Irvingiawombolu) as a Function of Moisture Content
OHAERI, O. H.1*
and E. G. OHAERI2
1.Department of Agricultural Engineering,Federal University of Technology,P. M. B. 1526, Owerri, Imo State
Nigeria
2.Department of Polymer and Textile Engineering,Federal University of Technology,P. M. B. 1526, Owerri
Imo State, Nigeria
*Corresponding author: E-mail: okezieohaeri@gmail.com
Abstract
Some physical and frictional properties of Dikanut (Irvingiawombolu) were evaluated as a function of moisture
content varying from 7.02% to 15.04% (dry basis). Regression models were equally developed to these effects.
All properties studied were found to have a polynomial response to moisture content increase within the
moisture content range studied (7.02% to 15.04% dry basis).The kernel dimensions increased from 44.00 to
47.73mm, 33.50 to 34.89mm, 20.60 to 21.79mm and 32.20 to 33.73mm for major, intermediate, minor and
equivalent diameters respectively as moisture content increased. The kernel volume and surface area increased
from 120.01mm3
to 158.56mm3
and 102.04mm2
to 131.64mm2
. Bulk density and true density increased from
3.64g/cm3
to 4.33g/cm3
, and 10.31g/cm3
to 12.26g/cm3
respectively with increase in the moisture content range
tested. Aspect ratio and sphericity and porosity increased from 0.74 to 0.79; 0.70 to 0.72; 0.41 to 0.60
respectively within the moisture content range studied. Angle of repose increased from 20.10 ̊ to 37.20 ̊ while
static coefficient of friction increased from 0.60 to 0.92 (plywood), 0.50 to 0.82 (mild steel), 0.37 to 0.70
(aluminum), 0.30 to 0.64 (plastic) as moisture content increased from 7.02 to 15.04% (dry basis)with plywood
giving the highest range of values. The relevant data obtained for this variety would be useful for design and
development of machines and equipment for processing and handling operations.
Keywords: physical properties, Dikanut, moisture content, postharvest, processing
1.0 INTRODUCTION
Dikanut is an extract of wild mango (IrvingiaSpp.)which is grown for its fruits and kernels popularly known as
ugiri and ogbono (Igbo) respectively in Nigeria. The edible fruit (Irvingiagabonensis) is eaten fresh or used to
make juice and the kernel when ground is used to make ogbono soup but the no-edible (Irvingiawombolu)is
solely grown for the production of ogbono from its kernels. The powder of the kernel is also used as ingredient
in other sauces like tomatoes and groundnut for a sticky effect and taste (Ehiem and Simonyan, 2012). Extracts
of ogbono seed can be used to reduce obesity, cholesterol and chances of developing degenerative diseases such
as diabetes, cancer, high blood pressure, kidney failure, heart attack and stroke (Leakey et al., 2005; Ngodiet al.,
2005). Processing Dikanut involves four stages: separating the mesocarp from hard endocarp (done manually
using knife to peel off the mesocarp or allow it to rot); cracking the stony endocarp with hammer or stone to
remove the kernel (splitting fresh fruit into two with sharp knife can also be used to extract the kernel); drying
the extracted kernel to storable moisture content and finally, grinding the kernel to powder. The kernel composed
about 62.8% lipids, 19.7% carbohydrates, 8.9% protein, 5.3% dietary fibre and 3.2% ash (Ejiofor, 1994).
CarbonizedDikanut shell which is an abundant agricultural waste can be relevant as an alternative to synthetic
carbon black for reinforcing both natural and synthetic rubber, thereby converting low value underutilized
resources to high value product (Adeosun, 2002).

Vol.4, No.14, 2014
16
The knowledge of physical and frictional (flow) properties of agricultural products as a function of its moisture
contentis important in providing essential engineering data required for design and development of machines,
structures and equipment for handling, dehusking, processing, transporting and storage of agricultural product.
Shape and size are relevant in designing equipment for grading, sorting, cleaning, dehulling and packaging.
Density with specific gravity are used for calculating thermal diffusivity in heat transfer, terminal velocity, mass,
bulk density and porosity are employed in storage, transportation and separation system(Oh et al., 2001; Urenaet
al., 2002). Therefore, the knowledge of the physical and frictional properties of food and agricultural product as
a function of moisture content is vital for decision making and design consideration during the design of
handling and processing machines and systems as well as the development of new consumer products.
Due to the hectic nature of the processing Dikanut (ogbono), mechanization of this process has been of high
interest in recent times so as to increase the efficiency of this production process. But the achievement of
optimum mechanization of this process cannot be made with total success without proper study of the various
physical and frictional properties of the Dikanut as a function of moisture content which is the essence of this
research.
