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Citation: Saldaña-Olguin, M.;
Quispe-Ciudad, B.J.; Aguirre, E.
Antioxidant Activity of Zingiber
officinale R. Extract Using Pressurized
Liquid Extraction Method.
AgriEngineering 2024,6, 3875–3890.
https://doi.org/10.3390/
agriengineering6040220
Academic Editor: Pankaj B. Pathare
Received: 10 September 2024
Revised: 13 October 2024
Accepted: 18 October 2024
Published: 24 October 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
Antioxidant Activity of Zingiber officinale R. Extract Using
Pressurized Liquid Extraction Method
Marlon Saldaña-Olguin *, Bernardo Junior Quispe-Ciudad and Elza Aguirre
Department of Agroindustrial Engineering, Faculty of Engineering, Universidad Nacional del Santa,
Nuevo Chimbote 02712, Peru; eaguirre@uns.edu.pe (E.A.)
*Correspondence: keim4772@gmail.com
Abstract: Global food demand is rising, leading to increased food waste, which contains underutilized
bioactive compounds. The Pressurized Liquid Extraction (PLE) method employs high temperature
and pressure to maintain the solvent in a liquid state above its boiling point, thereby minimizing
extraction time and solvent usage. Ginger waste is known to contain bioactive compounds with
significant antioxidant activity. We aimed to assess the effect of temperature, time, and particle size
on the total phenolic content (TPC) and antioxidant activity (AA) of ginger (Zingiber officinale R.)
waste aqueous extract using the PLE method. A Box–Behnken design with 16 runs was employed.
Each extraction utilized 40 g of the sample and was conducted at a constant pressure of 20 bar with a
solvent ratio of 27:1 mL/g. Data analysis was performed with Minitab
®
19.1 (64-bit). TPC ranged
from 10.42 to 14.1 mg GAE/g, and AA ranged from 72.9 to 111.9
µ
mol TE/g. The model explained
81.07% of AA’s total variability. Positive correlation was found between TPC and AA (Pearson’s
ρ
= 0.58, p< 0.05). The optimized extraction conditions were a temperature of 126
◦
C, an extraction
time of 38 min, and a particle size between 355 and 500
µ
m. Temperature significantly influenced
AA (p< 0.05), while time and particle size were not significant factors. To enhance future research,
conducting nutritional and functional studies on the extracted compounds would provide valuable
insights. Lastly, evaluating the economic feasibility of using PLE for ginger waste valorization should
be considered to support its commercial application.
Keywords: antioxidant activity; aqueous extract; ginger; phenolic compounds; pressurized liquid extraction
1. Introduction
Global food industry generates approximately 1.6 billion tons of food waste annually,
creating significant economic, environmental, and social impacts [
1
,
2
]. Despite the food
system’s success in increasing per-capita food supply by over 30% since 1961, this growth
has led to substantial waste and by-products, posing ethical, social, economic, and envi-
ronmental challenges. Addressing food waste is crucial, as it can be repurposed as natural
sources of bioactive compounds, organic fertilizers, animal feed, biopesticides or bioplas-
tics [
3
]. Therefore, due to the significant global impact of the increasing demand for food, a
larger volume of food waste is generated; however, these wastes are not properly utilized.
Ginger (Zingiber officinale Roscoe), a member of the Zingiberaceae family, has many
biologically active compounds with antioxidant properties. Gingerols, shogaols, and
zingerone were found in ginger waste [
4
]. In 2016, the global ginger production reached
3.3 million tons, resulting in a substantial volume of ginger waste generated by industry [
5
].
Ginger waste is typically either burned, discarded in landfills, or processed into ginger
waste meal, which serves as a low-quality feed for animals [4].
Ginger is renowned for its bioactive compounds, including phenolic compounds, which
exhibit potent antioxidant activity [
6
]. Antioxidants present in ginger waste can help shield
the body from oxidative stress and reduce the risk of chronic diseases such as cancer, diabetes,
and heart conditions. Notably, ginger peels demonstrate higher antioxidant activity compared
AgriEngineering 2024,6, 3875–3890. https://doi.org/10.3390/agriengineering6040220 https://www.mdpi.com/journal/agriengineering
AgriEngineering 2024,63876
to the root, leaf, and stem of the ginger plant [
4
]. Oxidative stress, primarily caused by reactive
oxygen species (ROS), can damage nucleic acids, proteins, and lipids, leading to diseases
such as cancer and aging [
7
]. Phenolic compounds play a crucial role in plant defense and
human health owing to their antioxidant properties [
8
,
9
]. They help prevent lipid and protein
oxidation and protect against microbial activity, thereby extending the shelf life of food and
beverages [
2
,
10
]. Given the increasing preference for natural antioxidants over synthetic ones
due to carcinogenic concerns [
11
], exploring effective extraction methods for these bioactive
compounds is of great interest. Polyphenols are traditionally extracted using solvents like
water, methanol, ethanol, or their mixtures [8,9].
A particular extraction method from PLE is Subcritical Water Extraction (SWE) method.
SWE emerges as an extraction method that employs liquid water under temperatures be-
tween 100
◦
C and 374
◦
C and high-pressure conditions, enhancing mass transfer rates,
absorption into the particle matrix, and selectivity [
12
]. Under these conditions, water’s
properties shift, resembling non-polar solvents like acetone, ethanol, or DMSO, significantly
reducing its dielectric constant and increasing its diffusivity [
6
]. This distinct property of
subcritical water enables it to be used as the sole extraction fluid, eliminating the need
for any co-solvents like acids, alkalis, catalysts, or organic solvents [
8
]. These changes
promote faster and higher-yield extractions [
13
]. SWE has been widely recognized for
its effectiveness in extracting various bioactive compounds from plant-based raw materi-
als [
14
]. SWE’s advantages include short extraction times, minimal downstream processing,
solvent recyclability, the non-requirement of catalysts, and the preservation of functional
groups [
15
]. Thus, SWE offers high selectivity, high extraction efficiency, low economic
costs, sustainability and a reduced environmental footprint compared to traditional extrac-
tion methods [
16
–
19
]. Furthermore, response surface methodology (RSM) can optimize the
extraction process, reducing the number of experiments, solvent usage, and saving time,
while also revealing the relationships between experimental factors and responses [20].
