Received: 4 June 2020
|
Revised: 4 July 2020
|
Accepted: 17 July 2020
DOI: 10.1111/jfpp.14813
ORIGINAL ARTICLE
Plum (Prunus domestica L.) leaves extract as a natural
antioxidant: Extraction process optimization and sunflower oil
oxidative stability evaluation
Nesren Elsayed
| Karima Said Mohamed Hammad
Department of Food Science, Faculty of
Agriculture, Cairo University, Egypt
Correspondence
Ekram Abd El-Salam Abd El-Salam,
Department of Food Science, Faculty of
Agriculture, Cairo University, Giza 12613,
Egypt.
Email: eaam2000@agr.cu.edu.eg
| Ekram Abd El-Salam Abd El-Salam
Abstract
The current work aimed to maximize the yield of total phenolic content (TPC) and associated antioxidant activity of plum leaf extract. The feasibility of using the optimal
extract as a natural antioxidant for retarding the oxidation process in sunflower oil
using rancimat method was also investigated. One-factor-at-a-time (OFAT) method
and 32 full factorial design were implemented in sequence to optimize the studied
factors (extraction time [ET] (10–60 min) and ethanol percentage [EtOH%] (0%–
75%)). DPPH scavenging ability of the obtained extracts were positively (r ≥ 0.7493)
and significantly (p < .0001) correlated to TPC yield. The optimal conditions for extraction plum leaves were 64.37% EtOH% and 60 min with desirability of 0.856. The
optimal plum leaves extract was subjected to HPLC analysis. At the same concentration (200 ppm), the protection factor of BHT and optimal plum leaves extract against
sunflower oxidation were insignificantly (p > .05) differed.
Practical applications
Plum (Prunus domestica L.) leaves are characterized by a high content of phenolic
compounds which varied between 66.50 to 143.7 mg GAE/g powder. Plum leaf extracts exhibited high scavenging activity against DPPH radicals. The protection factor of the freeze-dried optimal leaves extract was insignificantly differed than that
of BHT at the concentration of 200 ppm. These findings indicate that plum leaves
extract could efficiently retard the oxidation process of edible oils; and consequently,
improve their quality and extend their shelf life.
1 | I NTRO D U C TI O N
attributed to their phenolic compounds content (Moure et al., 2001;
Pokorný, 2007).
Plant leaves are a great source of bioactive materials that are uti-
Phenolic compounds present in all plants and comprise a group
lized in various food applications due to their functional properties
of greater than 8,000 identified compounds. These molecules are
(Bernhoft, 2010). In this context, the antiradical activities of leaf
considered as a secondary metabolite in the plant. They contribute
extract of several plants such as olive leaves (Andrikopoulos, Salta,
to plant growth, pollination; and protection against ultraviolet radia-
Mylona, Chiou, & Boskou, 2007; Farag, Mahmoud, & Basuny, 2007),
tion, pathogens, and environmental stresses. Polyphenols molecules
murta leaves (Rubilar et al., 2006, 2012), Ginkgo leaves (Kobus
are characterized by the existence of hydroxyl groups that are at-
et al., 2009), and thyme leaves (Beddows, Jagait, & Kelly, 2000)
tached to a benzene ring. The number of phenolic hydroxyl groups as
were evaluated and their abilities to retard edible oils oxidation
well as their location determine the antioxidant activity of phenolic
were determined. The antioxidant activity of plant leaf extracts was
compounds (Serra, Almeida, & Dinis, 2018).
J Food Process Preserv. 2020;00:e14813.
https://doi.org/10.1111/jfpp.14813
wileyonlinelibrary.com/journal/jfpp
© 2020 Wiley Periodicals LLC.
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ELSAYED Et AL.
Fatty acids composition in addition to the antioxidants that are nat-
and stirred for 10–60 min using a benchtop lab stirrer (Heidolph,
urally found in edible oils affect their oxidative stability, which is an
Germany) at maximum speed. All extraction experiments were con-
important parameter to evaluate the shelf life and quality of oils during
ducted at room temperature and repeated three times. The extract
processing and storage (Abril et al., 2019). Polyphenols are natural anti-
was filtered and kept at 8°C for further analysis. The significant ef-
oxidants that have a valuable role for retarding unsaturated fatty acids
fects of studied factors (extraction time [ET] and ethanol percent-
oxidation and provide an efficient protection against oxidative stress in
age [EtOH%]) were estimated using One-way ANOVA followed by
the human body (Farhoosh, Khodaparast, & Sharif, 2009).