1.1 OBJECTIVES:
• To determine the effect of moisture content on some physical and frictional properties of Dikanut
(Irvingiawolumbolu) necessary for the design of various separating, handling, storage, processing and drying
machines and systems.
• To develop regression models of these effects.
1.2 SCOPE OF WORK
This research covered the analysis of some physical properties (necessary for the design of selective kernel
separators, seed handling machine parts, machines and equipment) and frictional properties (necessary for
defining the flowabilty of the kernels thereby aiding in proper design decision making with regards to seed
conveyance) of Dikanut (Irvingiawolombolu)at a moisture content range of 7.02% to 15.04% (dry basis)
2.0 METHODOLOGY
Dikanuts ( Irvingiawombolu) used for this study were purchased from rural farmers in AmuzuoroIbeku village –
Umuahia, Abia State which is located in the rain forest vegetation zone of South eastern Nigeria and lies
between 04° 401 and 06° 141 N and 07° 101 and 08° E, due to unavailability of unprocessed Dika-nut kernels in
urban markets. About 27kg of Dikanut kernels were purchasedand after sorting out of whole nuts and removal of
debris, 25kg was gotten and was used to carry out the research.
Fig. 1.1b
De-husked Dikanut Kernel (ogbono)
Fig. 1.1a
Wild mango fruitand splinted
Dikanutkernel(Irvingiawombolu)

Vol.4, No.14, 2014
17
Fig. 2.1 Some Dikanut kernels placed in a plate
2.1 Seed Conditioning/Rewetting:
The initial moisture content of the Dika nuts was determined by oven method as described by ASAE (2003) and
was found to be 7.02% using Eqn. 1:
MC = [(w1 – w2)/w2] × 100% …1
Where w1= Initial weight of sample; w2= Final weight of sample;
MC = moisture content
During moisture conditioning of the nuts, a batch of 4.5kg (batch 1) was kept at the initial moisture content of
7.02% without any further addition of moisture. The rest of the seeds were then divided into 4 parts/batches of
4.5kg each and were conditioned to obtain four different moisture content levels between 10.2% to 15.2% dry
basis (hence giving a total of 5 batches). This was done by adding different calculated amounts of water to each
batch of the Dikanut (Irvingiawombolu), using Eqn. 2 (Zareiforoush et al., 2009);
Q = A (b – a ) / (100 – b) …2
Where Q = mass of water to be added (g)
A = Initial mass of sample (g)
a = Initial moisture content of sample (%dry Basis)
b = Final/desired moisture content (%dry basis)
They were mixed thoroughly and then sealed in cellophane bags.
The batches were kept in a refrigerator at 2⁰C - 5⁰C for about 5days to allow for even distribution of water
throughout the individual seeds of each sample. Before each test was started, the sample was exposed for about 2
hours for equilibration to occur (ASAE Standards, 2003).
2.2 Physical Properties:
The principal dimensions of the nuts were determined using a micro-meter screw guage with an accuracy of
0.02mm.
Similarly, the arithmetic mean diameter (F1), geometric mean diameter(F2), square mean diameter(F3),
equivalent diameter(De) were determined respectively using the formulae by and Asoegwu et al., (2006) given
in Eqns. 3-7;
F1 = (L1 + L2 + L3)/3 …3
F2 = (L1 × L2 × L3)1/3
…4
F3 = [(L1 L2 + L2 L3 + L 3 L1)/3]1/2
…5
De = (F1 + F2 + F3) /3 …6
Where L1, L2 and L3 = major, intermediate and minor diameters(triaxial dimensions)
The kernel aspect ratio was determined by using Eqn. 7 by Seifi and Alimardani (2010);
Ras = L2/L1 …7
Where Ras = Aspect Ratio;
Similarly, kernel surface area(As) and kernelvolume (V) was calculated using the following relationships shown
in Eqns. 8-10 (Subukola and Onwuka, 2011).
