Introduction
Light is a crucial environmental factor
that holds substantial sway over the growth and development of plants. It
stimulates plants to undergo diverse physiological, morphogenetic, and
metabolic adaptations to cope with shifting light conditions (Liu et al.
2018a; Liu et al. 2018b; Huang et al. 2021). Consequently, environmental
factors closely tied to plant growth and secondary metabolism often include
lighting (Radušienė et al. 2012). Plants of the same species
require varying light conditions throughout their growth stages, prompting farmers
to utilize diverse technical methods to adjust intensity with the aim of
meeting specific growth needs (Huang et al. 2016). Environmental
factors, e.g., light, play a key role in influencing plant growth, development,
and secondary metabolic processes (Janská et al. 2010; Zoratti et al.
2014), regulating growth rates, organ development, and plant functions and
behaviors (Akari et al. 2014).
Changes in light conditions due to
shading significantly affect photosynthesis, growth patterns, morphology,
anatomical structures, various aspects of cellular physiology and biochemistry,
as well as flowering timing and overall plant productivity (Dai et al.
2009; Deng et al. 2012). Under conditions of light deficiency, plant
growth is disrupted due to a lack of ATP and the energy supply needed for
photosynthesis (Niinemets 2015; Valladares and Niinemets 2018). Plant responses
to light deficiency or shading are related to physiological, biochemical,
anatomical, and leaf morphology processes (Valladares and Niinemets 2018).
Shading is commonly used to manage light intensity and can decrease the active
radiation accessible for plant photosynthesis. This decrease in light
availability can consequently influence both the photosynthesis process and
photomorphogenesis in plants (Dennis et al. 2020; Liu et al.
2020; Xu et al. 2020).
Plants generally contain active
compounds as secondary metabolites. Secondary metabolite compounds are
chemicals that typically possess bioactivity and function as protective agents
for plants against pests, diseases, either for the plant itself or its
environment (Maya et al. 2015). Soybean is one of the food commodities
rich in plant-based protein that contains bioactive compounds (Isanga and Zhang
2008). Currently, the growth of industries using soybeans as a raw material
continues to expand, leading to an increased demand for soybeans, coupled with
the public's awareness of nutritional adequacy (Zainuddin et al. 2022).
There are several challenges in soybean
cultivation under tree canopies, including competition for nutrients, water,
and light. Low light intensity is a major obstacle in the development of
soybeans as an understory crop beneath tree canopies, especially due to shading
from the main plants. Spatiotemporal shading has been found to influence the
accumulation of anthocyanins, proanthocyanidins, and sucrose in black soybean
seeds (Dennis et al. 2020). In this study, various shading treatments,
including no shading and shading under coconut trees that have not produced,
were applied on various soybean varieties known for their resistance and
tolerance. The objective is to gain a deep understanding of the influence of
various levels of shading intensity on soybean leaf metabolites to determine an
appropriate shading level beneficial for soybean growth and metabolite
accumulation. To achieve this goal, we identified a range of metabolite
components in soybean leaves, classified these metabolic substances, and determined
the trends of change in crucial metabolic substances under different levels of
shading intensity. Therefore, this study aims to analyze the impact of
different shading treatments on the secondary metabolite profile of soybean
plants.
Materials and Methods
Experimental
details and treatments
Experimental
material: The materials
employed for this research included soybean seeds (Glycine max L.) of
the following varieties: Anjasmoro (V1), Mutiara 1 (V2),
Denasa 1 (V3), Denasa 2 (V4), Dena 1 (V5), and
Dena 2 (V6). Additionally, a fertilizer mixture consisting of 25%
manure (Setia Tani, Tangerang, Indonesia) and 75% water hyacinth compost
(obtained from the local community in Medan Labuhan, Indonesia) was used. The
materials employed for cultivating the soybeans in small polybags included
soil, rice husks, and a small quantity of charcoal obtained from PT Bukit
Asam Tbk, Indonesia. Ethanol 70% was also utilized as a solvent in the
extraction process. In addition, for antibacterial testing, the materials included
Mueller Hinton Agar (MHA), methanol solvent, chloroform, sterile 9% NaCl, paper
disks (diameter: 6 mm), distilled water, and erythromycin. The bacterial
strains prepared were Escherichia coli (E. coli) and Staphylococcus
aureus (S. aureus).
The instruments used in this study
included the following: Gas Chromatography-Mass Spectrometry (GC-MS; Shimadzu
QP 2010), Fourier Transform Infrared (FTIR; Agilent Cary 630), a rotary
evaporator (IKA, RV 3 V), an oven, autoclave, hot plate with magnetic stirrer,
laminar airflow (LAF) hood, flame source (for sterilization), petri dishes,
vortex mixer, analytical balance (Mettler Toledo), calipers, and mortar.