Thus, the study aimed to evaluate the effect of temperature, time, and particle size
on the antioxidant activity of ginger (Zingiber officinale R.) waste extract through the PLE
method. This research seeks to address the growing demand for natural antioxidants,
leveraging green extraction techniques to minimize environmental impact and maximize
the yield and efficiency of antioxidants compound extraction from ginger.
2. Materials and Methods
2.1. Materials
Raw materials and supplies included powdered ginger peels processed from fresh
ginger purchased in a local supermarket located in 02804 Chimbote, Peru. The reagents
used were chromatographic grade methanol (J.T. Baker, Radnor, PA, USA), gallic acid mono-
hydrate
≥
98.5% ACS (Sigma-Aldrich, Shanghai, China), 2,2-diphenyl-1-picrylhydrazyl
(DPPH) 95% (Alfa Aesar, Karlsruhe, Germany), 6-hydroxy-2,5,7,8-tetramethylchroman-2-
carboxylic acid (Trolox) 97% (Sigma-Aldrich, Shanghai, China), sodium carbonate, Folin–
Ciocalteu reagent, and distilled water (ρ= 0.9982 g/cm3).
Common-use materials comprised 4% sodium hypochlorite (bleach), kitchen knives,
potato peelers, latex gloves, aluminum foil, paper towels, harvest crates, buckets, volumet-
ric pitchers, zip-lock bags, food cooler boxes, porcelain supports, metal spatulas, 250 mL
glass containers, steel knives, coolers, gel packs, and permanent markers.
2.2. Instruments
The equipment utilized throughout the procedural and analytical stages of the research
included the following: a multisolvent extractor (Top Industrie, series 2802.0000, Vaux-
le-Pénil, France), a sonicator (VWR International, SymphonyTM, 97043-942, Shanghai,
China), a spectrophotometer (Perkin Elmer
®
, LAMBDA 950, San Diego, CA, USA), an
analytical balance (Ohaus
®
, Discovery DV 214C, Aesch, Switzerland; RADWAG, model
PS 6000.R2, Radom, Poland), a water bath (Thermo Scientific™, AquaBath™, Waltham,
MA, USA), a muffle furnace (Thermolync, Type 1300 Furnace, Arlington, MA, USA), a
AgriEngineering 2024,63877
convection oven (POL-EKO, SLW 115, Wodzisław ´
Sl ˛aski, Poland), a water purifier (Thermo
Scientific™, Barnstead Nanopure
®
, model D 11911, Waltham, MA, USA), a tube shaker
(Thermolyne, model 16700 Maxi-Mix I, Boston, MA, USA), a magnetic stirrer (IKA, C-MAG
HS 7, Campinas, Brazil), and an electric sieve (RICELI, motor ½ HP, Lima, Peru).
The laboratory instruments included magnetic bars, 10 mL and 20 mL graduated
cylinders, 125 mL and 250 mL volumetric flasks, crucibles, 10 mL and 50 mL volumetric
pipettes, Petri dishes, micropipettes (100
µ
L, 250
µ
L, 500
µ
L, 1000
µ
L, and 5000
µ
L), mortars,
Whatman No. 40 filter paper (90 mm), pipettes, 250 mL and 500 mL graduated cylinders,
500 mL precipitation tubes, test tubes, vials, and flasks.
2.3. Sample Preparation and Characterization
The sample preparation process included washing raw material, sanitizing, cutting,
drying, grinding and sieving (see Figure 1).
AgriEngineering 2024, 6, FOR PEER REVIEW 3
2.2. Instruments
The equipment utilized throughout the procedural and analytical stages of the re-
search included the following: a multisolvent extractor (Top Industrie, series 2802.0000,
Vaux-le-Pénil, France), a sonicator (VWR International, SymphonyTM, 97043-942, Shang-
hai, China), a spectrophotometer (Perkin Elmer®, LAMBDA 950, San Diego, CA, USA), an
analytical balance (Ohaus®, Discovery DV 214C, Aesch, Swierland; RADWAG, model PS
6000.R2, Radom, Poland), a water bath (Thermo Scientific™, AquaBath™, Waltham, MA,
USA), a muffle furnace (Thermolync, Type 1300 Furnace, Arlington, MA, USA), a convec-
tion oven (POL-EKO, SLW 115, Wodzisław Śląski, Poland), a water purifier (Thermo Sci-
entific™, Barnstead Nanopure®, model D 11911, Waltham, MA, USA), a tube shaker (Ther-
molyne, model 16700 Maxi-Mix I, Boston, MA, USA), a magnetic stirrer (IKA, C-MAG HS
7, Campinas, Brazil), and an electric sieve (RICELI, motor ½ HP, Lima, Peru).
The laboratory instruments included magnetic bars, 10 mL and 20 mL graduated cyl-
inders, 125 mL and 250 mL volumetric flasks, crucibles, 10 mL and 50 mL volumetric pi-
pees, Petri dishes, micropipees (100 µL, 250 µL, 500 µL, 1000 µL, and 5000 µL), mortars,
Whatman No. 40 filter paper (90 mm), pipees, 250 mL and 500 mL graduated cylinders,
500 mL precipitation tubes, test tubes, vials, and flasks.
2.3. Sample Preparation and Characterization
The sample preparation process included washing raw material, sanitizing, cuing,
drying, grinding and sieving (see Figure 1).
