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Citation: Chatzimitakos, T.;
Athanasiadis, V.; Makrygiannis, I.;
Kalompatsios, D.; Bozinou, E.; Lalas,
S.I. Bioactive Compound Extraction of
Hemp (Cannabis sativa L.) Leaves
through Response Surface
Methodology Optimization.
AgriEngineering 2024,6, 1300–1318.
https://doi.org/10.3390/
agriengineering6020075
Received: 6 March 2024
Revised: 18 April 2024
Accepted: 29 April 2024
Published: 9 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
AgriEngineering
Article
Bioactive Compound Extraction of Hemp (Cannabis sativa L.)
Leaves through Response Surface Methodology Optimization
Theodoros Chatzimitakos , Vassilis Athanasiadis * , Ioannis Makrygiannis , Dimitrios Kalompatsios ,
Eleni Bozinou and Stavros I. Lalas
Department of Food Science and Nutrition, University of Thessaly, Terma N. Temponera Str., 43100 Karditsa,
Greece; tchatzimitakos@uth.gr (T.C.); ioanmakr1@uth.gr (I.M.); dkalompatsios@uth.gr (D.K.);
empozinou@uth.gr (E.B.); slalas@uth.gr (S.I.L.)
*Correspondence: vaathanasiadis@uth.gr; Tel.: +30-24410-64783
Abstract: Hemp, commonly known as Cannabis sativa L., is a medicinal plant species of the Cannabaceae
family. For the efficient extraction of C. sativa leaves using the conventional stirring process with
water as the solvent, three crucial extraction parameters (i.e., extraction duration, liquid–solid
ratio, and temperature) were investigated through the response surface methodology (RSM). The
concentrations of the extracted bioactive compounds (polyphenols, ascorbic acid, and carotenoids)
showed significant variations in the RSM design points, suggesting the importance of finding the
optimal extraction conditions in which liquid–solid ratio and extraction temperature were found
to have the highest impact. Further analysis was conducted on the optimal extract employing
several assays to determine their polyphenol content, total carotenoid content, color evaluation,
anti-inflammatory activity, and antioxidant capacity through FRAP, DPPH, and H
2
O
2
assays. A low
extraction time (30 min) at 50
◦
C and a high liquid–solid ratio (50:1) were required for the highest
possible yield of polyphenols. The total polyphenol content was determined to be 9.76 mg gallic acid
equivalents/g under optimum conditions, with pelargonin being the most abundant polyphenol
(1.51 mg/g) in C. sativa extracts. Ascorbic acid was measured at 282.23
µ
g/g and total carotenoids at
356.98
µ
g/g. Correlation analyses revealed that anti-inflammatory activity was negatively correlated
with specific polyphenols. As determined by DPPH (27.43
µ
mol ascorbic acid equivalents (AAE)/g),
FRAP (49.79
µ
mol AAE/g), and H
2
O
2
(230.95
µ
mol AAE/g) assays, the optimized aqueous extract
showed a high antioxidant capacity. Furthermore, it demonstrated considerable anti-inflammatory
activity at 17.89%, with the potential to increase to 75.12% under particular extraction conditions.
Given the high added-value of the aqueous extracts, the results of this study highlight the potential
utility of C. sativa leaves as a source of health-improving antioxidant compounds in the pharmaceutical
and food industries.
Keywords: Cannabis sativa; extraction; Box–Behnken design; polyphenols; antioxidant activity;
anti-inflammatory activity; HPLC-DAD; color evaluation; principal component analysis; partial least
squares analysis
1. Introduction
Herbaceous plants have been utilized by humans for the last 5000 years [
1
]. Developed
and domesticated agriculturally during the Bronze Age as traditional medicine, hemp
(Cannabis sativa L.) is a plant of the Cannabaceae family and was introduced to Europe from
central Asia [
2
]. Irrespective of its place of origin, the modern domesticated variety of C.
sativa L. is grown extensively around the globe, from Asia to North America, Europe,
and Africa [
3
]. The primary reason is that all aerial parts can be utilized in various
applications. From agriculture and phytoremediation to the cosmetic, pharmaceutical,
food, and construction sectors, this versatile crop has a low environmental effect while
serving several purposes. In terms of valorization, many useful industrial products can be
AgriEngineering 2024,6, 1300–1318. https://doi.org/10.3390/agriengineering6020075 https://www.mdpi.com/journal/agriengineering
AgriEngineering 2024,61301
extracted from this adaptable plant, e.g., apart from energy generation, straw fibers have
been utilized in the construction and textile industries [4].
Regarding nutrition, C. sativa is notable for the presence of the unique psychotropic
compound
δ
-9-tetrahydrocannabinol. Such cannabinoids and their pharmacological char-
acteristics have attracted great research interest, and it is the main reason why the plant
is illicit [
5
]. In addition, C. sativa seeds provide an excellent source of oil for human con-
sumption. Hempseed oil is generally recognized to be among the few seed oils that have
approximately 80% polyunsaturated fatty acids which are suggested as optimal for human
diets [
6
], such as
ω
-6 acid isomers,
γ
-linolenic, and
α
-linolenic acid [
7
]. Hemp seed oil
has shown beneficial effects on humans, such as decreasing hypertension and preventing
cardiovascular disease [
8
]. Terpenes, another group of molecules found in C. sativa, have a
vast role in the unique scent of the plant. In addition to their positive biological actions,
these volatile compounds, primarily mono- and sesquiterpenes, have anti-inflammatory
and antidepressant properties [
9
,
10
]. A major sesquiterpene called
β
-caryophyllene may
bind to the type 2 cannabinoid receptor, which could have a synergistic effect on health
benefits in conjunction with the other phytochemicals in this plant [11].
Natural antioxidants have recently gained popularity, given their potential to add
biological value to food and cosmetics [
12
,
13
]. Polyphenols, which are natural antioxidants,
have a beneficial effect on the nutritional content, taste, and shelf-life of food products.
They can delay lipid oxidation and other radical reactions that lead to food spoilage [
14
,
15
].
Polyphenols aid in the protection of plants, namely against detrimental UV radiation,
due to their antioxidant and radical scavenging characteristics [
16
]. These compounds
have the potential to slow the development of a wide range of illnesses and ailments,
including cardiovascular and neurological disorders, asthma, inflammatory disorders,
and cancers [
17
]. C. sativa is unique among plants in that it includes several types of
polyphenols, including flavonoids, phenolic acids, and cannflavins, a kind of prenylated
flavone [
18
]. Antioxidant, anti-inflammatory, neuroprotective, and antiparasitic properties
have previously been attributed to these compounds [
19
,
20
]. C. sativa possesses several
antioxidant compounds which can be beneficial for the wellness of people. With these
beneficial characteristics, cannabinoids A and B stand out among the normal flavones found
in C. sativa [
21
]. Under the impact of intense solar radiation and freezing temperatures,
cannflavin A was found in significant concentrations in certain C. sativa cultivars.
Polyphenols and other beneficial molecules can be obtained from a plant by extraction.