Treatments: This study was conducted from August
to November 2023 in the coconut plantation area of Medan City, while GC-MS
analysis was performed at the Integrated Research Laboratory of Universitas
Sumatera Utara, and FTIR analysis and antibacterial analysis were conducted at
the Plant Protection Laboratory at Universitas Medan Area. The soybean planting method is based on the research
conducted by Widiastuti and Latifah (2016). The treatments consisted of
shading levels (S) as the primary treatment, with three treatment levels,
namely no shading (S0), shading on non-producing plants (S1),
and shading on producing plants (S2). Soybean seeds of the V1,
V2, V3 V4, V5 and V6
varieties were used for the experiment. Eighteen treatment combinations (3 ´ 6) with
two replications was used, so the total was 36 research plots ´ 9
plants per plot = 324 plants, of which 2 were destructive samples. The size of
each plot was 1.5 × 2 m. The plants were cultivated using artificial planting
media in small polybags, supplemented with fertilizers. Watering was carried
out once a day, with a volume ranging from 440 to 550 mL per plant (Makarim et
al. 2017). Soybean pods form optimally within a temperature range of 26.6–32.0°C
(Widiastuti and Latifah 2016). Harvesting was done after 90 days when the
soybean plants are ready. Leaf samples from the soybean plants were then
prepared for isoflavone testing.
Morphology measurements
The method for assessing soybean
morphology combines research methodologies outlined by He et al. (2023)
and Vaccaro et al. (2022). Morphology
measurements were taken when the soybeans were ready for harvest, specifically
at 90 days. Parameters examined included leaf area, root length, and the dry
weight of the roots. Leaf area was determined through the analysis of digitized
images using ImageJ software (NIH, Bethesda, MD, USA), while lengths were
manually measured with a ruler. Dry weights were obtained using an analytical
balance following a drying process at approximately 90°C until a consistent
weight was achieved.
Metabolite
extraction for GC-MS analysis
A total of 100 g of young leaf samples
were crushed, macerated in 250 mL of 70% ethanol for 24 h, filtered and the
obtained filtrate was collected. The choice of young leaves was made because
secondary metabolites found in young leaves are more advantageous, thus
increasing the probability of achieving elevated levels of is flavonoids (Liu et
al. 2018). The residue was combined with 100 mL of 70% ethanol and left to
macerate for 24 h (Kudou et al. 1991). After this period, it was
filtered, and the filtrate was collected. The remaining residue underwent a second
filtration following its mixing with another 100 mL of 70% ethanol. The
filtrate obtained from this maceration process was then concentrated using a
rotary evaporator, yielding a concentrated extract. The concentrated extract
was heated for 30 mins at 50°C to produce an ethanol extract. The results were
obtained as yellow filtrate for 0-day soybean leaf fermentation. The obtained
filtrate was then evaporated at 50°C in a rotary evaporator until a
concentrated extract was acquired or nearly all ethanol was evaporated. This
extract was then placed in an oven at 40°C to evaporate any remaining solvent
before being weighed to determine the extraction yield.
GC-MS analysis
GC-MS testing was employed using a
RTx5MS column to identify compound contents in the extract (Wang et al.
2018). The column, with a length of 30 m, had injector and detector
temperatures set at 250°C, and operated within a temperature range of 50–300°C.
The temperature increased gradually from 50 to 120°C at a rate of 4°C/min, held
for 1 min, followed by a faster increase from 120 to 300°C at a rate of 6°C/min,
held for 5 min, with a total retention time (Rt) of 60 min. Helium was used as
the carrier gas. Compound identification utilized Wiley/NIST Library software
(Fernandes and Maharani 2019).
Initially, the sample was dissolved in
ethanol solvent, followed by injecting 1 µL of this sample into the GC
instrument inlet. The inlet conveys the sample to the column through the helium
mobile phase. The stationary phase of the RTx-5MS column, composed of 5%
diphenyl and 95% dimethyl polysiloxane, facilitates the separation process.
The concentration data from this
analysis were analyzed using the Kruskal-Wallis test to address this
hypothesis:
H1: There is a significant difference
in the concentration of metabolite compound extracts among the three treatment
groups.
FTIR
analysis
FTIR analysis was carried out on
soybean leaf extract with an objective of identifying functional groups in the compounds.