Figure 1. General scheme of the experimental procedure.
RAW MATERIAL
CUTTING AND
DRYING
GRINDING
SIEVING
EXTRACTION
Convective
drying 60 °C X
12 h
ESSAY
Zingiber
officinale R.
rhizomes
WASHIN G AND
SANITIZING
Sample
preparation
Experimental
procedure
Analysis
ASTM sieves
#35, #20, #10
Experimental
conditions
TPC
DPPH
5 min
3 mm mesh size
150 ppm NaClO
10 min
Figure 1. General scheme of the experimental procedure.
AgriEngineering 2024,63878
2.3.1. Raw Material
A total of 60.00 kg of fresh ginger rhizomes, uniform in size, color, and maturity,
were purchased from the Plaza Vea supermarket in Chimbote, Santa Province, Ancash
(UBIGEO 21801, latitude-9.07444, longitude-78.5936). Processing and sample preparation
were conducted in the Microbiology and Toxicology Laboratory at the National University
of Santa.
2.3.2. Washing and Sanitizing
The fresh ginger rhizomes were washed to remove impurities and then immersed in
a chlorinated solution at a concentration of 150 ppm for 10 min to reduce the microbial
load. Afterward, the raw material was placed on paper towels to remove any residual
aqueous solution.
2.3.3. Cutting and Drying
The rhizomes were then cut and peeled using a conventional potato peeler, aiming
to obtain peels with uniform thickness and size to facilitate drying. The yield at this stage
was 19.40%, producing 11.64 kg of ginger peels. Then, the ginger peels were subjected to a
drying process in a convection oven (POL-EKO, SWL 115, Poland) at 60
◦
C for 12 h. The
process was carried out at 60
◦
C. The final weight of the peels after this procedure was
3274.60 g, representing a yield of 5.46% relative to the initial weight of the raw material.
2.3.4. Grinding and Sieving
The grinding process was carried out for 5 min using a mill with a 3 mm mesh screen.
The purpose was to achieve a ground product with a relatively homogeneous particle size
close to the desired size, and to minimize losses in the subsequent sieving process. The
powder was then sieved through ASTM #45, ASTM #35, ASTM #20, and ASTM #10 mesh
sieves. Seventeen samples of approximately 100 g were fractionated using an analytical
balance (Ohaus
®
Discovery DV 214C, Switzerland). Then, the sample was vacuum-packed,
labeled, and stored at 4 ±1◦C.
2.3.5. Characterization
Proximal analysis was performed using 100 g of the sample at the Laboratorio de Química,
Instituto Tecnológico de la Producción (ITP) located in Ventanilla, Callao, 07046 Peru. The
analyses included the determination of moisture (FAO, Food and Nutrition Paper pp. 205 T
14/7, 1986), crude fat (LABS-ITP-FQ-003-2009, Rev. 00, 2009), crude protein (LABS-ITP-FQ-
001-2009, Rev. 00, 2009), and ash (Food and Nutrition Paper pp. 228 T 14/7, 1986).
2.4. Pressurized Liquid Extraction (PLE)
This procedure was carried out using a multi-solvent extractor with a capacity of
1.70 L (Top Industrie series 2802.0000, France), following the methodology described by
Barriga-Sánchez and Rosales-Hartshorn [21].
2.4.1. Step 1: Loading the Extraction Cell in the Extractor
For each extraction run, the extraction cell—a detachable hollow unit—was loaded
with alternating layers of the sample (solute) and 5 mm glass microspheres. Approximately
40 g of the sample was distributed into four layers of 10 g each, interspersed with five layers
of glass microspheres (totaling 525 g). The extraction cell was then placed into the apparatus
and filled with approximately 1080 mL of solvent, as specified by the manufacturer (see
Figure 2). The solvent used was sonicated distilled water. Sonication conditions were
30 min at 25 ◦C in a sonicator (VWR International, SymphonyTM, 97043-942, China).
AgriEngineering 2024,63879
AgriEngineering 2024, 6, FOR PEER REVIEW 5
2.4.2. Step 2: Establishing Operation Conditions
The control software of the equipment allowed for the establishment of the desired
temperature (°C), and pressure. The pressure was 20 bar and constant in all experiments.
The general procedure consisted of programming the preheater (coil type) and reactor
temperature according to the conditions of each run of the experimental design.
Figure 2. Extraction cell loading process.
2.4.3. Step 3: Operation Control
From the equipment’s software, once the operating parameters were configured, the
equipment was allowed to reach appropriate temperature and pressure conditions, veri-
fying this information from the sensors and the software graphs. The operation time was
manually controlled.
2.4.4. Step 4: Extract Discharge
Upon completion of the extraction time (for each of the experimental runs), the ex-
tract was discharged and cooled in an ice bath for 10 min. Each aqueous extract was stored
at 4 ± 1 °C until further analysis.
2.5. Total Phenolic Content (TPC) Assay
TPC were determined using a modified Singleton et al. [22] method. A gallic acid
calibration curve was prepared, and samples reacted with Folin reagent and sodium car-
bonate. Absorbance was measured at 750 nm using UV–VIS spectrophotometry. The re-
sults were expressed as mg GAE/g of sample based on the calibration curve.
2.6. Antioxidant Activity (AA) Assay
AA was measured using the DPPH method modified from Brand-Williams et al. [23]
and Kim et al. [24]. A DPPH solution and a calibration curve with Trolox were prepared,
with absorbances measured at 518 nm using UV–VIS spectrophotometry. Subsequently,
the sample solutions were prepared and diluted, with absorbances recorded after 60 min.
The results were calculated in µmol ET/g of the sample, based on the calibration curve.
Figure 2. Extraction cell loading process.
2.4.2. Step 2: Establishing Operation Conditions
The control software of the equipment allowed for the establishment of the desired
temperature (
◦
C), and pressure. The pressure was 20 bar and constant in all experiments.
