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Pakistan Journal of Scientific and Industrial ResearchSeries A: Physical Sciences
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Polish Academy of SciencesOlsztyn, PolandDr. A. Chauhan
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Rawenshaw University, Cuttack, India
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Pakistan Journal of Scientific and Industrial ResearchSeries A: Physical Sciences
Vol. 59, No. 2, May-June, 2016
Contents
(E)-N'-(2,4-dihydroxybenzylidene)nicotinohydrazide and its Metal Complexes:
Synthesis, Characterisation and Antitubercular Activity
Kehinde Olurotimi Ogunniran, Joseph Adeyemi Adekoya, Cyril Ehi-Eromosele,
Olayinka Oyewale Ajani, Akinlolu Kayode and Tadigoppula Narender 63
Quantification of Cr(VI)-Thymoquinone Complex Using Cyclic Voltammetry
Farah Kishwar, Khalid Mohammed Khan, Rubina Perween, Anila Anwar and Nasir Akhtar 76
Effect of Processing on Physicochemical Properties and Fatty Acid Composition
of Fluted Pumpkin (Telfairia occidentalis) Seed Oil
Jacob Olabode Alademeyin and Jacob Olalekan Arawande 83
Liberation Studies of Padhrar Coal by Using Fractionation Method,
XRD Analysis and Megascopic and Microscopic Techniques
Muhammad Shahzad, Zulfiqar Ali, Yasir Majeed, Zaka Emad,
Muhammad Aaqib and Bilal Adeel 90
Modeling the Land Suitability Using GIS and AHP for Cotton Cultivation
in Punjab, Pakistan
Nabila Naz and Haroon Rasheed 96
Quality Variation Minimizer: A New Approach for Quality Improvement
in textile industry
Muhammad Amin, Muhammad Amanullah and Atif Akbar 109
Effect of Different Processing Stages on the Crystallinity % and Tensile
Strength of 100% Cotton Fabric
Zahid Hussain, Muhammad Qamar Tusief , Sharjeel Abid, Muhammad Tauseef Khawer,
Nabeel Amin and Mudassar Abbas 114
Short Communication
Biosorption Characteristics of Water Hyacinth (Eichhornia crassipes) in the
Removal of Nickel (II) Ion under Isothermal Condition
Chidi Obi and Sylvester Eigbiremonlen 118
(E)-N'-(2, 4-dihydroxybenzylidene)nicotinohydrazide and
its Metal Complexes: Synthesis, Characterisation
and Antitubercular Activity
Kehinde Olurotimi Ogunnirana*, Joseph Adeyemi Adekoyaa, Cyril Ehi-Eromoselea,Olayinka Oyewale Ajania, Akinlolu Kayodea and Tadigoppula Narenderb
aDepartment of Chemistry, College of Science and Technology, Covenant University,
PMB, 1023, Ota, Ogun State, NigeriabMedicinal and Process Chemistry Division, CSIR-Central Drug Research Institute, Lucknow, India
(received April 15, 2015; revised August 6, 2015; accepted August 7, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 63-75
Introduction
Human tuberculosis (TB) has re-emerged with devastating
consequences on global public health and it is currently
one of the most widespread infectious diseases. In
addition, it is the leading cause of death due to a single
infectious agent among human adults in the world (Jenkins
et al., 2011). Mycobacterium tuberculosis is one of the
most harmful pathogens of mankind, infecting one-
third of the global population and claiming two million
lives every year (Stewart et al., 2003). Tuberculosis
spreads by aerosols from patients with pulmonary disease
(Phillip and Graham, 2004). Mycobacterial infection
has increased in number worldwide due to a global
increase in the number of patients with HIV infection
and AIDS disease, increase in number of elderly patients
and the emergence of resistant tuberculosis. Tuberculosis
arises in two different ways: either from a recent infection
with M. tuberculosis or from the reactivation of dormant
tubercle bacillus after initial infection. As a consequence,
the present level of tuberculosis comprises both indivi-
duals with �new� exogenous infections and those with
a reactivation of �old� endogenous disease (De Backer
et al., 2006). In terms of absolute number of TB cases,
22 countries of the world have the highest TB burden
with at least 270 cases per 100,000 populations. Among
the top five ranking countries are India, China, Indonesia,
South Africa and Nigeria (Harper, 2007; Laughon,
2007). The situation has become more deplorable than
it appeared as 0.5 million new cases due to multidrug-
resistant (MDR) TB were recorded in 2010 (WHO,
2013). The alarming estimates exposes that 0.22 billion
people may acquire TB and 79 million could die due
to TB by the year 2030.
Effective TB treatment is difficult, due to the unusual
structure and chemical composition of the Mycobacterium
cell wall, which makes many antibiotics ineffective and
hinders the entry of drugs (Jia et al., 2005). TB disease
can be treated by taking several drugs for 6 to 9 months*Author for correspondence;
E-mail: [email protected]
Abstract. Nicotinic acid hydrazide and 2,4-dihydoxylbenzaldehyde were condensed at 20 °C to form an
acylhydrazone (H3L1) with ONO coordination pattern. The structure of the acylhydrazone was elucidated
by using CHN analyzer, ESI mass spectrometry, IR, 1H NMR, 13C NMR and 2D NMR such as COSY and
HSQC. Thereafter, five novel metal complexes [Mn(II), Fe(II), Pt(II) Zn(II) and Pd(II)] of the hydrazone
ligand were synthesized and their structural characterization were achieved by several physicochemical
methods namely: elemental analysis, electronic spectra, infrared, EPR, molar conductivity and powder
X-ray diffraction studies. An octahedral geometry was suggested for both Pd(II) and Zn(II) complexes
while both Mn(II) and Fe(II) complexes conformed with tetrahedral pyramidal. However, Pt(II) complex
agreed with tetrahedral geometry. In vitro antitubercular activity study of the ligand and the metal complexes
were evaluated against Mycobacterium tuberculosis, H37Rv, by using micro-diluted method. The results
obtained revealed that (PtL1) (MIC = 0.56 mg/mL), (ZnL1) (MIC = 0.61 mg/mL), (MnL1) (MIC = 0.71 mg/mL)
and (FeL1) (MIC = 0.82 mg/mL), exhibited a significant activity when compared with first line drugs such
as isoniazid (INH) (MIC = 0.9 mg/mL). H3L1 exhibited lesser antitubercular activity with MIC value of
1.02 mg/mL. However, the metal complexes displayed higher cytotoxicity but were found to be non-
significant different (P > 0.05) to isoniazid drug.
Keywords: hydrazones, metal complexes, electron spin resonance, thermogravimetry, powder X-ray
diffraction, antitubercular agents
63
which includes the first 2 months of isoniazid, rifampicin,
pyrazinamide, and ethambutol in the intensive phase
and after that period isoniazid and rifampicin in the
continuous phase (Jindani et al., 2004). Most of the drugs
in the current tuberculosis regime result from the research
performed over 50 years ago (Sacchettini et al., 2008).
With the global emergence of multidrug-resistant
tuberculosis (MDR-TB) and extensively drug-resistant
tuberculosis (XDR-TB) there is an urgent need to develop
new anti-mycobacterial agents.
In the search for new compounds, isoniazid derivatives
have been found to possess potential tuberculostatic
activities (Aboul-Fadl et al., 2011). Hydrazones are
important compounds for drug design, as possible
ligands for metal complexes, organocatalysis and also
for the syntheses of heterocyclic compounds. These
compounds have interesting biological properties,
such as anti-inflammatory, analgesic, anticonvulsant,
antituberculous, antitumor, anti-HIV and antimicrobial
activity (Nataliya et al., 2010). In the present work, an
acylhydrazone obtained in the reaction of nicotinic
hydrazide with 2,4-dihydroxybenzaldehyde and its
Mn(II), Fe(II), Pt(II) Zn(II) and Pd(II) complexes were
characterised and tested for their antimycobacterial
activity. The presence of the hydrazine pharmacophore
in these compounds is expected to contribute to high
antimycobacterial activity.
Materials and Methods
Measurements. All the chemicals and solvents used
were reagent grade and used without further treatment
unless otherwise noted. Nicotinic acid hydrazide and
2,4-dihydroxylbenzaldehyde were purchased from
Sigma-Aldrich. ESI-MS spectrum of the ligand was
obtained using Agilent 6520 Q-TOF mass spectrometer.
The % of carbon, nitrogen and hydrogen in the synthesized
hydrazones and metal complexes were determined by
using Vario EL CHNS analyzer. 1H,
13C and 2D NMR
(COSY, HSQC and DEPT 135) spectra of the hydrazone
were recorded by using Bruker AMX 300 FT-NMR
spectrometer with DMSO-d6 at sophisticated analytical
instruments facility, Central Drug Research Institute,
Lucknow, India. The infrared spectroscopy of the hydrazone
and the metal complexes were recorded on Perkin-Elmer
RX-1 Fourier Transform Infrared Spectrometer using
KBr pellets in the range of 4000-400 cm-1. The electronic
data of the hydrazone and the metal complexes were
obtained in methanol/DMSO by using Perkin Elmer
Spectro UV-visible Double Beam UVD spectrometer
in the range of 200-700 nm. The molar conductivities
of the metal complexes at ambient temperature in DMF
solution (10-3 M) were measured using systronics-304
conductivity meter at Chemistry Department, Covenant
University, Canaan Land, Sango-Ota, Ogun State, Nigeria.
The magnetic measurements of paramagnetic metal
complexes in powder form were measured at room
temperature by using vibrating susceptibity magnometer
(PAR 155) with magnetic field of -10 to +10 kOe at
Instrumentation Center, Indian Institute of Technology,
Roorkee, India. TGA/DTA thermograph of the metal
complexes were obtained by heating the complexes at
the rate of 10 °C/min under inert atmosphere by using
thermogravimetric analyzer TGA Q500 V20.8 Build
34 model at Indian Institute of Science and Technology,
Hyderabad, India. The EPR spectra of the metal
complexes at 77 K were recorded on a Varian E-112
spectrometer using TCNE as the standard, with 100
KHz modulation frequency, modulation amplitude 2 G
and 9.1 GHz microwave frequency at sophisticated
analytical instruments facility, Indian Institute of
Technology, Bombay, India. Powder X-ray diffraction
data for one of the metal complexes were collected by
using Powder X-ray diffractometer at Instrumentation
center, Indian Institute of Technology, Roorkee, India.
Powder diffraction data was recorded on a Bruker AXS
D8 Advance diffractometer operating in the q:q mode,
equipped with a secondary beam graphite monochromator,
a Na(Tl)I scintillation counter, and pulse-height amplifier
discrimination. CuKa radiation (l=1.5418 Å) was used.
The X-ray generator and diffractometer settings were
40 kV, 40 mA, DS 0.5°, AS 0.5°, and RS 0.1 mm.
Experimental conditions were step scan mode, with
5 < q <105°, Dq = 0.02°, and t = 30 s/step. Silicon NBS
640b was used as an external standard.
Synthesis of (E)-N¢-(2,4-dihydroxybenzylidene)
nicotinohydrazide. The synthetic methods previously
described by Cui et al. (2012) were modified and adopted.
The nicotinic acid hydrazide (10 mmol, 1.37 g) was
dissolved in 20 mL of absolute ethanol by heating gently
on water bath. The solution obtained was mixed with
ethanolic solution of 2,4-dihydroxylbenzaldehyde (10
mmol ) in a round bottom flask. The mixture was stirred
at 20 °C for 6 h after which it was allowed to stand at
ambient temperature for 24 h. The precipitate formed
was filtered and washed several times with ethanol.
The precipitate was recrystallized in mixture of methanol
and chloroform (1:1). It was filtered off, washed with
ether and dried in vacuum. The purity of the hydrazone
64 Kehinde Olurotimi Ogunniran et al.
was confirmed by single spot displayed by TLC using
methanol: chloroform (2:8) mixture.
Yield 1.82 g (70.8%); light pink solid; mp: 273-275 °C;
Rf = 0.83 (CHCl3/CH3OH, 4:1, at RT.). 1H-NMR
(DMSO-d6) d: 12.16 (s, 1H, NH), 11.36 (s,1H, Ar-
OH(2)), 10.06 (s, 1H, Ar-OH(3)), 9.10 (s, 1H, H(4)), 8.80
(d, J= 3.84 Hz,1H, H(5)), 8.55 (s, 1H, H-CN), 8.30 ( dt,
J = 7.9 Hz, 1H, H(7)), 7.60 (dd, J1 = 4.80 Hz, J
2 = 7.9
Hz, 1H, H(8)), 7.39 (d, J = 8.43 Hz, 1H, H(9)) 6.42 (dd,
J1 = 2.1 Hz, J
2 = 8.43 Hz. 1H, H(10)) 6.36 (d, J = 2.1Hz,
1H, H(11)) ppm. 13
C-NMR (DMSO-d6) d: 161.3 (C1),
161.2 (C2), 159.8 (CO), 159.3 (C4), 149.9 (C5), 148.8
(NCH), 135.6 (C7), 131.6 (C8), 129.1 (C9), 123.9
(C10), 110.7 (C11), 108.1 (C12), 102.9 (C13) ppm. IR
(KBr) cm-1: 3432 (ArOH), 3158 (NH), 1639 (C=O),
1508 (C=N), 1466 (N-N), 1353 (C-O), 1165(C-N). MS
(ESI+): in m/z: 258.0 [M + H]+. Anal. calcd. for
C14H12N3O3 (256.26): C, 65.62; H, 4.68; N, 16.39.
Found: C, 65.89; H, 4.53; N, 16.16 (Scheme 1).
Preparation of Mn(II), Fe(II), Pt(II), Zn(II) and
Pd(II) complexes of (E)-N¢-(2,4-dihydroxybenzyli-
dene) nicotinohydrazide (H3L1). [Mn(H3L
1)2].H2O
(1). To an ethanolic solution of H3L1 (10 mmole, 2.57 g),
10 mmole equivalent of [Mn(CH3COO)2.4H2O]
dissolved in absolute ethanol were added slowly after
which 2 drops of TEA was added. The mixture was
refluxed at 80 °C for 4 h. The brown product obtained
was allowed to stand at ambient temperature for 24 h
after which it was filtered, washed with absolute ethanol
followed by ether and dried over P4O10 in vacuo.
[Fe(H3L1)2Cl2] (2). 10 mmol (2.57 g) of ethanolic
solution of H3L1 was dissolved in absolute ethanol and
mixed directly ethanolic solution of anhydrous FeCl2
(10 mmol, 1.27 g). The mixture was refluxed at 80 °C
for 5 h. The solution obtained was allowed to stand at
ambient temperature for 48 h. The black precipitate
formed was filtered and washed thrice with 30 mL of
cold ethanol, followed by ether and dried in vacuo.
[Pt(H3L1)Cl]Cl (3). 10 mmole (2.66 g) of PtCl2 was
heated to dryness in 1 mL of conc. HCl in a round
bottom flask, after which 10 mL of distilled water was
added. The mixture was stirred at 60 °C for 30 min
before adding ethanolic solution of H3L1 (10 mmol,
2.57 g) in 10 mL of absolute ethanol. The mixture was
then refluxed for 3 h. The green precipitate formed was
filtered after cooling the solution in ice blocks, washed
with 30 mL of cold ethanol and dried over P4O10 in
vacuo.
[Zn(H3L1)(CH3COO)2H2O] (4). 10 mmol (1.83 g) of
Zn(CH3COO)2 was dissolved in 20 mL of mixture of
absolute ethanol and distilled water (1:1). The solution
obtained was added gradually to ethanolic solution of
H3L1 (10 mmol in 20 mL of ethanol) in a round bottom
flask after which two drops of TEA were added. The
solution was refluxed at 80 °C for 4 h. The yellow preci-
pitate formed was allowed to cool to ambient temperature
after which it was filtered, washed with 30 mL of ethanol
and then with 10 mL of ether. The precipitate was then
dried by using vacuum rotary evaporator.
[Pd(H3L1)Cl] (5). 10 mmol (1.77 g) of PdCl2 was
dissolved in 10 mL of DMF while 10 mmol of H3L1
was dissolved in 10 mL of absolute ethanol. The solutions
were mixed together in a round bottom flask and refluxed
at 80 °C for 6 h after which the solution was left at
room temperature for 48 h. The dark brown precipitate
formed was filtered and washed with cold ethanol and
then dried over P4O10 in vacuo.
Antimycobacterial activity study. MIC determination.
All the compounds were screened for their in vitro
antimycobacterial activities against isoniazid (ATCC
35822) resistant strains of M. tuberculosis, using the
micro plate Alamar Blue assay (MABA) (Sivakumar
and Rajasekaran, 2013). A serial dilution of the com-
pounds was made directly on the plate. The final drug
concentrations tested were 0.01- 20.0 µg/mL. The plates
were covered and sealed with parafilm and incubated
at 37 °C for 5 days. After this time, 25 µL of a freshly
prepared 1:1 mixture of Alamar Blue reagent and 10%
tween 80 was added to the plate and incubated for 24 h.
A blue colour in the well indicated no bacterial growth,
and a pink colour indicated growth. The minimal
inhibition concentration (MIC) was defined as the lowest
drug concentration, which prevented a colour change
from blue to pink.
Cytotoxicity study. Cytotoxicity of H3L1
and some of
its synthesized metal complexes were determined with
the Vero cell line ATCC CCL-81 using an MTS assay
(Protopopova et al., 2005).
65Antitubercular Activity of Acylhydrazone
Scheme 1. Synthetic pathway for H3L1.
HN
N
HN2
O +
HO
OH H
O6 h
N
O
NH
N
H
HO OH
EtOH, 20 °C
Results and Discussion
Mass spectrum of H3L1. The molecular mass of the
hydrazone was ascertained with the use of mass spectrum
obtained by the use of Agilent 6520 Q-TOF mass
spectrophotometer (ESI). The spectrum (Fig. 1) showed
the molecular ion peak at m/z 258.0 which is in
agreement with the calculated molecular mass of the
compound within the precision limit of ± 0.02.
1H NMR spectrum of H3L
1. The
1H NMR spectrum
of the hydrazone (Fig. 2) showed a sharp singlet peak
which integrated as one hydrogen at d = 12.16 ppm.
It was assigned to an imine proton NH(1) (Pavia et al.,
2008). Apart from the fact that the proton is attached
to a highly electronegative element, the effect of neighbo-
ring atoms contributed to a high decrease in electron
density around the imine proton (Sankar et al., 2010;
Jursic et al., 2002). The infrared spectrum confirmed
the involvement of the imine proton in interhydrogen
bonding formation with hydroxyl group (NH�
OH)
during tautomerism and thereby contributing to decrease
in electron density around the proton. Thus, the proton
resonated downfield (Mustafa et al., 2009). The singlet
peak observed was as a result of the absence of protons
on its neighbouring atoms and thus there was no coupling
interaction. The two other singlet peaks in the downfield
region of the spectrum at 11.36 ppm and 10.06 ppm
were assigned to the two aromatic protons OH(2) and
OH(3), respectively (Al-Shaalan, 2011; Patel and Patel,
2011). The high d ppm values could be attributed to
their attachment to high electronegative element and
thereby making each of the peaks to resonate as a single
peak downfield. Also in the pyridine moiety, H(4) resona-
ted as a single peak with a slight shoulder at 9.10 ppm.
Other aromatic protons resonated within the region
8.80-6.36 ppm. The doublet of doublet peak which
integrates as one proton at 7.60 ppm was assigned to
proton H(8) which is at the meta position to nitrogen
atom in the pyridine moiety. The splitting observed is
as a result of the coupling effect from adjacent protons.
The doublet peak at 8.8 ppm was assigned to a proton
H(5) at the ortho position to N(1) atom in the pyridine
moiety. The doublet signal observed was attributed to
the coupling effect from proton H(8) which is at the
meta position. The doublet of triplet peak at 8.30 ppm
was assigned to the proton H(7) which is adjacent to
N(1) atom. Also, the proton in between the two aromatic
group H(9) was assigned to a doublet peak at 7.39 ppm.
The doublet peak observed is as a result meta coupling
effect from H(10). Meanwhile, H(10) was assigned to
doublet of a doublet at 6.42 ppm, while a doublet at
6.36 ppm was assigned to H(11). The proton at 6.42 ppm
experienced meta coupling effect from H(9) and ortho
coupling effect from H(11) and therefore resonated as
doublet of doublet. Also the doublet signal at 6.36 ppm
assigned to H(11) resulted from the ortho coupling
effect from proton H(10) at 6.42 ppm.
COSY spectrum of H3L1. The COSY experiment was
used to confirm the protons assignment above. The COSY
spectrum shown in Fig. 3 revealed the 1H-
1H coupling
interactions in the molecule (Berger and Sicker, 2009;
66
Fig. 2.1HNMR spectrum of H3L1 recorded in
DMSO-d6 at 300 MHz.
N
O
N
OH
HH11
4
1
3HO
H8
H5
H7
NH
H6
H9
H10
12 11 10 9 8 7 6
1.0
0
0.9
9
1.1
8
1.0
81
.13
1.1
01
.20
1.1
41
.13
2.3
4
Fig. 1. ESI mass spectrum for H3L1.
100
90
80
70
60
50
40
30
20
10
0700,0
684.4629.0
610.6
600.0
568.0537.2
500.0400.0
391.2
339.1
300.0
200.0298.9223.1
200.0
179.0177.9
137.8
100.0m/z
259.0
258.0 2.70+006
Kehinde Olurotimi Ogunniran et al.