As = πBL1
2
/(2L1 – B) ...8
V = πB2
L1
2
/6(2L1 – 3) ...9

Vol.4, No.14, 2014
18
Where B = (L2L3)0.5
…10
And π = Mathematical constant
The bulk density which is the ratio of the mass of the kernels to its total volume was determined by filling up a
1000mL beaker with samples, striking off the top level without kernel being compacted in any way, weighing
the set up and subtracting the weight of the beaker. Eqn. 11 was used (Amin et al., 2004; Subukola and Onwuka,
2011) to determine bulk density;
ϱbulk = bulk kernel mass/ 1000mL …11
The true density was determined using toluene displacement method. Toluene was used in place of water
because it is absorbed by the kernels to a lesser extent and also has a low surface tension with low dissolution
power too (Aydin, 2002). 500mL of toluene was put in 1000mL graduated measuring cylinder. Kernels from
each batch were first weighed using an electronic weighing balance and then immersed in toluene in six
replicates. The amount of displacement was recorded as the volume. Hence true density (ϱtrue) was obtained
using Eqn. (12);
ϱtrue = W / (V2 – V1) …12
where V2 = final volume, V1 = initial volume,W = weight of kernel
Porosity ( ε ) was determined as a function of the volume fraction (fv = ϱbulk /ϱtrue). The porosity expressed in
percentage was calculated using Eqn. 13 (Asoegwu et al., 2006; Joshi et al., 1993; Deshpande et al., 1993; Suthar
and Das, 1996; Nelson, 2002);
ε = (1 - fv) × 100% …13
Sphericity( ф )was calculated using Eqn. 14 by Asoegwu et al., (2006) and Gupta and Das (1997)
Ф = F2/L1 …14
Where F2 = geometric mean diameter
The angle of repose (θr) was determined at different moisture contents using square box method. In this method,
a specially constructed square box with removable front cover was used. The box was filled with the kernels
from each batch; the front cover was then quickly removed, allowing the kernels to flow to its natural angle. The
height (H) of the kernels in the box as well as the length of spread (L) was measured and Eqn. 15 (Kingly et al.,
2006) was used to determine the angle of repose for the different moisture contents:
Fig 2.2Measurement of angle of repose using square box method
Θr = tan-1
(H/L) …15
Where H = maximum height of kernels(mm); L = spread length(mm);
Θr= angle of repose
The static coefficient of friction of the various sample batches was determined against four (4) different
structural materials, namely; mild steel, aluminium, plywood and plastic. A carton was filled up to the brim with
samples from each batch at a time and placed inverted on the structural surface lying on an adjustable tilting
table. The carton was raised slightly so as to prevent the edges from touching the surface of the structural
material. One edge of the entire set up was raised gradually using the tilt table screw device until the inverted
carton of samples started to slide down and the angle of tilt (α) was read off using a protractor. Eqn. 16 was then
used to determine the values of the static coefficient of friction (µ) on these structural surfaces at different
moisture content levels (Singh and Goswami, 1996; Isik, 2007)
µ = tan α …16
All resultant values generated in this research were statistically analysed using Microsoft office excel 2010.
Regression models of the effect of moisture on all the properties were developed and discussed.

Vol.4, No.14, 2014
19
3. RESULT AND DISCUSSION
3.1 REWETTING
The following amount of water were added to the African locust beans after being calculated with respect to
mass of samples contained in each batch and its initial and desired moisture content level.
Table 3.1 Rewetting parameters for batches of samples
Batch A(kg) Q (kg) b (% db)
1 4.50 Nil 7.02
2 4.50 0.12 9.45
3 4.50 0.22 11.36
4 4.50 0.31 12.97
5 4.50 0.43 15.04
3.2 PHYSICAL PROPERTIES
Some of the physical properties of the kernels, grouped into five (5) batches of moisture content values given in
Table 3.1were respectively analysed.
3.2.1 Seed dimensions
Fig. 3.1 Effect of moisture content on seed dimensions
The following regression models were developed to this effect and given in Eqns. 17-20;
L1 = 0.019M2
+ 0.0285M + 42.859 (R2
= 0.9828) …17
L2 = 0.0014M2
+ 0.1468M + 32.39 (R2
= 0.9978) …18
L3 = 0.0078M2
–0.025M + 20.39 (R2
= 0.9996) …19
De = 0.0081M2
+ 0.0092M + 31.735 (R2
= 0.9896) …20
Major, intermediate, minor and equivalent diameters were seen to have exhibited a polynomial increase as
moisture content increases. This is due to the fact that the kernel dimensions increase when the kernel absorbs
more moisture because more matter is then added to the kernel; hence there is an expansion in its dimensions.
Zeifouroush et al. (2009) reported a linear response too for paddy grain. Amin et al. (2004), Subukola and
Onwuka (2011), Seifi and Alimardani (2010) and Tavakoli et al. (2009) all posited linear response of seed
dimensions to moisture increase for lentil seeds, locust bean (Parkiafillicoidea), corn and barley grain
respectively. However, it has been found that a polynomial model had a higher coefficient of determination (R2
)
and thus is the model recommended in this work for the dimensions of Dikanut (Irvingiawombolu).