In order to maximize the extraction yield, many extraction methods have been developed
and employed. However, in many cases, sophisticated equipment or tailored solvents
are needed. As such, the employment of more simple, yet efficient, extraction methods,
that are human and environmentally friendly at the same time, is gaining attention [
22
].
The conventional stirring method is a simple and commonly employed technique for the
effective extraction of bioactive compounds [
23
]. Several factors, including the temperature,
solvent, extraction duration, and liquid–solid ratio are known to have a crucial influence on
the extraction of polyphenols and other bioactive compounds. For instance, although tem-
perature can promote the extraction of various compounds, some compounds can degrade
upon heating at high temperatures [
23
]. As such, fine-tuning the above parameters can
significantly enhance the extraction yield. In order to avoid tedious optimization workflows
that may miss optimum parameters, the use of statistical methods is highly suggested. As
such, the above parameters can be optimized through the response surface methodology
(RSM) to guarantee the highest extraction yield [
24
]. As such, using an improved stirring
procedure with water can be a promising procedure to extract compounds [25].
Although various attempts have been carried out to obtain extracts from C. sativa
leaves, in most cases, the use of either organic solvents or mixtures with water is being
employed. This results in increased extraction costs and higher environmental impact, while
additional steps are needed to eliminate the organic solvent prior to the usage of the extract.
Despite the above efforts, there are no detailed studies regarding the optimization of the
extraction using only water. In addition, the studies that report the use of water to prepare
AgriEngineering 2024,61302
extracts focus solely on a few compounds (such as polyphenols) without taking into account
other compounds with antioxidant properties, such as ascorbic acid and carotenoids. To that
end, the study aimed to optimize the extraction parameters of C. sativa leaves with water as
a solvent, including the temperature, extraction time, and liquid–solid ratio through RSM.
In addition, the bioactive compound concentration (polyphenols, carotenoids, and ascorbic
acid) of C. sativa extracts was measured, whereas optimum conditions were revealed
through a partial least squares (PLS) model. The influence of these parameters was further
explored with color evaluation, the antioxidant activity from three assays, and the anti-
inflammatory activity of the extracts. All the above were conducted to determine whether
C. sativa leaves could serve as a feasible source of antioxidant compounds, with a particular
focus on their potential use in the food and pharmaceutical sectors.
2. Materials and Methods
2.1. Chemicals and Reagents
Gallic acid, ethanol, and the Folin–Ciocalteu reagent were bought from Panreac Co.
(Barcelona, Spain). Hydrochloric acid, methanol, L-ascorbic acid, phosphate buffer solution,
aluminum chloride, 2,2-diphenyl-1-picrylhydrazyl (DPPH) 2,4,6-tris(2-pyridyl)-s-triazine
(TPTZ), trichloroacetic acid, albumin,
β
-carotene analytical standard, and all chemical
HPLC standards for the polyphenol determination were obtained from Sigma-Aldrich
(Darmstadt, Germany). Anhydrous sodium carbonate was bought from Penta (Prague,
Czech Republic). Iron (III) chloride was purchased from Merck (Darmstadt, Germany).
Hydrogen peroxide (35% v/v) was purchased from Chemco (Malsch, Germany). Deionized
water from a deionizing column was used for all the experiments.
2.2. Hemp Leaf Material
For all experiments, hemp (Cannabis sativa var. Finola) leaves (without flowering and
fruiting tops) were donated by CBD Extraction I.K.E. (Farsala, Greece), gathered from the
Krokio area in Almiros region (at 39
◦
20
′
58
′′
N and 22
◦
75
′
16
′′
E, based on Google Earth
version 9.185.0.0). The leaves were rinsed extensively with distilled water and dried with
paper towels. The sample underwent freeze-drying using a Biobase BK-FD10P freeze-dryer
(Jinan, China). The moisture level of the fresh leaves was measured to be 79.12
±
1.26%.
The dried C. sativa leaves were then ground to a fine powder (<400
µ
m diameter) using a
blender. Finally, until further analysis, the powder was preserved at −40 ◦C.
2.3. Hemp Leaf Extraction
To identify the optimal conditions for the extraction of bioactive compounds from
C. sativa leaves, different quantities of the dried powder (0.40, 0.57, and 1 g) were weighted
and inserted into screw-capped glass bottles, and 20 mL of water was added to achieve a
liquid–solid ratio of 50:1, 35:1, and 20:1, respectively. The mixtures were heated at 20, 50,
and 80
◦
C for 30, 90, or 150 min, under continuous stirring at 500 rpm. After the extraction
was completed, the samples were centrifuged for 10 min at 10,000
×
gin a NEYA 16R
centrifuge (Remi Elektrotechnik Ltd., Palghar, India). Finally, the supernatants were stored
at
−
40
◦
C. All experiments were carried out in various combinations of the examined
parameters, the coded levels of which are shown in Table 1.
Table 1. The actual and coded levels of the independent variables that were used to optimize
the process.
Independent Variables Coded Units
Coded Levels
−1 0 1
Liquid–solid ratio (mL/g) X120 35 50
T(◦C) X220 50 80
t(min) X330 90 150
AgriEngineering 2024,61303
2.4. Optimization with Response Surface Methodology (RSM), Experimental Design, and
Model Validation
The RSM technique was employed to achieve optimal efficiency in extracting the
bioactive compounds and evaluating the antioxidant activity from the C. sativa aqueous
extracts. Therefore, the main objective of the design was to effectively maximize the levels
of these values. This was accomplished by optimizing the liquid–solid ratio (R, mL/g),
extraction time (t, min), and extraction temperature (T,
◦
C). The optimization process was
based on an experiment that utilized a Box–Behnken design with a main impact screening
arrangement. The experiment consisted of 15 design points, including 3 center points.
According to the experimental design, three levels of process variables were created. The
overall model significance, as shown by the R
2
and p-values, and the significance of the
model coefficients, as represented by the equations, were assessed using the analysis of
variance (ANOVA) and summary-of-fit tests, with a minimum level of 95% confidence.
In addition, the response variable was predicted as a function of the examined inde-
pendent factors using a second-order polynomial model, as illustrated in Equation (1):
Yk=β0+
2
∑
i=1
βiXi+
2
∑
i=1
βiiX2
i+
2
∑
i=1
3
∑
j=i+1
βijXiXj(1)
The predicted response variable is denoted as Y
k
, while the independent variables are
X
i
and X
j
. The intercept and regression coefficients for the linear, quadratic, and interaction
terms of the model are denoted as β0,βi,βii, and βij, respectively.
To determine the greatest peak area and assess the effect of a substantial independent
variable on the response, the RSM was applied. The development of three-dimensional
surface response graphs was initiated to represent the model equation visually.
Furthermore, the model validation process involved comparing the model’s predic-
tions with the actual outcomes to measure its accuracy. This step was essential to ensure
that the model was reliable and could be used for future predictions. The data were divided
into three subsets: training, validation, and test. The model parameters were learned from
the training subset. The validation subset was used to adjust the model parameters and
select a model that can predict well. The test subset was used to measure the performance
of the final model. In the case of our study, we used k-fold cross-validation to validate the
model’s predictive ability. The statistics of model validation are given in Table S1.