Silverstein and Webster, 2002). The absence of off
diagonal peak at 12.10, 11.36, 10.04, 9.10 and 8.54 ppm
confirmed their assignment to H(1), ArOH(2), ArOH(3),
H(4) and H(6), respectively. This confirmed that absence
of coupling interaction around the protons in the mole-
cule. However, COSY spectrum confirmed that the
multiplet peak at 7.60 ppm, assigned to H(8), experienced
coupling interaction with a doublet of triplet peak at
8.30 ppm and at the same time correlated with a doublet
peak at 8.80 ppm. The two peaks were assigned to H(7)
and H(5), respectively. COSY spectrum also confirmed
that the proton at 6.42 ppm correlated with H(9) at
7.39 ppm and and H(11) at 6.36 ppm, respectively. The
resonances at 6.42 ppm H(10) and 6.36 ppm H(11)
confirmed that the two protons were attached to neighbo-
ring carbon atoms. The schematic contour plot of 1H-
1H COSY experiment for H3L
1, (Fig. 3), along with the
observed coupling interaction is given in (Fig. 4).
13C NMR spectrum of H3L
1. The
13C NMR spectrum
(Fig. 5) displayed the presence of 13 magnetically different
carbon atoms in the molecule which corresponded to
the number of carbon atoms in the molecule. The assign-
ments were done on the basis of proton decoupled 13
C
spectrum. The two carbon atoms attached to hydroxyl
group in the resorcinol ring [C(1) and C(2)] resonated
at 162.12 ppm and 160.95 ppm downfield, respectively
due to the effect of the attached electronegative element
coupled with the fact that both carbons are sp2 carbon.
Thus, a high decrease in electron density around the
two carbon atoms is expected. Also, the peak at 159.56
ppm was assigned to the carbonyl carbon, C(3), which
appeared downfield due to the conjugative effect of
N(2)-N(3) core in the hydrazone. The two adjacent carbon
atoms to N(1) appeared downfield at 152.31 ppm and
149.65 ppm, respectively, because both of them were
affected by electronegative effect of N(1). The lower
field of carbon atom, 148.56 ppm, C(6) is due to exten-
sive p electron delocalization of N(2)-N(3)=C(6) bond
chain. The remaining two carbons in the pyridine moiety,
C(7) and C(10), were assigned to the peak at 135.37 ppm
and 123.62 ppm, respectively. The resonances assigned
to resorcinol carbons are C(8), 131.32; C(12), 107.84;
C(13), 102.71 ppm. The non-protonated carbons, C(11)
and C(9), resonated downfield due to the conjugative
effect of C(6)=N(3) on C(11) and electronic effect
exerted by adjacent carbonyl on C(9).
67Antitubercular Activity of Acylhydrazone
Fig. 3. 1H-1H COSY spectrum of H3L1 at 300 MHz.
ppm
ppm
1H-
13C HSQC spectrum of H3L
1.
1H-
13C correlation
spectrum (Fig. 6) was used to confirm both the proton
and carbon assignments. HSQC spectrum showed all
the protonated carbon in the molecule. Only 8 carbon
Fig. 5. 13C NMR spectrum of H3L1 in DMSO-d6
at 75 MHz.
N10
5N1
NH
79
4
3
O
23
6
HO OH8
13
12
21
11
105110115120125130135140145150155160 ppm
Fig. 4. 1H-1H COSY spectrum schematic structure
of H3L1.
N
8H
O
2
7H
H5
H4
NH1
N
HO
H6
9H
OH3
H11
H10
(C, H, N) obtained are in good agreement with the
calculated values. The magnetic susceptibility values
recorded at room temperature for Mn(II) and Ni(II)
complexes compete favourably with the calculated spin
only magnetic values. The conductivity ability of the
metal complexes is very low, thus suggesting non-
electrolytic character of the synthesized complexes.
68
Fig. 6. 1H- 13C COSY spectrum of H3L1 at 75 MHz.
Table 1. Analytical data of H3L1metal complexes
Complex Colour M. pt. Found (Calc.), % LM
meff
(% Yield) (°C) M C H N S cm2/mol mB
[Mn(H3L
1)(CH
3COO)
2].H
2O (1) Brown >300 12.52 41.52 4.21 9.22 32 5.82
(62.5) (12.95) (45.55) (4.27) (9.37)
[Fe(H3L
1)Cl
2] (2) Black >300 14.12 40.59 3.22 10.72 56 5.3
(51.2) (14.58) (40.77) (2.63) (10.97)
[Pt(H3L
1)C1].C1 (3) Green >300 36.89 29.51 2.04 7.53 28 -
(65.0) (37.28) (29.84) (2.12) (8.03)
[Zn(H3L
1)(CH
3COO)
2H
2O] (4) Yellow >300 14.17 41.73 4.27 9.56 30 -
(78.8) (14.26) (41.51) (4.17) (9.16)
[Pd(H3L
1)C1] (5) Brown >300 26.53 39.03 2.53 10.56 26 -
(65.06) (26.66) (39.12) (2.78) (10.53)
L1 = C
13H
8N
3O
3
atoms appeared in the correlated spectrum and this
matches the structure of the hydrazone. 13
C-NMR peaks
at 161.12, 160.95, 159.55, 128.85 and 110.49 ppm did
not make any correlation in the HSQC spectrum and
therefore suggested that these carbons are non-protonated
as assigned. However, all the protonated carbons showed
correlation with the attached proton at the assigned
position. Based on this, the schematic diagram for the
hydrazone 1H-
13C relation is as shown in Fig. 7.
Analytical data of H3L1metal complexes. All the metal
complexes of H3L1 synthesized possess characteristics
colour as reported in Table 1. The metal complexes were
found to possess high melting point (300 °C) due to the
coordination bonds. They were found to be sparingly
soluble in methanol, DMF and THF but are soluble in
DMSO and pyridine. The results of elemental analyses
Infrared spectra. The spectra interpretation was done
by comparing the spectra of each of the metal complexes
with the spectrum of the ligand, H3L1. The tentative
infrared spectra assignments of H3L1 and its metal
complexes are presented in Table 2 while some of the
spectra are shown in Fig. 8. The significant changes in
the vibration bands of the metal complexes when com-
pared to that of the ligand were observed by Davidson
(2010). A strong peak at 3432.27 cm-1 attributed to OH
2
Fig. 7. Schematic diagram of 1H- 13C COSY for
H3L1.
N
HO
8H
7H
H5
H4
O
NH1
N
H6
H11
H10
OH
9H
3
Kehinde Olurotimi Ogunniran et al.
stretching vibration in the spectrum of the ligand was
observed to have shifted to lower wavelength with
reduction in intensity, in Mn(II), Pt(II) and Zn(II)
complexes. The observation could be as a result of
coordination of the OH vibration group to the metal
ions in the complexes. However, the peak was observed
at higher wavelength in Fe(II) and Pd(II) complexes
(3396.89 and 3384.43 cm-1, respectively). The higher
wavelength observed, coupled with broadness of the
peak, is as a result of the coordination to the central
metal through in enol form.
A strong and broad band at 3384.43 cm-1 attributed to
the CO stretching vibration in the spectrum of H3L1
also appeared in the spectral of the metal complexes
with significant changes in wavelength and intensity
due to effect of the coordination. The peak appeared as
a medium peak at the lower wavelength in all of the
complexes except in Mn(II) complex where it appeared
at 1660.01 cm-1. The observation could be attributed to
the coordination of CO to the central metal in the com-
plexes.
The azomethine band, which appeared in the spectrum
of H3L1 at 1508.62 cm-1 as a medium band, appeared
in all of the metal complexes at a higher wavelength.
The increase in u(C=N) in the spectra of metal com-
plexes is due to the increase in the double bond character
off-setting the loss of electron density via donation to
the central metal and thus further confirmed the coordi-
nation of H3L1
through the azomethine atom. The
conjugate effect of the coordination through u(C=N) in
the complexes was noticed on u(N-N) bands which
appeared as weak band in the metal complexes.
UV/Visible spectra. The electronic absorption data of
the ligand and the metal complexes in methanol at
ambient temperature are recorded in Table 3. The
electronic transitions were interpreted by comparing
the spectrum of the ligand to the spectra of the corres-
ponding metal complexes. Mn(II) is of d5 configuration
which is high spin complex with doubly forbidden
transition. Tunabe-Sugano diagram shows that the
Russell-Saunders term for d5 high spin system is
6S
with 6A1g as the ground term symbol. This term gives
rise to 4G, 4D and 4P excited states. Thus, there is no
sextet spin multiplicity for octahedral complexes, the
transitions are Laporte forbidden and spin forbidden.
In the spectrum of the Mn(II) complexes, five notable
transition were observed. The high intense transitions
at ca. 44248, 33333 and 30864 cm-1 were due to transition
of ligand chromophoric groups in the complex. The
transition had undergone bathochromic shift toward
visible region due to coordination effect. However, two
low intense forbidden transitions were observed in
69Antitubercular Activity of Acylhydrazone
Table 2. Infrared spectra assignments for metal complexes of H3L1
Ligand/metal u(OH) u(C=O) u(C=N) u(N-N) d(C-O)df. d(C-N)df.
complex cm-1
cm-1
cm-1
cm-1
cm-1
cm-1
H3L1
3432.27 vs 1639.5 s,b 1508.62 m 1466.91 m 1353.0 s 1165.23 s
[Mn(H3L
1)(CH
3COO)
2H
2O] (1) 3402.5 s,b 1660.01 m 1574.32 ms 1476.99 w 1354.03 m 1181.03 m
[Fe(H3L
1)Cl
2] (2) 3396.89 s,b 1593.74 m 1539.29 m 1438.24 w 1347.51 m 1136.67 s
[Pt(H3L
1)Cl].Cl (3) 3413.3 s,b 1609.39 m 1535.93 m 1437.95 w 1212.35 m 1129.2 m
[Zn(H3L
1)(CH
3COO)
2H
2O] (4) 3414.47 s,b 1604.31 m 1524.69 w 1491.26 w 1374.39 m 1190.56 s
[Pd(H3L
1)Cl] (5) 3384.43 b 1606.25 m 1532.06 m 1435.82 w 1211.94 m 1129.63 s
Fig. 8. TGA/DTA spectrum of [Mn(H3L1)(CH3
COO)2 ]. H2O (1).
De
riv.
we
igh
t (%
/ C
)
Sample: Mn L1Size: 10.0510 mgMethod: RampComment: Mn L1
File: D:\TGA\KVSN\CDRI\L1.001Operator: AKRun Date: 27-Nov-2012 13.54Instrument: TGA Q500 V20.8 Build 34
TGA
0.8
0.6
0.4
0.2
0.0
100
80
60
40
O
We
igh
t (%
)
43.98 CO
80.00 CO
291.04 CO
325.60 CO
361.85 CO
375.45 CO
399.13 CO
380.91 CO
390.55 CO
422.00 CO
539.57 CO
631.64 CO
587.74 CO
336.55 CO
0 100 200 300 400 500 600 700
Universal V4. 5A TA Instruments
Temperature (°C)
Mn(II) complex at ca 24390 and 19920 cm-1
. They were
assigned to 4T1g (G) ¬ 6A1g and 4T1g (G) ¬ 6A1g,
respectively which is the characteristics of octahedral
geometry but the presence of a weak shoulder at ca
24390 cm-1 suggested a distorted tetrahedral pyramidal
complex. This could be pictured as octahedral complex
with one corner occupied by a lone pair of electrons
(Brik et al., 2011).
The absorption spectrum of Fe(II) complex adapted
greatly to the literatures (Munde et al., 2012). Only one
band with low intensity was observed in the visible
region at ca. 22124 cm-1. This showed that Fe(II) is of
d6 configuration with high spin system and ground
term symbol of 5D. The d-d transition observed was
assigned to 2Al2 ¬2B2. However, two absorptions at
ca. 33113 and ca. 27700 cm-1 were attributed to n®p*
transitions in the ligand. The band at ca. 40816 cm-1
was assigned to n®p*transition in the ligand had
completely disappeared while bathochromic shift was
observed in n®p*transition. The observation was
attributed to the coordination of the ligand to the metal
ion in the complexes.
Magnetic moment data of Pt(II) complex indicate that
the complex is a diamagnetic with d8 configuration
which favours square-planar geometry (Brik et al.,
2011). This is also supported by a failed attempt to get
the esr data for the complex. The electronic spectrum
of the complex showed transition bands at ca. 33003,
28818 and 16891 cm-1. The strong band at ca. 33003
cm-1 was attributed to the n®p* transitions in the
coordinated ligand while the bands at ca. 28818 and
16891 cm-1 were assigned to 1Eg ¬
1A1g and
1A2g ¬
1A1g transitions.
The electronic spectrum of Zn(II) complex showed a
shoulder band at ca. 362232 cm-1 which was assigned
to the n®p* transition within the ligand moiety. Also
the spectrum showed a strong band at ca. 30211cm-1.
This was assigned to the n®p* transition which could
be as a result of cumulative effect of transitions of
azomethine and amide chromophoric groups of the
ligand. The bathochromic shift observed was attributed
to the coordination effect. Zn(II) ion is of d10
configuration,
therefore no d-d transition is expected, but metal-ligand
charge transfer band was observed at ca. 23256 cm-1
due to yellow colour of the complex. No appreciable
band was observed below ca. 20,000 cm-1 which is in
accordance with d10
configuration of Zn(II) ion.
The Pd(II) ion has a d10
configuration with a term
symbol, 1S. Due to the filled state of the d- orbital, d-d
transition was not observed for Pd(II) complex as
expected. However, intra-ligand bands were observed
with hypsochromic shift for n®p* transition and
bathcromic shift for n®p* transitions. Thus, support
the coordination of the ligand to the central metal ion
in the complex.
TGA/DTA analysis. The thermal behaviour of
compounds (1), (2) and (5) was investigated by using
a non-isothermal thermo gravimetric, TG, and differential
TG. The samples were heated at a rate of 10 °C min-1
under N2 atmosphere from 0-700 °C. The selected
thermograms obtained are as represented in Fig. 8. The
TG/DTG curve of compound (1) showed 4% weight
loss between 43 and 160 °C. This corresponds to loss
of water molecule which is outside the coordination
sphere of the complex (Artur et al., 2013). However,
the thermogram shows three major decomposition steps.
The first step occurred between 291 and 352 °C (3%
weight loss). This corresponds to loss of uncoordinated
hydroxyl group in the complex. The major weight loss
70
Table 3. UV/Visible data of H3L1 and its metal com-
plexes
Compound Transition Ground Transition
cm-1
term
symbol
H3L
140816 - p®p* ( C=C) ar
33113 n®p* (C=N )
29940 n®p*( C=O)
Mn(H3L
1)(CH
344248
6S p®p* ( C=C) ar
COO)2]H
2O (1) 33333 n®p* (C=N )
30864 n®p*( C=O)
243904T1g (G) ¬
6A1g
199204T
2g (G) ¬
6A1g
[Fe(H3L
1)Cl
2] (2) 33113
5D n®p* (C=N )
27700 n®p*( C=O)
221242A1
2¬
2B
2
Pt(H3L
1)2.Cl
2 (3) 330033D n®p*
288181Eg¬
1A1g
168911A2g ¬
1A1g
Zn(HL1)2)(CH
336232
1S n®p* (C=N )
COO)2 (4) 30211 n®p*( C=O)
23256 MLCT
Pd(HL1)2.Cl
2 (5) 423731S p®p* ( C=C) ar
31646 n®p* (C=N )
26316 n®p*( C=O)
Kehinde Olurotimi Ogunniran et al.
occurred in the second step is attributed to a loss of
2CH3CO between 361-475 °C. The remaining part of
the ligand decomposed to metal oxide in the third step
(475-676 °C) of the decomposition.
The TGA/DTA curves for compound (2) shows one
major decomposition in Fig. 9. The stability range
extended from ambient temperature to 225 °C. The
decomposition of the complex occurred in three stages
as indicated by DTA peaks at 367, 557 and 595 °C.
The first decomposition started at 225 °C and ended at
475 °C. This corresponded to 57% (Calcd. 58%) of the
weight loss for the compound which is mainly the H3L1
ligand. The second and the third steps were attributed
to the loss of the coordinated chloride atoms. This
occurred between 530 and 575 °C and 575 and 625
°C
with a weight loss of 8.7% (Calcd. 9%) in each step.
The % weight of the residue, which was assumed to be
the metal oxide, is 23% (Calcd. of 24%).
The TGA/DTA curves for compound (5) (Fig. 10) shows
an endothermic peak at 102 °C which indicates the
presence of water molecules outside the coordinated
sphere of the complex. The 8% (Calcd. 7.7%) weight
loss occurred between 88 and 161 °C is accounted for
the loss of the water molecule. Decompositions observed
between 220 and 340 °C; and 340 and 465
°C are
attributed to the partial loss of the coordinated ligand
in the complex. As thermogram shows, the complex
did not decompose fully to the residue within the
temperature range of study.
71Antitubercular Activity of Acylhydrazone
Fig. 9. TGA/DTA spectrum of [Fe(H3L1) Cl2] (2).
2.0
1.5
1.0
0.5
0.0
De
riv.
we
igh
t (%
/ C
)O
100
80
60
40
20
We
igh
t (%
)
356.20 CO
367.59 CO
379.11 CO
540.02 CO
565.14 CO
589.28 CO
557.31 CO595.90 C
O
613.97 CO
Universal V4. 5A TA Instruments
0 100 200 300 400 500 600 700
Sample: Fe L1Size: 6.3700 mgMethod: RampComment: Fe L1
File: D:\TGA\KVSN\22-10-12\Fe L1.001Operator: AKRun Date: 31-Oct-2012 10:00Instrument: TGA Q500 V20.8 Build 34
TGA
Temperature (°C)
Electron paramagnetic resonance study. The EPR
spectra of Mn(II) and Fe(II) complexes were recorded
in liquid nitrogen at 77 K. Mn(II) complexes show wide
range of geometry. Reports by Charles and Horacia1
(999); Hamed and Neilands (1994) and Pilbrow (1990)
have reported that EPR have been used successfully to
affirm the geometries of Mn(II) complexes. The EPR
spectrum of [Mn(H3L1) (CH3COO)2] H2O in frozen
DMSO at 77 K exhibited 2 g values at g^ = 1.7445 and
g|| = 2.1240 with no hyperfine splitting (Fig. 11). The
gav isotropic value was found to be 2.0569. This value
is very close to the free electron spin value of 2.0023.
This is consistent with the typical manganese (II) ion
and could be responsible for the absence of spin orbital
coupling in the ground state 6A1 without any sextet term
of higher energy (Charles and Horacia,1999; Pilbrow,
1990). The spectrum is broad spectrum which is probably
due to polar interactions and enhanced spin lattice relaxa-
tion in the complex. The observed g|| > g^
> 2 suggested a
monomeric complex with tetrahedral pyramidal geometry.
The spectrum of [Fe(H3L1)Cl2] in frozen DMSO at 77
K shows hyperfine splitting with giso = 2.0204, g^ =
1.9937 and g|| = 1.9865 (Fig. 12). The spectrum hyperfine
splitting constants, A|| and A^ were found to be 14 mT
and 8 mT, respectively. The splitting pattern suggested
Fig. 10. TGA/DTA spectrum of [Pd(H3L1)Cl]H2O
(5).
0.25
0.20
0.15
0.10
0.5
0.00
Deriv.
weig
ht (%
/ C
)O
Weig
ht (%
)
120
100
80
60
40
298.72 CO
161.11 CO
86.98 CO
102.93 CO
278.89 CO
327.77 CO
426.89 CO
401.65 CO
465.07 CO
0 100 200 300 400 500 600 700
Universal V4.TA Instruments
Temperature ( C)O
Sample: Pd L1Size: 6.9180 mgMethod: RampComment: Pd L1
File: D:\TGA\KVSN\CDRI\Pd1.001Operator: AKRun Date: 29-Nov-2012 09.35Instrument: TGA Q500 V20.8 Build 34
TGA
pentagonal coordinated environment for d6 Fe(II) in
the complexes (Huang and Haight Jun, 1969). However,
since g^ > g|| and A|| > A^, which is the characteristic
of tetrahedral base complex, the complex was considered
to be tetrahedral base pyramidal.
Powder X-ray diffraction study. The powder XRD
pattern for [Mn(H3L1)(CH3COO)2]H2O recorded on a
Bruker AXS D8 advance diffractometer operating in
the q:q mode, equipped with a secondary beam graphite
72
Fig. 11. EPR spectrum of [Mn(H3L1) (CH3COO)2]
H2O.
-1500
-1000
-500
0
500
1000
1500
Inte
nsity
B (mT)
200 250 300 350 400 450
-3000
-2000
-1000
0
1000
2000
3000
200 250 300 350 400 450
Inte
nsity
B (mT)
Fig. 12. EPR spectrum of [Fe(H3L1)Cl2] in DMSO
at 77 K.
400
300
200
100
0
Lin
(C
ounts
)
5 10 20 30 40 50 60 70 80 90 100 110 120
2-Theta
1
2
3456
7
89
10
1112
Fig. 13. X-ray diffraction pattern of [Mn(H3L1)
(CH3COO)2]H2O (1).
monochromator is shown in Fig. 13. Table 4 shows the
selected diffraction data obtained which were indexed
(Hesse, 1948). Mn(II) complex was scanned between
5° and 120° at a wavelength of 1.543 Å. The diffrac-
tograms and associated data depict the 2q value for
each peak, the relative intensity and inter-planar spacing
(d-values). The X-ray diffraction patterns of the complex
with respect to major peaks of relative intensity greater
than 10% were indexed using a computer programme
(Kozakov et al., 2011; Harikumaran and Thankamani,
2009). The selected indexed data yielded the Miller
indices (hkl), the unit cell parameters and the unit cell
volume. The unit cell of Mn(II) complex yielded values
of lattice constants: a = 12.441 Å, b = 14.582 Å and c
= 7.842 Å, and a unit cell volume V = 1227.2353 Å3. In
concurrence with these cell parameters, conditions such
as a ¹ b ¹ c and a = g = 90° ¹ b required for a monoclinic
sample were tested and found to be satisfactory. Hence,
it can be concluded that [Mn(H3L1) (CH3COO)2]H2O
complex has a monoclinic crystal system.