The general procedure consisted of programming the preheater (coil type) and reactor
temperature according to the conditions of each run of the experimental design.
2.4.3. Step 3: Operation Control
From the equipment’s software, once the operating parameters were configured,
the equipment was allowed to reach appropriate temperature and pressure conditions,
verifying this information from the sensors and the software graphs. The operation time
was manually controlled.
2.4.4. Step 4: Extract Discharge
Upon completion of the extraction time (for each of the experimental runs), the extract
was discharged and cooled in an ice bath for 10 min. Each aqueous extract was stored at
4±1◦C until further analysis.
2.5. Total Phenolic Content (TPC) Assay
TPC were determined using a modified Singleton et al. [
22
] method. A gallic acid
calibration curve was prepared, and samples reacted with Folin reagent and sodium
carbonate. Absorbance was measured at 750 nm using UV–VIS spectrophotometry. The
results were expressed as mg GAE/g of sample based on the calibration curve.
2.6. Antioxidant Activity (AA) Assay
AA was measured using the DPPH method modified from Brand-Williams et al. [
23
]
and Kim et al. [
24
]. A DPPH solution and a calibration curve with Trolox were prepared,
with absorbances measured at 518 nm using UV–VIS spectrophotometry. Subsequently, the
sample solutions were prepared and diluted, with absorbances recorded after 60 min. The
results were calculated in µmol ET/g of the sample, based on the calibration curve.
2.7. Experimental Design
A Box–Behnken 3
3
design with four center points and 16 runs was created using
Minitab
®
19.1 (64-bit). The design included the following three independent variables (fac-
tors): temperature (100–130
◦
C), extraction time (15–45 min), and particle size (A, B and C,
AgriEngineering 2024,63880
according to <355–500>, <500–850> and <850–2000>
µ
m, respectively). High-temperature
(between 100
◦
C and 374
◦
C) and high-pressure conditions enhances mass transfer rates, ab-
sorption into the particle matrix, and selectivity [
12
]. The response variables were TPC (mg
GAE/g sample) and AA (
µ
mol TE/g sample) from aqueous extracts of ginger peel powder.
2.8. Greenness Character and Applicability of the Method
To assess the greenness and applicability of the method, two established tools, AGREE [
25
]
and BAGI [
26
], were employed. The AGREE analysis indicates the alignment of the method
with green analytical chemistry principles. Additionally, the BAGI tool evaluates the balance
between green chemistry and method applicability.
2.9. Data Analysis
An analysis of variance (ANOVA) was used to evaluate the effect of factors on the
response variables. Then, standardized effects, response surface diagrams, and correlating
response variables with Pearson’s product–moment test were performed. Minitab
®
19.1
(64-bit) software was used for analysis.
3. Results
3.1. Material Characterization
The characterization of dried ginger peel powder brought the following results: the
total carbohydrate content was 64.08%, the moisture content was 11.25%, the ash content
was 10.69%, the crude fat content was 7.62% and the crude protein content was 6.36%.
3.2. Extraction, TPC, and AA Assay Results
The pressure was set at 20 bar and the average volume of extracts obtained from
experimental procedures was 867.0
±
18.6 mL. Table 1shows extract volume according to
each sample out of 16.
Table 1. Assay results.
Sample
Extracts
Sample
Material
(g)
Water
Volume
(mL)
S/F
(g/g)
Conditions
Yield
(%)
Responses 2
Temperature
(◦C)
Time
(min)
Particle
Size
(µm) 1
TPC
(mg GAE/g Sample)
AA
(µmol TE/g Sample)
M1 40.02 1092 27.24 100 15 B 3.66 10.84 11.28 117.46 117.90
M2 40.06 1088 27.11 115 30 B 3.68 11.27 10.89 111.94 112.02
M3 40.01 1091 27.22 115 30 B 3.67 12.43 12.76 106.70 104.83
M4 40.01 1083 27.02 100 30 C 3.69 10.29 10.42 89.80 89.53
M5 40.04 1095 27.30 115 30 B 3.66 12.16 12.32 98.19 98.87
M6 40.02 1083 27.01 115 45 A 3.70 13.22 13.45 107.85 109.16
M7 40.01 1110 27.69 130 30 A 3.60 13.73 14.10 106.52 106.12
M8 40.03 1057 26.36 130 15 B 3.79 12.68 12.36 91.83 91.66
M9 40.00 1093 27.28 115 45 C 3.66 12.53 12.23 94.33 93.99
M10 40.07 1083 26.98 115 15 A 3.70 12.06 12.40 90.98 95.68
M11 40.01 1081 26.97 130 30 C 3.70 12.93 12.79 90.36 90.26
M12 40.01 1081 26.97 100 45 B 3.70 10.95 11.02 72.88 72.30
M13 40.02 1096 27.34 115 30 B 3.65 13.06 12.61 88.12 88.80
M14 40.06 1083 26.99 115 15 C 3.70 10.96 11.08 78.60 79.07
M15 40.07 1082 26.95 130 45 B 3.70 12.90 13.07 88.79 89.72
M16 40.03 1110 27.68 100 30 A 3.61 10.40 10.85 73.95 73.34
1Retained powder (µm): 355 < size A < 500; 500 < size B < 850; 850 < size C < 2000. 2Duplicate measures.
3.3. ANOVA of TPC and AA Responses
The values of R
2
and adjusted R
2
for TPC were 92.15% and 79.58%, respectively. The
values of R
2
and adjusted R
2
for AA were 81.07% and 50.78%, respectively. The ANOVA
showed that the variation in the temperature had a significant effect (
α
= 0.05) on the TPC
and AA values (see Table 2).
AgriEngineering 2024,63881
Table 2. ANOVA results of TPC and AA.