Antibacterial
activity test
The antibacterial activity was
evaluated by examining the growth inhibition of bacteria, specifically S.
aureus and E. coli, using the agar disk diffusion method
(Kirby-Bauer) (Sun et al. 2019). For the medium, 6.8 g of MHA was used,
which was first autoclaved and then heated on a hot plate while stirring with a
magnetic stirrer (Choi et al. 2019). Pure bacterial cultures were
revitalized on the solid medium by streaking E. coli and S. aureus-containing
needles in a zigzag pattern near a flame. Two bacterial cultures were prepared
for each bacterium. This process was conducted under sterile conditions in a
Laminar Airflow Hood (LAF) and incubated at 37°C for 24 h (Teow et al.
2016).
Additionally, a statistical analysis
was conducted on the antimicrobial index data to address these hypotheses: H2:
There is a significant difference in the antibacterial compound extract
abilities of the three treatments against E. coli. H3: There is a significant
difference in the antibacterial compound extract abilities of the three treatments
against S. aureus.
The hypothesis testing utilized the
Kruskal-Wallis test, which is a non-parametric method for comparing three or
more independent groups. Prior to conducting the Kruskal-Wallis test, two
prerequisite analyses were performed: normality and homogeneity tests. These
tests were conducted to assess whether the data meets the assumptions required
for parametric statistical tests. The significance value used for all tests was
set at 0.05.
Table 1: Soybean leaf area on day 90 (cm2)
|
Varieties |
Shading |
||
|
S0 |
S1 |
S2 |
|
|
V1: Anjasmoro |
2230.4 |
2471.5 |
2521.2 |
|
V2: Mutiara 1 |
2179.6 |
2278.6 |
2368.5 |
|
V3: Denasa 1 |
2034.6 |
2380.2 |
2520.1 |
|
V4: Denasa 2 |
2178.9 |
2231.4 |
2432.6 |
|
V5: Dena 1 |
1989.5 |
2068.5 |
2231.6 |
|
V6: Dena 2 |
1876.3 |
2138.6 |
2248.6 |
Table 2: Soybean root length on day 90 (cm)
|
Varieties |
Shading |
||
|
S0 |
S1 |
S2 |
|
|
V1: Anjasmoro |
53.90 |
42.50 |
23.50 |
|
V2: Mutiara 1 |
48.70 |
33.75 |
26.80 |
|
V3: Denasa 1 |
41.92 |
30.45 |
22.50 |
|
V4: Denasa 2 |
61.77 |
46.88 |
31.33 |
|
V5: Dena 1 |
68.80 |
48.70 |
34.72 |
|
V6: Dena 2 |
56.35 |
32.30 |
26.33 |
Table 3: Dry weight of soybean roots on day 90 (g)
|
Varieties |
Shading |
||
|
S0 |
S1 |
S2 |
|
|
V1: Anjasmoro |
11.86 |
10.72 |
8.76 |
|
V2: Mutiara 1 |
9.76 |
9.12 |
7.65 |
|
V3: Denasa 1 |
8.33 |
7.98 |
7.32 |
|
V4: Denasa 2 |
10.23 |
9.23 |
6.22 |
|
V5: Dena 1 |
6.72 |
7.42 |
6.06 |
|
V6: Dena 2 |
7.83 |
7.21 |
5.32 |
%20447-455_files/image002.jpg)
Fig. 1: FTIR spectra
of soybean extracts extracted from the no shade treatment
Results
Morphology
The results for soybean plant
morphology on day 90 are presented in Table 1, 2 and 3 for leaf area (cm˛),
root length (cm) and dry weight of roots (g), respectively. Analysis reveals
that across all shading treatments, variety V1 (Anjasmoro)
consistently demonstrates superior performance in terms of leaf area, root
length, and dry weight of roots. Notably, V1 consistently exhibits
the highest leaf area and root dry weight compared to other varieties.
Additionally, while V4 (Denasa 2) shows the longest root length
under shading conditions (S1 and S2), variety V5
(Dena 1) displays the longest root length in the absence of shading (S0).
These findings suggest that Anjasmoro may be the most resilient variety,
showing robust growth regardless of shading conditions.
GC-MS
analysis results
The chemical compounds resulting from
the GC-MS analysis of extracts from various varieties of soybean leaves and
different shading variations can be observed in Table 4. It is evident that in the
no-shading variations (S₀), variety V₁ exhibits
several peaks corresponding to alkaloid compounds like thymol, with a
concentration of 7%. Conversely, other varieties (V2–V6)
predominantly produce saturated acids such as n-hexadecanoic acid, ranging from
20 to 45%. Under shading on non-producing plants (S1) conditions,
all varieties display peaks corresponding to antioxidant compounds from the
flavonoid group, particularly hydroxy-1. Variety V2 stands out with
the highest concentration of flavonoids, reaching up to 35.2%. However, shading
on producing plants (S2) leads to the presence of non-flavonoid
compounds (e.g., 11-bromoundecanoic
acid and azelaoyl chloride) across all varieties (V1–V6).