Tukey's test at p < .05. Degree of association between variables
Plum (Prunus domestica L.) is a deciduous tree and belongs to the
was calculated as Pearson correlation coefficient using XLSTAT
Rosaceae family. There are more than 2,000 varieties of plum and the
2014.5.03 software (Addinsoft, USA). The three levels of studied
major species are found in Europe and Australia (Mocan et al., 2018).
factors that significantly resulted in the highest polyphenol yield
Several studies have been conducted to investigate the total phe-
and radical scavenging activity were estimated and used for further
nolic content of plum leaves and its associated antioxidant activity
experimental design.
(Gougoulias, 2015; Mocan et al., 2018). However, based on our knowledge, there is no single study has been conducted to optimize the ex-
Full factorial design (FFD)
traction process of plum leaves and evaluate the ability of the obtained
RSM using 32FFD was further implemented to maximize the yield
extract to extend oxidative stability of edible oils. Therefore, response
of extracted polyphenols and its corresponding antioxidant activity.
surface methodology (RSM) using 32 full factorial design (FFD) was
The chosen three levels of ET (X1) an EtOH% (X 2) that result in the
implemented to maximize the TPC yield of plum leaves extract and
highest polyphenol yield and radical scavenging activity (Figure 1)
its scavenging activity against DPPH radicals. Furthermore, rancimat
were 40, 50, and 60 min and 25%, 50%, and 75%, respectively.
method was used to investigate the ability of optimal plum leaves ex-
Experimental data were subjected to Browne–Forsythe and One-
tract to extend the oxidative stability of sunflower oil.
way ANOVA test with post hoc Tukey's test (p < .05) to assess responses variance homogeneity and significant effects among various
treatments (combinations), respectively. Design-Expert version 11
2 | M ATE R I A L S A N D M E TH O DS
(Stat-Ease, Inc., USA) was used to implement 32 FFD. Reduced cubic
model including linear, squared, and interaction terms (Equation 1)
2.1 | Materials
2.1.1 | Plant material and chemicals
was used to fit experimental data.
Y = b0 +
k
∑
j=1
b j Xj +
k
∑
j=1
b𝑗𝑗 X2j +
k
∑
b𝑖𝑗 Xi Xj +
i<j
k
∑
b𝑖𝑗 X2i Xj +
i<j
k
∑
b𝑖𝑗 Xi X2j + ei
i<j
(1)
Leaves (5 months old) of plum tree (Prunus domestica L. SSP Hollywood)
where, Y is the predicted response; b0 is the intercept; bj, bjj, and bij
were detached from 5 years old trees grafted on “Marianna” plum
are coefficients of linear, quadratic, and interaction effects of coded
root stock and grown in sandy soil at the Faculty of Agriculture farm
independent variables (Xi and Xj ), respectively; and ei is the error.
[Latitude 30° 1′ 38.0208″ (N), Longitude 31° 11′ 39.5628″(E)], Cairo
Statistical significance of the model and their various terms were de-
University, Egypt in the summer of 2019. Plum trees were received the
termined using analysis of variance (ANOVA). Lack of fit test in ad-
normal agriculture practices and cultivated under flood irrigation sys-
dition to R 2, adjusted R 2 and predicted R 2 values were used to check
tem. Plum leaves were identified by Dr. Ibrahim Hmmam, Pomology
the adequacy of generated models.
Department, Faculty of Agriculture, Cairo University. The leaves were
dried at 40°C in a forced air oven (Shel-lab, USA) until constant weight,
then they were grinded with analytical mill (Cole-Parmer, USA), sieved
2.2.2 | Total phenolic compounds
up to 50 mesh and stored in a dark place at room temperature till
analysis. Free antioxidant sunflower oil was obtained from Cairo Oil
Folin–Ciocâlteu assay (Hosseini, Bolourian, Yaghoubi Hamgini, &
and Soap Company (Egypt). Folin–Ciocalteu reagent, Gallic acid, and
Ghanuni Mahababadi, 2018) was used to determine Total phenolic
2,2-diphenyl-1-picrylhydrazyl (DPPH) were purchased from Sigma
compounds (TPC) of dried plum leaves extract and expressed as mg
Chemical Co., Ltd (St. Louis, MO, USA).
Gallic acid equivalents (GAE)/g powder.