2.5. Bioactive Compound Determination
2.5.1. Total Polyphenol Content (TPC)
An established methodology [
26
] was applied to determine TPC. Briefly, 0.10 mL
of a sample properly diluted extract was mixed with 0.10 mL of Folin–Ciocalteu reagent
and after 2 min, 0.80 mL of 5% w/vaqueous sodium carbonate solution was added. The
mixture was incubated for 20 min at 40
◦
C and the absorbance was measured at 740 nm in
a Shimadzu UV-1700 PharmaSpec Spectrophotometer (Kyoto, Japan). The total polyphenol
concentration (C
TP
) was calculated from a gallic acid calibration curve. Total polyphenol
yield (Y
TP
) was determined as mg gallic acid equivalents (GAE) per g of dry weight (dw)
using the following Equation (2):
TPC (mg GAE/g dw)=CTP ×V
w(2)
where the volume of the extraction medium is indicated with V(expressed in L) and the
dry weight of the sample is w(expressed in g).
2.5.2. HPLC Quantification of Polyphenolic Compounds
Individual polyphenols were identified and quantified from the sample extracts using
High-Performance Liquid Chromatography (HPLC), according to our prior research [
27
].
A Shimadzu CBM-20A liquid chromatograph and a Shimadzu SPD-M20A diode array
AgriEngineering 2024,61304
detector (DAD) (both purchased by Shimadzu Europa GmbH, Duisburg, Germany) were
employed for the analysis of the C. sativa extracts. The separation of the compounds was
performed in a Phenomenex Luna C18(2) column from Phenomenex Inc. in Torrance,
California, with the temperature at 40
◦
C (100 Å, 5
µ
m, 4.6 mm
×
250 mm). The mobile
phase included 0.5% aqueous formic acid (A) and 0.5% formic acid in acetonitrile/water
(3:2) (B). The gradient program had a total of 70 min and was set as follows: initially
from 0 to 40% B for 40 min, then to 50% B in 10 min, to 70% B in another 10 min, and
finally constant for 10 min. The flow rate of the mobile phase was set at 1 mL/min. The
identification of the compounds was made through comparing the absorbance spectrum
and retention time to pure standards. The quantification was conducted through calibration
curves (0–50 µg/mL), and the results were given in mg/g.
2.5.3. Ascorbic Acid Content (AAC)
Ascorbic acid content (AAC) was evaluated with a previously established method [
28
].
In an Eppendorf tube, a quantity of 100
µ
L sample extract along with 500
µ
L of 10% (v/v)
Folin–Ciocalteu reagent was mixed with 900
µ
L of 10% (w/v) trichloroacetic acid. The ab-
sorbance was measured at 760 nm after 10 min. Ascorbic acid was the calibration standard.
2.5.4. Total Carotenoids Content (TCC)
A slightly modified method introduced by Ayour et al. [
29
] was employed to deter-
mine the total carotenoid content (TCC) of the analyzed extracts. In brief, the samples
underwent a ten-fold dilution during the preparation process; consequently, the absorbance
measurement was recorded at 450 nm. Using a calibration curve from
β
-carotene, the TCC
was quantified in mg of β-carotene equivalents per gram of dried weight.
2.6. Antioxidant Capacity of the Extracts
2.6.1. Ferric-Reducing Antioxidant Power (FRAP) Assay
An established methodology [
30
] was used for the evaluation of FRAP. In a 2 mL
Eppendorf tube, 100
µ
L of the properly diluted sample was mixed with 100
µ
L of FeCl
3
solution (4 mM in 0.05 M HCl). The mixture was incubated at 37
◦
C for 30 min. A quantity
of 1800
µ
L of TPTZ solution (1 mM in 0.05 M HCl) was immediately added and the
absorbance was measured after 5 min at 620 nm. The ferric-reducing power (P
R
) was
calculated using an ascorbic acid calibration curve (C
AA
) in 0.05 M HCl with ranging values
(50–500
µ
M). The P
R
was calculated as
µ
mol of ascorbic acid equivalents (AAE) per gram
of dw using Equation (3):
PR(µmol AAE/g dw) = CAA ×V
w(3)
where Vrepresents (in L) the entire volume of the extraction medium and w(in g) represents
the dried weight of the material.
2.6.2. DPPH•Antiradical Activity Assay
The extracted polyphenols from the dried material were evaluated for their antiradical
activity (A
AR
) using a slightly modified DPPH
•
method by Shehata et al. [
31
]. Briefly,
50
µ
L of the properly diluted sample was mixed with a quantity of 1950
µ
L of a 0.1 mM
DPPH
•
solution in methanol, with the solution being kept at room temperature for 30 min
in the dark right after. The absorbance was measured right after at 515 nm. Moreover,
a blank solution sample was used instead of the sample, including DPPH
•
solution and
methanol, with the absorbance immediately being measured. To calculate the percentage
of scavenging, Equation (4) was employed:
% Scavenging =Acontrol −Asample
Acontrol
×100 (4)
AgriEngineering 2024,61305
The ascorbic acid calibration curve in Equation (5) was used to evaluate the antiradical
activity (AAR), which was expressed as µmol AAE per gram of dw:
AAR (µmol AAE/g dw)=CAA ×V
w(5)
where Vrepresents (in L) the entire volume of the extraction medium and w(in g) represents
the dried weight of the material.
2.6.3. Hydrogen Peroxide (H2O2) Scavenging Assay
A previous method [
28
] was applied for the H
2
O
2
scavenging assay. A quantity of
400
µ
L of the properly diluted extract and 600
µ
L of an H
2
O
2
solution (40 mM, made in
phosphate buffer, pH 7.4) was added into an Eppendorf tube. The absorbance was recorded
right after 10 min at 230 nm. The scavenging capacity of the H
2
O
2
was expressed as follows:
% Scavenging of H2O2=A0−(A−Ac)
A0
×100 (6)
where the absorbances of the blank solution, the extract solution in the absence of hydrogen
peroxide, and the sample are denoted by A0,Ac, and A, respectively.
The concentration of ascorbic acid ranged in the calibration curve (C
AA
, 50–500
µ
mol/L
in 0.05 M HCl) and the following Equation (7) was used to determine the anti-hydrogen
peroxide activity (AAHP) as µmol AAE per g of dw:
AAHP (µmol AAE/g dw)=CAA ×V
w(7)
where Vdenotes the volume of the extraction medium (in L), and wis the dry weight of
the sample.
2.7. Biological and Physicochemical Parameters of the Extracts
2.7.1. Assessment of In Vitro Anti-Inflammatory Activity
The
in vitro
evaluation of the anti-inflammatory properties of the C. sativa extracts was
conducted using the albumin denaturation assay [
32
]. Briefly, a mixture containing egg
albumin and PBS (pH = 6.4) with a 0.1:1.4 mL ratio (mixture A), respectively, was prepared.
Then, 400
µ
L of the sample extract or standard were mixed with 600
µ
L of the mixture
A in a 1.5 mL Eppendorf tube, and then, the tubes were incubated at 37
◦
C for 15 min.