Their presence slightly interferes with the presence of hydroxy-1 compounds,
evidenced by their highest concentration being around 26.1%.
Statistical
analysis using data on metabolite concentrations in soybean leaves was
conducted to address H1, including tests for normality and homogeneity. The
normality test revealed that the data are non-normally distributed but
homogenous. This led to the Kruskal-Wallis test as shown in Table 5, where the
Asym. Sig. value is 0.085, exceeding the significant threshold of 0.05.
Therefore, H1 is rejected, indicating no difference between treatments in
metabolite concentrations.
FTIR
results
The FTIR spectra of soybean extracts
with three shading variations (no shade, shade on non-producing plants, and
shade on producing plants) and several soybean varieties can be observed in
Fig. 1, 2 and 3.
Based on Fig. 1, 2 and 3, the FTIR
spectrum profiles exhibit distinctive patterns, and they share similar spectrum
patterns. Differences are noticeable in the absorbance values and wavelength
intensity readings of the FTIR spectra. This indicates that the compounds
present in the treatments are not significantly different. Interpretation of
the FTIR spectrum reveals several functional groups within the soybean extract.
At peaks of 3317–3628 cm-1, the presence of N-H groups indicates the
presence of amine compounds. Within the wavenumber range of 2532–3130 cm-1,
the presence of -OH groups suggests bonds with acidic compounds. The stretching
of single bonds in the C-H group occurs at 2855–2963 cm-1,
indicating the presence of alkane compounds. Additionally, C-H groups appear at
3010–3087 cm-1, indicating the presence of alkene compounds.
Moreover, the FTIR spectrum shows stretching of double bonds, specifically the
C = O group of ketone compounds at the wavenumber 1734 cm-1, and the
presence of C = C aromatic bonds at the wavenumber range of 1591–1604 cm-1.
These interpretations are supported by the results of phytochemical screening,
where the N-H or amine group signifies the presence of alkaloid metabolites.
The alcohol functional group indicates the presence of steroid and tannin
compound metabolites. Additionally, the C=C aromatic group indicates the
presence of flavonoid and tannin compound metabolites. In conclusion, the
shading treatments do not yield distinct FTIR spectra for each variety.
Antibacterial
activity test results
Antibacterial activity was assessed by
culturing S. aureus and E. coli bacteria on MHA media in petri
dishes. Filter paper discs, measuring 6 mm in diameter, were treated with leaf
extracts from various soybean varieties (Glycine max L.) with 3
repetitions. The negative control used distilled water. The positive control in
the antibacterial test used the antibiotic erythromycin. The antibacterial
activity of extracted compounds from various soybean varieties under varying
shade treatments against S. aureus and E. coli can be observed by
the formation of inhibition zones after incubating the petri dishes for 24 h at
37°C. The inhibition zones formed in the antibacterial test for soybean leaves
of various varieties without shade can be seen in Fig. 4 and 5 (E. coli
and S. aureus).%20447-455_files/image004.jpg)
Table 6 shows information about the
diameter of the inhibition zones of soybean leaf extracts against E. coli
and S. aureus bacteria. It is observed that the inhibition zone diameter
produced by the leaf extracts from various soybean varieties using the paper disc
method against E. coli is larger.
The statistical analysis on normality
and homogeneity tests of the antimicrobial index for E. coli and S.
aureus was conducted prior to the Kruskal-Wallis test. The normality test
for both datasets shows normal distribution, except for S. aureus under
treatment S2. The homogeneity test for all datasets indicates
homogeneous variance (P > 0.05).
Based on these results, the decision to address the three hypotheses involves
employing the Kruskal-Wallis test. The test outcomes for each dataset are
presented in Table 7 and 8.
Based on the Asymp. Sig. data from the
Kruskal-Wallis tests in Table 7 and 8, only the Kruskal-Wallis test data for
the antimicrobial index against E. coli has a value below the
significance level of 0.05. The acceptance criteria for the hypotheses are met,
specifically, if the significance value obtained from the Kruskal-Wallis test
is < 0.05. Therefore, only H2 is accepted, while H3 is rejected. This
indicates a significant difference in the antibacterial compounds extracted
using the three different Table 7: Results of the Kruskal-Wallis test
for the antimicrobial index data of the three treatments against E. coli
|
Test Statistics a,b |
|
|
|
Antimicrobial
Index (E. coli) |
|
Kruskal-Wallis
H |
8.588 |
|
DF |
2 |
|
Asymp.