Variation Source TPC AA
Contribution pContribution p
Model 92.15% 0.02 81.07% 0.15
Lineal 89.12% 0.01 38.69% 0.15
Temperature 61.88% 0.00 23.14% 0.03
Time 10.99% 0.07 9.23% 0.12
Particle 16.25% 0.06 6.32% 0.59
Quadratic 1.70% 0.61 24.80% 0.12
Temperature*Temperature
1.68% 0.35 14.35% 0.07
Time*Time 0.02% 0.93 10.45% 0.16
Two-factor interaction 1.33% 0.68 17.58% 0.19
Temperature*Particle 1.33% 0.68 17.58% 0.19
Error 7.85% - 18.93% -
Lack of fit 0.57% 0.89 4.21% 0.69
Pure error 7.29% - 14.73% -
Total 100.00% - 100.00% -
3.4. Graphical Analysis of TPC and AA Responses
Figure 3shows a Pareto chart used to determine the magnitude and importance of the
effects in the model. In the displayed charts, the bars that cross the reference line (2.57) are
statistically significant. Therefore, temperature is significant with the current model terms
for TPC and AA.
AgriEngineering 2024, 6, FOR PEER REVIEW 7
M16 40.03 1110 27.68 100 30 A 3.61 10.40 10.85 73.95 73.34
1
Retained powder (µm): 355 < size A < 500; 500 < size B < 850; 850 < size C < 2000.
2
Duplicate
measures.
3.3. ANOVA of TPC and AA Responses
The values of R
2
and adjusted R
2
for TPC were 92.15% and 79.58%, respectively. The
values of R
2
and adjusted R
2
for AA were 81.07% and 50.78%, respectively. The ANOVA
showed that the variation in the temperature had a significant effect (α = 0.05) on the TPC
and AA values (see Table 2).
Table 2. ANOVA results of TPC and AA.
Variation Source TPC AA
Contribution p Contribution p
Model 92.15% 0.02 81.07% 0.15
Lineal 89.12% 0.01 38.69% 0.15
Temperature 61.88% 0.00 23.14% 0.03
Time 10.99% 0.07 9.23% 0.12
Particle 16.25% 0.06 6.32% 0.59
Quadratic 1.70% 0.61 24.80% 0.12
Temperature*Temperature 1.68% 0.35 14.35% 0.07
Time*Time 0.02% 0.93 10.45% 0.16
Two-factor interaction 1.33% 0.68 17.58% 0.19
Temperature*Particle 1.33% 0.68 17.58% 0.19
Error 7.85% - 18.93% -
Lack of fit 0.57% 0.89 4.21% 0.69
Pure error 7.29% - 14.73% -
Total 100.00% - 100.00% -
3.4. Graphical Analysis of TPC and AA Responses
Figure 3 shows a Pareto chart used to determine the magnitude and importance of
the effects in the model. In the displayed charts, the bars that cross the reference line (2.57)
are statistically significant. Therefore, temperature is significant with the current model
terms for TPC and AA.
(a)
AgriEngineering 2024, 6, FOR PEER REVIEW 8
(b)
Figure 3. Pareto chart of standardized effects for: (a) TPC response; (b) AA response.
Figure 4 shows the main factors effects on TPC and AA. For TPC, the temperature
and time levels have a greater magnitude effect than particle size. Moreover, higher levels
of temperature and time and small particle size produced higher AA values. For AA, the
temperature and time levels have a greater magnitude of effect than particle size. Inter-
mediate levels of temperature and time are those that maximize antioxidant activity val-
ues, while the smallest particle size produced higher antioxidant activity values.
(a)
Figure 3. Pareto chart of standardized effects for: (a) TPC response; (b) AA response.
AgriEngineering 2024,63882
Figure 4shows the main factors effects on TPC and AA. For TPC, the temperature
and time levels have a greater magnitude effect than particle size. Moreover, higher levels
of temperature and time and small particle size produced higher AA values. For AA,
the temperature and time levels have a greater magnitude of effect than particle size.
Intermediate levels of temperature and time are those that maximize antioxidant activity
values, while the smallest particle size produced higher antioxidant activity values.
AgriEngineering 2024, 6, FOR PEER REVIEW 8
(b)
Figure 3. Pareto chart of standardized effects for: (a) TPC response; (b) AA response.
Figure 4 shows the main factors effects on TPC and AA. For TPC, the temperature
and time levels have a greater magnitude effect than particle size. Moreover, higher levels
of temperature and time and small particle size produced higher AA values. For AA, the
temperature and time levels have a greater magnitude of effect than particle size. Inter-
mediate levels of temperature and time are those that maximize antioxidant activity val-
ues, while the smallest particle size produced higher antioxidant activity values.
(a)
AgriEngineering 2024, 6, FOR PEER REVIEW 9
(b)
Figure 4. Main effects plot were A,B and C are the particle sizes 355 < size A < 500; 500 < size B <
850; 850 < size C < 2000 for: (a) TPC response; (b) AA response.
Figure 5 shows the interaction plot for TPC. There was no interaction between factors.
Figure 5. Interaction plot for TPC response.
Figure 6 shows the interaction plot for AA. The interaction among the evaluated fac-
tors indicates the relationship between particle size and AA depends on temperature.
Higher values of temperature and particle size produce lower AA values.
Figure 4. Main effects plot were A, B and C are the particle sizes 355 < size A < 500; 500 < size B < 850;
850 < size C < 2000 for: (a) TPC response; (b) AA response.
Figure 5shows the interaction plot for TPC. There was no interaction between factors.
AgriEngineering 2024,63883
AgriEngineering 2024, 6, FOR PEER REVIEW 9
(b)
Figure 4. Main effects plot were A,B and C are the particle sizes 355 < size A < 500; 500 < size B <
850; 850 < size C < 2000 for: (a) TPC response; (b) AA response.