2.2 | Methods
2.2.3 | Determination of antioxidant activity
2.2.1 | Experimental design and statistical analysis
The antiradical activity of the obtained extracts against DPPH
radicals was determined according to the methodology described
Preliminary extraction experiments
by Fang et al. (2014). DPPH inhibition percentage (DPPH IN%)
To extract polyphenols, 5 gm Prunus domestica dried leaves pow-
was Spectrophotometrically (Unico UV-2000, USA) determined at
der were added to 100 ml aqueous ethanol solution (0%–75%)
517 nm and calculated using Equation (2):
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F I G U R E 1 Effect of extraction time and ethanol percentage on total polyphenols content [TPC (mg GAE/g dried powder] of plum leaves
and its associated DPPH radical scavenging activity [DPPH IN%]
DPPH IN% =
(
A517nm of DPPH solution − A517nm of DPPH and extract solution
A517nm of DPPH solution
)
× 100
(2)
2.2.4 | HPLC analysis
2.2.5 | Physical and chemical properties of
sunflower oil
Refractive index, acid value, and peroxide value of sunflower oil
were determined according to AOCS official methods (AOCS, 2009).
The optimal plum leaves extract was analyzed using an Agilent
1260 series HPLC system (Agilent technologies Inc. CA, USA).
2.2.6 | GC analysis of fatty acids
The separation was carried out using C18 column (100 mm ×
4.6 mm i.d., 5 μm). The mobile phase consisted of (A) water 0.2%
Fatty acids methyl esters (FAMEs) preparation
H3PO 4, (B) methanol, and (C) acetonitrile at a flow rate 0.6 ml/min.
The FAMEs were prepared using a cold saponification method ac-
Gradient elution was according to the following scheme: 0–11 min
cording to ISO standard No. 12966-2 (ISO, 2011).
(96% A, 2% B); 11–13 min (50% A, 25% B); 13–17 min (40% A, 30%
B); 17–20.5 min (50% B, 50% C), and 20.5–30 min (96% A, 2% B).
Identification of FAMEs
Detection wavelength was set at 284 nm. The injection volume
The FAMEs were analyzed by an Agilent 6,890 series gas chro-
was 20 μl and the column temperature was maintained at 30°C.
matography equipped by a flam ionization detector and DB23
Compounds were identified by comparing their retention time
(60 m × 0.32 mm × 0.25 µm) capillary column (Agilent technologies
with those from authentic standards. Calibration curves were
Inc. CA, USA). The carrier gas was N2 at a flow rate of 1.6 ml/min and
used to calculate the compound amounts.
split ratio of 50:1. The column oven temperature was programed at
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initial temperature of 150°C for 1 min; rising at 10°C/min to 170°C
for TPC of quince and cranberry leaves, which were found to be
and held for 5 min; rising at 5°C/min to 220°C and held for 3 min.
varied from 89.57 to 175.36 and from 89.81 to 127.64 mg GAE/g
The injector and detector temperature were set at 250 and 270°C,
dry matter, respectively (Teleszko & Wojdyło, 2015). Also, TPC of
respectively. Gases flow rates for the detector were 450, 40, and
black tea was ranged between 80.5 to 134.9 mg GAE/g dry mat-
25 ml/min for air, H2, and N2, respectively. Fatty acid standards were
ter (Khokhar & Magnusdottir, 2002). Moreover, Gougoulias (2015)
used to identify the peaks.
found that TPC of plum leaves was 9.381 mg GAE/g dry matter,
which is lower than our results. These variations in TPC could be
attributed to several factors, such as plum leaves variety, climate,
2.2.7 | Rancimat test
cultivation conditions, harvesting time, extraction methods, and
solvent type (Brahmi, Mechri, Dhibi, & Hammami, 2013; Nashwa
The optimal plum leaves extract was first concentrated using ro-
& Abdel-Aziz, 2014).