Sig. |
0.014 |
a Kruskal Wallis test; b Grouping
variable: treatment
Table 8: Results of the Kruskal-Wallis test for the
antimicrobial index data of the three treatments against S. aureus
|
Test statisticsa,b |
|
|
|
Antimicrobial Index (S. aureus) |
|
Kruskal-Wallis H |
5.746 |
|
DF |
2 |
|
Asymp. Sig. |
0.057 |
a Kruskal Wallis test; b Grouping variable:
treatment
%20447-455_files/image006.jpg)
Fig. 3: FTIR spectra
of soybean extracts extracted from the shade on producing plants treatment
treatments, with the optimal treatment
being S2, involving shading in soybean varieties on non-producing
plants.
Discussion
The research results indicate that
expanding soybean cultivation areas under shading on a large scale is crucial
for the development of healthy soybeans. Varied shading intensities resulted in
alterations in the morphology and bioaccumulation of soybean plants. Across all
soybean varieties (V1–V6) subjected to shading treatments
S0 and S1, there was a notable reduction in leaf area
compared to those treated with S2. Notably, V1 exhibited
the largest leaf area, measuring 2521.2 cm˛. Diminished light intensity can
decelerate both photosynthesis and the breakdown of trehalose 6-phosphate
(Tre6P), which serves as a growth stimulant in plants. As a result, plants
might produce larger leaves to optimize light absorption (Göbel and Fichtner
2023). For root length, we observed varying outcomes across different shading
intensities. In treatment S0, variety V5 exhibited the
longest root, measuring 68.80 cm in length. Meanwhile, Variety V4
displayed the longest root length under shading conditions (S1 and S2),
with lengths of 41.88 cm and 31.33 cm, respectively. Moreover, V1
emerges as the top-performing variety in terms of dry root weight (g) across
all conditions of S0, S1 and S2. The dry root
weight of V1 reaches 11.86 g. These results from all tests indicate
that V1 (Anjasmoro variety) exhibits vigorous growth irrespective of
shading conditions. The growth of plants, encompassing both leaf and root
development, can be constrained by competition for carbon resources (Vaccaro et al. 2022). Achieving a balance
between the utilization and storage of fixed carbon in leaves becomes crucial.
When an excess of newly fixed carbon is allocated to the synthesis of sucrose,
it does not lead to a significant growth increase (He et al. 2023).
Table 5: Results of the
Kruskal-Wallis test for the concentration of metabolite compound extracts among
the three treatments
|
Test Statistics a,b |
|
|
|
Metabolite
Compound Percentage |
|
Kruskal-Wallis
H |
4.938 |
|
DF |
2 |
|
Asymp.
Sig. |
0.085 |
a Kruskal Wallis test; b Grouping
variable: treatment
Table 4: GC-MS analysis
results of soybean leaf extracts
|
Sample Code |
Retention Time |
Compounds |
Concentration (%) |
|
(minutes) |
|||
|
S0V1 |
1.39 |
Hydrazine |
1.810 |
|
7.55 |
Thymol |
7.016 |
|
|
18.04 |
Nonanedioic acid, monemethyl |
6.199 |
|
|
18.69 |
n-Hexadecanoic acid |
20.331 |
|
|
20.49 |
Undecanoic acid, hydroxy-, 1 |
26.107 |
|
|
20.77 |
Octadedcanoic acid |
3.23 |
|
|
20.85 |
Tetradecanoic acid |
2.20 |
|
|
20.98 |
11-Hexadecen-1-ol, (z) |
6.046 |
|
|
21.11 |
Cyclopentadecanol |
1.779 |
|
|