Figure 5 shows the interaction plot for TPC. There was no interaction between factors.
Figure 5. Interaction plot for TPC response.
Figure 6 shows the interaction plot for AA. The interaction among the evaluated fac-
tors indicates the relationship between particle size and AA depends on temperature.
Higher values of temperature and particle size produce lower AA values.
Figure 5. Interaction plot for TPC response.
Figure 6shows the interaction plot for AA. The interaction among the evaluated
factors indicates the relationship between particle size and AA depends on temperature.
Higher values of temperature and particle size produce lower AA values.
AgriEngineering 2024, 6, FOR PEER REVIEW 10
Figure 6. Interaction plot for AA response.
Figure 7 shows 3D surface plots for TPC response at different particle sizes (A, B, and
C), where A represents the smallest particle size and C the largest. Particle size A yielded
the highest TPC, followed by size B, and lastly size C. Furthermore, increasing the levels
of temperature and time enhances the response, regardless of the selected particle size.
Figure 7. Three-dimensional surface plots of TPC response for: (A) particle size A; (B) particle size
B; (C) particle size C.
Figure 6. Interaction plot for AA response.
AgriEngineering 2024,63884
Figure 7shows 3D surface plots for TPC response at different particle sizes (A, B, and C),
where A represents the smallest particle size and C the largest. Particle size A yielded the
highest TPC, followed by size B, and lastly size C. Furthermore, increasing the levels of
temperature and time enhances the response, regardless of the selected particle size.
AgriEngineering 2024, 6, FOR PEER REVIEW 10
Figure 6. Interaction plot for AA response.
Figure 7 shows 3D surface plots for TPC response at different particle sizes (A, B, and
C), where A represents the smallest particle size and C the largest. Particle size A yielded
the highest TPC, followed by size B, and lastly size C. Furthermore, increasing the levels
of temperature and time enhances the response, regardless of the selected particle size.
Figure 7. Three-dimensional surface plots of TPC response for: (A) particle size A; (B) particle size
B; (C) particle size C.
Figure 7. Three-dimensional surface plots of TPC response for: (A) particle size A; (B) particle size B;
(C) particle size C.
Figure 8shows 3D surface plots for AA response at different particle sizes (A, B, and C),
with A being the smallest particle size and C the largest. Particle size A yielded the highest
antioxidant activity, followed by size B, and lastly size C. All plots depict a response surface
with a single maximum.
AgriEngineering 2024,63885
AgriEngineering 2024, 6, FOR PEER REVIEW 11
Figure 8 shows 3D surface plots for AA response at different particle sizes (A, B, and
C), with A being the smallest particle size and C the largest. Particle size A yielded the
highest antioxidant activity, followed by size B, and lastly size C. All plots depict a re-
sponse surface with a single maximum.
Figure 8. Three-dimensional surface plots of AA response for: (A) particle size A; (B) particle size
B; (C) particle size C.
3.5. TPC and AA Correlation
A positive and significant correlation between TPC and AA responses was found (see
Table 3).
Table 3. Pearson correlation output.
Response 1 Response 2 ρ 95% CI p
TPC AA 0.58 (0.07; 0.85) 0.03
3.6. Response Optimization
The optimized values for temperature and time factors were 126.36 °C, 37.73 min,
and a particle size of <355–500> µm. Adjusted values show that the model predicts a TPC
value of 112.44 and an AA value of 14.20. However, the standard error of the fit is higher
for TPC compared to AA, indicating that the TPC predictions are less precise. The confi-
dence and prediction intervals for TPC are also wider than those for AA, suggesting
greater variability and uncertainty in the TPC estimates. This is further reflected in the
broad prediction interval for TPC, which indicates a significant range in potential future
observations (see Table 4).
Figure 8. Three-dimensional surface plots of AA response for: (A) particle size A; (B) particle size B;
(C) particle size C.
3.5. TPC and AA Correlation
A positive and significant correlation between TPC and AA responses was found (see
Table 3).
Table 3. Pearson correlation output.
Response 1 Response 2 ρ95% CI p
TPC AA 0.58 (0.07; 0.85) 0.03
3.6. Response Optimization
The optimized values for temperature and time factors were 126.36
◦
C, 37.73 min, and a
particle size of <355–500>
µ
m. Adjusted values show that the model predicts a TPC value
of 112.44 and an AA value of 14.20. However, the standard error of the fit is higher for TPC
compared to AA, indicating that the TPC predictions are less precise. The confidence and
prediction intervals for TPC are also wider than those for AA, suggesting greater variability
and uncertainty in the TPC estimates. This is further reflected in the broad prediction interval
for TPC, which indicates a significant range in potential future observations (see Table 4).
AgriEngineering 2024,63886
Table 4. Multiple response optimization.
Response Fit SE Fit 95% CI 95% PI
TPC 112.44 6.77 (95.03; 129.85) (84.30; 140.58)
AA 14.20 0.38 (13.23; 15.17) (12.64; 15.76)
3.7. AGREE and BAGI Evaluation
The AGREE analysis resulted in a score of 0.61, indicating moderate alignment with
green analytical chemistry principles. Additionally, the BAGI tool produced a score of
75.0, demonstrating a good balance between green chemistry and method applicability.
(see Figure 9). These results highlight the method’s potential for sustainable use while
maintaining practical effectiveness.
AgriEngineering 2024, 6, FOR PEER REVIEW 12
Table 4. Multiple response optimization.
Response Fit SE Fit 95% CI 95% PI
TPC 112.44 6.77 (95.03; 129.85) (84.30; 140.58)
AA 14.20 0.38 (13.23; 15.17) (12.64; 15.76)
3.7. AGREE and BAGI Evaluation
The AGREE analysis resulted in a score of 0.61, indicating moderate alignment with
green analytical chemistry principles. Additionally, the BAGI tool produced a score of
75.0, demonstrating a good balance between green chemistry and method applicability.