Afterwards, the mixture was heated at 70
◦
C for 5 min. The absorbance was then recorded
at 660 nm. To determine the inhibition of protein denaturation, the following equation
was used:
% Inhibition =
Absorbancesample
Absorbancecontrol
−1
×100 (8)
2.7.2. Color Evaluation
The CIELAB color of the C. sativa extracts was measured by using a prior established
method [
33
] with the use of a colorimeter (Lovibond CAM-System 500, The Tintometer
Ltd., Amesbury, UK), where the CIELAB parameters (L*,a*, and b*) measured the aqueous
extracts. Three parameters were fundamental to measure the color of the aqueous extracts:
The L* value which denotes the lightness of a color, ranging from 0 to 100 (black to white).
The a* value specifies the degree of redness (negative values) or greenness (positive values)
in a color. Likewise, the b* value measures the extent of yellowness (negative values) or
blueness (positive values) in a color. The measure of color intensity is expressed as C
ab
or
C* (chroma or saturation). The hue angle (h
ab
or H) and psychological coordinate chroma
(Cab*) were measured by the following equations:
C∗
ab =q(a∗2+(b∗2(9)
AgriEngineering 2024,61306
ho
ab =arctanb*
a*(10)
2.8. Statistical Analysis
The statistical analysis related to the response surface methodology, distribution
analysis, and model validation were applicable through the JMP
®
Pro 16 software (SAS,
Cary, NC, USA). The extraction procedures were repeated a minimum of twice for each
batch of the C. sativa extract, and the quantitative analysis was conducted in triplicate. The
results are represented as means and standard deviations. The multivariate correlation
analysis (MCA), principal component analysis (PCA), and partial least squares (PLS) were
performed through the JMP®Pro 16 software (SAS, Cary, NC, USA).
3. Results and Discussion
The aim of this research was to maximize the extraction of polyphenols from C. sativa.
To enhance the effectiveness of recovering the bioactive compounds, several extraction
parameters were examined, such as the extraction duration (30–150 min) and the extraction
temperature (20–50
◦
C). To verify the appropriate ratio of liquid–solid and produce the
finest results, corresponding experiments were conducted within the range of 20–50 mL/g.
Furthermore, to evaluate the impact of certain components and improve the efficiency of
water extraction, the response surface methodology (RSM) was employed. The efficacy of
the RSM and model adequacy were evaluated by employing the ANOVA and summary-of-
fit tests to compare the experimental values with the anticipated values.
3.1. Total Polyphenol Content and Antioxidant Activity of the Extracts
Polyphenolic compounds are among the most widely recognized categories of com-
pounds found in natural products. There is significant potential for the implementation of
these compounds in several sectors, including the food and pharmaceutical sectors [
34
,
35
].
The measured responses and predictions for the TPC, as well as of the FRAP, DPPH, and
H
2
O
2
assays for each prepared extract, are presented in Table 2. It can be noted that the
predicted and actual measurements have a low variance. The range of the TPC values
for the extracts was between 7.28 and 9.77 mg GAE/g dw. The variation in TPC may be
attributed to several parameters of the extraction process, including the liquid–solid ratio,
temperature, time, and extraction solvent, as per Koraqi et al. [
36
]. In a study conducted
by Aazza et al. [
37
], the TPC of the Moroccan C. sativa plant was measured with different
extraction solvents (i.e., water, ethanol, methanol, hexane, dichloromethane, ethyl acetate,
and chloroform). When water was utilized as a solvent, the TPC was ~8 mg GAE/g dw,
whereas ethanol and methanol achieved more than two-fold increased polyphenol recovery.
In another study [
38
] with ethanolic mixtures or water used as extraction solvents, young
and mature C. sativa extracts were analyzed. The TPC value from aqueous extracts was com-
parable to our study and was measured at 8.44 and 6.21 mg/g dw, respectively. Mixtures
consisting of 50% ethanol were again found preferable, achieving a two-fold increase in
recovered polyphenols. Our objective was to obtain an extract using green and food-grade
solvents, with water being a highly cost-effective option. A similar hydroethanolic solution
could be employed in a future investigation.
Regarding the antioxidant tests, the data displayed a greater variance compared to
those of the TPC. For instance, the corresponding range for the FRAP assay was from 35.07
to 49.63
µ
mol AAE/g, in the DPPH assay was from 12.05 to 27.34
µ
mol AAE/g revealing a
two-fold increase, and in the H
2
O
2
assay was from 98.58 to 238.56
µ
mol AAE/g, showing
almost a two-and-a-half-fold increase. In the previously mentioned study by Aazza [
38
],
the antioxidant capacity was measured at ~140
µ
mol AAE/g dw, three-fold higher than
our results, highlighting this interesting trend. As such, via the optimization process of the
extraction, the antioxidant properties of the extracts can significantly be improved.
AgriEngineering 2024,61307
Table 2. Experimental findings for the three investigated independent variables and the dependent
variables’ responses.
Design
Point
Independent Variables
Responses
TPC (mg GAE/g dw) FRAP (µmol AAE/g)
DPPH (
µ
mol AAE/g)
Hydrogen Peroxide
(µmol AAE/g)
X1
(R, mL/g)
X2
(T,◦C)
X3
(t, min) Actual
Predicted
Actual
Predicted
Actual
Predicted
Actual
Predicted
1−1 (20) −1 (20) 0 (90) 7.28 7.17 41.49 40.54 18.68 17.91 110.91 110.75
2−1 (20) 1 (80) 0 (90) 8.53 8.50 37.72 37.70 18.43 19.14 120.25 120.77
3 1 (50) −1 (20) 0 (90) 8.78 8.81 45.36 45.38 25.52 24.81 229.85 229.33
4 1 (50) 1 (80) 0 (90) 8.32 8.43 39.90 40.85 12.05 12.82 198.05 198.21
5 0 (35) −1 (20) −1 (30) 8.42 8.42 43.61 43.88 26.94 27.48 164.02 174.19
6 0 (35) −1 (20) 1 (150) 8.59 8.67 42.64 43.30 18.07 19.01 163.36 153.88
7 0 (35) 1 (80) −1 (30) 9.60 9.52 45.67 45.01 22.29 21.35 148.92 158.40
8 0 (35) 1 (80) 1 (150) 8.51 8.51 35.07 34.80 14.92 14.38 158.73 148.57
9−1 (20) 0 (50) −1 (30) 8.58 8.69 44.69 45.37 24.15 24.38 98.58 88.58
10 1 (50) 0 (50) −1 (30) 9.77 9.74 49.63 49.34 27.34 27.52 238.56 228.92
11 −1 (20) 0 (50) 1 (150) 8.53 8.56 39.64 39.94 19.69 19.52 106.19 115.83
12 1 (50) 0 (50) 1 (150) 9.21 9.10 44.65 43.97 17.17 16.94 161.51 171.51
13 0 (35) 0 (50) 0 (90) 9.20 9.21 45.51 45.88 18.70 18.19 146.60 149.81
14 0 (35) 0 (50) 0 (90) 9.20 9.21 46.01 45.88 18.56 18.19 153.46 149.81
15 0 (35) 0 (50) 0 (90) 9.23 9.21 46.11 45.88 17.32 18.19 149.36 149.81
Table 3presents the measured amounts of the identified polyphenols with the use of
HPLC-DAD. Several types of polyphenols were quantified such as anthocyanins (pelargonin,
from 0.41 to 1.53 mg/g dw), phenolic acids (ferulic acid, from 0.07 to 0.17 mg/g dw), and
flavonoids (luteolin-7-glucoside, from 0.18 to 0.34 mg/g, and kaempferol-3-glucoside, from
0.18 to 0.44 mg/g dw), with a total sum of 0.84–2.84 mg/g dw. Pelargonin measured
concentration was highest in the design point 10, which was previously indicated as the
optimum sample, whereas the design points 4 and 12 had the highest concentrations in the
other three polyphenols, revealing an interesting trend. In all three optimum design points,
the liquid–solid ratio had a major impact on the extraction of polyphenols compared to the
other variables, which will be further discussed below.