Antimycobacterial results. Based on the results of the
biological studies, it was found that the complexes
(PtL1) (MIC = 0.56 µg/mL), (ZnL
1) (MIC = 0.61
µg/mL), (MnL1) (MIC = 0.71 mg/mL) and (FeL
1) (MIC
= 0.82 mg/mL), exhibited a significant activity when
compared with first line drugs such as isoniazid (INH)
(MIC = 0.9 mg/mL). These results suggest that they
may be selectively targeted to M. tuberculosis growth.
They are therefore regarded as potential drug candidates
but the results of the toxicity study indicated that the
complexes are more toxic than the isoniazid and the
ligand. However, Fe(II) complex of H3L1 displayed the
Kehinde Olurotimi Ogunniran et al.
lowest toxicity with the IC50 value of 0.92 µM which
were found to be non significant different (P >0.05) to
that of the ligand (H3L1) (IC50 = 3.01mg/mL) and
isoniazid (INH) (IC50 = 4.72 mM). The results obtained
were displayed in bar chart (Fig. 15).
73Antitubercular Activity of Acylhydrazone
Table 4. X-ray powder diffraction data for [Mn(H3L
1)
(CH3COO)
2] H
2O (1)
2q d (Å) Count Intensity h k lcount (%)
8.076 10.9383 212 10.9 1 0 09.393 5.5773 105 5.57 4 9 813.865 3.8474 115 3.84 5 4 516.800 3.1415 162 42.2 1 0 018.724 2.6809 166 43.2 1 0 019.790 2.5411 155 40.3 1 1 023.098 1.3748 95.8 24.9 2 0 025.738 1.3420 92.7 24.1 2 1 025.993 1.15342 93.2 24.2 2 2 032.067 1.04703 86 22.3 3 0 048.277 0.92321 89.4 23.2 3 1 1
Table 5. MIC (µg/mL) and IC50 (µM) values of the
metal complexes of H3L1
Entry MIC IC50
(µg/mL) (µM)
MnL1
0.71 0.85FeL
10.82 0.92
PtL1
0.56 1.03ZnL
10.61 1.17
H3L1
1.02 3.01INH 0.92 4.72
Fig. 15. Comparison of MIC/IC50 values of the metal
complexes with the ligand and isoniazid drug.
**
6
5
4
3
2
1
0
MIC
(m
g/m
L)
IC50(m
M)
MnL1 FeL1 PtL1 ZnL1 H3L1 INH
** ** **
**
**
**(p > 0.05)
MIC values IC50 values
Fig. 14. The proposed structures for the complexes.
[Mn(H3L
1)(CH
3COO)
2]H
2O (1)
Zn(H3L
1)2(CH
3OO)
2 (4)
[Fe(H3L
1)(Cl
2)] (2)
Pt(H3L
1)2Cl
2 (3)
Conclusion
In conclusion, H3L1 was successfully synthesized at
20 °C from nicotinic hydrazide by stirring in the presence
of 2,4-dihydroxybenzaldehyde. The hydrazone is a
derivative of isoniazid and therefore was expected to
possess antitubercular activity. The structure of the
hydrazone was successfully elucidated by using mass
spectrum, CHN analysis, 1H,
13C and 2D NMR. All the
efforts made to grow single crystals of the metal com-
plexes did not yield positive results. Hence, the physical
properties and structures of the metal complexes were
elucidated using different spectroscopic analysis. The
information obtained was supported with the use of
powder X-ray analysis which leads to the model
structures of the complexes. The in vitro antitubercular
study confirmed the efficacies of the compounds. The
metal complexes were found to be more active than
isoniazid however, they were found to be more toxic
against the vero cell than isoniazid.
Acknowledgement
The authors express their gratitude to CISR and TWAS
for financial support through TWAS-CSIR fellowship
granted to carry out this work. We thank all the staff of
SAIF Central Drug Research Institute, Lucknow, Indian
Institute of Technology, Roorkee, and Indian Institute
of Technology, Bombay, India for their technical support.
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75Antitubercular Activity of Acylhydrazone
Quantification of Cr(VI)-Thymoquinone Complex
Using Cyclic Voltammetry
Farah Kishwara*, Khalid Mohammed Khanb, Rubina Perweena,Anila Anwara and Nasir Akhtara
aDepartment of Chemistry, Federal Urdu University of Arts, Science and Technology,
Gulshan-e-Iqbal Campus, Karachi-75300, PakistanbH.E.J. Research Institute of Chemistry, International Centre for Chemical and Biological Sciences,
University of Karachi, Karachi-75270, Pakistan
(received December 23, 2014; revised August 10, 2015; accepted August 24, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 76-82
Abstract. Quantitative studies of Cr(VI)- thymoquinone complex have been performed by cyclic voltammetry.
For this purpose glassy carbon, platinum and saturated calomel electrodes were used as working, auxiliary
and reference electrodes, respectively. The effects of concentrations and metal-ligand ratios on Cr(VI)-
thymoquinone complex were investigated. Effect of concentration was found to follow Randles-Sevcik
equation. Calibration curve method with linear regression line confirms that cyclic voltammetry can be
used for quantification of Cr(VI)-thymoquinone complex for pharmaceutical assay. Complete complex
formation seems to occur at metal ligand ratio 1:1. Results indicate quasi-reversible electron transfer
mechanism. E° and diffusion coefficient of complex at different concentrations and metal ligand ratios
were also calculated and found to be 0.244±0.01 V and 3.45´10-5 cm2s-1, respectively. The values of transfer
coefficients, a and b, were found to be 0.716±0.02-1.231±0.01 and 0.814±0.01-0.906±0.01, respectively.
Keywords: Cr(VI)-thymoquinone complex, quantitative studies, cyclic voltammetry
Introduction
Electrochemical methods are now emerging as powerful
and versatile analytical techniques and have found vast
applications in many important fields including pharma-
ceutical industry, and biological and environmental
applications (Farghaly et al., 2014; Tsai et al., 2011;
Sivasubramanian and Sangaranarayanan, 2011; Beitollahi
et al., 2008). The use of electro analysis is increasing
day by day due to high sensitivity, reduction in solvent
and sample consumption, high-speed and low operating
cost (Almeida et al., 2013; Halls et al., 2013; Yuzhi Li
et al., 2013; Cheng et al., 2012; Aaboubi and Housni,
2012; Farghaly et al., 2005; Farghaly and Ghandour,
2005). Especially voltammetric techniques have proved
to be more suitable to investigate the redox properties
of drugs and biological analytes (Baghbamidi et al.,
2012; Karaaslan and Suzen, 2011).
Cyclic voltammetry is a potentiodynamic electrochemical
technique. The primary function of this technique is to
give qualitative information regarding various electro-
chemical processes, although it is equally beneficial for
quantitative analysis. It is useful in finding out the
mechanisms and kinetics of different electrochemical
reactions, rates of oxidation/reduction processes, stability
of different oxidation states etc. Furthermore, this tech-
nique helps to give information about the presence of
intermediates in various redox reactions (Skoog et al.,
1998; Braun, 1983). Hence, this technique is very popular
and reliable electrochemical technique and widely used
nowadays (Beitollahi and Mostafavi, 2014; Molaakbari
et al., 2014; Baghbamidi et al., 2012; Beitollahi and
Sheikhshoaie, 2011; Beitollahi et al., 2012).
Thymoquinone (2-methyl-5-isopropyl-1,4-benzoquinone)
is a phytochemical compound (Fig. 1). It is found as
an active component in the plant of Nigella sativa (Ali
and Blunden, 2003). It possesses several biological
activities (Gali-Muhtasib et al., 2008; Syed, 2008;
Badary et al., 2007; El-Mahdy et al., 2005). Most of
its pharmacological properties are due to its antioxidant
property (Mansour et al., 2002). In addition, it has the
ability to form complexes. Previously its complexation
with some redox active metals using potentiometry has
been investigated by Kishwar et al. (2012). The comple-
xation of thymoquinone with iron by cyclic voltammetry
has also been examined qualitatively and quantitatively
by Kishwar and Haq (2013).
Chromium (VI) has been reported as a highly toxic
element. It is a strong irritant; as a result it may cause
different types of allergic reactions. Its inhalation can*Author for correspondence; E-mail: [email protected]
76
cause irritation and damage to nose, lungs, stomach and
intestine and ingestion could result in stomach mal-
functioning and ulcers, convulsions, damage to kidney
and liver and even death. In case of chronic exposure
it might cause pulmonary fibrosis and lung cancer.
However, certain reducing substances in the food could
reduce Cr(VI) to Cr(III) (Stoecker, 1999; Lukaski,
1999). Seeds of Nigella sativa are commonly used in
our food and thymoquinone in these seeds can also
perform the same function. Cr(VI) forms a vast variety
of complexes (Sharpe, 1996) including complex with
thymoquinone (Kishwar et al., 2012). Hence, it could
be helpful in case of Cr(VI) toxicity. In this view quan-
titative study of Cr(VI)-thymoquinone complex was
performed in order to get useful information regarding
complexation of Cr(VI) and thymoquinone.
Materials and Methods
Instrumentation. Cyclic voltammeter. CHI�760 D
Electrochemical work station was used. Three electrodes
were used, a glassy carbon working electrode (Model
number = CHI 104, area of the electrode = 0.07065 cm2),
saturated calomel reference electrode and a platinum
wire auxiliary electrode. The working electrode was re-
polished using alumina, the particles� size of which was
0.3 micron.
Chemicals. Thymoquinone was purchased from MP
Biomedicals, LLC, whereas sodium chloride and
potassium dichromate from E. Merck.
Sample preparation. Supporting electrolyte. 0.1 M
solution of NaCl.
Analyte solution. The 5´10-3 M solution of thymo-
quinone (TQ) and equimolar solution of K2Cr2O7 were
prepared as measuring solution. TQ was analyte. 10%
methanol was also used in their preparation in addition
to 0.1 M NaCl.
Cyclic voltammetric studies. At first CV of supporting
electrolyte was run to get base-line and then 15.0 mL
of 5´10-3 M solution of analyte and equimolar solution
of metal were run to get overlay. The scan rate and current
sensitivity were 0.1 V/s and 1´10-4 A/V, respectively.
The potential range was set from -0.40 V to +0.80 V
and then reversed back to -0.40 V. In order to observe
effect of several parameters, the complexation was
studied by varying metal ligand ratio and concentration.
Complex solutions having different metal-ligand ratio
from 1:0.5 to 1:5 and concentrations from 0.02´10-3 M
to 1.2´10-3 M were prepared in order to investigate
effect of these changes on complex formation.
Results and Discussion
Effect of concentration on voltammograms of Cr(VI)-
thymoquinone complex. Calibration curve method
was used to judge effect of concentration on Cr(VI)-
thymoquinone complex. For this Randles-Sevcik equa-
tion (Greef et al., 1985) was used which is as follows:
Ip = 0.4463 nFACo* (nF n D°/RT)½
where:
Ip = peak current (A); n = scan rate (V/s); n = number
of electron transferred; F = Faraday�s constant; A = area
of electrode (cm2); Co* = concentration of Cr(VI)-
thymoquinone complex (moles/cm3); D° = diffusion
coefficient of Cr(VI)-thymoquinone complex (cm2 s -1);
T = 25 ± 2 °C and R = rate constant.
The cyclic voltammograms at different concentration
showed dependence of Ip on concentration of the com-
plex. At low concentrations, 0.02´10-3 M and 0.1´10-3 M,
no oxidative wave of forward scan was observed whereas
the reduction peak could be seen clearly during the
reverse scan. It was observed that one small peak (1)
is also appearing in addition to a bigger peak (3) in both
forward as well as in reverse scan (Fig. 2a). It may be
due to complications in the reaction. It is possible that
the metal needs low concentration of the ligand for
complex formation and excess ligand may be responsible
for giving bigger peaks, as these peaks (i.e., peak 3 and
4) were observed within the potential range similar to
that of thymoquinone (Table 1-2). Direct increase of
current with concentration (0.02´10-3 M to 1.2´10-3 M)
shows that effect of concentration follows Randles -
Sevcik equation (Fig. 2b). Calibration curve along with
Fig. 1. Structure of thymoquinone.
H C3
O
O
3CH
3CH
77Cr(VI)-Thymoquinone Complex
least square fit line showed no major deviation from
zero, which points out towards the possibility that no
adsorption has occurred on electrode surface. These
results indicate that calibration curve method can be
used for quantification of Cr(VI)-thymoquinone complex
within a wide range i.e. (0.2´ 10-3 M to 1.2´10-3 M).
Effect of metal-ligand ratio. For this purpose cyclic
voltammograms of complex solutions having metal-
ligand ratios 1:0.5 -1:5 were studied. The overlay reveals
Farah Kishwar et al.
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
Cu
rre
nt/
le.lA
3
2
1
4
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Potential/V vs SCE
Fig. 2a. Cyclic- voltammograms of Cr(VI)-thy-
moquinone complex showing effect of
concentration (concentrations of complex
solutions = 0.02×10-3 M, 0.1×10-3 M,
0.2×10-3 M, 0.4×10-3 M, 0.6×10-3 M,
0.8×10-3 M, 1×10-3 M, 1.2×10-3 M).
10
8
6
4
2
0
Ip (
in 1
0-5 A
)
Ipa (A) Ipc (A)
R =0.9422
R =0.95812
0 0.5 1.51
Conc. x 10-3 M
Fig. 2b. Plot of anodic and cathodic peak current
against concentration of Cr(VI)-thymo-
quinone complex.
Table 2. The values of Ep, Ep/2, Ep-Ep/2, Epa-Epc,, Ip, ana, bnb and diffusion coefficients from cyclic voltammograms
of Cr(VI)-thymoquinone complex with different concentrations
Concen- Epa Epa/2 Epa-Epa/2 Ipa Ipa/Ipc bnb D (cm2 s-1)
tration (V) (V) (V) ×10-5(A) =0.048/Epa-
(10-3 M) Epa/2
0.02 -0.236 ± 0.01 -0.29 ± 0.01 0.054 ± 0.011 1.431 ± 0.01 1.15 0.889 ± 0.01 -
0.4 -0.238 ± 0.01 -0.292 ± 0.01 0.054 ± 0.011 1.507 ± 0.01 0.88 0.889 ± 0.01 1.90 × 10-5
0.6 -0.24 ± 0.01 -0.294 ± 0.01 0.054 ± 0.011 2.143 ± 0.01 0.566 0.889 ± 0.01 1.7 × 10-5
0.8 -0.247 ± 0.02 -0.3 ± 0.02 0.053 ± 0.012 3.419 ± 0.02 0.65 0.906 ± 0.02 2.44 × 10-5
1.0 -0.248 ± 0.02 -0.301 ± 0.02 0.053 ± 0.011 4.099 ± 0.02 0.61 0.906 ± 0.01 2.24 × 10-5
1.2 -0.248 ± 0.02 -0.301 ± 0.02 0.053± 0.012 4.252 ± 0.02 0.508 0.906 ± 0.02 1.68 × 10-5
Concen- Epc Epc/2 Epc-Epc/2 Epa-Epc Ipc ana D (cm2 s-1)
tration (V) (V) (V) (V) ×10-5(A) =0.048/Epc-
(10-3 M) Epc/2
0.02 -0.377 ± 0.02 -0.333 ± 0.02 -0.041 ± 0.011 0.141 ± 0.01 1.248 ± 0.02 1.2 ± 0.02 -
0.4 -0.375 ± 0.01 -0.33 ± 0.01 -0.045 ± 0.012 0.137 ± 0.01 1.706 ± 0.01 1.1 ± 0.01 2.43 × 10-5
0.6 -0.353 ± 0.01 -0.312 ± 0.01 -0.041 ± 0.011 0.113 ± 0.01 3.785 ± 0.01 1.20 ± 0.01 5.31 × 10-5
0.8 -0.348 ± 0.01 -0.295 ± 0.01 -0.053 ± 0.011 0.101 ± 0.01 5.248 ± 0.01 0.906 ± 0.01 5.75 × 10-5
1.0 -0.342 ± 0.02 -0.294 ± 0.02 -0.048 ± 0.012 0.094 ± 0.01 6.716 ± 0.02 1.0 ± 0.01 6.02 × 10-5
1.2 -0.35 ± 0.01 -0.301 ± 0.01 -0.049 ± 0.012 0.102 ± 0.01 8.37 ± 0.01 0.980 ± 0.01 6.50 × 10-5
Table 1. Electrochemical parameters of cyclic voltam-
mograms of thymoquinone, Cr(VI), and Cr(VI)-thymo-
quinone complex
Ipa Ipc Epa Epc
(×10-5 A) (×10-5 A) (V) (V)
TQ 1.017 2.717 -0.242 -0.326
±0.01 ±0.01 ±0.01 ±0.01
Cr(VI) - - - -
Cr(VI)-TQ complex 0.246 1.142 0.179 -0.210
±0.01 ±0.01 ±0.01 ±0.01
78
that complete complexation occurred at metal ligand
ratio 1:1 because here anodic and cathodic peaks (1 and
2) were observed in a potential range entirely different
from that of thymoquinone (Table 3, Fig. 3). By further
increasing metal-ligand ratio, sudden change in the peak
potential was observed. These observations suggest that
probably thymoquinone suppressed the metal at high
concentrations and its own peaks (3 and 4) became
prominent. At higher metal-ligand ratios i.e., 1:3-1:5
distortion in the anodic peaks was observed which may
be due to the superimposition of two peaks lying very
close to each other. These two peaks may be of complex
and thymoquinone.
In case of effect of metal-ligand ratio the cyclic voltam-
mograms seem to fulfill the criteria for quasi-reversible
reactions (Bard and Faulkner, 2001). As Ipa/Ipc was not
equal to one and DEp was found to be greater than 59/n
mV, it seems that quasi-reversible behaviour is favoured
(Table 3). For each case a and b were also calculated
using the relation 0.048/Ep-Ep/2 which were found in
the range of 0.716 ± 0.02 to 1.231 ± 0.01 and 0.814 ±
0.01to 0.906 ± 0.01, respectively. Effect of metal-ligand
ratio on peak potential (Epa and Epc) gave a straight line
with very good R2 value (Fig. 4), showing independence
of peak potential on change in metal ligand ratio.
An increase in Ipa and Ipc was noted with the increase
in metal-ligand ratio but the peak current became nearly
constant at metal-ligand ratio 1:3, showing nearly
maximum complexation below this ratio (Fig. 5).
Analysis of diffusion coefficient for Cr(VI)-thymo-
quinone complex. Diffusion coefficient of different
complexes and compounds is an important constant and
it could be easily and accurately measured by cyclic
Fig. 3. Cyclic- voltammograms of Cr(VI)-thymo-
quinone complex showing effect of metal
- ligand ratio (Metal-ligand Ratio = 1:0.5,
1:1,1:2,1:3, 1:4, 1:5, concentration of
TQ = 5×10-3 M, concentration of Cr(VI)
solution = 5×10-3 M).
2.0
1.0
0
-1.0
-2.0
-3.0
-4.0
-5.0
-6.0
-7.0
-8.0
3
2
1
4
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Potential/V vs SCE
Cu
rre
nt/1
e-5
A
Table 3. The values of Ep, Ep/2, Epa-Epc, Ip and ana, bnb and diffusion coefficients from cyclic voltammograms of
Cr(VI)-thymoquinone complex with different metal-ligand ratios
Ratio L/M Epa Epa/2 Epa-Epa/2 Ipa Ipa/Ipc bnb D (cm2 s-1)
(V) (V) (V) ×10-5(A) =0.048/Epa-
Epa/2
0.5 0.149 ± 0.01 0.09 ± 0.01 0.059 ± 0.01 0.2 ± 0.01 0.256 ± 0.01 0.814 ± 0.01 -
1 0.179 ± 0.01 0.12 ± 0.01 0.059 ± 0.01 0.246 ± 0.01 0.215 ± 0.01 0.814 ± 0.01 -
2 -0.232 ± 0.01 -0.29 ± 0.01 0.058 ± 0.01 0.932 ± 0.01 0.46 ± 0.01 0.828 ± 0.01 4.64 × 10-6
3 -0.236 ± 0.02 -0.291 ± 0.01 0.055 ± 0.02 1.431 ± 0.01 0.434 ± 0.01 0.873 ± 0.02 1.09 × 10-5
4 -0.239 ± 0.01 -0.292 ± 0.01 0.053 ± 0.01 1.411 ± 0.01 0.352 ± 0.01 0.906 ± 0.01 1.06 × 10-5
5 a - - - - -
Ratio L/M Epc Epc/2 Epc-Epc/2 Epa - Epc Ipc ana D (cm2 s-1)
(V) (V) (V) (V) ×10-5(A) =0.048/Epc-
Epc/2
0.5 -0.17 ± 0.01 -0.103 ± 0.02 -0.067 ± 0.02 0.021 ± 0.02 0.78 ± 0.01 0.716 ± 0.02 -
1 -0.21 ± 0.01 -0.15 ± 0.01 -0.06 ± 0.01 0.031 ± 0.02 1.142 ± 0.01 0.8 ± 0.01 -
2 -0.338 ± 0.01 -0.299 ± 0.01 -0.039 ± 0.01 0.106 ± 0.01 2.028 ± 0.01 1.231 ± 0.01 2.2 × 10-5
3 -0.346 ± 0.01 -0.302 ± 0.01 -0.044 ± 0.01 0.11 ± 0.01 3.3 ± 0.01 1.091 ± 0.01 5.82 × 10-5
4 -0.37 ± 0.01 -0.31 ± 0.01 -0.06 ± 0.02 0.131 ± 0.02 4.011 ± 0.01 0.8 ± 0.01 8.59 × 10-5
5 - - - - - -
a = peak distorted.