(see Figure 9). These results highlight the method’s potential for sustainable use while
maintaining practical effectiveness.
(a) (b)
Figure 9. Metric tools evaluation for: (a) AGREE tool: the color gradient represents the alignment
with green analytical chemistry principles; (b) BAGI tool: the blue shades represent the score, with
darker blue indicating a stronger balance between green chemistry and method applicability.
4. Discussion
In terms of the experimental factors, previous studies highlight the critical role of
temperature in the extraction of phenolic compounds and antioxidant activity in plant
extracts. Temperature influences both the solubility of these compounds and the diffusion
rate of solutes within the solvent [6,27,28]. Furthermore, Siti Nur Khairunisa et al. [29]
underscore that maintaining appropriate pressure is crucial to ensuring that water re-
mains in its liquid state during the extraction process. Regarding extraction time, Nour-
bakhsh Amiri et al. [27] indicate that excessive durations can lead to a reduction in bioac-
tive compound yield due to their instability at elevated temperatures. As for particle size,
smaller particles increase the contact surface area between solute and solvent, thereby en-
hancing extraction efficiency. However, if the particle size is too small, it may lead to chan-
neling effects, which could hinder mass transfer [27,30].
In terms of valorizing Zingiber officinale, ginger peels demonstrate antioxidant prop-
erties that may surpass those of conventional synthetic antioxidants [31]. Mao et al. [32]
highlighted that the health benefits of ginger are primarily aributed to its phenolic com-
pounds. Additionally, numerous researchers have carried out studies on Zingiber officinale
Roscoe [33–37] and other ginger species, including Zingiber zerumbet [29,38,39], Zingiber
montanum [40], Zingiber officinale rubrum, and Zingiber officinale amarum [35]. The primary
contribution of this work, compared to previous studies on ginger, lies in the evaluation
of the phenolic content and antioxidant activity of Zingiber officinale peel extract using the
PLE method with water as the sole solvent. While previous studies have emphasized the
health benefits of ginger due to the presence of phenolic compounds, few studies have
addressed the valorization of ginger residues, specifically peels, using sustainable
Figure 9. Metric tools evaluation for: (a) AGREE tool: the color gradient represents the alignment
with green analytical chemistry principles; (b) BAGI tool: the blue shades represent the score, with
darker blue indicating a stronger balance between green chemistry and method applicability.
4. Discussion
In terms of the experimental factors, previous studies highlight the critical role of
temperature in the extraction of phenolic compounds and antioxidant activity in plant
extracts. Temperature influences both the solubility of these compounds and the diffusion
rate of solutes within the solvent [
6
,
27
,
28
]. Furthermore, Siti Nur Khairunisa et al. [
29
]
underscore that maintaining appropriate pressure is crucial to ensuring that water remains
in its liquid state during the extraction process. Regarding extraction time, Nourbakhsh
Amiri et al. [
27
] indicate that excessive durations can lead to a reduction in bioactive
compound yield due to their instability at elevated temperatures. As for particle size,
smaller particles increase the contact surface area between solute and solvent, thereby
enhancing extraction efficiency. However, if the particle size is too small, it may lead to
channeling effects, which could hinder mass transfer [27,30].
AgriEngineering 2024,63887
In terms of valorizing Zingiber officinale, ginger peels demonstrate antioxidant properties
that may surpass those of conventional synthetic antioxidants [
31
]. Mao et al. [
32
] highlighted
that the health benefits of ginger are primarily attributed to its phenolic compounds. Addition-
ally, numerous researchers have carried out studies on Zingiber officinale Roscoe [
33
–
37
] and other
ginger species, including Zingiber zerumbet [29,38,39], Zingiber montanum [40], Zingiber officinale
rubrum, and Zingiber officinale amarum [
35
]. The primary contribution of this work, compared
to previous studies on ginger, lies in the evaluation of the phenolic content and antioxidant
activity of Zingiber officinale peel extract using the PLE method with water as the sole solvent.
While previous studies have emphasized the health benefits of ginger due to the presence of
phenolic compounds, few studies have addressed the valorization of ginger residues, specifically
peels, using sustainable techniques like PLE. Furthermore, although numerous authors [
33
–
37
]
have explored the antioxidant activity of ginger and other related species, this study provides
an optimization of the extraction process in terms of temperature and time, improving both
efficiency and solvent usage, which represents a significant advance in the valorization of food
industry by-products.
Regarding the results obtained from other ginger varieties, Mahmudati et al. [
35
]
reported a maximum TPC value of 22.97 mg GAE/g using the decoction method for
Zingiber officinale var. rubrum. Additionally, the highest antioxidant activity observed
was 79.83% inhibition, as measured by the DPPH method through the infusion process.
Various authors have also experimented with different raw materials. Barriga-Sánchez
and Rosales-Hartshorn [
21
] analyzed extracts from cherimoya leaves (Annona cherimola
Mill), obtaining a maximum TPC of 5.6 g GAE/100 g of dry leaf using hot water extraction
under high pressure and temperature (130
◦
C). However, the highest antioxidant activity
observed was 0.86 mg TE/mg of dry ethanolic extract (70% v/v). Similarly, Sánchez-
Gonzáles et al. [
30
] studied grape seeds (Vitis vinifera), achieving antioxidant activity values
of 1628.15 ±80.32 µmol TE/g dry weight through the PLE method.
However, the quantification of TPC and AA is highly dependent on the extraction
method and conditions employed. Several authors have utilized the PLE method [
21
,
29
,
30
,
37–39,41], while ultrasound-assisted extraction has also been widely applied [33,34,36,40].