tary evaporator (EYELA rotary evaporator N-1000, Japan) at 40°C
Several analytical methods have been adopted to evaluate the
and then, freeze-dried (Edward freeze dryer (3,983), England). The
antioxidant activity of different materials. Among these analytical
lyophilized extract at concentration of 100, 200, and 400 ppm
methods, DPPH method is extensively used to evaluate in vitro scav-
GAE and BHT at 200 ppm were individually added to a free anti-
enging ability of plant extracts against free DPPH radicals due to its
oxidant sunflower oil. The tested oil samples (2.5 g) were placed in
efficiency, simplicity, and cheapness (Kandi & Charles, 2019). DPPH
rancimat tubes (Metrohm's 743, Switzerland), which were exposed
IN% was increased from 60.07 ± 0.26 to 80.51 ± 0.89% as ET in-
to air flow rate of 20 L/h and elevated temperature of 110°C. The
creased from 10 to 40 min, respectively (Figure 1b); however, further
organic acids, which were produced as a result of thermal de-
increase in ET significantly decreased DPPH IN% with low decre-
composition of oil, were absorbed in deionized water filling the
ment to 77.01 ± 1.41%. The correlation between TPC and DPPH
measuring vessel. Conductivity measuring cell was used to con-
IN% of the tested extracts was significant (p < .001) and positive
tinuously monitor the formed organic acids. The induction periods
(r = 0.7853). Tohidi, Rahimmalek, and Arzani (2017) reported that
of different samples were used to calculate the protection factor
polyphenols have a capability to scavenge reactive oxygen interme-
using Equation (3)
diate compounds without any further support of oxidative reactions.
Protection factor =
induction period of sample with antioxidant
induction period of sample without antioxidant (control)
3.1.2 | Effect of ethanol percentage (EtOH%)
(3)
At ET of 50 min, Data illustrated in Figure 1c show that increas-
3 | R E S U LT S A N D D I S CU S S I O N
ing EtOH% to 50% significantly (p < .05) increased TPC yield to
119.12 ± 1.75 mg GAE/g powder; however, further increase in
3.1 | Preliminary extraction experiments
EtOH% insignificantly (p > .05) decreased the yield of TPC to
116.23 ± 1.53 mg GAE/g powder. Similar trend was observed during
Preliminary extraction experiments were conducted to investigate
extraction polyphenols from Myrtuscommunis L. leaves using differ-
the individual effect of studied factors (ET and EtOH%) on the yield
ent EtOH% (Dahmoune, Nayak, Moussi, Remini, & Madani, 2015).
of polyphenols and its corresponding antioxidant activity. Thus,
The reason behind this decrement in TPC yield at higher EtOH%
through all preliminary experiments only one factor was changed
could be attributed to the binding affinity of polyphenols for pro-
while the other extraction conditions were kept constant. The ob-
tein (Papadopoulou & Frazier, 2004). Higher EtOH% induce protein
tained results are outlined in Figure 1.
denaturation which in turn decreased the dissolution of polyphenols leading to low TPC yield (Dahmoune et al., 2015). Furthermore,
the low TPC yield at high EtOH% might be ascribed to the effect of
3.1.1 | Effect of extraction time (ET)
ethanol on barrier properties of the plant cell membrane (Frontuto
et al., 2019).
Different ET (10–60 min) were used to extract polyphenols
Moreover, the highest TPC yield at EtOH% of 50% could be
from dried plum leaves powder at EtOH% of 50% (Figure 1a).
returned to the polarity of ethanol water mixture. The polarity of
The highest and lowest yields of TPC were 119.40 ± 1.50 and
ethanol is lower than that of water. Thus, adding water to etha-
94.63 ± 4.72 mg GAE/g powder which were obtained at ET of
nol increases its polarity regarding the proportion of added water.
60 and 10 min, respectively. In general, it could be noted that
Polyphenols are polar compounds, which are more soluble in eth-
extending ET increased TPC yield until it reached equilibrium at
anol concentration of 50% (high polarity) other than high ethanol
50 min and there was no change in TPC yield after that. Similar
concentration of 75% (low polarity) according to the principle of “like
behavior was observed during extraction polyphenols from olive
dissolve like” (Dahmoune et al., 2015; Zhang et al., 2007).
leaves (Mkaouar, Gelicus, Bahloul, Allaf, & Kechaou, 2016). The
Similar to TPC recovery pattern, the DPPH IN% of the obtained
obtained results are consistent with previously reported data
extracts was significantly (p < .05) increased as EtOH% increased to
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YTPC(mg GAE∕g powder) = 119.10 + 2.32X1 + 15.52X2 + 6.25X1 X2 − 1.98X21
50% and significantly decreased after that. The correlation between
TPC and DPPH IN% of the tested extracts was significant (p < .001)
−18.35X22 + 11.68X12 X2 + 9.02X1 X22
and positive (r = 0.9638).