79Cr(VI)-Thymoquinone Complex
voltammetry (Anwer, 2006). Diffusion coefficient of
the complex was determined using Randles- Sevcik
(Greef et al., 1985) equation by varying concentrations
and metal-ligand ratios (Table 2-3). No reasonable
effect of varying concentration or metal-ligand ratio on
diffusion coefficient was observed and its value remained
nearly the same under all above mentioned conditions.
Area of electrode (A) was 0.07065 cm2 whereas number
of electron transfer (n) was supposed to be 3 (Sharpe,
1996).
Analysis of E°, a characteristic property. For Cr(VI)-
thymoquinone complex values of E° were determined
at different concentrations and metal-ligand ratios and
it was found to be approximately constant at all concen-
tration and ratios (Table 4), except in first two cases of
metal- ligand ratio. This change may be due to the fact
Table 4. Half wave potential (E°= E1/2) for Cr(VI)-
thymoquinone complex at different concentrations and
metal-ligand ratios
Concentration (E°)a Ratio (E°)a
(10-3 M) (V) L/M (V)
0.02 0.264 ± 0.01 0.5 0.12 ± 0.02
0.4 0.265 ± 0.01 1 0.15 ± 0.02
0.6 0.267 ± 0.01 2 0.261 ± 0.02
0.8 0.274 ± 0.02 3 0.264 ± 0.01
1.0 0.275 ± 0.02 4 0.266 ± 0.01
1.2 0.275 ± 0.02 5 -
-1E.15
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
R =0.93562
R =0.92312
M:L Ratio
Epc Epc/2
E (
V)
pc
1 2 3 400
0
-0.05
-0.1
-0.15
-0.2
-0.25
-0.3
-0.35
-0.4
1 2 3 4E
(
V)
pa
M:L Ratio
R =0.99322
Epa Epa/2
Fig. 4. Variation of anodic and cathodic peak potentials with change of metal-ligand ratio in cyclic voltammo-
grams of Cr(VI)-thymoquinone complex.
I (
in 1
0 A
)p
-5
5
4
3
2
1
0
Ipa x10-5 Ipa x10
-5
1 2 3 40
M:L Ratio
Fig. 5. Variation of anodic and cathodic peak
currents with change of metal-ligand ratio
in cyclic voltammograms of Cr(VI)-thymo-
quinone complex.
R = 12
that in case of metal- ligand ratio 1:0.5 and 1:1anodic
and cathodic peaks (1 and 2 in Fig. 3) were observed
in a potential range entirely different than rest of the
metal-ligand ratios. Hence, it is suggested that change
in peak potential, both in Epa and Epc (Table 3), resulted
in changed values of E°.
Briefly, quantitative studies of Cr(VI)-thymoquinone
complex were performed at glassy carbon electrode
against saturated calomel electrode which include
determination of E°, D, a, and b. Effects of different
parameters, i.e., concentration and metal ligand ratio,
on complexation were observed by varying these
parameters. Horizontal base line indicates the purity of
the base electrolyte. The Cr(VI)-thymoquinone complex
seems to be stable at lower concentrations. It was also
suggested that here best complex formation occurred
at metal-ligand ratio 1:1. At higher metal- ligand ratios
(i.e., 1:2 to 1:5) distortion in the anodic peak was observed
which may be due to the presence of two peaks very
close to each other. Present study reveals that calibration
80 Farah Kishwar et al.
curve method by cyclic voltammetry can be helpful in
quantification of Cr(VI)-thymoquinone complex. E°
was observed to be approximately constant. Diffusion
coefficient was calculated using Randles- Sevick
equation. The values of transfer coefficients, a, and b
were also determined at different concentrations and
metal-ligand ratios.
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82 Farah Kishwar et al.
Effect of Processing on Physicochemical Properties
and Fatty Acid Composition of Fluted Pumpkin
(Telfairia occidentalis) Seed Oil
Jacob Olabode Alademeyin and Jacob Olalekan Arawande*Department of Science Laboratory Technology, Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria
(received February 17, 2015; revised August 18, 2015; accepted August 19, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 83-89
Abstract. This paper reports the physicochemical properties and fatty acid composition of the seed oil
extracted from fluted pumpkin (Telfairia occidentalis). The extracted oil was degummed, neutralised and
bleached. The oil yield was 42.26±0.20%. The specific gravity (at 25 °C) of the oil was 0.923±0.003 and
the refractive index (at 25 °C) was 1.475±0.002. Processing of the crude oil resulted in progressive decrease
in turbidity, colour, free fatty acid, acid value, peroxide value and saponification value. However, there
was increase in smoke point (243.00±0.03 to 253.00±0.03 °C), flash point (285.00±1.20 to 304.00±1.10 °C)
and fire point (345.00±1.10 to 358.00±1.55 °C) as well as iodine value (113.00 to 121.50 g/100 g) and
fatty acid composition during the processing of the oil. The fatty acids detected in the oil samples were
myristic, palmitic, stearic, oleic, arachidic, behenic, linoleic and linolenic acids. The predominant fatty
acid was oleic acid (47.40-47.90%) followed by linoleic acid (26.36-30.44%) while the least fatty acid
was linolenic acid (0.01-0.05%).
Keywords: fluted pumpkin, seed oil, degumming, neutralisation, bleaching, fatty acid composition
Introduction
Cucurbitaceae is one of the largest floras which consist
of nearly 100 genera and 750 species. The plant family
is known for its great genetic diversity and wide spread
adaptation which include tropical and sub-tropical
regions, arid desert and temperate locations. Cucurbits
are known for their high protein and oil content (Giwa
et al., 2010).
The seed and seed oil characteristic of some members
of the cucurbits are documented in literature and they
are generally referred to as melons. Telfairia occidentalis
is a member of the cucurbitaceae family, commonly
called fluted pumpkin. Its common names include,
Fluted guard, Colestillada (Spanish), Krobanko (Ghana),
Gonugbe (Sierra Leone) and Ugu (South-East, Nigeria)
(Chukwuonso et al., 2010). The crop is grown mainly
for the leaves, which constitute an important component
of the diet in many West African countries (Gill, 1992).
Fluted pumpkin (T. occidentialis Hook F.) is one of the
food crops with considerable value of energy and protein
that is grown in Nigeria. It is a vine with large lobed
leaves and long twisting tendrils (Chukwuonso et al.,
2010; Okoli and Mgbeogu, 1983). Although pumpkin
seeds are rich in oil storage reserves, it presently has
very low commercial value as an oil seed but is poten-
tially valuable as a high protein oil seed for human and
animal food (Nkang et al., 2003). The seeds of fluted
pumpkin are valuable both as an oil seed (54%) and
also as a protein source (27%) with a fairly well balanced
amino acid composition (Hamed et al., 2008; Akwaowo
et al., 2000). Unfortunately, 78-91% of the fruits wasted
annually (Fagbemi et al., 2005).
There are many studies on the proximate composition
and nutritive value of leaves and seeds of T. occidentalis
(Effiong et al., 2009; Hamed et al., 2008; Akwaowo
et al., 2000; Giami and Isichei, 1999; Giami and Bekebian,
1992; Asiegbu, 1987), scanty information is available
on the physicochemical properties of the seed oil and
fatty acid profile (Bello et al., 2011), but little or no
information on physicochemical properties and fatty
acid composition of the processed (refined) seed oil.
Oil seeds tend to contain a much larger proportion of
solid material associated with the requiring careful
reduction in size and usually some heat treatment before
being processed or solvent extracted to recover the oil
(Gunstone and Norris, 1983). The chemical composition
of oil extract consequently gives a qualitative identifi-
cation of oil, and is an important area in the selective
application guide in the commercialization and utility
of oil products (Salunkhe et al., 1992). A number of
seed oils have been characterized for the identification*Author for correspondence; E-mail: [email protected]
83
of several fatty acids of nutritional and nutraceutical
importance but the vast majority have not been adequa-
tely evaluated.
Like all vegetable oils, fluted pumpkin (T. occidentalis)
seed oil is composed of triglycerides (98-99%) and
other substances in unsaponifiable fractions (non-
glycerides) which are also known as the �minor com-
ponent� (NRC, 2001). Crude T. occidentalis seed oil
produced by solvent extraction contains both oil-soluble
and oil-insoluble substances that need to be removed
(with minimum loss of oil, and damage to the nature
of the oil �glycerides� and tocopherol). The oil-insoluble
materials may be removed through filtration. However,
the soluble material must be removed by several methods
which lead to production of edible oil. And such methods
include degumming, neutralisation, bleaching and deodo-
rization (Gunstone and Norris, 1983). Crude T. occidentalis
seed oil contains phosphatides (hydratable and non-
hydratable) as one of the soluble materials, hydratable
one can be removed with 2-3% hot water while the
non-hydratable ones are removed with addition of
phosphoric or citric acid (Lusas, 2002; Gunstone and
Norris, 1983). These phosphatides are referred to as
gum and removal of this gum is called degumming.
The next stage is neutralisation which is the process
that removes free fatty acids from the degummed oil.
This is done by mixing a calculated volume of a specific
concentration of caustic soda (sodium hydroxide) with
the oil at a definite temperature (60-80 °C) and atmos-
pheric pressure (1.01325×104 N/M2), for a definite time
and with prescribed agitation conditions. The alkali
treatment is designed to remove the undesirable crude
oil impurities without saponifying any degummed oil
which would increase refining loss (Erickson et al.,
1980). Vegetable oils generally contain colour pigments
which are predominantly yellow, red and green. The
yellow, orange and red pigments are known as
�carotenoids�. Other colours include chlorophyll, steroid,
tocopherols and gozypoll (Bernardini, 1973). Colour
pigment must be removed so as to produce oil of brighter
colour acceptable to consumers and these pigments are
removed from the oil with bleaching earth or fullers�
earth (John, 1990) through a process known as bleaching.
The present work was aimed to assess the physico-
chemical properties and fatty acid composition of crude,
degummed, neutralised and bleached Telfairia occidentalis
seed oil with a view of establishing the effects of these
processes on the parameters.
Materials and Methods
Collection and sample preparation . Fluted pumpkin
fruits were bought from a local market in Owo, Ondo
State, Nigeria. The fruits were sliced, and the seeds
were removed. The seeds were cleaned and freed from
unwanted materials before they were shelled manually.
The seeds of good quality were sliced into pieces, sun
dried and smoothly milled into powdery form. The
flour was then stored into tight container prior to oil
extraction.
Oil extraction. The powdered fluted pumpkin sample
was then subjected to soxhlet extraction using hexane
as solvent.
Ground seeds (50 g) were placed into a cellulose paper
cone and extracted, using, hexane (B.P 65 °C) in a
Soxhlet extractor for 8 h. The oil was then recovered
by simple distillation and residual solvent was removed
by drying in a hot air oven at 45 °C for 2 h (Erickson
et al., 1980; Bligh and Dyer, 1959). The extracted oil
was stored in an air-tight bottle for further analysis.
Refining process. The crude T. occidentalis seeds oil
extracted was then subjected to degumming, neutrali-
sation and bleaching processes.
Degumming process. 400 cm3 of the crude oil was
heated to temperature of 70 °C followed by addition of
0.80 cm3 of 50% phosphoric acid and the mixture was
then vigorously stirred for 10 min. Thereafter 10 cm3
of water heated to 80 °C was added and whole mixture
agitated for another 10 min. The agitation was stopped
and the mixture was allowed to stand undisturbed for
1 h so that the mixture was separated into two layers
i.e. oil and gum. The gum was drained off while the oil
obtained was termed as degummed oil (Salunkhe et al.,
1992, Carlson, 1991; Erickson et al., 1980). The degum-
med oil was further subjected to alkali neutralisation.
Neutralisation process. Degummed oil (200 cm3) sample
was heated up to 70 °C with constant stirring in a beaker,
then 3.3 cm3 of 3.59 M (20 Baume) sodium hydroxide
solution was added to the oil with vigorous stirring and
the temperature rose to 90 °C. Thereafter, 10 cm3 of
saturated solution of sodium chloride (an electrolyte)
was added and the resulting mixture was stirred
vigorously at 90 °C for 30 min. Then left undisturbed
in a separating funnel for 6 h resulting into separated
two layers, the lower layer which is known as �soap
stock� was then heated to 90 °C and washed with
water then heated to 95 °C. The washing was done six
Jacob Olalekan Arawande et al.84
consecutive times to remove any excess caustic soda
and water soluble gum remaining in the oil (Salunkhe
et al., 1992, Erickson et al., 1980). The resulting neutral
oil was then dried in a hot air oven, and later cooled in
the desiccators. The dried oil was further bleached.
Bleaching processes. Neutralised (100 cm3) oil was
heated to 75 °C with constant agitation. Then 1 g of the
bleaching earth was added and the mixture was heated
to 110 °C with constant stirring for 45 min (Salunkhe
et al., 1992, Erickson et al., 1980). It was then filtered
and the resulting oil was termed as bleached oil.
Physicochemical characterisation of the oil samples.
The crude, degummed, neutralised and bleached oil
samples were analysed for physicochemical properties.
The moisture content and specific gravity were deter-
mined according to AOAC (1990), while the refractive
index was measured using Abbey Refractometer coupled
with thermometer (ASTM, 1985). The colour was
determined using Lovibond Tintometer (Model 520).The
colour of crude oil was determined in half (½") inch
cell while that of degummed, neutralised and bleached
oils were determined in 1� inch cell. The flash and fire
points were measured using GallenKamp Authomatic
Pensky-Martens flash point and fire point tester with
thermometer while the smoke point was determined
using Cleveland Open Cup apparatus (Lawson, 1995;
ASTM, 1985). The temperature at which turbidity was
first detectable also measured using Palm Test turbidity
tube (ASTM, 1985). The free fatty acid, acid value,
saponification value and peroxide value were determined
using methods described by AOAC (1990), while iodine
value was determined by method described by Morris
(1999) and Pearson (1976).
Fatty acids identification. The oil samples were con-
verted to fatty acid methyl esters (FAMEs) using the
method described by Oshodi (1996) and Hall (1982).
The fatty acid methyl esters were analysed using an HP
6890 gas chromatograph fitted with flame ionization
detector and powered with HP chemistation Rev.09.01
[206] software. The career gas was helium at pressure
of 19 psi.The FAMEs sample (1.5 µL) was injected and
the separation was carried out on an HP capillary column
(HP-INNowax; cross-linked PEG); 30.0 m length,
0.32 mm i.d., and 0.50 µm film thickness. The oven
temperature was held initially at 60 °C for 2 min, incre-
ased from 180 °C at 12 °C/min to 320 °C at 14 °C/min
and then maintained at 320 °C for 5.0 min. The tempera-
ture of the injection port and the detector were set at
250 °C and 300 °C, respectively. The peaks were identified
by comparison with standard fatty acid methyl esters
(ASTM, 1985).
Results and Discussion
Table 1 shows physicochemical parameter of crude,
degummed, neutralised and bleached oils obtained from
fluted pumpkin seeds. Fluted pumpkin seeds contain
42.26±0.20% crude oil and the oil is light yellow with
colour unit of 20 in half inch cell. This high yield value
indicates that the processing of the oil for industrial or
edible purpose would be economical. The oil yield is
lesser than 54% reported for T. occidentalis (Akwaowa
et al., 2000). There was no moisture in the crude sample
analysed and this implied that the oil may have prolong
shelf life. The colour of degummed, neutralised and
bleached oil samples were 10.0, 8.0 and 5.0 lovibond
unit, respectively, in one inch cell. This was calculated
based on the expression (5R+Y-B):
where:
R = the red pigment, Y = yellow pigment; B = blue
pigment.
The progressive decrease in colour from crude oil to
bleached oil was as a result of phosphoric acid used for
degumming and bleaching earth specifically used to
remove colour pigments during bleaching (Abitogun
and Oshodi, 2010; Bernardini, 1973). There was no
remarkable difference in the values of both specific
gravity (0.923-0.924) and refractive index (1.474-1.475)
of crude, degummed, neutralised and bleached oil
samples. The specific gravities of the oil sample were
slightly higher than the specific gravity (0.913) of
pumpkin seed kernel oil reported by Mohammed (2004).
The refractive indices of the oil samples were slightly
higher than 1.4721 reported for Adenopus breviflorus
Benth seed oil (Akintayo and Bayer, 2002). The smoke
point (°C) for crude, degummed, neutralised and bleached
oils were 243.00±0.03, 248.00±0.01, 250.00±0.02 and
253.00±0.03, respectively, while that of flash point (°C)
were 285.00±1.20, 289.00±1.30, 290.00±1.00 and
304.00±1.10 and that of fire points (°C) were 345.00±
1.10, 350.00±1.25, 353.00±1.20 and 358.00±1.55,
respectively. The progressive increase in values of
smoke, flash and fire points from crude oil to bleached
oil might be as a result of removal of impurities such
as volatile organic material and the residual extraction
solvent during the oil processing (Erickson et al., 1980).
The high smoke, flash and fire points of the oil suggest
85Fluted Pumpkin Seed Oil Analysis
that it can be suitable for deep frying purpose (Bello
et al., 2011; Akintayo and Bayer, 2002). Free fatty acid
(FFA) and acid values are among the characteristics
features that are necessary for the confirmation of the
identity and edibility of oil. FFA can stimulate hydrolytic
deterioration of oils to form off- flavour components.
The free fatty acid (% oleic acid) for the crude, degummed,
neutralised and bleached oils were 1.83±0.10, 1.08±0.20,
0.48±0.10 and 0.60±0.12, respectively while acid values
(mgKOH/g) were 3.64±0.29, 2.14±0.15, 0.94±0.25 and
1.18±0.14, respectively. These values are relatively low
that suggests application of the oil as good edible oil
(Akintayo and Bayer, 2002).
It is noted that oil samples containing low FFA give
high smoke, flash and fire points and this quality will
enhance the suitability of the oil for deep fry cooking
(Akintayo and Bayer, 2002). The FFA and acid value
decreased from crude oil to neutralised oil but sudden
increase is observed in bleached oil. The decrease in
FFA and acid value is due to the effective use of caustic
alkali in neutralisation of the oil sample which led to
reduction in the free fatty acids, acid values and other
impurities while the increase in FFA and acid values of
bleached oil is a result of acidic nature of bleaching
earth used for colour removal (Salunkhe et al., 1992;
Bernardini, 1973). The acid values of the samples were
lower than the minimum acceptable value of 4.0% (for
crude oil) recommended by the Codex Alimentarius
Commission for oil seed (Abayeh et al., 1998). The
peroxide values (meq/peroxide/kg) of crude, degummed,
neutralised and bleached oil are 1.80±0.28, 0.90±0.12,
0.50±0.07 and 0.30±0.19, respectively. The low value
was an indication that the oil has a high resistance to
peroxidation and low rate of spoilage (Abayeh et al.,
1998). This value is low as compared to the maximum
acceptable value of 10 meq/KOH/g set by the Codex
Alimentarius Commission for edible oils (Abayeh
et al., 1998). The oil is thus stable and will not easily
go rancid. The iodine value (g/100 g) of the crude,
degummed, neutralised and bleached oils are 113.00
±0.17, 115.70±0.12, 116.20±0.18 and 121.50± 0.20,
respectively. The iodine value of the oil samples classi-
fied the oil among the semi drying oil (Fernando and
Akujobi, 1987). In addition, the high iodine value of
the oils indicates that the oil contains more unsaturated
fatty acid than saturated fatty acid (Nielsen, 1994) since
iodine value is a measure of the extent of unsaturation
of fatty acid present in fats and oils (Nielsen, 1994).
The iodine values are comparable to iodine value of
112.10 g/100 g for Adenopus Benth seed oil (Das et al.,
2002) and 121.03 g/100 g for African pea, Dacryodes
edulis (Ajiwe et al., 1997). On the other hand, the values
obtained are higher than 83.50 g/100 g reported as
iodine value of Moringa oleifera seed oil (Ogbunugafor
et al., 2011). Moreover, the iodine value of the oil incre-
ases progressively at each stage of the processing owing
to gradual removal of some impurities present in it. The
saponification values (mg/KOH/g oil) of the crude,
Table 1. Physicochemical parameters of crude, degummed, neutralised and bleached oils obtained from fluted
pumpkin seeds
Parameters Crude oil Degummed oil Neutralised oil Bleached oil
Specific gravity (at 25 °C) 0.924±0.002 0.923±0.003 0.923±0.003 0.923±0.002
Refractive index (at 25 °C) 1.475±0.002 1.474±0.004 1.474±0.003 1.474±0.010
Moisture content (%) 0.00 ± 0.00 0.00 ± 0.00 0.00 0.00±0.00
Turbidity point (JTU) 9.00±0.20 5.00±0.25 5.00±0.15 4.00±0.10
Smoke point (°C) 243.00±0.03 248.00±0.01 250.00±0.02 253.00±0.03
Flash point (°C) 285.00±1.20 289.00±1.30 290.00±1.00 304.00±1.10
Fire point (°C) 345.00±1.10 350.00± 1.25 353.00± 1.20 358.00±1.55
Colour (unit) 20.0 10.0 8.0 5.0
Free fatty acid (%) as Oleic 1.83±0.10 1.08±0.20 0.48±0.10 0.60±0.12
Acid value (mg/KOH/g) 3.64±0.29 2.14±0.20 0.94±0.25 1.18±0.14
Iodine value (g/100 g) 113.00± 0.17 115.70±0.12 116.20±0.18 121.50±0.20
Peroxide value (meq peroxide/kg) 1.80±0.28 0.90±0.12 0.50±0.07 0.30± 0.19
Saponification value (mg/KOH/g oil) 198.20±1.82 194.30±1.09 186.60±1.40 180.00± 0.06
Yield (%) = 42.26±0.20
Mean ± standard deviation of triplicate determination.