Table 3. Coded values of the three independent variables under investigation and the actual concen-
tration of the polyphenolic compounds, represented in mg/g dw.
Design
Point
Independent Variables Pelargonin Ferulic
Acid Luteolin-7-Glucoside Kaempferol-3-Glucoside
X1(R, mL/g) X2(T,◦C) X3(t, min)
1−1 (20) −1 (20) 0 (90) 0.41 0.07 0.25 0.18
2−1 (20) 1 (80) 0 (90) 0.46 0.11 0.22 0.21
3 1 (50) −1 (20) 0 (90) 0.55 0.16 0.33 0.44
4 1 (50) 1 (80) 0 (90) 0.88 0.17 0.34 0.44
5 0 (35) −1 (20) −1 (30) 0.68 0.13 0.26 0.31
6 0 (35) −1 (20) 1 (150) 0.75 0.12 0.32 0.31
7 0 (35) 1 (80) −1 (30) 1.31 0.14 0.27 0.31
8 0 (35) 1 (80) 1 (150) 0.47 0.14 0.26 0.31
9−1 (20) 0 (50) −1 (30) 0.83 0.09 0.18 0.18
10 1 (50) 0 (50) −1 (30) 1.53 0.16 0.31 0.43
11 −1 (20) 0 (50) 1 (150) 0.74 0.09 0.2 0.19
12 1 (50) 0 (50) 1 (150) 1.08 0.17 0.33 0.44
13 0 (35) 0 (50) 0 (90) 1.27 0.13 0.25 0.33
14 0 (35) 0 (50) 0 (90) 1.26 0.13 0.26 0.34
15 0 (35) 0 (50) 0 (90) 1.27 0.13 0.27 0.32
3.2. Other Bioactive Compounds, and Biological and Physicochemical Determination of Extracts
Carotenoids are pigments present in many plants that have a significant association
with color properties. The amount of carotenoids present is an important quality criterion
since it directly affects the visual features of the end product [
39
]. Regarding the content
of both total carotenoids and ascorbic acid, along with the anti-inflammatory activity, the
AgriEngineering 2024,61308
results are shown in Table 4and reveal an interesting trend. The TCC ranged from 283.27
to 371.29
µ
g CtE/g, whereas AAC ranged from 215.88 to 526.94
µ
g/g, suggesting almost a
two-and-a-half-fold difference between the design points 11 and 7. In a study by Spano
et al. [
40
], similar TCC was observed between seven cultivars of C. sativa which ranged
from 106 to 317
µ
g/g. They used ultrasonication-assisted extraction with methanol as the
extraction solvent. It was observed that the impact of the seven cultivars highly affected the
TCC. Finally, the anti-inflammatory activity ranged from 7.82 to 75.12%, revealing a ten-fold
difference between the design points 2 and 3. The interesting trend lies in the fact that
the samples with a high TCC showed a low AAC and low anti-inflammatory activity, and
vice versa. For example, the design point 2 sample showed the highest anti-inflammatory
activity, but moderate AAC. A possible synergism of polyphenols, carotenoids, ascorbic
acid, and other unidentified cannabinoids could explain this trend.
Table 4. Coded values of the three investigated independent variables and the actual concentration
of the total carotenoids, ascorbic acid content, and anti-inflammatory activity.
Design Point Independent Variables Carotenoids
(µg CtE/g)
Ascorbic Acid
(µg/g)
Anti-Inflammatory
Activity (%)
X1(R, mL/g) X2(T,◦C) X3(t, min)
1−1 (20) −1 (20) 0 (90) 288.02 410.18 35.5
2−1 (20) 1 (80) 0 (90) 322.56 442.06 75.12
3 1 (50) −1 (20) 0 (90) 328.2 434.54 7.82
4 1 (50) 1 (80) 0 (90) 305.28 407.05 50.48
5 0 (35) −1 (20) −1 (30) 283.27 402.03 8.30
6 0 (35) −1 (20) 1 (150) 342.12 455.64 17.16
7 0 (35) 1 (80) −1 (30) 361.57 526.94 67.51
8 0 (35) 1 (80) 1 (150) 290.71 381.12 62.66
9−1 (20) 0 (50) −1 (30) 333.51 444.19 64.10
10 1 (50) 0 (50) −1 (30) 353.67 264.83 9.68
11 −1 (20) 0 (50) 1 (150) 338.83 215.88 63.37
12 1 (50) 0 (50) 1 (150) 371.12 323.14 33.29
13 0 (35) 0 (50) 0 (90) 360.51 248.93 53.64
14 0 (35) 0 (50) 0 (90) 371.29 272.56 48.31
15 0 (35) 0 (50) 0 (90) 365.66 257.03 49.60
Figure 1contains the results of the color analysis. The L* coordinate showed consider-
able variance among the samples, with the design points 9 and 11 having the lowest values
(~32) and the design point 4 (~50) having the highest value. A similar trend regarding
these samples was also observed in the C* coordinate. The design point 9 gave the lowest
value in the C* coordinate (~16), whereas the design point 4 was found among the highest
values (~30). However, these values were considerably low, resulting in non-saturated
(achromatic) extracts. In the case of hue, most extracts had values above 60, as the majority
of the extracts had a greenish-to-brownish color.
The developed models are suggested to be well fitting by the second-order polynomial
equations (models), statistical parameters, and coefficients (
≥
0.97) which are shown in
Table 5. The desirability functions and plots of the actual response versus the predicted
response for each examined parameter are presented in Figures S1–S4, which will be
further explained below. Figure 2presents the three-dimensional response plots for the
TPC. Regarding the TPC, it can be concluded that the highest performance was with
relatively medium values for the parameters X
1
(~50 mL/g) and X
2
(~50
◦
C), as can be
seen in Figure 2A. The optimum conditions were ~45 mL/g and ~20 min of extraction in
Figure 2B, whereas ~65
◦
C and ~25 min of extraction was the best combination as could be
concluded in Figure 2C. The interpretation of the three-dimensional response plots for the
rest of the responses in Figures S5–S7 lies on a similar rationale.