21.25 |
Cis-9-Tetradecan-1-ol |
2.356 |
|
|
24.36 |
Hexadecanoic acid, 2, 3 – dihyd |
2.750 |
|
|
S0V2 |
17.95 |
Undecanoic acid,
hydroxy-, 1 |
2.687 |
|
18.42 |
n-Hexadecanoic
acid |
31.768 |
|
|
20.18 |
Undecanoic acid,
hydroxy-, 1 |
27.809 |
|
|
20.35 |
Octadecanoic
acid |
19.233 |
|
|
20.86 |
11-Hexadecen-1-ol,
(z)- |
2.307 |
|
|
S0V3 |
17.92 |
Azelaoyl
chloride |
5.719 |
|
18.26 |
n-Hexadecanoic
acid |
43.605 |
|
|
S0V4 |
1.42 |
Hydrazine |
84.319 |
|
18.34 |
n-Hexadecanoic
acid |
8.083 |
|
|
20.17 |
Undecanoic acid,
hydroxy-, 1 |
2.200 |
|
|
S0V5 |
1.42 |
Hydrazine |
79.873 |
|
18.32 |
n-Hexadecanoic
acid |
12.548 |
|
|
20.18 |
Undecanoic acid,
hydroxy-, 1 |
2.583 |
|
|
S0V6 |
18.15 |
n-Hexadecanoic
acid |
4.820 |
|
19.96 |
Undecanoic acid,
hydroxy-, 1 |
1.430 |
|
|
S1V1 |
1.42 |
Acetic acid,
hydroxy- |
43.632 |
|
17.93 |
Azelaoyl chloride |
6.852 |
|
|
18.26 |
n-Hexadecanoic
acid |
34.272 |
|
|
20.08 |
Undecanoic acid,
hydroxy-, 1 |
11.13 |
|
|
S1V2 |
1.42 |
1, 2-Ethanediol |
4.190 |
|
18.46 |
n-Hexadecanoic
acid |
36.858 |
|
|
20.25 |
Undecanoic acid,
hydroxy-, 1 |
35.206 |
|
|
20.42 |
Octadecanoic
acid |
15.904 |
|
|
S1V3 |
1.42 |
Hydrazine |
94.866 |
|
18.16 |
n-Hexadecanoic
acid |
5.134 |
|
|
S1V4 |
20.25 |
Undecanoic acid,
hydroxy-, 1 |
33.242 |
|
18.47 |
n-Hexadecanoic
acid |
38.823 |
|
|
20.43 |
Octadecanoic acid |
14.961 |
|
|
S1V5 |
1.40 |
Hydrazine |
79.304 |
|
17.91 |
Azelaoyl
chloride |
5.895 |
|
|
18.14 |
Tridecanoic acid |
1.341 |
|
|
18.30 |
n-Hexadecanoic
acid |
5.076 |
|
|
19.59 |
3-Cyclohexene-1-acetaldehyde |
1.894 |
|
|
20.16 |
Udecenoic acid,
hydroxy-,1 |
4.302 |
|
|
S1V6 |
1.40 |
Silane |
72.881 |
|
17.92 |
Azelaoyl
chloride |
8.500 |
|
|
18.29 |
n-Hexadecanoic
acid |
6.910 |
|
|
20.17 |
Udecenoic acid,
hydroxy-,1 |
7.718 |
|
|
S2V1 |
1.43 |
Acetic acid,
hydroxy |
2.272 |
|
6.23 |
3-Cyclohexen-1-ol,
4-methyl |
3.207 |
|
|
6.77 |
1,
6-octadien-3-ol, 3, 7-dimet |
3.016 |
|
|
9.22 |
Thymol |
20.321 |
|
|
17.91 |
Undecanoic acid,
hydroxy-, 1 |
3.298 |
|
|
18.25 |
n-Hexadecanoic
acid |
12.371 |
|
|
S2V2 |
1.402 |
Carbonic
dihydrazide |
11.738 |
|
17.92 |
Azelaoyl
chloride |
7.215 |
|
|
18.30 |
n-Hexadecanoic
acid |
36.346 |
|
|
20.07 |
Undecanoic acid,
hydroxy-, 1 |
21.912 |
|
|
20.24 |
11-Bromoundecanoic
acid |
17.952 |
|
|
S2V3 |
1.411 |
Carbonic
dihydrazide |
26.265 |
|
17.92 |
Undecanoic acid,
hydroxy-, 1 |
8.315 |
|
|
18.27 |
n-Hexadecanoic
acid |
31.564 |
|
|
S2V4 |
1.40 |
Carbonic
dihydrazide |
20.241 |
|
17.93 |
Undecanoic acid,
hydroxy-, 1 |
9.06 |
|
|
18.28 |
n-Hexadecanoic
acid |
36.084 |
|
|
20.07 |
Undecanoic acid,
hydroxy-, 1 |
19.71 |
|
|
20.22 |
11-Bromoundecanoic
acid |
11.514 |
|
|
S2V5 |
1.42 |
Acetic acid,
hydroxy |
83.052 |
|
17.93 |
Azelaoyl
chloride |
16.948 |
|
|
S2V6 |
1.42 |
1, 2-Ethanediol |
89.167 |
|
17.93 |
Azelaoyl
chloride |
7.875 |
|
|
19.69 |
Undecanal |
2.151 |
Table 6: Inhibition
zone diameter and antimicrobial index of antibacterial activity test for
various varieties of soybean leaves
|
Sample Code |
Inhibition Zone Diameter (mm) |
Antimicrobial Index |
||
|
S. aureus |
E. coli |
S. aureus |
E. coli |
|
|
S0V1 |
8.36 |
10.00 |
0.40 |
0.66 |
|
S0V2 |
8.40 |
9.36 |
0.40 |
0.56 |
|
S0V3 |