(4)
YDPPHIN% = 75.95 − 1.75X1 + 6.27X2 + 0.524X1 X2
3.2 | Full factorial design
+3.34X21 − 13.40X22 − 0.563X21 X2 + 1.63X1 X22
(5)
Three level FFD was found to be more adequate to fit experi-
Multiple regression analysis in addition to analysis of variance
mental data comparing with other experimental designs (Rakić,
(ANOVA) was implemented to assess the adequacy of the obtained
Kasagić-Vujanović, Jovanović, Jančić-Stojanović, & Ivanović, 2014).
models to represent the variability of response variables (Table 2).
Therefore, in the present study three level FFD was chosen. Mean
Low probability values (p < .0001) of the obtained models indicate
values of the actual and predicated values of TPC yield of plum
their high significances. Despite, regression coefficients of X1 X2 , X21 X2
leaves extracts and their DPPH IN% are listed in Table 1. Probability
and X1 X22 for DPPH IN% which were insignificant (p > .05), all other
values of Brown–Forsythe's test (p ≥ .2486) and one-way ANOVA
regression coefficients were significant (p ≤ .0320). Adjusted R 2
(p < .0001) indicate that all dependent variables are homoscedas-
values of the obtained models were 0.9921 and 0.9647 for TPC
tic and significantly differed. Implementing the preceding tests
and DPPH IN% and their lack of fit were insignificant (p = .9368
are essential for performing FFD (Granato, de Araújo Calado, &
and 0.0800, respectively), which show the ability of the obtained
Jarvis, 2014).
models to explain 99.21% and 96.47% of the variance, respectively
TPC yield of various extracts were significantly (p < .0001)
(Pedro, Granato, & Rosso, 2016). According to Maran, Priya, and
varied between 66.50 ± 0.10 mg GAE/g powder (run 1) and
Manikandan (2014), the desirable value of signal-to-noise ratio (ad-
143.57 ± 2.52 mg GAE/g powder (run 9), while the DPPH IN% were
equate precision) is greater than 4. In the present study, adequate
significantly (p < .0001) varied between 55.74 ± 0.77% (run 4) and
precision values were greater than 28 which indicate the adequacy
80.51 ± 0.90% (run 2). The correlation between TPC and DPPH IN%
of the signals. CV% values of the obtained models were lower than
under applied extraction conditions was significant (p < .0001) and
2.3 which indicates high degree of experimental values precisions
positive (r = 0.7493).
and adequately of the obtained models (Maran et al., 2014).
Results of fitting experimental data using various models are
outlined in Table 2. Despite the cubic model which was confounding
or aliased, the reduced cubic model was the most suitable model to
3.2.1 | Effect of extraction process variables
fit experimental data. As, the reduced cubic model was significant
(p < .0001), lack of fit was insignificant (p ≥ .0800) and it has the
Equations (4) and (5) were used to draw 3D plots (Figure 2c,d) which
highest values of R2, adjusted R 2 and predicted R 2.
were used in addition to perturbation plots (Figure 2a,b) to illustrate
In terms of coded factors, reduced cubic models (Equations 4 and
5) were adopted to the following forms.
the interactive and individual effects of studied factors on response
variables. Perturbation plots were drawn at midpoint of studied
TA B L E 1 Full factorial design (FFD), experimental means, and predicted values of plum leaves extract total polyphenols content (TPC)
and their radical scavenging activity against DPPH
Run
Extraction time (min) X1
Ethanol conc.
(%) X2
TPC yield (mg GAE/gm powder)
DPPH inhibition %
Experimental
Experimental
Predicted
f
c
Predicted
1
40
25
66.50 ± 0.10
66.48
61.09 ± 0.69
60.82
2
40
50
114.77b ± 5.01
114.80
80.51a ± 0.90
81.04
c
b
3
40
75
108.40 ± 1.50
108.38
71.46 ± 0.38
71.19
4
50
25
85.20 d ± 0.60
85.23
55.74d ± 0.77
56.28
b
a
5
50
50
119.17 ± 1.75
119.10
77.01 ± 2.52
75.95
6
50
75
116.23b ± 1.53
116.27
68.29b ± 1.22
68.82
e
7
60
25
76.67 ± 0.42
76.65
8
60
50
119.40 b ± 1.50
119.43
75
a
9
60
143.57 ± 2.52
P (Browne–Forsythe)
P (ANOVA)
Note: Values are expressed as means ± standard deviations of three replicates.
Different letters in the same column indicate significant differences at p < .05.
143.55
59.80
cd
± 2.40
59.54
77.01a ± 1.41
77.54
b
72.27 ± 1.53
0.2486
0.9298
<0.0001
<0.0001
72.01
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TA B L E 2 Adequacy of the tested models and ANOVA analysis of the reduced cubic-order models in addition to their statistical
parameters
Std. Dev.