AgriEngineering 2024,61309
AgriEngineering 2024, 6 1309
12 1 (50) 0 (50) 1 (150) 371.12 323.14 33.29
13 0 (35) 0 (50) 0 (90) 360.51 248.93 53.64
14 0 (35) 0 (50) 0 (90) 371.29 272.56 48.31
15 0 (35) 0 (50) 0 (90) 365.66 257.03 49.60
Figure 1 contains the results of the color analysis. The L* coordinate showed
considerable variance among the samples, with the design points 9 and 11 having the
lowest values (~32) and the design point 4 (~50) having the highest value. A similar trend
regarding these samples was also observed in the C* coordinate. The design point 9 gave
the lowest value in the C* coordinate (~16), whereas the design point 4 was found among
the highest values (~30). However, these values were considerably low, resulting in non-
saturated (achromatic) extracts. In the case of hue, most extracts had values above 60, as
the majority of the extracts had a greenish-to-brownish color.
Figure 1. Color coordinate analysis of the C. sativa extracts.
The developed models are suggested to be well fiing by the second-order
polynomial equations (models), statistical parameters, and coefficients (≥0.97) which are
shown in Table 5. The desirability functions and plots of the actual response versus the
predicted response for each examined parameter are presented in Figures S1–S4, which
will be further explained below. Figure 2 presents the three-dimensional response plots
for the TPC. Regarding the TPC, it can be concluded that the highest performance was
with relatively medium values for the parameters X1 (~50 mL/g) and X2 (~50 °C), as can be
seen in Figure 2A. The optimum conditions were ~45 mL/g and ~20 min of extraction in
Figure 2B, whereas ~65 °C and ~25 min of extraction was the best combination as could be
concluded in Figure 2C. The interpretation of the three-dimensional response plots for the
rest of the responses in Figures S5–S7 lies on a similar rationale.
Figure 1. Color coordinate analysis of the C. sativa extracts.
AgriEngineering 2024, 6 1310
Table 5. Mathematical models generated through the RSM that were used to optimize the extraction
process of C. sativa. The models contained only significant terms.
Responses Second-Order Polynomial Equations (Models) R2 P Eq.
TPC Y = –1.98 + 0.2X1 + 0.13X2 + 0.001X3 − 0.002X12 − 0.001X22 + 0.0001X32 − 0.001X1X2
− 0.0001X1X3 − 0.0001X2X3 0.9873 0.0003 (11)
FRAP
Y
= 24.37 + 0.47X1 + 0.52X2 + 0.04X3 − 0.004X12 − 0.004X22 − 0.0001X32 − 0.001X1X2
+ 0.0001X1X3 − 0.001X2X3 0.9795 0.0011 (12)
DPPH Y = 21.73 + 0.21X1 + 0.21X2 + 0.04X3 − 0.16X12 − 0.001X22 + 0.001X32 − 0.007X1X2 −
0.002X1X3 + 0.0002X2X3 0.9784 0.0012 (13)
Hydrogen
Peroxide
Y = –5.72 + 5.37X1 − 0.76X2 + 0.74X3 + 0.02X12 + 0.01X22 − 0.001X32 − 0.02X1X2 −
0.02X1X3 + 0.002X2X3 0.966 0.0036 (14)
Figure 2. The optimal extraction of the C. sativa extracts is shown in 3D graphs that show the impact
of the process variables considered in the response (total polyphenol content—TPC, mg GAE/g).
Plot (A), covariation of X1 and X2; plot (B), covariation of X1 and X3; plot (C), covariation of X2 and
X3.
3.3. Optimal Extraction Conditions
Improving efficiency necessitates optimizing the extraction parameters. Diverse
bioactive component structures could introduce challenges to the extraction procedure
due to variations in solubility and polarity [41]. Moreover, the yield, composition, and
antioxidant activity of the extract are significantly impacted by the extraction method and
a multitude of processing parameters; thus, it is imperative to optimize this procedure
[42]. Over the past decade, there has been a notable emphasis on extraction methods that
aim to reduce the utilization of environmentally hazardous and noxious solvents,
safeguard human health, and minimize energy consumption. The implementation of
Figure 2. The optimal extraction of the C. sativa extracts is shown in 3D graphs that show the impact
of the process variables considered in the response (total polyphenol content—TPC, mg GAE/g). Plot
(A), covariation of X1and X2; plot (B), covariation of X1and X3; plot (C), covariation of X2and X3.
AgriEngineering 2024,61310
Table 5. Mathematical models generated through the RSM that were used to optimize the extraction
process of C. sativa. The models contained only significant terms.
Responses Second-Order Polynomial Equations (Models) R2PEq.
TPC
Y= –1.98 + 0.2X
1
+ 0.13X
2
+ 0.001X
3−
0.002X
12−
0.001X
22
+
0.0001X32−0.001X1X2−0.0001X1X3−0.0001X2X30.9873 0.0003 (11)
FRAP
Y= 24.37 + 0.47X
1
+ 0.52X
2
+ 0.04X
3−
0.004X
12−
0.004X
22−
0.0001X32−0.001X1X2+ 0.0001X1X3−0.001X2X30.9795 0.0011 (12)
DPPH Y= 21.73 + 0.21X1+ 0.21X2+ 0.04X3−0.16X12−0.001X22+
0.001X32−0.007X1X2−0.002X1X3+ 0.0002X2X30.9784 0.0012 (13)
Hydrogen Peroxide Y= –5.72 + 5.37X1−0.76X2+ 0.74X3+ 0.02X12+ 0.01X22−
0.001X32−0.02X1X2−0.02X1X3+ 0.002X2X30.966 0.0036 (14)
3.3. Optimal Extraction Conditions
Improving efficiency necessitates optimizing the extraction parameters. Diverse bioac-
tive component structures could introduce challenges to the extraction procedure due to
variations in solubility and polarity [
41
]. Moreover, the yield, composition, and antiox-
idant activity of the extract are significantly impacted by the extraction method and a
multitude of processing parameters; thus, it is imperative to optimize this procedure [
42
].
Over the past decade, there has been a notable emphasis on extraction methods that aim
to reduce the utilization of environmentally hazardous and noxious solvents, safeguard
human health, and minimize energy consumption. The implementation of environmentally
friendly solvents such as water is key for this approach [
43
]. Despite not being the optimum
extraction solvent, water could effectively extract polyphenols across plant tissues [
44
].
For instance, flavonoid glucosides are more water-soluble compounds than aglycones [
45
],
as was previously observed through HPLC-DAD analysis. The liquid–solid ratio is also
a parameter that is often investigated and optimized. As the liquid–solid ratio increases,
the amount of compounds obtained also increases, irrespective of the solvent used [
46
].
Furthermore, it is essential to optimize the extraction duration and temperature to reduce
the energy usage caused by the procedure. A thorough evaluation must be conducted to
determine the effect of time on extraction, given that previous studies have established the
effectiveness of both rapid [
47
] and prolonged [
48
] extraction durations. Inevitably, several
factors influence the duration of the extraction, one of which is the matrix composition.
Moreover, elevated temperatures are known to have an advantageous impact on extrac-
tion processes by facilitating increased solubility of solutes and strengthening diffusion
coefficients. Despite this, it is necessary to be aware that polyphenolic compounds could
decompose beyond a certain threshold, given the fact that polyphenols are thermolabile
compounds [
49
]. The usual temperature range for conventional extraction methods to
produce the greatest recovery of polyphenols is typically between 40 and 80
◦
C [
50
,
51
]. It
should be noted that differences in polarity and solubility among bioactive components
may introduce complexity into the extraction process due to their diverse structures.