18
23
28
33
38
43
48
6.5 8.5 10.5 12.5 14.5
SEEDDIMENSIONS(mm)
MOISTURE CONTENT (%)
L1
L2
L3
De

Vol.4, No.14, 2014
20
3.2.2 Seed volume and surface area
Fig. 3.2 Effect of moisture content on seed volume and surface area
The following regression models (Eqns. 21and 22) were developed to these effects;
V = -0.354M2
+ 12.59M + 48.908 (R2
= 0.9908) ...21
AS = -0.3681M2
+ 11.805M + 37.542 (R2
= 0.951) …22
This is does not agree with the suggestions of some researchers like Seifi and Alarmadani (2010) who suggested
a linear model for seed volume and seed surface area as moisture content of corn increased. Zareiforoush et al.
(2009) posited a linear model for these properties too for paddy rice.
3.2.5 Density
Fig. 3.3 Effect of moisture content on densities
Eqns. 23 and 24 are regression models generated to this effect
ϱb= - 0.0062M2
+ 0.222M + 2.384 (R2
= 0.9974) …23
ϱt= -0.0029M2
+ 0.3223M + 8.1491(R2
= 0.9796) …24
From Fig. 3.3, densities (bulk and true) showed a polynomial increase though a linear behaviour was suggested
by Amin et al. (2004) for both the bulk and true densities of lentil seeds with respect to moisture content variance.
Asoiro and Ani (2011) suggested an average safe storage density of yam bean to bean to be 1.02g/cm3
and
1.00g/cm3
respectively for true and bulk densities. Nimkar and Chattopadhyay (2001) posited a linear model for
the density of green gram which had the range of 807g/cm3
to 708g/cm3
(bulk density) and 1363g/cm3
to1292g/cm3
(true density).
90
100
110
120
130
140
150
160
6 8 10 12 14
SEEDVOLUME(mm3)ANDSEED
SURFACEAREA(mm2)
V
As
3
4
5
6
7
8
9
10
11
12
6.5 8.5 10.5 12.5 14.5
BULKDENSITY(g/cm3)ANDTRUE
DENSITY(g/cm3)
Bulk density
True Density

Vol.4, No.14, 2014
21
3.2.4 Aspect ratio,sphericity and porosity
Fig. 3.4 Effect of moisture content on aspect ratio,sphericity and porosity
The following regression models (Eqns. 25-27) were developed to this effect;
ASRa = -0.0007M2
- 0.0212M + 0.6185 (R2
= 0.9914) …25
Ф = -0.0002M2
+ 0.0077M + 0.6577 (R2
= 0.9964) …26
ε = 0.0014M2
– 0.0084M + 0.3984 (R2
= 0.9994) …27
Similarly, Fig. 3.4 shows a polynomial increase for all the dimensionless properties studied in this research.Seifi
and Alimardani(2010) suggested a linear response for the aspect ratio and sphericity of corn. Subukola and
Onwuka (2011) and Zareiforoush et al. (2009) suggested a linear behaviour too for the sphericity of
Parkiafillicoidea specie of locust bean and paddy grain respectively. Nimkar and Chattopadhyay (2001)
suggested a linear increasing response of porosity for green gram. Kingly, et al. (2006), Subukola and Onwuka
(2011) and Tavakoli et al. (2009) suggested a decrease in porosity of pomegranate seeds, Parkiafillicoideaspecie
of locust bean and barley grains respectively with increasing moisture content.
3.3 FRICTIONAL PROPERTIES
Some frictional properties of Dikanut which were studied as a function of moisture content gave the following
results which define seed flowability as a function of moisture content;
0.38
0.43
0.48
0.53
0.58
0.63
0.68
0.73
0.78
6.5 8.5 10.5 12.5 14.5
ASPECTRATIO,SPHERICITYANDPOROSITY
Aspect ratio
Sphericity
Porosity

Vol.4, No.14, 2014
22
3.3.1 Static coefficient of friction
Fig. 3.5 Effect of moisture content on static coefficient of friction
The static coefficients of friction of friction on the five different surfaces and at five different moisture content
levels are shown in Fig. 3.5 above. It can be observed that the static coefficient of friction for all the structural
surfaces tested in the experiment had a polynomial increase as moisture content increases, with plywood having
the highest coefficient followed by mild steel and lastly plastic. These are given in Eqns. 28 - 31:
µPLW = -0.0001M2
+ 0.0436M + 0.2949 (R2
= 0.993) …28
µMS = 0.001M2
+ 0.0199M + 0.308 (R2
= 0.9948) …29
µAL = 0.0004M2
+ 0.0318M + 0.1259 (R2
= 0.9974) …30
µPL = -0.0006M2
+ 0.0558M - 0.0603 (R2
= 0.9981) …31
This is due to the fact that the kernels become stickier as moisture content increases, leading to more resistance
to relative motion between nuts and the surface. This increase in resistance therefore leads to an increase in the
coefficient of static friction. It was also observed that the coefficient of static friction also varied with surfaces,
this was as a result of the dependency of frictional properties and mechanical behaviour of a material on the
microstructure of the material. Structural material grains shape and their crystallographic orientation are two
features of microstructure that affect friction on this material. Due to the difference in crystallographic
orientation of the grains which creates a difference in the surface texture, the material grains of the two surfaces
prevent sliding freely on surface. The rougher the grains, the more the surfaces interlock, resulting in more
resistance to relative motion between them which equally leads to increase in the coefficient of static friction.