9.00 |
10.70 |
0.50 |
0.78 |
|
S0V4 |
9.53 |
10.20 |
0.59 |
0.70 |
|
S0V5 |
9.03 |
11.97 |
0.51 |
0.99 |
|
S0V6 |
8.90 |
9.87 |
0.48 |
0.64 |
|
S1V1 |
10.03 |
11.57 |
0.67 |
0.93 |
|
S1V2 |
10.43 |
11.70 |
0.74 |
0.95 |
|
S1V3 |
8.70 |
11.77 |
0.45 |
0.96 |
|
S1V4 |
9.60 |
11.23 |
0.60 |
0.87 |
|
S1V5 |
10.17 |
13.50 |
0.70 |
1.25 |
|
S1V6 |
9.90 |
11.20 |
0.65 |
0.87 |
|
S2V1 |
8.30 |
10.07 |
0.38 |
0.68 |
|
S2V2 |
8.57 |
11.00 |
0.43 |
0.83 |
|
S2V3 |
9.97 |
10.01 |
0.66 |
0.68 |
|
S2V4 |
8.60 |
9.80 |
0.43 |
0.63 |
|
S2V5 |
9.97 |
9.97 |
0.66 |
0.66 |
|
S2V6 |
8.73 |
10.10 |
0.45 |
0.68 |
Note: The diameter of the paper discs
used is 6 mm
%20447-455_files/image008.jpg)
Fig. 2: FTIR spectra
of soybean extracts extracted from the shade on non-producing plants treatment
Research findings have demonstrated
that shading significantly influences leaf gas exchange, leaf pigments, and the
secondary metabolites in plants (Yusof et al. 2021). Based on the
results of GC-MS analysis, extracts from soybean leaves under no shading (S0)
conditions in various varieties (V1–V6) contain
alkaloids. In addition, the results show that thymol compound appears at the
second peak, with a retention time of 7.559 mins and a concentration of 7.016%.
The phytochemical group present in soybean leaf extract from shading on
non-producing plants (S1) in all varieties (V1–V6)
is flavonoids, with the highest concentration of 35.2% observed in variety V2.
Flavonoids are a group of hydroxy phenols with high antioxidant activity,
exhibiting various bioactivities, including antibacterial, anticancer,
anti-inflammatory, immune system enhancement (Tungmunnithum et al.
2018), and anti-diabetic properties (Sarian et al. 2017).
Moreover, GC-MS testing results indicate the presence of n-hexadecanoic acid, which is a saturated fatty acid (Eastwood 2003), at the eighth peak, with a retention time of 18.693 mins and a concentration of 20.331%. Furthermore, the analysis reveals the presence of undecanoic acid, hydroxy-, 1 at the seventeenth peak, with a retention time of 20.731 mins and a concentration of 26.107%. However, shading on producing plants (S2) leads to the presence of non-flavonoid or organic halide compounds, such as 11-bromoundecanoic acid and azelaoyl chloride, in all varieties (V1–V6). Their presence causes a slight disruption in the presence of hydroxy-1 compounds, as indicated by their highest concentration reaching around 26.1% in variety V2. Moreover, treatments involving shading on non-producing plants (S1) and shading on producing plants (S2) result in the production of flavonoid and phenol compounds with antioxidant properties. In conclusion, the most efficient treatment for generating antioxidant compounds is shading on non-producing plants (S1).
The FTIR
analysis results identify that the soybean leaf extract contains strong
out-of-plane bending vibrations of C-H for substituted benzene rings,
indicating the presence of phenols and flavonoids in soybean leaf extract
(Corcoran et al. 2022). The identification of benzenoid compounds
through FTIR spectroscopy supports the findings from phytochemical examination,
detecting the presence of phenols and flavonoids.