To that end, in order to identify the highest anticipated values for the TPC and
antioxidant activity (measured by the FRAP, DPPH, and H
2
O
2
assays), the desirability
function was employed. The highest values of the assays were accomplished through
different extraction conditions. To effectively quantify the TPC from C. sativa at a predicted
value of 9.80 mg GAE/g dw, a 30 min extraction was required with a 44:1 liquid–solid ratio
at 55
◦
C. The same duration was necessitated for the maximum predicted response for
the antioxidant assays FRAP and DPPH, whereas a significantly low temperature (26
◦
C)
was required for the optimum H
2
O
2
scavenging activity. Further details about the optimal
extraction conditions are depicted in Table 6below.
AgriEngineering 2024,61311
Table 6. Maximum predicted responses and optimum extraction conditions for the dependent
variables.
Responses
Optimal Conditions
Maximum Predicted
Response
R(mL/g)
(X1)
T(◦C)
(X2)
t(min)
(X3)
TPC (mg GAE/g) 9.80 ±0.20 44 55 30
FRAP (µmol AAE/g) 49.34 ±2.02 50 50 30
DPPH (µmol AAE/g) 30.41 ±2.38 47 30 30
Hydrogen Peroxide (µmol AAE/g) 241.67 ±27.80 50 26 50
3.4. Principal Component Analysis (PCA) and Multivariate Correlation Analysis (MCA)
A PCA was employed to derive more information from the variables and conduct a
more thorough data analysis. The focus of this investigation was to examine whether there
was a correlation between the TPC and anti-inflammatory activity, antioxidant compounds
(ascorbic acid and individual polyphenols), antioxidant assays (i.e., FRAP, DPPH, and
H
2
O
2
), color coordinates, and carotenoids. The two principal components illustrated in
Figure 3were chosen based on their eigenvalues > 1. These components accounted for a
combined 74.00% of the variance. The results indicated whether the parameters exhibited a
negative or a positive correlation. For example, the anthocyanin pelargonin has a positive
correlation with the TPC and antioxidant assays, as one would anticipate. A key point to
highlight, due to its intriguing discovery, was the negative correlation between the ascorbic
acid concentration and the variables in question. This is noteworthy since ascorbic acid is
a chemical compound of huge biological significance. Furthermore, another unexpected
negative correlation that might be observed was the one between the anti-inflammatory
and H
2
O
2
scavenging activity, which might occur due to synergism or antagonism between
the bioactive compounds.
AgriEngineering 2024, 6 1312
Furthermore, another unexpected negative correlation that might be observed was the one
between the anti-inflammatory and H2O2 scavenging activity, which might occur due to
synergism or antagonism between the bioactive compounds.
Figure 3. Principal component analysis (PCA) for the measured variables.
In addition, an MCA was conducted to further assess the association between the
examined variables. The main advantage of this analysis over the previous is its capacity
to quantify how much positive or negative correlation the variables have with one
another. The color map used in this context employs a color scale that represents
correlation values ranging from −1 to 1, as indicated in the following caption. The
outcomes of this analysis are shown in Figure 4, where the previously mentioned negative
correlation between ascorbic acid and the TPC seems non-significantly high (<0.8).
Furthermore, interesting enough were the findings that the anti-inflammatory activity had
a negative correlation (>0.6) with individual polyphenols luteolin-7-glucoside, along with
the H2O2 scavenging activity. On the contrary, the TCC had a strong positive correlation
(>0.8) with pelargonin, and a generally good positive correlation with the TPC and FRAP,
but not with DPPH and H2O2 assays.
Figure 3. Principal component analysis (PCA) for the measured variables.
AgriEngineering 2024,61312
In addition, an MCA was conducted to further assess the association between the
examined variables. The main advantage of this analysis over the previous is its capacity
to quantify how much positive or negative correlation the variables have with one another.
The color map used in this context employs a color scale that represents correlation values
ranging from
−
1 to 1, as indicated in the following caption. The outcomes of this analysis
are shown in Figure 4, where the previously mentioned negative correlation between
ascorbic acid and the TPC seems non-significantly high (<0.8). Furthermore, interesting
enough were the findings that the anti-inflammatory activity had a negative correlation
(>0.6) with individual polyphenols luteolin-7-glucoside, along with the H
2
O
2
scavenging
activity. On the contrary, the TCC had a strong positive correlation (>0.8) with pelargonin,
and a generally good positive correlation with the TPC and FRAP, but not with DPPH and
H2O2assays.
AgriEngineering 2024, 6 1313
Figure 4. Multivariate correlation analysis of measured variables.
3.5. Partial Least Squares (PLS) Analysis
A PLS analysis was conducted to determine the essential extraction parameters (X1,
X2, and X3). Figure 5 depicts the application of PLS analysis to generate a correlation
loading plot, which graphically displays the extraction conditions of C. sativa. A higher
variable significance for the projection (VIP) factor, especially over 0.8, indicates a bigger
contribution from this variable. According to the results, X1 and X3 (i.e., liquid–solid ratio
and extraction duration) were shown to be the key factors affecting the extraction of the
bioactive compounds, demonstrating a much higher relevance compared to the other
variables. This trend was also confirmed in the assessment of the H2O2 scavenging activity.
As mentioned before, it is advantageous for the mixture to have high values of this ratio
and a low extraction time (i.e., 50 mL/g and 40 min) in order to ensure favorable results.
Factor X2 (extraction temperature) has lile effect on the optimization of the extraction
process. It was observed that extracting for more than 45 min had an adverse effect on the
extraction process, which has also been reported elsewhere [52], whereas the temperature
was found to reach a plateau at 50 °C.
Figure 4. Multivariate correlation analysis of measured variables.
3.5. Partial Least Squares (PLS) Analysis
A PLS analysis was conducted to determine the essential extraction parameters (X
1
,
X
2
, and X
3
). Figure 5depicts the application of PLS analysis to generate a correlation
loading plot, which graphically displays the extraction conditions of C. sativa. A higher
variable significance for the projection (VIP) factor, especially over 0.8, indicates a bigger
contribution from this variable. According to the results, X
1
and X
3
(i.e., liquid–solid
ratio and extraction duration) were shown to be the key factors affecting the extraction of
the bioactive compounds, demonstrating a much higher relevance compared to the other
AgriEngineering 2024,61313
variables. This trend was also confirmed in the assessment of the H
2
O
2
scavenging activity.
As mentioned before, it is advantageous for the mixture to have high values of this ratio
and a low extraction time (i.e., 50 mL/g and 40 min) in order to ensure favorable results.
Factor X
2
(extraction temperature) has little effect on the optimization of the extraction
process. It was observed that extracting for more than 45 min had an adverse effect on the
extraction process, which has also been reported elsewhere [
52
], whereas the temperature
was found to reach a plateau at 50 ◦C.
AgriEngineering 2024, 6 1314
Figure 5. Partial least squares (PLS) prediction profiler of each examined variable and desirability
function with extrapolation control for the optimization of the C. sativa extracts.