R2
Adjusted R2
Predicted R2
PRESS
Prob < F
Prob (Lack
of Fit)
Linear
12.02
0.7607
0.7408
0.6997
4,351.53
<0.0001
<0.0001
2FI
11.42
0.7931
0.7661
0.7257
3,975.40
0.0706
<0.0001
Model
TPC
Quadratic
6.74
0.9341
0.9184
0.8931
1549.95
<0.0001
<0.0001
Reduced Cubic
2.10
0.9942
0.9921
0.9884
168.81
<0.0001
0.9368
2.10
0.9942
0.9921
0.9884
168.81
<0.0001
0.9368
*
Cubic
DPPH IN%
Linear
7.09
0.3443
0.2896
0.2056
1,462.62
0.0063
<0.0001
2FI
7.24
0.3461
0.2608
0.1848
1,500.86
0.8041
<0.0001
Quadratic
1.68
0.9677
0.9601
0.9470
97.53
<0.0001
0.0611
Reduced Cubic
1.58
0.9742
0.9647
0.9507
90.76
<0.0001
0.0800
1.58
0.9742
0.9647
0.9507
90.76
0.1194
0.0800
*
Cubic
ANOVA analysis and statistical parameters of reduced cubic-order models
Source
Model
DPPH IN%
TPC
RC
SS
p value
RC
S
p value
119.10
14,408.90
<.0001
75.95
1793.63
<.0001
X1
2.32
32.20
.0141
−1.75
18.36
.0139
X2
15.52
1,444.60
<.0001
6.27
236.07
<.0001
6.25
468.75
<.0001
0.5241
3.30
.2651
X 21
−1.98
23.60
.0320
67.05
<.0001
X 22
1,077.53
<.0001
1.27
.4848
10.64
.0530
X1X2
3.34
−18.35
2020.34
<.0001
−13.40
X 21 X 2
11.68
546.00
<.0001
−0.5632
X 1 X 22
9.02
325.20
<.0001
Residual
83.68
Lack of fit
0.0300
Pure error
C.V.%
Adeq precision
47.48
.9368
7.62
83.65
Cor total
Mean
1.63
14,492.59
105.54
1.99
67.4622
.0800
39.86
1841.11
69.24
2.28
28.7802
*Aliased model.
variables (coded 0.0 (50 min, 50%)) to compare the influences of
these variables on different responses.
The results in Figure 2c show that the highest TPC yield was observed at the highest levels of ET and EtOH%. All regression coefficients for TPC yield were statistically significant (p ≤ .0320) which
Effect of extraction process variables on TPC yield
result in curvilinear change of TPC yield for all investigated factors.
Data in Figure 2a illustrate that TPC yield was more sensitive to
Two distinct effects of EtOH% on TPC yield were observed. At low
the change in EtOH% than the change in ET. As, the line (B) that
ET, it could be noted that increasing EtOH% increased the yield of
represent the change of TPC yield with changing EtOH% at con-
TPC till maximum which was observed at the range of 55%–65% and
stant ET is highly curved, while the line (A) that represent the
further increase in EtOH% decreased the yield of TPC. At high ET,
change of TPC yield with changing ET at constant EtOH% is ap-
increase EtOH% resulted in progressive increment of TPC yield.
proximately flat line. Furthermore, regression coefficients of
linear terms for ET and EtOH% were 2.32 and 15.52 (Table 2),
Effect of extraction process variables on DPPH IN%
respectively, which show greater effect of EtOH% than ET on
Data in Figure 2b indicate that DPPH IN% was sensitive to the change
TPC yield.
of both EtOH% and ET. However, its sensitivity against the change in
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7 of 11
F I G U R E 2 Perturbation and response surface plots of total polyphenols content [TPC (mg GAE/ g dried powder] of plum leaves (a & c)
and its associated DPPH radical scavenging activity [DPPH I%] (b & d)
EtOH% is higher. The values of regression coefficient of linear terms
activity were 60 min and EtOH% of 64.37% with desirability of
of both ET and EtOH% sustained the preceding observation. As,
0.856. To validate the obtained model, extraction of plum leaves
their values were −1.75 and 6.27, respectively. Significances of linear
powder using aqueous ethanol solution (65%) for 60 min were car-
and quadratic terms for DPPH IN% resulted in a curvilinear change
ried out. The predicted and validated values of TPC yield and DPPH
of DPPH IN% for all investigated variables (Figure 2d). Two different
IN% were 135.57 and 136.67 ± 2.52 mg GAE/g powder and 77.24
trends were observed as a result of increasing EtOH%. For EtOH%
and 76.54 ± 0.86%, respectively. The predicted and validated values
lower than 62%, DPPH IN% was increased as EtOH% increased.
are very close indicating the adequacy of the obtained models to
However, for percentages higher than 62%, increasing EtOH% re-
predict experimental data.
sulted in a decrease of DPPH IN%.