Additionally, infusion and decoction techniques [
35
], as well as organic solvent extrac-
tion [
21
,
30
,
34
,
38
], have been used in various studies. Regarding findings from previous
studies using the PLE method, Razak et al. [37] reported AA values of 71.46 ±2.44% inhi-
bition. Similarly, Siti Nur Khairunisa et al. [
39
] observed maximum AA values of 63.26%
inhibition at 170
◦
C, with an extraction time of 20 min and a solvent-to-sample ratio of
20 mL/g for Zingiber zerumbet. These results can be explained because the PLE method
enhances mass transfer rates, absorption into the particle matrix, and selectivity [12].
On the other hand, using the ultrasound-assisted extraction method, Murphy et al. [
36
]
reported a maximum TPC value of 1039.64 mg GAE/g dry weight and AA values of 54.50%
in ethanolic extract. Similarly, Contreras-López et al. [
33
] achieved maximum TPC values
of 17.11 mg GAE/100 g and AA values of 157.15 mg TE/100 g. Additionally, in extracts
of Zingiber montanum, Thepthong et al. [
40
] obtained a maximum TPC of 71.45
±
1.45 mg
GAE/g in methanolic extract and a maximum AA value with an IC
50
of 38.89
±
0.27
µ
g/mL
in ethanolic extract.
Similarly, Jorge-Montalvo et al. [
34
] obtained higher TPC values using the maceration method,
reporting 10.03
±
0.14 mg GAE/g sample (25
◦
C for 24 h). Higher AA values were achieved using
the reflux method, with an IC
50
of 0.72
±
0.05 mg dry matter/mL (85
◦
C for 12 h). Conventional
methods, however, often face challenges such as extended extraction times and significant solvent
loss. In contrast, the PLE method offers a faster, less polluting, and more cost-effective alternative.
AgriEngineering 2024,63888
Regarding the results obtained from the statistical analysis, findings similar to those
of Siti Nur Khairunisa [
29
] were observed, where a fractional factorial design indicated
that temperature was the significant factor, contributing 38.36%. Yulianto et al. [
41
] also
identified temperature as a significant factor in the extraction process using a central
composite design. In this study, the application of the Box–Behnken design to optimize
extraction conditions represents a noteworthy innovation aimed at maximizing antioxidant
activity, with potential industrial applications. The significant positive correlation between
total phenolic content and antioxidant activity further emphasizes the relevance of this by-
product as a viable source of natural antioxidants, which may surpass the use of synthetic
antioxidants in various industrial contexts [11].
Despite the promising results, the study faced several limitations related to the ex-
traction equipment employed. The thermal fragility of certain components necessitated
restrictions on the operational conditions, capping the maximum operating temperature
at 130
◦
C. This limitation hindered a comprehensive investigation of temperature effects
across a broader range. Furthermore, the presence of starch in the dried ginger rhizome led
to the formation of a cake, which impeded the complete recovery of the operating water
volume and consequently reduced the extract yield (w/v). Therefore, these factors should
be carefully considered when designing future experiments.
This article enhances the scientific understanding of extracting bioactive compounds
from ginger by demonstrating the effectiveness of utilizing the PLE method. It establishes
a foundation for future research and applications within the pharmaceutical and food
industries, particularly for developing supplements and functional foods. This approach
also supports the valorization of agricultural by-products and the creation of high-value
natural products. Moreover, by employing water as the sole extraction solvent, we promote
a sustainable and pollution-free industry.
5. Conclusions
Considering that temperature was found to be the significant factor in the extraction
process and acknowledging that temperature constraints were a limiting factor in this
research due to equipment operational conditions, further studies are recommended to
optimize temperature for the extraction of bioactive compounds with antioxidant activity.
Additionally, the precise control of storage conditions is advised to maintain the stability of
sample composition and preserve product quality for subsequent analysis.
The continuous monitoring of these parameters is suggested to ensure consistency
and quality in future experiments. Although particle size was not significant, further inves-
tigation into its impact could provide insights and potential improvements in extraction
efficiency.
Finally, future directions could include more detailed investigations into the com-
pounds and toxicity of the extracts, as well as sensory and nutritional studies to evaluate
the impact on food products. Additionally, exploring other parameters in the PLE method
could provide additional insights and improve extraction efficiency.
Author Contributions: Conceptualization, M.S.-O. and E.A.; Data curation, M.S.-O.; Formal analysis,
M.S.-O.; Funding acquisition, M.S.-O.; Investigation, M.S.-O. and B.J.Q.-C.; Methodology, M.S.-O.;
Project administration, M.S.-O.; Resources, M.S.-O.; Software, M.S.-O.; Supervision, E.A.; Validation,
M.S.-O. and E.A.; Visualization, B.J.Q.-C.; Writing—original draft, M.S.-O.; Writing—review and
editing, M.S.-O. All authors have read and agreed to the published version of the manuscript.
Funding: The APC was funded by the Vicerrectorado de Investigacion (VRIN) of the Universidad
Nacional del Santa, Peru. The funding for this project is legally supported by the “Reglamento
de otorgamiento de subvenciones económicas”, which was approved through RESOLUCIÓN N
◦
343-2024-CU-R-UNS. Based on this regulation, the results of the first grant competition were officially
approved by RESOLUCIÓN N◦508-2024-CU-R-UNS.
AgriEngineering 2024,63889
Data Availability Statement: The original contributions presented in the study are included in the
article; further inquiries can be directed to the corresponding author.
Acknowledgments: The authors gratefully acknowledge the support and contributions of various
individuals and institutions that made this research possible. We extend our sincere appreciation
to the personnel of the Instituto Tecnologico de la Produccion (ITP) for their invaluable assistance
during the experimental work. Special thanks are due to the Vicerrectorado de Investigacion (VRIN)
of the Universidad Nacional del Santa for providing financial support through a research scholarship,
which facilitated the execution of this study. We also extend our gratitude to Elza Berta Aguirre
Vargas for her expert guidance and invaluable advice throughout this research.
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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