The results of the experimental analysis and the values provided by the PLS model
are highly correlated (0.9999) and do not deviate from each other (p < 0.0001). Table 7
represents the PLS-predicted values along with the experimental values of the TPC and
antioxidant assays, in which the optimum parameters were found to be 50 mL/g, 30 min
extraction at 50 °C. Table 8 depicts the values of several parameters (individual
antioxidant compounds, and physicochemical and biological properties) in these
optimum extraction conditions.
Table 7. Maximum desirability for all examined variables using the partial least squares (PLS)
prediction profiler under the optimal extraction conditions (X1:50, X2:50, X3:30).
Variab l es PLS Model Values Experimental Values
TPC (mg GAE/g) 9.74 9.76 ± 0.47
FRAP (µmol AAE/g) 49.34 49.79 ± 3.24
DPPH (µmol AAE/g) 27.52 27.43 ± 0.63
Hydrogen Peroxide (µmol AAE/g) 228.92 230.95 ± 9.7
Figure 5. Partial least squares (PLS) prediction profiler of each examined variable and desirability
function with extrapolation control for the optimization of the C. sativa extracts.
The results of the experimental analysis and the values provided by the PLS model are
highly correlated (0.9999) and do not deviate from each other (p< 0.0001). Table 7represents
the PLS-predicted values along with the experimental values of the TPC and antioxidant
assays, in which the optimum parameters were found to be 50 mL/g, 30 min extraction at
50
◦
C. Table 8depicts the values of several parameters (individual antioxidant compounds,
and physicochemical and biological properties) in these optimum extraction conditions.
AgriEngineering 2024,61314
Table 7. Maximum desirability for all examined variables using the partial least squares (PLS)
prediction profiler under the optimal extraction conditions (X1:50, X2:50, X3:30).
Variables PLS Model Values Experimental Values
TPC (mg GAE/g) 9.74 9.76 ±0.47
FRAP (µmol AAE/g) 49.34 49.79 ±3.24
DPPH (µmol AAE/g) 27.52 27.43 ±0.63
Hydrogen Peroxide (µmol AAE/g) 228.92 230.95 ±9.7
Table 8. Different parameter and polyphenolic compound analyses under optimal extraction condi-
tions (X1:50, X2:50, X3:30).
Parameters Optimal Extract
Carotenoids (µg CtE/g) 356.98 ±24.63
Ascorbic Acid (µg/g) 282.23 ±10.72
Anti-Inflammatory Activity (%) 17.58 ±0.69
L* 44.8 ±0.2
C* 29.5 ±0.5
Hue 71.4 ±0.1
Polyphenolic compounds (mg/g)
Pelargonin 1.51 ±0.07
Ferulic Acid 0.17 ±0.01
Luteolin-7-glucoside 0.35 ±0.02
Kaempferol-3-glucoside 0.45 ±0.02
Following optimization, the TPC showed minor modifications from the initial RSM
results, leading to 9.76 mg GAE/g, whereas major polyphenols pelargonin, ferulic acid,
luteolin-7-glucoside, and kaempferol-3-glucoside reached values of 1.51, 0.17, 0.35, and
0.45 mg/g, respectively. In a study by Ferrante et al. [
53
], the TPC of four aqueous extracts
of four cultivars of C. sativa were measured from 4.7 to 8.1 mg GAE/g dw using ultrasound-
assisted extraction, leading to comparable results with our study. It should be noted that
by using the same extraction solvent (i.e., water) but employing a different extraction
method which was more simple, we were able to achieve a more effective extraction of
polyphenols. This outcome could be attributed to the process of optimizing the extraction
conditions. In a similar study, Izzo et al. [
54
] studied the polyphenolic compounds of four
commercial C. sativa inflorescence methanolic extracts. They averaged ~30 mg GAE/g, a
value higher than we found. The hemp cultivar and extraction solvent could explain these
differences in polyphenol recovery. However, the measured individual non-cannabinoid
polyphenols were significantly lower than our values, as they averaged ~0.015 mg/g of
luteolin-7-glucoside, ~0.015 mg/g of kaempferol-3-glucoside, and 0.023 mg/g of ferulic
acid. Regarding TCC, Irakli et al. [
4
] investigated the antioxidant properties of seven differ-
ent industrial cultivars of defatted hemp seeds. The concentration of the total carotenoids
(expressed as the sum of
β
-carotene and zeaxanthin) ranged from 14 to 43
µ
g/g dw, whereas
our optimal extract yielded 356.98
µ
g CtE/g. Specifically, methanolic hemp seed extract of
Finola var. was found to have 29
µ
g/g of total carotenoids, indicating a significantly lower
value compared to hemp leaf extract.
AgriEngineering 2024,61315
4. Conclusions
The purpose of this study was to examine the best extraction method for bioactive
components recovered from C. sativa by thoroughly investigating and optimizing various
conditions. Water, a high-polarity environmentally friendly solvent, was used for the
experiments. In contrast to the PLS analysis, which revealed which parameters had a major
impact on the extraction, the RSM enabled the correct adjustment of the extraction parame-
ters. Reducing the extraction time and increasing the liquid–solid ratio were determined to
have a positive impact on the effectiveness. The extraction process was determined to be
quite unaffected by the temperature. Overall, a combination of the liquid–solid ratio (50:1),
extraction time (30 min), and temperature (50
◦
C) were found to be the most preferable
for maximum polyphenol recovery (9.76 mg GAE/g). On the other hand, the suitable
adjustment of the parameters would lead to extracts with a high anti-inflammatory effect
(75%). However, a negative correlation between the total individual polyphenols and
anti-inflammatory activity was revealed through the use of MCA and PCA. The findings
of the study highlight the potential of C. sativa to supply the pharmaceutical and food
sectors with bioactive compounds that improve health, owing to the high added-value of
the water extracts.
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/agriengineering6020075/s1, Table S1 presents the model validation
statistics using the k-fold cross-validation technique. Figures S1–S4 comprise the plots that illustrate
the comparison between the actual response and the predicted response for each parameter under
examination, accompanied by the desirability functions. Figures S5–S7 present the three-dimensional
response plots for the remaining responses.
Author Contributions: Conceptualization, V.A., T.C. and S.I.L.; methodology, V.A., T.C. and S.I.L.;
software, V.A. and T.C.; validation, V.A., T.C., D.K., I.M. and E.B.; formal analysis, V.A. and T.C.;
investigation, V.A. and T.C.; resources, S.I.L.; data curation, V.A., T.C. and S.I.L.; writing—original
draft preparation, V.A. and D.K.; writing—review and editing, V.A., T.C., D.K., I.M., E.B. and S.I.L.;
visualization, V.A. and T.C.; supervision, S.I.L.; project administration, S.I.L. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All related data and methods are presented in this paper. Additional
inquiries should be addressed to the corresponding author.
Acknowledgments: The authors would like to thank the CBD Extraction I.K.E. (Farsala, Greece) for
donating hemp (Cannabis sativa var. Finola) leaf material.
Conflicts of Interest: The authors declare no conflicts of interest.
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