86 Jacob Olalekan Arawande et al.
degummed, neutralised and bleached oil samples are
198.20±1.82, 194.30±1.09, 186.60±1.40 and 180.00±0.06,
respectively. The saponification values are relatively
lower as compared to palm kernel oil (253.781 mg/
KOH/g) (Arawande, 2013) and this indicates that the
oil will not be good for soap making.
Table 2 depicts the fatty acid composition of crude,
degummed, neutralised and bleached oils obtained from
fluted pumpkin (T. occidentalis) seeds. The fatty acids
detected in the crude, degummed, neutralised and bleached
oil samples are myristic, palmitic, stearic, arachidic,
behenic, palmitoleic, oleic, linoleic and linolenic acids.
The amount (%) of fatty acids in the crude oil are:
myristic, 0.12; palmitic acid, 10.80; stearic acid, 0.24;
arachidic acid, 1.17; behenic acid, 0.42; oleic acid,
47.40; linoleic acid, 26.36 and linolenic acid, 0.01. Also,
the values (%) of these fatty acids in degummed oil are
myristic acid (0.30), palmitic acid (12.40), stearic acid
(1.65), arachidic acid (1.70), behenic acid (0.46),
palmitoleic acid (0.17), oleic acid (47.86), linoleic acid
(27.36) and linolenic acid (0.01), respectively. In
addition, the following fatty acids are present in the
neutralised oil sample, myristic acid (0.45%), palmitic
acid (13.00%), stearic acid (1.80%), arachidic acid
(1.83%), behenic acid (0.51%), palmitoleic acid (0.25%),
oleic acid (47.90%), linoleic acid (28.20%) and linolenic
acid (0.01%). Moreover the amount (%) of fatty acids
present in the bleached oil is reported as follows: myristic
acid is 0.63, palmitic acid is 13.57, stearic acid is 2.33,
arachidic acid is 2.00, behenic acid is 0.62, palmitoleic
acid is 0.30, oleic acid is 49.02, linoleic acid is 30.44
and linolenic acid is 0.05. In all the oil samples, oleic
acid has the highest composition and next to it is linoleic
acid. Palmitoleic acid was not detected in the crude oil
but was detected in degummed, neutralised and bleached
oil samples. It is observed that fatty acid value increased
as the processing progressed from one stage to another.
The predominant fatty acid was oleic acid (47.40-
47.90%) and this high content suggest the oil to have
ability to reduce incidence of coronary heart disease
(CHD) because oleic acid decreases total cholesterol
(10%) and LDL cholesterol (Dennys et al., 2006). Also
the high level of unsaturated fatty acids content of the
oil in conjunction with its low free fatty acid and acid
values confirm the reasons for its edibility.
Table 3 gives the summary of the total fatty composi-
tion of the oil. The values for saturated fatty acid in
crude, degummed, neutralised and bleached oils are
12.75, 16.52, 17.59 and 19.15%, respectively, that of
mono-unsaturated fatty acids are 47.40, 48.03, 48.15
and 49.32%, respectively, while that of polyunsatu-
rated fatty acids are 26.37, 27.37, 28.21 and 30.49%,
respectively. The total fatty acid in crude, degummed,
neutralised and bleached oils are 86.52, 91.92, 93.95
and 98.96%, respectively. There is progressive increase
in the saturated, monounsaturated, polyunsaturated
and total fatty acid of fluted pumpkin seed oil during
processing from crude oil to bleached oil. In addition,
these results show that the proportion of unsaturated
fatty acids was much higher i.e., 73.77%-79.81%, and
that of saturated fatty acids was less than 25%. This
is in accordance with the values reported for other
vegetable oils (Salman and Tanver, 2005; Raie et al.,
1992) which is characteristics for these oils.
Table 2. Fatty acid composition of crude, degummed, neutralised and bleached oils obtained from fluted pumpkin
seeds
Fatty acid Fatty acids Carbon number Crude oil Degummed oil Neutralised oil Bleached oil
methylester (%) (%) (%) (%)
Myristate Myristic 14:0 0.12 0.30 0.45 0.63
Palmitate Palmitic 16:0 10.80 12.40 13.00 13.57
Stearate Stearic 18:0 0.24 1.65 1.80 2.33
Arachidate Arachidic 20:0 1.17 1.70 1.83 2.00
Behenoate Behenic 22.0 0.42 0.46 0.51 0.62
Palmitoleate Palmitoleic 16:1 N.D 0.17 0.25 0.30
Oleate Oleic 18:1 47.40 47.86 47.90 49.02
Linoleate Linoleic 18:2 26.36 27.36 28.20 30.44
Linolenate Linolenic 18:3 0.01 0.01 0.01 0.05
ND = not detected.
87Fluted Pumpkin Seed Oil Analysis
Conclusion
Processing of fluted pumpkin seed oil from crude to
bleached oil increases iodine value, smoke point, flash
point, fire point, saturated, monounsaturated, polyunsatu-
rated and total fatty acid composition of the oil. Whereas,
processing of the oil from crude oil to bleached oil
resulted in decrease in values of saponification, peroxide,
colour and turbidity. It is obvious that if the bleached
oil is deodourized, it will supply essential fatty acid
needed in the body and the final refined oil will be of
high quality in terms of physicochemical properties and
this will enhance its edibility. It is further suggested
that the bleached oil should be deodourised and analysed
for physicochemical properties and fatty acid compo-
sition.
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89Fluted Pumpkin Seed Oil Analysis
Liberation Studies of Padhrar Coal By Using Fractionation Method,
XRD Analysis and Megascopic and Microscopic Techniques
Muhammad Shahzad*, Zulfiqar Ali, Yasir Majeed, Zaka Emad,Muhammad Aaqib and Bilal Adeel
Mining Engineering Department, University of Engineering & Technology, Lahore, Pakistan
(received October 2, 2014; revised September 14, 2015; accepted December 31, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 90-95
Abstract. This research study aims to establish liberation characteristics of Padhrar coal by using various
methods including fractionation method, megascopic and microscopic analysis and X-ray diffraction (XRD)
technique. Sieve analysis revealed that more than 83% of the coal lied in the medium particle size range
of -26.670+6.680 mm. The results of fractionation analysis indicated that most of the sulphur was found
in the smaller sized fractions having particle size less than 6.680 mm while most of the ash was found to
be associated at larger particle size (+26.670 mm) and at relatively smaller particle size (-6.680 mm). It
was found that Padhrar coal consisted of three major minerals namely; quartz, pyrite and kaolinite. These
minerals were found to be associated with organic matter at different particle size levels, thus making the
nature of the Padhrar coal more complex for its cleaning.
Keywords: liberation study, Padhrar coal, fractionation method, XRD of coal, megascopic analysis,
microscopy of coal
Introduction
Coal is a complex heterogeneous mixture of organic
and inorganic constituents (Ural, 2007). These are
usually present in the form of solid, liquid, and gaseous
phases intimately mixed with each other (Liu et al.,
2005; Vassilev and Vassileva, 1996). Although, the
nature of coal (rank and type) and the utilization of coal
in different processes are primarily dependent upon its
organic components (Liu et al., 2005; Vassilev and
Tascon, 2003), but the amount and type of inorganic
matter also play a key role in defining the end use.
The inorganic matter of coal is generally classified into
three major classes namely; dissolved salts, discrete
particles, and inorganic elements mingled within organic
constituents (Vessilev and Vassileva, 1996; Ward, 2002).
Dissolved salts are usually present in the pore water of
coal. Discrete inorganic particles which may be
crystalline or non-crystalline represents true mineral
components of coal (Ward, 2002). The crystalline
mineral matter typically comprised of oxides-hydroxides,
sulphides-sulphosalts, sulphates, silicates, carbonates,
phosphates, vanadates, tungstates, chlorides, native
elements, and other mineral classes (Ural, 2007; Vassilev
and Vassileva, 1996).
Punjab area of Pakistan has a coal resource potential
of 596 million tonnes. Coal reserves in Punjab are
located in the Eastern and the Central Salt Range and
in the Makerwal area of Surghar range. In the recent
past, the coal resources of Punjab have been defined to
be located in the seven zones, Padhrar coal zone is one
of them containing coal resource of 63.83 million tonnes
(Snowden, 2010).
Until now, Padhrar coal could not find its applications
in electricity production and cement industry due to its
low quality. It contains large amounts of mineral matter
and sulphur. Few attempts were made to upgrade the
quality of Padhrar coal through physical cleaning
methods (Shahzad et al., 2015). But these were not
proved successful due to the lack of or poor knowledge
of liberation characteristics of Padhrar coal.
Most of the difficulties associated with coal utilization
arise from inorganic mineral matter rather than maceral
composition. Mineral matter act as a diluent in coal,
displacing combustible material with non-combustible
matter (Ward, 2002).
The form and quantity of inorganic components have
profound effect on the behaviour of coal during
combustion. Quartz particles give rise to erosion in the
grinding mills and on exposed surfaces of the furnace.
Sulphur in various forms have long been recognized as
a cause of corrosion of furnace and boiler pipes as well
as the major source of environmental pollution during
burning of coal. Slagging in furnace is mostly associated
with iron sulphides, siderite or calcite (Creelman and*Author for correspondance; E-mail: [email protected]
90
Ward, 1996). These problems demand the removal of
undesirable elements from coal before its final use in
the industry. Since the nature and distribution of mineral
matter present in the coal, have fundamental effect on
the coal cleaning technologies, the characterization and
liberation studies of the mineral matter are critically
important (López and Ward, 2008; Ward, 2002).
Several methods are available to determine the type
and amount of mineral matter and to assess their
associations with coal macerals. Fractionation method
was used by several researchers (Cloke et al., 2002;
Spears and Booth, 2002) to study associations of mineral
components with organic material of coal in different
size fractions. Microscopic (petrographic) study of coal
has long been used to study the kinds and amounts of
macerals and minerals. It was also utilized to determine
the degree of coalification by measuring the percentage
of reflectance in reflected light (Valentim et al., 2006).
The powder X-ray diffraction (XRD) is probably the
most widely employed technique both for qualitative
and quantitative analysis of minerals in coal (Ritz and
Klika, 2010; Saikia et al., 2007; Ward et al., 2001;
Wertz and Collins, 1998).
This study aims to determine the quality of coal from
Padhrar area of district Khushab, Punjab, Pakistan.
Another objective of the present research is to establish
the liberation characteristics of this coal. Moreover, this
paper also describes the nature of associations of mineral
matter with coal matrix.
Materials and Methods
Coal sample in bulk amount was collected from Punjab
Mineral Development Corporation (PUNJMIN) coal
mine located in Padhrar coalfield of Punjab, Pakistan.
Since primitive methods of mining are being used in
Punjab coal mines, it was not possible to take sample
by using mechanical means.
The sample was collected from a coal stock pile in
accordance to ASTM D-2234 and ASTM D-6883. The
production of the mine was about 80-90 metric tonnes
per day. There were three piles each containing
approximately 20 metric tonnes of coal (the amount of
coal that was loaded into a single truck). These stockpiles
contained fresh coal that was mined at that day. The
height of each coal pile was measured to be less than
1.5 meter. The top surface of each pile was levelled.
For the collection of gross sample, four points were
selected in each coal pile in such a way that the pile
was divided into four equal parts by drawing two
perpendicular lines on its top surface and the centre of
each quarter was selected as one of the four sampling
points. Four increments each having an amount of 5 kg
were taken from each point by using a shovel. The
number of increments collected in this way were totaled
to be 48 and the total amount of the gross sample
collected from three coal stockpiles was found
approximately equal to 240 kg. The gross sample was
preserved into two plastic drums already lined with
plastic sheets of 0.4 mm size along their inner walls.
The gross sample was crushed by using Denver
laboratory jaw crusher set at ¾ inches. The gross sample
was further divided into four sub samples by the standard
procedure of coning and quartering. Sieve analysis was
performed on one sub-sample. The individual sieve
fractions were weighed and subsequent weights were
recorded. After that, all the size fractions were further
ground to -0.177 mm by using laboratory disc mill.
These ground fractions were analyzed for their ash and
sulphur contents following ASTM standard test methods
(ASTM D-3174, ASTM E-775). All these tests were
conducted on air-dried basis.
ASTM standards (D-3173, 3174 and 3175) were used
to perform a proximate analysis on another coal sub
sample obtained from bulk sample which was first
ground to -0.177 mm. The percentage of total sulphur
was also determined for this sub-sample by using
standard test method (ASTM E-775). Bomb calorimeter
was utilized to find out the calorific value of coal sample.
Megascopic pictures of randomly selected coal pieces
were captured on the site while microscopic photographs
were taken by using MM6C-AF-2 microscope (Fig. 1)
in the reflected light. A very fine size coal sample
(-0.150 mm) was prepared and subjected to X-ray
diffraction analysis using computer controlled Philips
XPERT PRO diffractometer system with Cu Ka
radiation having a wavelength of 1.54 Å. The scan
range was kept starting from 4.990° and ending at
120.000° with step size of 0.035°. The total measuring
time was observed to be 8.15 min. The data thus obtained
was stored in a digital format. JCPDS Powder Diffraction
File was used to identify the minerals from diffractogram.
Results and Discussion
Sieve analysis. The results of sieve analysis are given
in Table 1. The graph between cumulative mass
percentage and average aperture size of each fraction
91Liberation Studies of Padhrar Coal
Padhrar coal to sub-bituminous C type class (ASTM
D388). It also contains higher amounts of ash and
sulphur which restrict its use in power generation and
cement manufacturing.
Fractionation method. The results of ash and sulphur
determination tests performed on all the seven fractions
are given in Table 3. It can be seen that vast variations
exist in the values of ash and sulphur at various coal
particle sizes. It is interesting to note that the largest-
sized fraction (+26.670 mm) has the highest amount of
ash contents while the lowest values of total sulphur
percentage are also found in the same size class. The
ash contents were the lowest for the medium-sized
fraction i.e., for the size range of -13.330+6.680 mm.
The sulphur contents were the highest in the size range
of -6.680+3.327 mm. The ash contents in other size
fractions vary from 30.53 to 35.33 %. These abrupt
variations in the fractionation analysis of Padhrar coal
may be attributed from the complex associations of
mineral matter with the organic materials. It may be
safely concluded that Padhrar coal contains various
minerals in different forms which have associations at
different levels of particle size and liberate accordingly.
The highest ash contents in the largest-sized fraction
may be due to the presence of mineral matter in the
form of nodules or bands at larger sizes. When these
is shown in Fig. 2. It may be noted that about 83.48%
of the total mass lies in the size range of -26.67
mm+6.68mm.
Proximate analysis. The results of proximate analysis
and combustion analysis (sulphur and calorific value)
of the Padhrar coal samples are given in Table 2. The
results obtained from the proximate analysis categorize
92 Muhammad Shahzad et al.
Table 2. Summary of results of proximate analysis
Characteristic Value
Moisture content (%) 3.13
Ash content (%) 32.40
Volatile matter (%) 34.07
Fixed carbon (%) 30.40
Total sulphur (%) 5.60
Calorific value (Kcal/kg) 4753
Table 3. Results of ash and sulphur determination tests
performed on different size fractions of Padhrar coal
Aperture size Average ash content Total sulphur
(mm) (%) (%)
+26.670 48.96 3.430
-26.670+18.860 30.63 5.280
-18.860+13.330 34.33 5.490
-13.330+6.680 25.30 4.935
-6.680+3.327 35.33 7.050
-3.327+1.680 32.40 6.556
-1.680 30.53 6.934
Fig. 1. MM6C-AF-2 microscope.
0 5 10 15 20 25 30 35 40Aperture size (mm)
120
100
80
60
40
20
0
Cum
ula
tive m
ass %
Passing
Retaining
Fig. 2. Graph between aperture size and cumulative
mass percentage.
Table 1. Masses of the fractions retained at sieves of
different mesh size
Fraction size Mass retained Cumulative mass
(mm) (%) Passing (%) Retained (%)
+38.00 0.00 100.00 0.00
-38.00+26.67 1.57 98.43 1.57
-26.67+18.86 22.50 75.93 24.07
-18.86+13.33 28.34 47.59 52.41
-13.33+6.68 32.64 14.95 85.05
-6.68+3.327 8.47 6.48 93.52
-3.327+1.680 2.90 3.58 96.42
- 1.680 3.58 0.00 100.00
large-sized particles are broken, these nodules are
fragmented into very fine particles which go down into
the smaller-sized fractions leaving more organic rich
coal particles in the medium-sized fractions.
X-ray diffraction analysis. Diffractogram of Padhrar
coal sample is shown in Fig. 3 while Table 4 presents
the major minerals identified by the peak pattern. These
minerals are kaolinite, quartz and pyrite.
Megascopic and microscopic analysis. Megascopic
and microscopic studies were also performed on Padhrar
coal samples. Figure 4 shows bands and nodules of
quartz packed in the coal. It may be noted that quartz
is present in the form of amalgamates of loosely adhered
fine particles. The size of these amalgamates varies
approximately from 5 mm to greater than 25 mm. These
masses of quartz are found to be the major reason of
higher ash contents at larger coal particle sizes. Since
these amalgamates consist of fine particles loosely
bound together, when the lumps of coal are broken,
they are separated from the coal and disintegrate into
individual fine grains. These disintegrated fine grains
ultimately go into the finer fractions during sieve
analysis, leaving the organic rich coal particles in the
middle fractions.
Figure 5 shows that pyrite is present in various forms
in the Padhrar coal. It is found both in thick and thin
bands as well as in the form of nodules. These bands or
nodules are relatively smaller than the quartz masses,
having greater adherence among the particles. When
the coal lumps are broken, these nodules and bands
separate from the organic materials and pass down into
the medium sized coal fractions due to their relatively
larger size. This results an increased total sulphur
percentage in the medium sized (-6.680+3.271 mm)
coal particles. In Fig. 5(B) a clay rich coal band can be
seen at the left side just before the pyrite nodule. It
represents that clayey material is intimately associated
with organic matter and does not liberate at coarser
sizes, thus causing higher ash values in lower size classes.
Figure 6 presents the microscopic photographs of
selected Padhrar coal pieces taken in reflected light by
using MM6C-AF-2 microscope. A very thin band of
quartz (white) can be seen in the top left corner of the
picture. Pyrite particles (golden colour) are unevenly
distributed in the coal while clayey particles (dirty
white) are associated with coal matrix at finer sizes.
93Liberation Studies of Padhrar Coal
Table 4. Pattern list of Padhrar Coal
Ref. Code Compound Chemical Score
name formula
00-001-0527 Kaolinite Al2Si2O5(OH)4 12
00-005-0490 Quartz SiO2 25
00-042-1340 Pyrite FeS2 16
10 20 30 40 50 60 70 80 90 100 110
Position (2°q)
800
600
400
200
0
Counts
Fig. 3. X-ray diffractogram of Padhrar coal sample
(K=Kaolinit; Q=Quartz; P=Pyrite).
Fig. 4. (A) Lump of coal containing quartz
particles in the form of concentrated masses
and bands, (B) Quartz and pyrite nodules
along with coal particles resulted by the
breakage of a coal lump.
All the above results confirm that the Padhrar coal
exhibits both epigenetic and syngenetic nature. Mineral
matter is present in the form of large nodules, bands,
and amalgamates of fine materials as well as in the
form of finer particles intimately associated with coal
matrix at very small size.
94 Muhammad Shahzad et al.
Fig. 5. (A) Pyrite and quartz bands of medium thickness in the coal, (B) Pyrite nodules and thin bands in
the coal lump.
Conclusion
This research work was conducted to assess the quality
and liberation characteristics of Padhrar coal. The results
of proximate analysis categorized the Padhrar coal into
sub-bituminous C type class. It was also showed that
Padhrar coal contained high amount of sulphur and
Fig. 6. Microscopic views of coal particles.
95Liberation Studies of Padhrar Coal
mineral matter. Sieve analysis revealed that more than
83% of the coal lied in the medium particle size range
of -26.670+6.680 mm. The results of fractionation analysis
indicated that most of the sulphur was found in the smaller
sized fractions having particle size less than 6.680 mm
while most of the ash was found to be associated at larger
particle size (+26.670 mm) and at relatively smaller
particle size (-6.680 mm). X-ray diffraction (XRD) studies
showed the presence of three major minerals namely;
kaolinite, pyrite and quartz. The megascopic and
microscopic analysis confirmed their presence. These
minerals were found to be present in the form of nodules
of different sizes, thick and thin bands, fine particles
intimately associated with coal matrix, and amalgamates
of fine mineral particles concentrated at different spots.
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Modeling the Land Suitability using GIS and AHP for
Cotton Cultivation in Punjab, Pakistan
Nabila Naza and Haroon Rasheedb*aDepartment of Computer and Software Engineering, Bahria University, Karachi Campus 13,
National Stadium Road, Karachi-75260, PakistanbDepartment of Electrical Engineering, Bahria University, Karachi Campus 13,
National Stadium Road, Karachi-75260, Pakistan
(received October 29, 2014; revised September 10, 2015; accepted October 19, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 96-108
Abstract. The main goal of this research was to establish a spatial model for identification of suitable land
for cotton in Punjab, Pakistan through evaluation of multidisciplinary variables by applying geographic
information system (GIS) and analytical hierarchy process (AHP) approach. In this model, rivers were
used as constraint and seven factors were temperature, soil physical and chemical properties, soil pH,
aridity classes, agro-ecological zones, and river command area. On the basis of these parameters suitability
maps were generated. By pair-wise comparison matrix (PWCM) of AHP, weights were extracted by means
of principal Eigen vector by Saaty�s method, with accepted consistency ratio of 0.09. Multi-criteria
evaluation (MCE) employing weighted linear combination aggregates all suitability maps to generate final
suitability map. It was found that more potential sites exist along with existing cotton practiced area. The
result provided important information for farmers to establish linkage between policy decisions and
regulatory actions and to improve agricultural land management.