%20447-455_files/image010.jpg)
Fig. 4: Inhibition
zones in the antibacterial test against E. coli with various soybean
extracts and shading treatments
%20447-455_files/image012.jpg)
Fig. 5: Inhibition
zones in the antibacterial test against S. aureus with various soybean
extracts and shading treatments
The antibacterial test results showed
that the positive control, represented by the antibiotic erythromycin,
exhibited antibacterial activity against both S. aureus and E. coli,
while the negative control, consisting of distilled water, showed no
antibacterial activity. The extract demonstrated antibacterial activity, as
indicated by the formation of clear zones around the paper discs after
incubation for 24 h at 37°C (Daneshzadeh et al. 2019).
The largest inhibitory zone diameter
produced by leaf extracts from various soybean varieties without shading
treatment (S0) against S. aureus, was 9.53 mm, and against E.
coli, it was 11.97 mm. The smallest effective concentration inhibiting
bacterial growth was observed in the chloroform fraction against S. aureus
(8.36 mm) and against E. coli (9.36 mm). The inhibitory zone diameters
obtained from each fraction against S. aureus indicate that the leaf
extract from various soybean varieties without shading treatment (S0)
has weak antibacterial activity, while it exhibits strong antibacterial
activity against E. coli. The largest inhibitory zone diameter produced
by leaf extracts from various soybean varieties under shading treatment against
S. aureus, was 10.43 mm, and against E. coli, it was 13.5 mm. The
smallest effective concentration inhibiting bacterial growth was observed in
the chloroform fraction against S. aureus (8.7 mm) and against E.
coli (11.20 mm). The inhibitory zone diameters against S. aureus
indicate that the leaf extract from various soybean varieties under shading
treatment has weak antibacterial activity, while it exhibits
strong antibacterial activity against E. coli.
The largest inhibitory zone diameter
produced by leaf extracts from various soybean varieties under shading
treatment against S. aureus, was 9.97 mm, and against E. coli, it
was 10.07 mm. The smallest effective concentration inhibiting bacterial growth
was against S. aureus (8.3 mm) and against E. coli (9.8 mm). The
inhibitory zone diameters against S. aureus and
E. coli indicate that the leaf extract from various soybean varieties
under shading treatment has weak antibacterial activity. According to Souza et
al. (2020), antibacterial activity < 5 mm is considered weak, 5–10 mm is
moderate, 11–20 mm is strong, and > 20 mm is very strong. As a result,
shading on non-producing plants (S1) emerges as the most efficient
treatment, with the Mutiara 1 variety (V2) proving to be superior
against S. aureus. Meanwhile, the Dena 1 variety (V5)
exhibits strong antibacterial activity against E. coli. The ability of
soybean leaf extract to inhibit bacteria is attributed to the presence of
secondary metabolites in soybean leaves such as alkaloids, tannins, steroids,
terpenoids, and flavonoids, which have the capability to inhibit the growth of
bacteria. The primary mechanism through which alkaloid compounds impede
bacterial growth is by interfering with the formation of peptidoglycan
components in the bacterial cell walls. This disruption ultimately leads to the
lysis or breaking down, of the bacterial cells (Daneshzadeh et al.
2019).
Conclusion
This study combines traditional
Indonesian soybean cultivation with modern scientific analysis to investigate
the impact of shading techniques, like using non-fruit-bearing coconut trees,
on local soybean varieties. The research reveals that different shading
intensities affect the soybean leaves' metabolic substances, identifying
important phytochemicals and demonstrating their potential antimicrobial
properties against bacteria like S. aureus and E. coli. The results
demonstrated the highest inhibitory efficacy against S. aureus, which is
10.43 mm (S1V2), and against E. coli, which is
13.50 mm (S1V5). Therefore, shading on non-producing
plants (S1) stands out as the most effective treatment, highlighting
Mutiara 1 (V2) and Dena 1 (V5) as the best varieties.
Significantly, it finds that certain shading treatments enhance these
antimicrobial properties, bridging traditional agricultural practices with
modern science, and offering new insights for sustainable farming and natural
antibacterial agents.
Acknowledgements
The authors thanks to the Ministry of
Education, Culture, Research, and Technology, Directorate General of Higher
Education, Research, and Technology, through the Universitas Medan Area, for
funding this research under the 'Doctoral Dissertation Research Grant' with
research grant number 0557/E5.5/AL.04/2023.
Author
Contributions
DWP and S conceptualized the study and
developed the methodology. DWP conducted the investigation and visualized the
data. ZN provided resources and supervised the study. DWP and S wrote the
original manuscript. ZN revised the manuscript. All authors agreed to the final
version of the manuscript.
Conflicts of
Interest
All authors declare no conflict of
interest.
Data Availability
Data presented in this study will be
available on a fair request to the corresponding author.
Ethics Approval
Not applicable to this paper.
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