3.2.2 | Optimal extraction process conditions
determination and model validation
3.3 | HPLC analysis
Identification and quantification of phenolic components of the
plum leaves extract obtained at optimal conditions were determined
Desirability function was implemented to maximize the yield of TPC
using HPLC (Figure 3 and Table 3). The total identified components
and its associated radical scavenging activity. The optimum condi-
were 18 compounds. The major identified phenolic compounds
tions for extracting TPC with the highest DPPH radical scavenging
were o-Coumaric acid, Rosmarinic acid, Resveratrol, Quercetin,
8 of 11
|
FIGURE 3
ELSAYED Et AL.
HPLC profile of phenolic compounds in plum leaves extract obtained at optimal conditions
TA B L E 3 Phenolic compounds of plum leaves extract obtained
at optimal conditions
Compounds
Retention time
(min)
Concentration
(µg/g leaf)
Guitard, Paul, Nardello-Rataj, & Aubry, 2016; Gülçin, 2010; Lesjak
et al., 2018; Peñalvo et al., 2016), which showed high scavenging activities against DPPH radicals. Thus, high antioxidant activity of the
obtained extracts could be related to its content of phenolic com-
Gallic acid
3.71
24 ± 1.41
pounds and its synergistic effect with other ingredients present in
Catechin
8.956
18 ± 0.80
the same extract (Xu et al., 2017).
Chlorogenic acid
9.38
81 ± 1.50
Vanillic acid
9.693
499 ± 25.00
164 ± 10.00
3.4 | Rancimat test
Caffeic acid
10.14
Syringic acid
10.402
p-Coumaric acid
12.933
61 ± 4.50
Benzoic acid
14.075
2,991 ± 225.00
fatty acids composition of the tested oil sample are in accordance
Ferulic acid
15.016
1888 ± 112.01
with those specified values for sunflower oil (Codex Alimentarius
Rutin
16.318
1,359 ± 51.33
Commission, 2005) which indicates the authenticity of the oil sam-
Ellagic acid
16.956
264 ± 7.50
ple. Also, data in Table 4 and Figure 4 indicates that the sunflower oil
o-Coumaric acid
17.517
6,836 ± 570.01
is rich with polyunsaturated fatty acids (greater than 88%) especially
Resveratrol
19.465
4,060 ± 152.00
essential fatty acids (ω6 and ω3) (Delplanque, 2000).
Quercetin
21.332
3,839 ± 113.00
3,300 ± 80.00
Rosmarinic acid
21.805
4,465 ± 51.99
Naringenin
22.286
1844 ± 66.00
Myricetin
23.301
3,180 ± 21.00
Kaempferol
24.595
2,678 ± 75.00
Note: Values are expressed as means ± standard deviations of two
replicates.
Physical and chemical characteristics of sunflower oil are listed
in Table 4. Refractive index value, acid value, peroxide value, and
The influence of optimal plum leaves extract on oxidative stability of sunflower oil was investigated using rancimat method and
the obtained results are listed in Table 5. Adding antioxidants to
sunflower oil significantly (p < .05) increased its induction period.
The induction period of sunflower oil containing plum leaves extract at 200 ppm GAE was 4.730 ± 0.007 hr, which was close to
the induction period of sunflower oil containing BHT at the same
concentration. Moreover, data in Table 5 reveal that increasing
plum extract concentration from 100 to 400 (ppm GAE) had insig-
Syringic acid, Myricetin, Benzoic acid, and Kaempferol. Several
nificant (p > .05) effect on extending the induction period of sun-
studies have been conducted to examine the antioxidant activity
flower. Despite the highest protection factor was recorded for BHT
of identified phenolic compounds (Erkan, Ayranci, & Ayranci, 2008;
(1.187 ± 0.012), it was insignificantly (p > .05) differed than that
|
ELSAYED Et AL.