Keywords: cotton, multi criteria evaluation, analytic hierarchy process, land suitability, geographic
information systems, pair-wise comparison matrix
Introduction
Cotton is Pakistan�s main cash crop and is planted on
15% of its total arable land during the Kharif season.
This production is concentrated in two provinces with
Punjab accounting for nearly 75% and Sindh nearly
25% of this arable land. An estimated 1.6 million farmers
grow cotton in Pakistan. The textile sector is the largest
industrial sector in Pakistan and accounts for about 40%
of the industrial labour force (Shafiq-ur-Rehman, 2015).
Pakistan is the 4th largest producer, 7th largest consumer,
7th largest importer, and 12th largest exporter of cotton
in the world (FAS-USDA, 2015).
As a semi-industrialized country, Pakistan is heavily
dependent on the agricultural crops such as cotton. As
a result, the policymakers are facing a great challenge
to carry out agricultural reform for sustainability and
optimization of resources. To meet the increasing demand
of agri-products, it is not feasible to bring more area
under cultivation due to limited resources. To tackle this,
focus has shifted from extensive to intensive farming
for precision farming and sustainable agriculture (Perveen
et al., 2013). To deal with the complexities for selecting
the best location for agricultural production, necessitate
the use of geo-spatial domain which accelerates the rate
of adoption of sustainable agriculture and benefit the
farmers and decision makers in agriculture planning
(Joshua et al., 2013).
One of the most important applications of GIS is the
display and analysis of data for environmental decision-
making (Eastman, 1999). GIS-based MCDA combines
and transforms spatial and aspatial data (input) into a
resultant decision (output) (Malczewski, 2004). The
hierarchical model i.e., analytical hierarchy process (AHP)
consists of objectives, criteria and sub-criteria. To evaluate
the criteria, scoring was made with preference scale,
and a pair-wise comparison matrix (PWCM) was created,
for which consistency should be below 0.10 (Akinci
et al., 2013). Multi-criteria evaluation (MCE) model
finds solutions to decision-making problems character-
ized by multiple factors, and weighted linear combination
(WLC) aggregate them into a final land suitability index
(Khoi and Murayama, 2010).
Using GIS based MCE model, cotton land suitability
in Sindh, Pakistan was evaluated by agro-informatics
data of soil, ground water availability, irrigation methods,
climate, land use, cropping patterns and agro-ecological*Author for correspondence;
E-mail: [email protected]
96
zones (Perveen et al., 2013). For the delineation of suitable
soil for Zero-Till Wheat cultivation in Gujranwala,
Pakistan based on soil profile, ground water survey data
and satellite imagery was employed with GIS framework
using weighted overlay (Iqbal and Mehdi, 2008). To
achieve the goal of land suitability assessment in
Sheikhupura and Nankana Sahib Districts of Punjab,
Pakistan, GIS based AHP model was employed by using
factors of soil texture and water supply along with other
factors for production of rice (Waqar et al., 2013).
Delineation of land suitability for agricultural production
was employed by applying GIS based AHP approach
in Yusufeli district of Artvin city, Turkey, using soil,
land use capability class, land use capability sub-class,
soil depth, slope, aspect, elevation, and erosion degree
to identify highly, moderately, marginally and unsuitable
areas (Akinci et al., 2013). GIS and MCDA model using
AHP technique employed for suitable agricultural land
in Greater Karu, Nasarawa state, Nigeria using soil,
slope, water bodies and geological maps to indicate
highly, moderately and not suitable areas (Joshua et al.,
2013). For the identification of suitable areas for cropland
in the Tam Dao National Park region, Vietnam, a GIS-
based MCE model using biophysical factors and Landsat
ETM+ imagery indicated the location and extent of
crop farming areas at different suitability levels (Khoi
and Murayama, 2010).
The main research problem was the cotton requirements
and limited cotton cultivation in the study area. Also a
constant growth in Pakistani yarn exports was observed
from 2009-2013 (Hussain, 2014). This research aims
to identify suitable land for cotton cultivation in Punjab,
Pakistan using the GIS based AHP modeling. It starts
from geometric correction, digitization of the re-sampled
maps to form the vector layers, then rasterization for
all the factors and constraint was performed. After
standardizing the maps for suitability, AHP was applied
to weight the factors using Saaty�s PWCM for suitability
analysis. Finally, the MCE technique was applied using
WLC for all the factors and constraints, to decide the
land suitability for cotton cultivation in the study area.
Materials and Methods
Site description. Punjab is the second largest province
of Pakistan in terms of land area at 205,344 km2 with
coordinate extending from 27° 42' 20.16'' N to 34° 00'
59.04'' N latitude and from 69° 20' 6'' E to 75° 22' 49.44''
E longitude. The province is bordered by Kashmir to
the north-east, the Indian states of Punjab and Rajasthan
to the east, Sindh to the south, Balochistan to the
southwest, and Khyber Pakhtunkhwa to the west and
Islamabad to the north. Temperature ranges from -2°-
45 °C. Climatically, Punjab has three major seasons
namely; hot weather (April to June); rainy season (July
to September); mild weather (October to March). Six
rivers of Punjab named Indus, Jhelum, Beas, Chenab,
Ravi and Sutlej provide heavy irrigation system by
canals throughout the province. The province is mainly
a fertile region along the river valleys. The region
contains Thal and Cholistan deserts. Despite of its
tropical wet and dry climate, extensive irrigation makes
it a rich agricultural region. Wheat and cotton are the
major crops. Cotton and rice are important cash crops
(Saif, 2014).
Criteria for cotton cultivation. In the study area, cotton
is sown in May or June and picking starts in September
or October. The data source (Table 1) describes the
source for obtaining all the factors and constraint utilized
for evaluating the cotton land suitability. The suitable
criteria (Arain et al., 2014) for cotton cultivation was
characterized by the factors: canal command area; soil
pH (6.6-8.4); soil chemical properties classes (Acid
soils: slightly acid, neutral soils: non-calcareous to slight
calcareous, mildly alkaline soils: non-calcareous to
strongly calcareous, moderately alkaline soils: non-
calcareous to strongly calcareous); mean maximum
annual temperature (34 °C); soil physical properties
Modeling the Land for Cotton Cultivation
Table 1. Data source for the research work
Data Source
Statistical data for cotton production
for Punjab and
Statistical data for area sown under
Cotton for Punjab. (Rafique, 2013)
Digital scanned map of Punjab rivers,
Digital scanned map of soil physical
properties,
Digital scanned map of soil pH and
Digital scanned map of soil chemical
properties. (Panagos et al., 2011)
Shape file of mean maximum annual
temperature. (ICIMOD, 2008)
Digital map of canal command area. (Brabben, 2000)
Shape file of Punjab administrative
boundary. (PCO, 2013)
Digital map of aridity classes and
Digital map of agro-ecological zones. (PARC, 2007)
97
classes (River plain and terraces:non-calcareous and
calcareous loamy soils); aridity classes (Arid (Kharif,
Rabi) and Arid (Kharif), Hyper-arid (Rabi); agro-
ecological zones (irrigated plains-D.G. Khan irrigated
and irrigated plains-cotton zone). Rivers were used as
constraint. The detailed description of suitability (Table
2) for factors and constraint describes both suitable and
not suitable criteria for cotton cultivation (Arain et al.,
2014). These values were in agreement with those
considered in the literature.
Developing GIS based AHP model. For cotton land
suitability, GIS-based AHP model (Fig. 1) describes all
the steps used in this study. The geometric rectification
of the downloaded digital scanned maps, which were
originally constructed in geographic latitude longitude
projection, for all the factors and constraint was done
in Erdas Imagine 9.2. Using rectification module of
Erdas Imagine, Linear rubber sheet map transformation
was used. Coordinates were assigned by using about
10 easily recognizable GCPs. Maps of all the criteria
were geo-referenced to WGS84. First polynomial order
and nearest neighborhood re-sampling method computes
the new coordinates for output image. The projection
of shape file of Punjab administrative boundary was
already in WGS84 UTM zone 42N so all the geometri-
cally rectified factor and constraint maps were then
Nabila Naz and Haroon Rasheed
Table 2. Standardized criteria for the constraint and
factors used in this work
Layer Standardization description of factors and constraint
Canal Suitable: Canal command areacommand Not suitable:area 1. Rivers
2. Non-canal command area
Soil pH Suitable: 6.6 - 8.4Not suitable:
1. 6.1 - 6.52. > 8.4
Soil Suitable:chemical 1. Acid soils: slightly acidproperties 2. Neutral soils: non to slight calcareous
3. Mildly alkaline soils: non to slight calcareous4. Mildly alkaline soils: moderately calcareous5. Mildly alkaline soils: strongly calcareous6. Moderately alkaline soils: non to slight calcareous7. Moderately alkaline soils: moderately calcareous8. Moderately alkaline soils: strongly calcareous
Not suitable:1. Salt affected soils: saline2. Salt affected soils: saline-sodic3. Salt affected soils: slight to strong saline-sodic4. Miscellaneous areas
Mean Suitable: 34 °Cmaximum Not suitable:annual 1. 26 °Ctemperature 2. 28 °C
3. 30 °C4. 32 °C
Soil Suitable:physical 1. River plain and terraces: non calcareous loamy soilproperties 2. River plain and terraces: calcareous loamy soils
Not suitable:1. River plain and terraces: non calcareous clayey soil2. River plain and terraces: calcareous sandy soil, dune3. River plain and terraces: calcareous clayey soils4. River plain and terraces: salt affected soils5. Piedmont plains: non-calcareous loamy soils6. Piedmont plains: non-calcareous clayey soils7. Piedmont plains: calcareous sandy soils8. Piedmont plains: calcareous loamy soils9. Piedmont plains: calcareous clayey soils10. Piedmont plains: salt affected soils11. Loess and weathered rock plains12. Mountains and hills13. Seasonally flooded soils14. Sand dunes and sandy soils
Aridity Suitable:classes 1. Arid (Kharif, Rabi)
2. Arid (Kharif), Hyper-arid (Rabi)Not suitable:
1. Humid2. Sub-humid3. Semi-arid
Agro- Suitable:ecological 1. Irrigated plains: D.G. Khan irrigatedzones 2. Irrigated plains: cotton zone
Not suitable:1. Barani region: high rainfall2. Barani region: low rainfall3. Thal region: arid zone4. Thal region: irrigated zone5. Marginal land: Suleiman mountains6. Irrigated plains: central mixed zone7. Marginal land: Cholistan8. Irrigated plains: rice zone
River Suitable: Landconstraint Not suitable: RiversFig. 1. The research procedure used in this study.
98
re-projected to WGS84 UTM zone 42N using ArcGIS
10.2.2. In ArcGIS 10.2.2 all factors and constraint were
then manually digitized using the projected shape file
of Punjab administrative boundary with geometrically
rectified maps as base maps. In this work, the used
factors: canal command area (Fig. 2), soil pH ranges
(Fig. 3), soil chemical properties classes (Fig. 4), mean
maximum annual temperature (Fig. 5), soil physical
properties classes (Fig. 6), aridity classes (Fig. 7), agro-
ecological zones (Fig. 8), and constraint: Punjab rivers
(Fig. 9), describe all the classes in a particular layer of
the study area. For generating the standardized map for
each parameter and the final aggregated land suitability
map in Idrisi Selva, all the parameters should be in
raster form. For this, rasterization was performed in
Idrisi Selva with the resolution of 100 ´ 100 m.
The logic of aggregation demands that all the rasterized
criteria be standardized to the same scale to make
comparisons possible. Ranges of values that are not of
interest are explicitly set to 0 while, the range of interest
is set to 1 (Eastman, 2012). The re-class tool of Idrisi Selva
was used to standardize the input criteria. Standardized
criteria (Table 2) describe suitability and non suitability
for cotton cultivation (Arain et al., 2014). In this study,
the standardized factors: canal command area (Fig. 10),
soil pH ranges (Fig. 11), soil chemical properties classes
(Fig. 12), mean maximum annual temperature (Fig. 13),
soil physi-cal properties classes (Fig. 14), aridity classes
(Fig. 15), agro-ecological zones (Fig. 16), and constraint:
Punjab rivers (Fig. 17), describe only the suitable and
not suitable class in a particular layer for cotton
cultivation in the study area.
Analytic hierarchy process. In Idrisi Selva AHP method
was used to derive a set of weights for all the stan-
dardized factors by utilizing PWCM with the principal
Eigen vector of this matrix representing a best fit set of
weights by Saaty�s method. In the comparison matrix,
two factors were compared at a time in terms of their
importance on a scale from 1/9 to 9, where 1/9 indicates
that relative to the column factor, the row factor is less
important; 9 indicates that in relation to the column
factor, the row factor is more important (Eastman, 2012;
Teknomo, 2006). After constructing comparison matrix,
Eigen vectors were computed, which were the normalized
Modeling the Land for Cotton Cultivation 99
Fig. 2. Digitized map of Punjab canal command area. Fig. 3. Digitized map of Punjab soil pH.
Nabila Naz and Haroon Rasheed
Fig. 5. Digitized map of Punjab mean maximum
annual temperature.
100
Table 3. Pairwise Comparison Matrix of all the factors relevant to cotton crop land suitability
Factors Soil Canal Soil Mean annual Soil Aridity Agro-pH command chemical maximum physical classes ecological
area properties temperature properties zones
Soil pH 1 - - - - - -Canal command area 1/3 1 - - - - -Soil chemical properties 1 3 1 - - - -Mean annual maximum temperature 3 1 3 1 - - -Soil physical properties 5 3 5 5 1 - -Aridity classes 7 7 3 3 1 1 -Agro-ecological zones 9 7 5 5 3 1 1
Eigen vector of the matrix obtained by normalizing
each column of the matrix. Since vectors are normal-
ized, the elements in Eigen vector sum to 1. PWCM
(Table 3) represent the relative importance of all the
Table 4. Weights of all the factors used in the study
Factors Weights
Canal command area 0.0397
Soil pH 0.0471
Soil chemical properties 0.0567
Mean maximum annual temperature 0.0804
Soil physical properties 0.2072
Aridity classes 0.2335
Agro ecological zones 0.3354
Fig. 4. Digitized map of Punjab soil chemical
properties.
factors involved in this study. The hierarchy of weights
for all the factors (Table 4) was used for generating
final land suitability map.
Weights are used to derive consistency ratio (CR) which
indicates any inconsistency made in PWCM. If CR >
0.10, then some pair-wise values need to be reconsidered
until desired value of CR < 0.10 is reached (Eastman,
2012). The formula for consistency ratio (Kihoro et al.,
2013) was described in equation 1 and 2:
(lmax- n)CI = ________ ......................................... (1) (n - 1)
CICR = ____ ................................................ (2) RI
where:
lmax = maximum Eigen value, CI = Consistency Index,
CR = Consistency Ratio, RI = Random Index and n =
number of criteria in each PWCM. This step was
performed in Idrisi Selva. In this work, Eigen vector
has acceptable consistency ratio of 0.09.
GIS based MCE is concerned with the allocation of
land to suit a specific objective on the basis of variety
of attributes that the selected areas should possess. In
MCE, each standardized factor was combined by
weighted linear combination that is, each factor was
multiplied by a weight, with results being summed up,
which was then multiplied by the product of constraints.
The final image was measure of aggregated suitability
Modeling the Land for Cotton Cultivation
Fig. 7. Digitized map of Punjab aridity classes.
101
Fig. 6. Digitized map of Punjab soil physical
properties.
for non-constrained locations (Fig. 18). The formula
for WLC (Eastman, 1999) is given in equation 3:
s = S wixi . P cj .................................... (3)
where:
S = the composite suitability score, wi = weight of a factor,
xi = factor, cj = constraint, S = the sum of weighted factors,
and P = product of constraints.
Results and Discussion
The final suitability map (Fig. 19) generated by GIS
based AHP modeling technique for cotton cultivation
potential sites in Punjab by using decision support tool
of multi criteria. Number of hectares suitable for cotton
Nabila Naz and Haroon Rasheed
Fig. 9. Digitized map of Punjab river constraint.
Fig. 10. Standardized factor map for Punjab canal
command area.
Fig. 8. Digitized map of Punjab agro-ecological
zones.
102
Modeling the Land for Cotton Cultivation
Fig. 11. Standardized factor map of Punjab soil
pH.
Fig. 12. Standardized factor map of Punjab soil
chemical properties.
Fig. 13. Standardized factor map of Punjab mean
maximum annual temperature.
Fig. 14. Standardized factor map of Punjab soil
physical properties.
103
cultivation was 6141259.4685409 hectares with
production capability 27021541.66157996 bales and
area not suitable for cotton cultivation was
13811985.0537708 hectares. Of the study area, 31%
represents suitable area and 69% represents not suitable
area (Table 5) for cotton cultivation.
According to Punjab Development Statistics (Rafique,
2013), area sown under cotton for 2012 was 2533000
hectares with production for the year 2011-2012 was
11129000 bales. According to this statistical report,
existing cropland under cultivation covers 13% while
area not under cultivation was 87% of the study area.
The final cotton land suitability map represents 18%
increase in suitable area for cotton cultivation with an
increased potential in production of 15892541.66157996
bales were recorded.
According to Punjab Development Statistics (Rafique,
2013), highly suitable districts for cotton cultivation
were Rahim Yar Khan, Bahawalpur, Bahawalnagar,
Multan, Dera Ghazi Khan, Muzaffargarh, Vehari,
Khanewal, Sahiwal, Lodhran, and Rajanpur. Final cotton
land suitability map indicates that the districts with
large extent of suitability were Bahawalnagar, Bahawalpur,
Chiniot, Dera Ghazi Khan, Faisalabad, Jhang, Khanewal,
Nabila Naz and Haroon Rasheed
Table 5. Summary of statistical information by GIS based AHP model for cotton land suitability in Punjab, Pakistan
Statistics Area Production (Bales)
Area (hectare) Percentage (%)
Suitable land 6141259.4685409 31 27021541.66157996Not suitable land 13811985.0537708 69 0
Land under cultivation (2011-2012) 2533000 13 11129000
Potential increase by suitability map 3608259.4685409 18 15892541.66157996
Fig. 15. Standardized factor map of Punjab aridity
classes.
Fig. 16. Standardized factor map of Punjab agro-
ecological zones.
104
report (Cai et al., 2010); Punjab districts cotton produc-
tion (SUPARCO, 2012); major and minor cotton crop
areas (USDA, 2014); cotton production regions (FAO,
2004) who concluded the sites suitable for cotton
cultivation in Punjab, Pakistan.
Conclusion
For decision makers to select certain crop land suit-
ability is a complicated issue especially when based on
environmental factors. The GIS based MCE using AHP
procedure involves the utilization of geographical data,
the decision maker preferences, manipulation of data,
and preference according to specific decision rules. The
model implemented in this study, demonstrated a rational
and objective approach to make decisions in agricultural
applications in Punjab. MCE method was adequate
to integrate databases required for different kinds of
environmental variables in a GIS context.
In this study, application of GIS technique to identify
suitable areas for cotton crop in Punjab, Pakistan was
successful. The results obtained from this study, indicate
that the use of GIS and application of MCE using AHP
could provide a superior guide map for farmers and
decision makers at local level to select the appropriate
cultivation sites, crop management and diversification
operations to achieve better agricultural production.
The approach has been used in some studies in other
countries. This study clearly brought out the distribution
of cotton derived from the evaluation of environmental
variables in GIS context.
Additionally, the result of this study could be useful for
other investigators who could use these results for
diverse studies. This study has been done considering
soil physical and chemical properties, temperature,
water resources, aridity, and agro-ecological zones that
affected the suitability of cotton cultivation. Therefore,
it gives primary result.
The common thread in mapping cotton cultivation
suitability is to utilize correct environmental variables
to achieve greater accuracy. To accomplish this, the
study has focused deeply on those environmental
variables that are worth mentioning for cotton cultiva-
tion and accurate enough to provide perfect suitability
map.
Modeling the Land for Cotton Cultivation
Fig. 17. Standardized constraint map of Punjab
rivers.
Lodhran, Multan, Muzaffargarh, Okara, Pakpattan,
Rahim Yar Khan, Rajanpur, Sahiwal, Toba Tek Singh,
and Vehari. Districts with less extent suitable area were
Bhakkar, Chakwal, Khushab, Layyah, Mianwali, and
Sargodha. Districts not suitable for cotton cultivation
were Attock, Gujranwala, Gujrat, Hafizabad, Jhelum,
Kasur, Lahore, Mandi Bahauddin, Nankana Sahib,
Narowal, Rawalpindi, Sheikhupura, and Sialkot.
The final suitability map (Fig. 19) shows that the agro
ecological zone, aridity classes, soil physical properties
and temperature zones comprise the major part of the
cotton suitable land in this analysis. There are several
other crops grown under the cotton suitable areas that
necessitate their land suitability evaluation in order
to get better yields by optimally utilizing the present
resources. The areas which are not suitable for cotton
cultivation may be suitable for any other crop and
excellent results can be obtained by better management
in terms of millions of bales. This will eventually give
boost to economy of Pakistan, which also supports the
textile industrial sector.
The final land suitability map for cotton cultivation is
also in conformity with the findings of IWMI research
105
Nabila Naz and Haroon Rasheed106
Fig. 18. The process of combining criteria maps for cotton land suitability in Punjab, Pakistan, using GIS
based AHP model.