The material grains of plywood are rougher than those of mild steel and aluminium, hence, the reason for the
high coefficient of static friction with plywood. Therefore, the power demand of processing machines involving
friction increases with increase in moisture content and also with increase in coefficient of static friction. This
implies that in plywood constructed machines, higher power will be required than in similar machine constructed
with aluminium.
Asoiro and Ani (2011) posited linear increase for average values of coefficient of static friction of African yam
bean from aluminium to asbestos than plywood at a safe moisture content. Oje and Ugbor (1991) suggested a
linear increase too for oil bean seeds using galvanized steel, plywood, stainless steel, aluminium and mild steel
with a simultaneous increase in moisture content and equally posited that plywood gave highest values. Kingly et
al. (2006) posited a linear increase too for pomegranate seeds for various structural surfaces with plywood giving
the highest values.
0.25
0.35
0.45
0.55
0.65
0.75
0.85
0.95
6.5 8.5 10.5 12.5 14.5
STATICCOEFFICIENTOFFRICTION
PLW
MS
AL
PL

Vol.4, No.14, 2014
23
3.3.2 Angle of repose
Fig 3.8 Effect of moisture content on angle of repose
Fig 4.8 shows the angle repose at five different moisture contents and it was observed that the angle of repose
had a polynomial variation with increase in moisture content. This is given in Eqn. 32:
ΘR = 0.0155M2
+ 1.8892M + 5.7693 (R2
= 0.988) …32
Reason being that, the higher the moisture content, the higher the cohesion between the seeds. In terms of
flowability, the seeds are heavier and the inertia to move is increased. This increase in resistance to flow
prevents seeds from sliding on each other, thereby increasing the angle of repose of the seeds.Nimkar and
Chattopadhyay (2001) , Subukola and Onwuka (2011), Tavakoli et al. (2009) and Zareiforoush et al. (2009) all
suggested a linear increase too for green gram seeds, Parkiafillicoideaspecie of locust bean, barley grains and
paddy grains respectively.
These regression models generated in this research can be used to predict mathematically these respective
properties of Dikanut(Irvingiawombolu)within the moisture content range of 7.02 to 15.04% (dry basis).
CONCLUSIONS
1. The nut was found to be oblate spheroid in shape.
2. All properties studied were found to have a polynomial response to moisture content increase within the
moisture content range studied (7.02 to 15.04% dry basis).
3. The average dimensions; major, intermediate and minor and equivalent diameters increased from 44.00 to
47.73mm, 33.50 to 34.89mm, 20.60 to 21.79mm and 32.20 to 33.73mm respectively as moisture content
increased from 7.02 to 15.04% (dry basis).
4. The seed volume and the seed surface area ofIrvingiawomboluincreased from 120.01mm3
to 158.56mm3
and
102.04mm2
to 131.64mm2
within the range of moisture content tested.
5. Bulk density and true density increased from 3.64g/m3
to 4.33g/m3
, and 10.31g/m3
to 12.26g/m3
respectively
with increase in the moisture content range tested.
6. Aspect ratio and sphericity and porosity of Irvingiawomboluvaried with increase in the tested moisture
content range from 0.74 to 0.79; 0.70 to 0.72; 0.41 to 0.60 respectively.
7. Angle of repose increased from 20.10 ̊ to 37.20 ̊ while static coefficient of friction increased from 0.60 to
0.92 (plywood), 0.50 to 0.82 (mild steel), 0.37 to 0.70 (aluminum), 0.30 to 0.64 (plastic) as moisture content
increased from 7.02 to 15.04% (dry basis).
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