TA B L E 4
9 of 11
Physical and chemical properties of sunflower oil
Parameter
Sunflower oil
Refractive index at 20°C/20°C
1.4750 ± 0.00
Acid value (mg KOH/g oil)
0.27 ± 0.01
Peroxide value (m. equiv./kg oil)
0.67 ± 0.02
Fatty acids
Relative area percentage
Myristic acid
0.072 ± 0.005
Palmitic acid
6.506 ± 0.425
Palmitoleic acid
0.106 ± 0.003
Stearic acid
3.574 ± 0.162
Oleic acid
28.617 ± 1.723
Linoleic acid
59.527 ± 3.124
Alpha-linolenic acid
0.291 ± 0.012
Arachidic acid
0.271 ± 0.015
Behenic acid
0.729 ± 0.041
Total unsaturated fatty acids
88.541
Total saturated fatty acids
11.152
Unknowns
0.307
Note: Values are expressed as means ± standard deviations of two replicates.
FIGURE 4
Typical GC chromatogram of sunflower fatty acid methyl esters
for plum extract (200 ppm GAE). Ghosh, Upadhyay, Mahato, and
They ascribed the oxidative inhibition effect of Ginkgo leaves ex-
Mishra (2019) found that the induction periods of antioxidant free
tract to bioactive components of the extract and its interactions.
sunflower oil under the same air flow rate were 6.13 and 2.74 hr at
Additionally, Beddows et al. (2000) found that induction period of
100 and 110°C, respectively. Kobus et al. (2009) found that add-
sunflower oil at 105°C was increased from 8.3 to 9.9 and 11.4 hr
ing ethanolic Ginkgo leaves extract at concentration of 200 ppm
as a result of adding thyme and turmeric extract to the oil, respec-
to stripped triacylglycerols of rape seed oil increased its induc-
tively. They also found that adding mixture of both extracts to the
tion period from 8.14 hr for antioxidant free sample to 10.04 hr.
oil increased its induction period to 13.8 hr.
10 of 11
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ELSAYED Et AL.
TA B L E 5 Effect of the concentration of plum leaves extract on
oxidative stability of sun flower oil according to Rancimat analysis
Sample
Induction period
(hours)
Protection
factor
Control
4.230 c ± 0.078
–
a
1.187a ± 0.012
b
5.020 ± 0.042
BHT (200 ppm)
Plum extract (100 ppm
GAE)
4.650 ± 0.007
1.099b ± 0.022
Plum extract (200 ppm
GAE)
4.730 b ± 0.007
1.118ab ± 0.022
Plum extract (400 ppm
GAE)
4.720 b ± 0.051
1.116b ± 0.009
Note: Values are expressed as means ± standard deviations of two
replicates.
Different letters in the same column indicate significant differences at
p < .05.
4 | CO N C LU S I O N
The results of the present work showed that the antioxidant activity
of ethanolic plum leaves extracts was highly dependent on their phenolic compounds content. TPC yield and DPPH IN% of the obtained
extracts were more sensitive to EtOH% change than changing ET.
Reduced cubic model was found to be the best model to represent
experimental data. RSM using 32 FFD successively optimized the
extraction parameters (64.37% EtOH%, 60 min) and TPC yield and
DPPH IN% of optimal extract were 136.67 ± 2.52 mg GEA/g powder and 76.54 ± 0.86%, respectively. The major identified phenolic
components were o-Coumaric acid, Rosmarinic acid, Resveratrol,
Quercetin, Syringic acid, Myricetin, Benzoic acid, and Kaempferol.
Optimal plum leaves extract showed superior inhibitory characteristics for sunflower oxidation process. At concentration of 200 ppm
GAE, the protection factor of optimal plum leaves extract was non
statistically differed than that of BHT.
C O N FL I C T O F I N T E R E S T
The authors have declared no conflicts of interest for this article.
ORCID
Nesren Elsayed
https://orcid.org/0000-0002-7040-3142
Karima Said Mohamed Hammad
https://orcid.
org/0000-0003-4983-7920
Ekram Abd El-Salam Abd El-Salam
https://orcid.
org/0000-0003-1144-1672
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How to cite this article: Elsayed N, Hammad KSM, Abd
El-Salam EAE-S. Plum (Prunus domestica L.) leaves extract as a
natural antioxidant: Extraction process optimization and
sunflower oil oxidative stability evaluation. J Food Process
Preserv. 2020;00:e14813. https://doi.org/10.1111/jfpp.14813