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Nabila Naz and Haroon Rasheed108
Quality Variation Minimizer: A New Approach for Quality
Improvement in Textile Industry
Muhammad Amin*, Muhammad Amanullah and Atif AkbarDepartment of Statistics, Bahauddin Zakariya University, Multan, Pakistan
(received March 24, 2014; revised September 4, 2015; accepted October 9, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 109-113
Abstract. The main theme of this research is to introduce a new technique for quality improvement in
industries and services environment. The technique is called as quality variation minimizer (QVM), which
is used to test and compare product quality among multiple data groups, i.e. machines, operators, and
material etc. For the significant application, QVM is applied at Card department in spinning industry to
determine yarn grains quality by different machines. Then comparison of QVM is made with other already
developed techniques, i.e., coefficient variation (CV), sigma level etc. to determine yarn grains quality.
From the results determined by t-test and chi square test, it has been found that QVM is an effective method
to determine yarn grains quality with sample average near the target/demanded value as well as minimum
variation.
Keywords: coefficient of variation, quality variation minimizer, sigma level, yarn grains quality
Introduction
Quality is the only key to survive any organization or
company for a certain product or services. Quality means
fitness for use or to meet the customer needs and satis-
faction. In this situation, quality improvement activities
have become a part of the business culture and a way of
life (Gijo and Rao, 2005).
Across all types of industries, a well-known fact is that
�process and product variation� is the strongest factor
affecting the product and process costs, process yield,
product quality and customer satisfaction (Hild et al.,
2000). During the past half of the century, various tools,
i.e. statistical process control (SPC), quality circles
(QC), total quality management (TQM), benchmarking,
quality management system (QMS), enter resource
planning (ERP) packages, Kaizen and Six Sigma have
been developed for quality improvements and custo-
mer satisfaction (Antony et al., 2005; Dedhia, 2005;
Montgomery, 2005).
Nowadays, billions of dollar are spent annually on good
quality products in the world. In order to remain in the
main stream of global competition, market manufacturers
have to produce good quality products in various fields,
i.e. health, the internet, textile, food etc. (Dhillon, 2007).
In 2000, World fiber production was predicted 50 million
tonnes (Clapp et al., 2001). In the textile industry, to
ensure the product quality, quality control is necessary.
Statistical quality control (SQC) was first time used in
the yarn manufacturing industry in the late 1940s until
1950 (Clapp et al., 2001). In 1981, Milliken & the
Company (Textile Company) applied TQM in the
company to meet the customer demands. Currently,
there are 30,000 approximately textile-related companies
in the United States, of which mostly use TQM tools
for reducing cost and enhancing customer satisfaction
(Mukhopadhyay and Ray, 2006). Mukhopadhyay and
Ray (2006) applied Six Sigma to reduce yarn packing
defects and they had the techniques of Six Sigma i.e.,
control chart, sigma level, MSA, regression, etc. Feili
and Fekraty (2010) constructed the control charts on
the basis of probability and fuzzy theory to monitor the
yarn quality. They have found that fuzzy theory performs
better than probability theory for monitoring product
quality. Maros et al. (2011) applied control charts on
chenille yarn defect types to see the variations.
Textile industry and its markets both are so complex
and the customer is always demanding for better quality
of textile products and on time delivery with minimum
cost. To satisfy these challenges, companies are imple-
menting SQC techniques for their customer satisfactions
(Das, 2013). Acceptable quality is identified by the end
user while fabric makers are the main user of yarn
(Lord, 2003). In textile spinning industry, quality is valued
by utilizing the parameters yarn count (NEC), count
coefficient of variation (CV%) or evenness (consistency),
strength, CLSP, TPI (twist per inch), uniformity (Um%),
thin, thick places and so on. Yarn quality consistency
is measured by variance, standard deviation or CV of*Author for correspondence; E-mail: [email protected]
109
the yarn quality parameter according to yarn product
(Lord, 2003). One of the most important prerequisites
for a spinner is to keep the average count and count
variation within control. Most customers are interested
in average count, counts CV%, average CLSP and average
single yarn strength over a specified yarn length. Most
important parameter for measuring the yarn quality is
the yarn strength because fabric durability and structure
completely depend on yarn strength (Lord, 2003). Modern
high performance machine in knitting and weaving
mills require a constant yarn quality. Most of the success-
ful spinning mills have to produce the demanded quality
in close cooperation with knitter and weavers through
most constant and cost-efficient manner. In parliamentary
law to predict yarn behaviour, it is not sufficient merely
to use individual quality characteristic, i.e. CV% for the
valuation of a narration. Because CVs does not discri-
minate between the sources of erroneous beliefs, then
alternative methods are required for measuring yarn
quality (Lord, 2003).
Reliable and accurate tools invention are necessary for
evaluating the product quality. Customers are always
demanding excellent product quality with a process
average near the target (Demand) with minimum
variation. Now, the problem is arising how to measure
the process quality to meet the customer demand with
minimum variation. In the present study a new technique
Quality Variation Minimizer (QVM) has been introduced
to measure the product quality. The principal objectives
of QVM are as follows:
i. To predict product quality,
ii. To compare different brands/companies/depart-
ments, product quality,
iii. To detect a minimum and maximum product
quality variation.
This QVM was applied in the textile industry to compare
machines quality in terms of yarn grains quality.
Materials and Methods
Data and company. The data are taken from the Mehr
Dastgir Spinning (MDS) Mills Ltd. Multan, Pakistan.
The MDS manufacturing the following yarn products,
i.e., 7/1s, 10/1s and Super 40/1s. A sample from the
carding department for product 40/1s was taken which
is nominated fine yarn to produce various types of
clothes. The spinning industry for yarn manufacturing
contains the following departments: mixing, blow room,
carding, drawing (breaker and finisher), simplex, ring
and autocone. The card is the heart of spinning industry
and variation arises due to material, machine, man, and
the environment. Therefore, the sample of grains/yard
from the card machine wise was taken and detected
which machine produce excellent quality with minimum
variation and which has maximum variation. In this
spinning industry, 22 card machines were sets in card
department, however, machine Card MK5-12 was not
in working condition when the samples from this industry
of these machines were collected.
Methods. Different statistical tools were used for
measuring the required yarn quality. These tools include
mean, standard deviation, coefficient of variation (CV),
sigma level, QVM (our proposed technique), T-test, chi
square test with respective p-values. The formulation
of sigma level, process capability and QVM (proposed
technique) methods are given as under:
Sigma level. A statistical tool which is used to measure
the process quality level to meet the customer require-
ment, which is also a technique of six sigma and very
important tool for measuring the product quality. Sigma
level is grounded along the voice of customer, process
average and process variation (standard deviation).
Six Sigma is implemented at textile spinning mills of
India, reduction in delay procurement of material that
results sigma level increase from 1.80 to 2.4 and yield
improved from 62-76% (Das, 2005). Six Sigma is
implemented in textile industry to reduce dying shade
variation and the sigma level is increased from 0.81 to
2.34, saving annually over $40,000 (Das et al., 2007).
Mathematically sigma level is defined as:
USL _ x_ x
_ _ LSLSigma level = process shift + min
________ ,
_______
s s
öø
éë
éë
öø
öø
öø
where:
m = target or demanded average; x_
= the estimated
process mean; s = the estimated standard deviation
from the process; USL = the upper specification limit
and LSL = the lower specification limit.
Generally the process shift is 1.5 from the target i.e.,
m. Because some quality standards on average are m ±
3s and at one side, may be left or right, the distance of
target values. In other words, we can take the process
shift allowed variation given by the customer is on
Muhammad Amin et al.110
either side from the target value is 1.5 for further detail
about process shift of 1.5 (Bothe, 2002).
USL _ x_ x
_ _ LSLSigma level = 1.5 + minimum
_______ , _______
s s
öø
öø
There is direct relationship between sigma level and
customer expectations. So higher sigma level results
better customer expectations, meaning the fewer defects
are produced in the process. Sigma level is inversely
proportional to process standard deviation. It shows
that if process standard deviation increases, then the
sigma level is decreased. Similarly, if process standard
deviation is decreased, then the sigma level is increased.
Our main objective to minimize the process variation
is the main reason to use sigma level to measure the
process quality for comparison purposes (Breyfogle,
1999).
Process capability. Another measure for determining
the product quality is process capability (Cp). Cp measures
customer specifications in terms of acceptable spread
or dispersion, without reference to the target value
(Ehrlich, 2002). Cp mathematically is defined as:
USL _ LSLCp = __________
s
The difference is the tolerance of manufacturing process
and the voice of the customer.
According to Keller (2011), Montgomery (2005) and
Park (2003) the interpretation of Cp is as follows:
If Cp <1, then the process variation is greater than
allowable variation and more defective items are
produced
If Cp = 1, it indicates the sample variation exactly
equals the allowable variation (i.e. tolerance)
If Cp >1, then the process variation is smaller than
allowable and less defective items are produced.
The main limitation of this technique like CV is that it
does not see how process mean is away from the target
value. It just only sees the voice of the customer and
process standard deviation.
Quality variation minimizer. As the limitation of CV
and Cp techniques is discussed to assess the product
quality, now the question arises how these limitations
are swept to get reliable results regarding the product
quality and also how to measure the process quality to
meet the customer demand with minimum variation and
process average near the target? Here a new technique
has been introduced which has been called Quality
Variation Minimizer (QVM) to measure the product
quality. QVM mathematically is defined as:
| x_ _ m |QVM = Za ´ s +
______2_
n
where:
Z a 2
_= the critical value of the normal distribution at a
specified level of significance. QVM is affected by
level of significance, the absolute distance between
process average and the target value, process standard
deviation and the sample size. The smaller the value of
alpha indicates more reliable QVM results as compared
to QVM at larger alpha. As in the literature there are
standard quality testing tools, i.e. Cp, sigma level. This
proposed method QVM has been proved practically
that it performs in a better way than already developed
method with simple and minimum computation.
Results and Discussion
In Table 1, studies of yarn grains/yards quality of
different machines have been presented alongwith
performance of QVM. A comparison among QVM with
SD, CV, Cp and sigma level is depicted here.
From Table1, it is clear that on the basis of smaller
standard deviation and CV, the best quality was given
by machine Card MK5-19, while T-test says that process
average grains do not meet the customer needs and also
chi-square test indicates that the process variation is
not acceptable. Now, considering the machine Card
MK5-03, Card MK5-18 and Card MK5-22, these have
the process average near the target but their CV is so
large. Similar results are given by Cp because it depends
only on process standard deviation. On the other hand,
sigma level and QVM observes both target and process
variation. Table 1 also shows that sigma level is maxi-
mum where QVM is minimum and t-test is accepted.
This indicates that the process average met the target
or demanded value and also variation by chi-square test
was minimum of machine Card MK5-16 as compared
to other machines and therefore, considered as the best
machine for producing a good quality grains. On the
other hand bad quality was produced by Card MK5-01
as its sigma level was minimum and QVM was maxi-
mum. This machine also needed to be checked for the
mechanical faults. There are some situations where CV,
SD and QVM are minimum while sigma level, Cp is
maximum but it is not always true. QVM depends on
111Quality Variation Minimizer for Textile Industry
level of significance, absolute distance between process
average and target value and process standard deviation.
As the greater value of the level of significance may
provide similar results as other groups. So smaller value
gives more cleared results of QVM to discriminate the
product quality among the groups. QVM value will
move in the same direction with process standard
deviation and the absolute distance between average
and target value. These relationships are also shown in
correlation in Table 2. The significance of this proposed
technique is that it has been applied in the textile spinning
industry and the results show that this technique is
comparatively better than CV. So this proposed technique
may be used in other manufacturing industries to com-
pare the product quality by different machines, operators,
temperature level, humidity level etc.
Table 2. Correlation matrix among quality measures
QVM Mean SD CV Cp Sigma level ABS distance
QVM 1.0000 -0.5171 0.9070 0.9098 -0.8553 -0.9688 0.6576
Mean -0.5171 1.0000 -0.4419 -0.4653 0.3642 0.4490 -0.3826
SD 0.9070 -0.4419 1.0000 0.9995 -0.9768 -0.8392 0.2797
CV 0.9098 -0.4653 0.9995 1.0000 -0.9754 -0.8415 0.2862
Cp -0.8553 0.3642 -0.9768 -0.9754 1.0000 0.8224 -0.2016
Sigma level -0.9688 0.4490 -0.8392 -0.8415 0.8224 1.0000 -0.7053
ABS distance 0.6576 -0.3826 0.2797 0.2862 -0.2016 -0.7053 1.0000
ABS = absolute; QVM = quality variation minimizer; CV = coefficient of variation; Cp = process capability.
Table 1. Card department yarn grains/yards quality analysis by machines
Department Card Specification limits USL 61.5 Process shift 1.5
Grains/Yard machine wise analysis Target 60 Level of significance 0.05
LSL 58.5 Target SD 0.6
Descriptive and quality measuring statistics QVM QVM QVM Testing
Machines Mean SD CV Cp Sigma Sample Level of significance T-test P-value Deci- Chi- P-value Deci-
level size 0.1 0.05 0.01 sion square sion
Card MK5-01 59.35 1.35 2.27 0.37 2.13 20 2.6025 3.2800 4.1252 -2.15 0.0444 NO 95.841 0.0000 NO
Card MK5-02 59.95 0.72 1.21 0.69 3.5 86 1.1014 1.4600 1.9192 -0.67 0.5065 Yes 123.664 0.0040 NO
Card MK5-03 60 1.09 1.82 0.46 2.87 82 1.5849 2.1300 2.8173 -0.03 0.9776 Yes 267.654 0.0000 NO
Card MK5-04 60.28 0.81 1.34 0.62 3.01 79 1.4546 1.8600 2.3681 3.11 0.0027 NO 141.588 0.0000 NO
Card MK5-05 60.33 0.76 1.26 0.66 3.03 81 1.4367 1.8200 2.2965 3.94 0.0002 NO 128.658 0.0000 NO
Card MK5-06 60.3 0.87 1.44 0.58 2.89 82 1.5547 1.9900 2.5343 3.11 0.0026 NO 169.091 0.0000 NO
Card MK5-07 60.29 0.79 1.31 0.63 3.04 86 1.4308 1.8300 2.3235 3.35 0.0012 NO 147.356 0.0000 NO
Card MK5-08 59.88 0.99 1.65 0.51 2.9 81 1.5531 2.0500 2.6670 -1.13 0.2616 Yes 215.92 0.0000 NO
Card MK5-09 59.88 0.92 1.54 0.54 2.99 83 1.4646 1.9300 2.5095 -1.22 0.2259 Yes 194.751 0.0000 NO
Card MK5-10 59.91 0.87 1.45 0.57 3.12 88 1.3499 1.7800 2.3327 -0.96 0.341 Yes 182.808 0.0000 NO
Card MK5-11 60.06 0.76 1.26 0.66 3.4 85 1.1614 1.5400 2.0158 0.79 0.4305 Yes 133.412 0.0000 NO
Card MK5-13 59.7 0.89 1.5 0.56 2.84 87 1.5955 2.0400 2.6041 -3.15 0.0023 NO 190.323 0.0000 NO
Card MK5-14 60.37 0.95 1.57 0.53 2.69 69 1.7461 2.2200 2.8199 3.22 0.002 NO 170.576 0.0000 NO
Card MK5-15 60.18 0.79 1.32 0.63 3.16 81 1.3318 1.7300 2.2286 2.05 0.0434 NO 139.974 0.0000 NO
Card MK5-16 60.09 0.7 1.16 0.71 3.52 88 1.1011 1.4500 1.8917 1.16 0.2488 Yes 118.304 0.0140 NO
Card MK5-17 60.04 0.99 1.65 0.51 2.98 81 1.4709 1.9700 2.5890 0.33 0.7428 Yes 217.566 0.0000 NO
Card MK5-18 59.98 0.76 1.26 0.66 3.45 83 1.1188 1.5000 1.9753 -0.24 0.8132 Yes 130.863 0.0000 NO
Card MK5-19 59.56 0.68 1.14 0.74 3.06 89 1.4263 1.7700 2.1926 -6.16 0.0000 NO 112.431 0.0410 NO
Card MK5-20 59.69 0.94 1.58 0.53 2.76 65 1.6833 2.1600 2.7507 -2.68 0.0094 NO 158.602 0.0000 NO
Card MK5-21 59.52 1.01 1.7 0.49 2.51 84 1.9423 2.4500 3.0845 -4.32 0.0000 NO 235.576 0.0000 NO
Card MK5-22 59.97 1.1 1.84 0.45 2.83 81 1.6342 2.1900 2.8825 -0.26 0.7925 Yes 271.193 0.0000 NO
YES = no significant difference between process value and target value; NO = there is significant difference between process
value and target value.
112 Muhammad Amin et al.
Conclusion
Different machines of card department in textile industry
were studied and different quality measuring tools were
used for checking the machines quality. Most of the
spinning quality analyst uses only the CV for measuring
the yarn quality. They say that the machine with minimum
CV has better quality. From the CV formula, it was
found that this is the ratio of process SD to the process
mean. CV does not show the target mean but just gives
the ratio between process standard deviation and mean.
Similarly, the Cp measurement only sees the process
standard deviation and not the process mean. From the
present results and discussion, it has come to the point
that smaller the value of QVM results in larger quality
level of yarn product and QVM is minimum where
sigma level is maximum. This indicates that better
quality of product is produced. QVM may also be preferred
over CV for its computational ease. QVM technique
seems to be so simple to use and can be considered as
good as other techniques, i.e. regression and design of
experiments, etc., in the industry to test the quality over
the time to get quick and reliable results.
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continuous processes: how and why it differs?.
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2nd edition, McGraw-Hill, New York, USA.
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Technology, Science and Economics, pp. 276-300,
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Control, pp. 202-203, 5th edition, John Wiley &
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packing defects using Six Sigma methods: A case
study. Quality Engineering, 18: 189-206.
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tivity Promotion, pp.18-23, Asian Productivity
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Japan.
113Quality Variation Minimizer for Textile Industry
Effect of Different Processing Stages on the Crystallinity
% and Tensile Strength of 100% Cotton Fabric
Zahid Hussaina, Muhammad Qamar Tusief b*, Sharjeel Abidc ,Muhammad Tauseef Khawera, Nabeel Amind and Mudassar Abbasd
aInterloop (Pvt) Limited Khurrianwala-Jaranwala Road, Faisalabad, Pakistan
bDepartment of Fibre & Textile Technology, University of Agriculture, Faisalabad, PakistancAdvance Textile Material Engineering, National Textile University, Faisalabad, Pakistan
dSchool of Textile and Design, University of Management and Technology Lahore, Pakistan
(received August 31, 2015; revised October 14, 2015; accepted October 15, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 114-117
Abstract. In this study, 100 % cotton fabric was used to check the impact on fabric crystallinity and tensile
strength at different processing stages. Desizing, scouring, bleaching, mercerization and resin (only resin
& resin+softener) application were the processes performed on the fabric. X-Ray diffractometer and tensile
strength tester were used to determine the crystallinity index (CI) and tensile strength, respectively. Results
revealed that from scouring to mercerization crystallinity (CI) decreased while desizing and resin application
treatments showed no significant impact on the crystallinity (CI). In case of tensile strength, a decreasing
trend from desizing to resin application was observed.
Keywords: cotton fabric, tensile strength, crystallinity index, X-ray diffraction
Introduction
Cotton is mostly used natural cellulosic fibre in textile
products. Cellulose has crystalline and amorphous
regions. In crystalline region, atoms are arranged in a
close packing. There exist three dimensional arrangements
due to hydrogen bonding between them. On the other
hand in amorphous region, atoms are randomly arranged
because of the absence of hydrogen bonding. Number
of sites (hydroxyl groups) available in amorphous region
governs the bonding or reactivity in suitable conditions
for modification in performance properties of cotton
fibre, while a material with greater crystallinity percentage
has greater strength and a big influence on hardness,
density, transparency and diffusion of the material e.g.
cotton fibre or fabric (Parikh, 2007).
During different chemical treatments on cotton fibre or
fabric, the available sites in the amorphous region react
and get modified. Such modifications have impact on
the crystallinity and strength of the cotton fibre or fabric.
Much research work has been carried out to measure
the crystallinity of cotton by using X-ray diffraction
method. The crystallite orientation of cotton cellulose
was analysed by using improved method of X-ray
diffraction spectrometer (Creely et al., 1956). In an
other research two types of cotton were treated with
anhydrous ethylamine, diethylamine, pyridine and
aqueous solutions of sodium hydroxide (30%) and
potassium hydroxide (40%) for 15 min to 50 h to check
the impact on moisture regain tensile, mechanical,
swelling and optical properties. It was concluded that
anhydrous ethylamine, NaOH and KOH reduced the
crystallinity significantly, while diethylamine and
pyridine did not produce any significant change in
crystallinity of cotton (Pandey and Iyengar, 1969a).
The chemical modification of cotton fibre was also
analysed by applying different concentrations of the
chemica and their impact on different properties of
cotton fibre were observed and concluded that ethylamine,
EDA, KOH and LiOH reduced the crystallinity of cotton
by increasing the concentration (Pandey and Iyengar,
1969b). Cotton fibre and fabric treated with butane-
tetracarboxylic acid (BTCA) with the catalyst sodium
hypophosphite at different concentrations. In the same
line the cotton fibre treated with BTCA reduced its
crystallinity and strength at different concentrations
(Xu, 2003). Similarly, the impact of crosslinking and
bleaching treatment on crystallinity of cotton fibres was
determined by using formaldehyde and dimethy-
loldihydroxy ethylene urea (DMDHEU) or citric acid
(CA) as cross linking agents for bleached cotton fabric
(Parikh, 2007). While in another study the cotton was
treated with neutralized vegetable oil base macro-
molecular crosslinkers to check the impact on cotton
crystallinity by using X-ray diffraction methodology
(Ford etal., 2010).*Author for correspondence; E-mail:[email protected]
114
All the above mentioned studies reported on one specific
process treatment (surface modification) not on the
complete processing stages (from grey fabric to finish)
to analyse the gradual decreasing or increasing trend
of crystallinity which directly relates with the strength
of cotton fabric. The aim of the present study is to
analyse the impact of desizing, scouring, bleaching,
mercerization followed by the resin application in two
ways (one is only resin, while the other is resin along
with softener) on the fabric crystallinity and tensile
strength.
Materials and Methods
For this study, 100 % cotton fabric of 110*110/ 60*60
was used and analysed in reference to crystallinity and
tensile strength by using X-ray diffractometer and tensile
strength tester. Lab scale jigger machine was used to
perform the experimental work on fabric. Five chemical
treatments were applied from desizing to resin
application. For desizing, a -amylase 5% on the weight
of fabric BEISOL SED (CHT), 2 mL/L BEIXON Q
(Biodegradable sequesting agent, CHT) and 2 g/L
FELOSAN FOX (universal detergent with high washing
efficiency, CHT) were used for desizing treatment at
90-95 °C for 30 min followed by one hot wash and then
cold wash.
In caustic scouring treatment, 20 g/L NaOH flakes
(analytical grade), 1 g/L HEPTOL BNF (sequesting
agent for strong alkaline treatment, CHT) and 2 g/L
FELOSAN FOX (universal detergent with high washing
efficiency, CHT) were applied at 90-95 °C for 60 min
followed by 10 min hot wash and then cold wash. After
this, 2 g/L acetic acid was used to neutralize the fabric.
In bleaching process, 6 g/L hydrogen peroxide 50%
(commercial grade by Sitara peroxide), 2 g/L NaOH
flakes, 5 g/LCONTAVAN ICE (stabilizer, CHT), 1 g/L
HAPTOL BNF and 3 g/L FELOSAN FOX were used
at 90 °C for 60 min followed by hot wash and then cold
wash followed by neutralization with 2 g/L acetic acid.
In mercerization process, fabric was treated with 30 %
solution of caustic soda was used for 60 sec under
tension and then hot washed. After this, 2 g/L acetic
acid used to neutralize the fabric followed by cold wash.
Two different methods were used for the resin application.
In first method (only resin application), 30 g/L Reaknit
EC ( modified DMDHEU, CHT) and 20 g/L magnesium
chloride (catalyst) were used at 5-6 pH at normal
temperature. Fabric was padded at 70 % pick up, dried
at 120 °C for 1 min and cured at 150 °C for three min.
In the second method (resin+ softener), 30 g/L Reaknit
EC (DMDHEU, CHT), 30 g/L Tubingal SMF-L
(microemulsion of silicone, CHT) and 20 g/L magnesium
chloride were used at 5-6 pH at normal temperature.
Fabric was padded at 70 % pick up, dried at 120 °C for
1 min and then cured at 150 °C for 3 min.
All the above mentioned chemicals were commercial
grade of CHT chemical company and sourced from a
reputed textile mills Khurrianwala, Faisalabad, Pakistan
Caustic soda, magnesium chloride and acetic acid were
used of analytic grade.
X-ray diffractometer (Model Xpert Pro Pan Analytical)
was used to determine the crystallinity index (CI). In
X-ray diffraction, X-rays beams were incident on the
sample diffracted a beam whose angular dependence
was measured by photomultiplier detector travelling
upon a goniometer in circle and provide a graph of
intensity (counts) of X-ray photons detected as a function
of 2q (the angle between the detector position and the
direction of incident beam) as shown in Fig. 1.
From the Fig. 2, crystallinity (CI) can be measured by
the formula.
Crystallinity %= [(Ic � Iam)/Ic]*100
where:
Ic = the intensity of the principal cellulose peak at 2q=
22.7° ,
Iam = the intensity of amorphous peak at 2q= 18°.
Tensile strength tester Lloyd of Ametek Company was
used to check the impact on warp wise and weft wise
strength of the cotton fabric after every process.
Results and Discussion
Grey fabric. The values related to the crystallinity and
tensile strength (warp wise and weft wise) of the grey
fabric (Sample 1) are mentioned in Table 1. These
results reveal a decreasing trend in the crystallinity and
tensile strength of the fabric.
Desized fabric. The effect of desizing process on fabric
in respect of its crystallinity and tensile strength was
determined. The results given in Table 1 and further
illustrated in Fig. 2-3 depict non significant impact on
crystallinity % of the fabric after desizing. But the
tensile strength of the fabric reduced from 478.05 N to
439.03 on warp wise and from 469.7N to 433.11N on
Proceeing Stages Effect on Cotton Fabric 115
weft wise. Size material applies on yarn level before
weaving to provide adequate strength for the next
process which helps during the weaving process. But
during desizing size material removed from the fabric
that affects the strength of the fabric. Both these factors
reduce the strength after desizing in fabric form.
Scoured fabric. Both crystallinity and tensile strength
decreased after caustic scouring as shown in Table 1
and Fig. 2-4. During this process, natural impurities of
cotton removed by the reaction of caustic soda at high
temperature. Chemical degradation and the effect of
elevated temperature reduced the crystallinity from
68.11% to 67.23% and tensile strength about 20N.
Bleached fabric. Significant decrease in crystallinity
and tensile strength was observed after the bleaching
process as shown in Table 1 and Fig. 2-4. During bleaching
process, natural and colour impurities removed from
the fabric. Hydrogen bonding from the crystalline region
breaks due to the chemical degradation of the fabric
which reduced the crystallinity from 67.23% to 62.47%
and also the tensile strength reduced significantly.
Mercerized fabric. During mercerization caustic soda
reacts with the cellulose and breaks down the hydrogen
bonding of the atoms in crystalline region of the cotton
fibre. This provides greater number of sites for reaction.
Ultimately the pick up of the cotton fabric enhanced
(El Badry et al., 2013). Due to this crystallinity of the
Table 1. Results of crystallinity(%) and tensile strength
(warp & weft wise)
Sample Process steps Crystallinity Tensile Tensile
No (%) strength (N) strength (N)
warp wise weft wise
1 Grey 68.32 478.05 469.7
2 Desize 68.11 439.03 433.11
3 Scoured 67.23 419.03 412.4
4 Bleached 62.47 362.71 353.56
5 Mercerized 61.56 366.89 352.89
6 A Resin 64.77 247.08 226.09
6 B Resin+Softener 62.05 279.36 254.6
70
%
68
Cry
sta
llin
ity 66
64
62
60
0 1 2 3 4 5 6 7 8 9
Process steps
Cryst allinity %
y = -1.1129x + 70.494
R² = 0.659
Crystallinity % linear (crystallinity %)
Fig. 2. Graphical explanation of crystallinity at
different processing steps.
Tensile strength (N) warp wise
600
500
400
300
200
100
0
1 2 3 4 5 6 7 8 9
(N)
str
ength
Te
nsile
y = -36.861x + 554.61 R² = 0.9009
Tensile strength (N) warp wise
Linear (tensile strength (N) warp wise)
Process steps
Fig. 3. Graphical explanation of warp wise tensile
strength (N) at different processing steps.
116 Muhammad Qameer Tusif et al.
Inte
nsity (
co
un
ts)
10
00
05
00
0
035 40 45 50 55
Position [ 2°q] {CuK-a}
X-raytube
Sample
Detector
2q
w
Fig. 1. Shows the working principle of X-Ray
diffractometer and graphical output.
fabric decreased slightly from 62.47% to 61.56% in the
mercerized sample while the warp wise strength of the
fabric increased due to parallelization of the fibre along
vertical axis under tension as shown in Table 1 and
Fig. 2-4.
Resin finished fabric. After the resin application
crystallinity % of the fabric increased significantly from
61.56 to 64.77 due to the cross linking of resin finish
(DMDHEU). This helps to enhance the bonding and
close packing in the crystalline region of the fibre. On
the other hand the tensile strength of the fabric reduced
while the stiffness of the fabric enhanced by the resin
application as shown in Table 1 and Fig. 2-4.
Resin+softener finished fabric. After the application
of resin with softener, a slight increase of crystallinity
from 61.56% to 62.05% was observed in the fabric.
Because the effectiveness of resin crosslinking reduced
by the combination of softener, crystallinity of the fabric
enhanced on less % age as compared to only resin
application as shown in Table 1 and Fig. 2. Fabric
(softner+resin) was also soft comparatively from the
resin padded fabric. It was due to the softness induced
by the micro-emulsion silicon based softener. Resin
with softener finish impact on tensile strength was also
less as compared to only resin padded fabric due to the
flexibility enhanced by softener and less fiber stiffness
as shown in Table 1 and Fig. 3-4.
Conclusion
It is concluded from the above study that, 100% cotton
fabric significantly lose its degree of crystallinity
(crystallinity %) during caustic scouring, bleaching and
mercerization due to chemical degradation and elevated
temperature impact, while desizing has no significant
effect on crystallinity % (CI). But the resin application
increases the crystallinity % age due to the crosslinking
and bonding in the cotton fabric structure. Tensile
strength decreases during all the processing stages from
desizing to resin finish treatment. But scouring, bleaching
and resin application significantly reduces the tensile
strength of the fabric.
References
Creely, J. J., Sega, L. l., Ziifle, H. M. 1956. Determination
of the degree of crystallite orientation in cotton
fibers by means of the recording X-Ray diffraction
spectrometer. Textile Research Journal, 28: 789-
795.
El Badry, K., Saleh, S.M., Bahlood, S. O. 2013. effect
of mercerization techniques on cotton towels
properties. Journal of Applied Sciences Research.,
9: 2386.
Ford, E. N. J., Mendon, S. K., Thames, S. F., Rawlins,
J. W. 2010. X-ray diffraction of cotton treated with
neutralized vegetable oil-based macromolecular
crosslinkers. Journal of Engineered Fibres and
Fabrics, 5: 10-20.
Pandey, S. N., Iyengar, R. L. N. 1969a. Studies of
chemically modified cotton. Part I: Effect of
chemical treatments for varying periods on
crystallinity and certain other properties of cotton.
Textile Research. Journal, 39: 15-23.
Pandey, S. N., Iyengar, R. L. N. 1969b. Studies on
chemically modified cotton. Part II: Effect of
different concentrations of chemicals on crystallinity
and certain other properties of cotton. Textile
Research Journal, 39: 24-31.
Parikh, D.V. 2007. X-ray crystallinity of bleached and
crosslinked cottons.Textile Research Journal, 77:
612-616.
Xu, W. 2003. Effect of crosslinking treatment on the
crystallinity, crystallite size, and strength of cotton
fibres.Textile Research Journal, 73: 433-436.
1 2 3 4 5 6 7 8 9
Process steps
(N)
sT
ren
gth
Te
nsile
Tensile strength (N) weft wise
y = -39.959x + 557.27 R² = 0.9085
Tensile strength (N) weft wise
Linear ( tensile strength (N) weft wise)
600
500
400
300
200
100
0
Fig. 4. Graphical explanation of weft wise tensile
strength (N) at different processing steps.
Proceeing Stages Effect on Cotton Fabric 117
Contamination of toxic metals such as copper, lead,
zinc, nickel and chromium in the aquatic environment
is a matter of attention as studied by Kaewsarn and Yu
(2001). Heavy metal contamination may cause serious
health problems such as cancer and brain damage
(Mukhopadhyay, 2008).
The presence of nickel ions in surface water is a problem
of increasing importance in Nigeria.
The permissible limit of nickel according to World
Health Organization (WHO) in drinking water is 1 mg/L
as reported by Nemerow (1963). Current developed
methods for solving water contaminated related problems
include filtration, ion exchange, membrane separation,
nutrient stripping and adsorption. The adsorption
technology (biosorption) which utilizes natural biomass
materials is very effective for the detoxification of
metal-bearing industrial effluents.
Water hyacinth (Eichhornia crassipes) is known as one
of the �world�s worst aquatic weeds� (Malik, 2007). On
the other hand, it appears to be a valuable material with
a remediation property. Therefore, E. crassipes biomass
was used in this study to remove Ni (II) ion from
aqueous solution under isothermal condition and
isotherm model equations were used to analyse the
equilibrium data.
The leaves of water hyacinth were collected from Choba
River, Choba community in Obio-Akpor local government
area of Rivers state, Nigeria. The water hyacinth biomass
was sun-dried for two days. The biosorbent was prepared
by washing it with 0.1M HCl (to convert alignates to
alignic acid) and then rinsed with deionized water. The
leaves were then dried further in an oven for 24 h until
the leaves became crisp. After drying, the leaves were
ground by a manual grinder, to a constant size of
150 mm.
The nickel stock solution (1000 mg/L) was prepared
using analytical grade of CH3COOONi.4H2O and test
solution was prepared by dilution to the desired
concentrations. The biosorption study was carried out
by adopting a column reactor system under isothermal
condition. The column experiment was performed in a
packed bed consisting of a cotton wool and the biomass
with inner diameter of 30 mm and length of 500 mm.
The water hyacinth leaves powder (1 g) was used to
study the effect of pH, contact time and concentration
at 298 K. The supernatant obtained was analysed using
atomic absorption spectrophotometer (AAS). The
amount of metal ion biosorbed per gram of the biomass
qe was calculated using the equation below.
where:
qe = the amount of metal ion biosorbed per gram of the
biomass in mg/g
Ci = the initial concentration of the metal ion in mg/L,
Biosorption Characteristics of Water Hyacinth (Eichhornia crassipes)
in the Removal of Nickel (II) Ion under Isothermal Condition
Chidi Obi* and Sylvester EigbiremonlenPhysical Chemistry Unit, Department of Pure and Industrial Chemistry, University of Port Harcourt, P.M.B. 5323,
Port Harcourt, Rivers State, Nigeria
(received March 30, 2015; revised August 14, 2015; accepted August 19, 2015)
Pak. j. sci. ind. res. Ser. A: phys. sci. 2016 59(2) 118-120
Abstract. This study was taken to investigate the potentiality of water hyacinth (Eichhornia crassipes)
as an alternative biosorbent for the removal of Ni (II) ion from aqueous solution. The optimum pH, contact
time and concentration were found to be 6.0, 40 min and 1.0 mg/L under isothermal condition. The
biosorption of Ni (II) ion was found to decrease with increasing pH, initial concentration and contact time.
Results obtained were analysed with Langmuir and Freundlich biosorption models. The equilibrium data
fitted well to the Langmuir biosorption model with correlation coefficient (R2) value of 0.98. The monolayer
adsorption capacity was 0.29 mg/g. The removal of Ni (II) ion from aqueous solution using water hyacinth
biomass followed a monolayer biosorption.
Keywords: monolayer biosorption, water hyacinth, nickel removal
*Author for correspondence; E-mail: [email protected]
Short Communication
118
Ci-Ce 50qe =
´
M 1000
thereby reducing the rate of Ni (II) ion uptake (Turp
et al., 2011). Therefore, the optimum biosorption time
of 40 min was obtained.
The initial ion concentration serves as an important
driving force for overcoming mass transfer resistance
of Ni (II) ion between the aqueous and solid phases
(Pandey et al., 2007). The effect of different initial Ni
(II) concentrations on the water hyacinth biosorption
capacity is shown in Fig. 3. The biosorption of Ni (II)
ion showed a concentration dependency. The rate of
biosorption increased as the concentration of Ni (II) ion
increased from 20 - 60 mg/L. It was observed that Ni
(II) ion uptake decreased above 60 mg/L. This could
be as a result of the concentration gradient established
between Ni (II) ion and the biomass surface charges
due to precipitation. This observation is similar to the
research of Tsai and Chen (2010).
Ce = the equilibrium concentration of the metal ion in
mg/L
M = the mass of the biomass in g; 50 = the volume of
the metal ion in milliliters; 1000 = the conversion factor
to liters. The experiment was performed in triplicate
and the mean values taken for each parameter.
The effect of pH on the biosorption capacity is shown
in Fig. 1. The biosorption capacity of Ni (II) ion increased
initially and then became constant with increasing pH.
A pronounced dependence of Ni (II) ion biosorption on
the solution pH was observed (Abdullah and Devi
Prasad, 2010). This biosorption behaviuor at low pH
showed competition between Ni (II) ion and the net
positive charge on the biosorbent surface thereby
lowering the rate of uptake. However, as the pH
increased, the rate of biosorption increased and optimum
biosorption was achieved at pH6. This could be as a
result of the unsaturation of the metal binding sites on
the surface of the biomass indicating weak chemical
interaction between Ni (II) ion and the biomass surface
charges. At pH above 6, the biosorption of Ni (II) ion
decreased. At this point, precipitation of nickel (II)
hydroxide set in leading to a decrease in the rate of Ni
(II) ion uptake (Wang and Xing, 2002).
The effect of contact time on the biosorption of Ni (II)
ion using water hyacinth biomass is shown in Fig. 2.
The result obtained showed that there was an initial fast
uptake of Ni (II) ion followed by a slow and constant
biosorption. The increase in the rate of biosorption
within 10 - 40 min could be due to unsaturation of the
active sites of the biomass and at higher time,
precipitation of the nickel (II) hydroxide took place
Fig. 3. Effect of concentration on Ni (II) ion biosorption
using water hyacinth biomass at 298 K.
1.2
1
0.8
0.6
0.4
0.2
0
q
(mg
/g)
e
0 20 40 60 80 100 160
Ci (mg/L)
Fig. 2. Effect of contact time on Ni (II) ion biosorption
using water hyacinth biomass at 298 K.
1.2
1
0.8
0.6
0.4
0.2
0
q
(mg
/g)
e
Time (Mins)
0 10 20 30 40 50 60
1.2
1
0.8
0.6
0.4
0.2
0
q
(mg
/g)
e
0 2 4 6 8 10 12
pH
Fig. 1. Effect of pH on the biosorption of Ni (II)
ion using water hyacinth biomass at 298 K.
119Short Communication: Water Hyacinth for Nickel (II) Removal
The plot of Langmuir as the best model equation is
represented in Fig. 4. However, Langmuir isotherm
constants were determined from a plot of Ce/qe against
Ce as shown in Table 1 (Akbal and Camci, 2011). The
isotherm correlation coefficient (R2) of Langmuir was
0.98 indicating a physical type of biosorption with
monolayer capacity of 0.29 mg/g. The result of the
biosorpion capacity obtained was greater than the work
done by Hassan et al. (2010).
water ways and aquatic lives can be transformed into
a useful source for pollution control.
References
Abdullah, M.A., Devi Prasad, A.G. 2010. Biosorption
of nickel (II) from aqueous solutions and electroplating
wastewater using tamarind (Tamarindus indica L.)
bark. Australian Journal of Basic and Applied
Sciences, 4: 3591-3601.
Akbal, F., Camci, S. 2011. Copper, chromium and nickel
removal from metal plating wastewater by electro-
coagulation. Desalination, 269: 214-222.
Hasan, S.H., Ranjan, D., Talat, M. 2010. Water hyacinth
biomass (WHB) for the biosorption of hexavalent
chromium: Optimization of process parameters.
Bio Resources, 5: 563-575.
Kaewsarn, P., Yu, Q. 2001. Cadmium (II) removal from
aqueous solutions by pre-treated biomass of
marine alga Padina Sp. Environmental Pollution,
112 : 209-213.
Malik, A. 2007. Environmental challenge vis a vis
opportunity: The case of water hyacinth. Environment
International, 33: 122-138.
Mukhopadhyay, M. 2008. Role of surface properties
during biosorption of copper by pretreated
Aspergillus niger biomass. Colloids and Surfaces
A: Physicochemical and Engineering Aspects,
329: 95-99.
Nemerow, N.L. 1963. Theories and Practices of
Industrial Waste Treatment. 557 pp., Addison-Wesley
Pub. Co. Inc., Reading, Massachusettes, USA.
Pandey, P.K., Choubey, S., Verma, Y., Pandey, M.,
Kamal, S.S.K., Chandrashekhar, K. 2007. Biosorptive
removal of Ni (II) from wastewater and industrial
effluent. International Journal of Environmental
Research and Public Health, 4: 332-339.
Tsai, W.T., Chen, H.R. 2010. Removal of malachite
green from aqueous solution using low-cost
Chlorella based biomass. Journal of Hazardous
Materials, 175: 844-849.
Turp, S.M., Eren, B., Ates, A. 2011. Prediction of
adsorption efficiency for the removal of nickel (II)
ions by zeolite using artificial neural network
(ANN) approach. Fresenius Environmental Bulletin,
20: 3158-3165.
Wang, K., Xing, B. 2002. Adsorption and desorption
of cadmium by goethite pretreated with phosphate.
Chemosphere, 48: 665-670.
1.2
10
8
6
4
2
0
C
/qe
e
0 0.5 1.5 2 2.5 3.51 3Ce
y=3.445x+0.201R =0.9752
Fig. 4. A plot showing Langmuir adsorption model
of Ni (II) ion using water hyacinth biomass.
Table 1. The Langmuir parameters
Model equation Parameters water hyacinth
Langmuir KL 0.06
Qmax (mg/g) 0.29
R2 0.98
The biosorption process was dependent on the pH of
the aqueous solution, contact time and concentration
of Ni (II) ion in the solution. The optimum contact time
and pH of 40 min and 6 were obtained for the biosorption
process. The equilibrium data obtained fitted well to
Langmuir adsorption model equation with a linear
correlation coefficient (R2) of 0.98 indicating a
monolayer type of adsorption.
However, the results obtained from the column reactor
system under isothermal condition have shown that
water hyacinth leaves which is termed as nuisance to
120 Chidi Obi and Sylvester Eigbiremonlen
