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Molecules 2022, 27, 6792. https://doi.org/10.3390/molecules27206792 www.mdpi.com/journal/molecules
Article
Comprehensive Comparison of Two Color Varieties of Perillae
Folium by GC-MS-Based Metabolomic Approach
Jiabao Chen 1,2,†, Dan Zhang 1,2,†, Qian Wang 1,2, Aitong Yang 1,2, Yuguang Zheng 1,3,* and Lei Wang 1,2,*
1 Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, College of
Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
2 International Joint Research Center on Resource Utilization and Quality Evaluation of Traditional Chinese
Medicine of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
3 Department of Pharmaceutical Engineering, Hebei Chemical and Pharmaceutical College,
Shijiazhuang 050026, China
* Correspondence: zyg314@163.com (Y.Z.); wanglei1031@126.com (L.W.)
† These authors contributed equally to this work.
Abstract: Perillae Folium (PF), the leaf of Perilla frutescens (L.) Britt, is extensively used as culinary
vegetable in many countries. It can be divided into two major varietal forms based on leaf color
variation, including purple PF (Perilla frutescens var. arguta) and green PF (P. frutescens var. fru-
tescens). The aroma of purple and green PF is discrepant. To figure out the divergence of chemical
composition in purple and green PF, gas chromatography–tandem mass spectrometry (GC-MS) was
applied to analyze compounds in purple and green PF. A total of 54 compounds were identified
and relatively quantified. Multivariate statistical methods, including principal component analysis
(PCA), orthogonal partial least-squares discrimination analysis (OPLS-DA) and clustering analysis
(CA), were used to screen the chemical markers for discrimination of purple and green PF. Seven
compounds that accumulated discrepantly in green and purple PF were characterized as chemical
markers for the discrimination of the purple and green PF. Among these 7 marker compounds,
limonene, shisool and perillaldehyde that from the same branch of the terpenoid biosynthetic path-
way were with relatively higher contents in purple PF, while perilla ketone, isoegomaketone, to-
copheryl and squalene on other branch pathways were higher in green PF. The results of the present
study are expected to provide theoretical support for the development and utilization of PF re-
sources.
Keywords: perilla leaf; chemical composition; GC-MS; multivariate statistical analysis; biosynthetic
pathway
1. Introduction
Perilla frutescens (L.) Britt. is an annual herbal plant that belongs to the family of La-
miaceae [1,2]. The leaf of P. frutescens (L.) Britt, also called Perillae Folium (PF), has been
extensively used in many countries as a culinary vegetable. Based on plant leaf color var-
iation, PF can be divided into two major varietal forms that are circulated in China, in-
cluding purple PF (P. frutescens var. arguta) and green PF (P. frutescens var. frutescens) [3].
P. frutescens var. arguta and P. frutescens var. frutescens are considered the same species in
plant taxonomy, but there are large differences in practical application. Purple PF is
widely used as a natural food pigment and a genuine medicinal plant for the treatment of
food poisoning, coughs and gastritis [4–6]. Purple PF is believed to have efficacy in exte-
rior relief, dispersing cold, easing stomach pain, reducing phlegm and relieving coughs
and asthma [7]. Traditionally, it has been used to alleviate a variety of symptoms, includ-
ing coughs, colds, fever, allergies and some intestinal diseases [8,9]. Unlike purple PF,
Citation: Chen, J.; Zhang, D.;
Wang, Q.; Yang, A.; Zheng, Y.;
Wang, L. Comprehensive
Comparison of Two Color Varieties
of Perillae Folium by GC-MS-Based
Metabolomic Approach. Molecules
2022, 27, 6792. https://doi.org/
10.3390/molecules27206792
Academic Editor: Domenico
Montesano
Received: 12 September 2022
Accepted: 9 October 2022
Published: 11 October 2022
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Molecules 2022, 27, 6792 2 of 10
green PF is consumed only as a vegetable or industrial preservative and is not used as a
traditional Chinese medicine in China [3].
Phytochemical studies indicate that PF is rich in volatile compounds [10–12], flavo-
noids [13,14], anthocyanins [15], fatty acids [16,17] and phenolic compounds [18,19]. Com-
pounds and extractions of PF showed various biological activities, such as antioxidant,
antimicrobial, antiallergic, antidepressant, anti-inflammatory and anticancer effects [20–
24]. Metabolites in foods or natural herbs differ by varietal forms, which may produce
effects on their quality and effectiveness. Therefore, it is necessary to clarify the chemical
differences of different PF. Huang et al. [25] compared the content and composition of the
volatiles of purple and green PF, obtained by SFE, HS-SPME and hydrodistillation. A total
of 64 volatile compounds were identified in purple and green PF by GC-MS, with 29 com-
ponents simultaneously found in both of them. Tabanca et al. [26] identified 27 volatile
compounds in purple and green PF by GC-MS, with only 8 compounds present simulta-
neously in both of them. Fan et al. [27] reported that a total of 57 nonvolatile chemical
components and 105 volatile chemical components were characterized in leaves, stems
and seeds of different varieties of perilla by ultrahigh-performance liquid chromatog-
raphy coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS)
and GC-MS. Furthermore, 27 nonvolatile constituents and 16 volatile constituents were
identified as potential markers for discriminating perilla between different varieties. De-
guchi et al. [28], using high-performance liquid chromatography (HPLC), reported that
the main phenolic compound rosmarinic acid content was higher in green PF compared
with purple PF. Zheng et al. [29] investigated the difference in the chemical compositions
between green PF and purple PF by rapid resolution liquid chromatography coupled with
quadruple time-of-flight mass spectrometry (RRLC-Q/TOF-MS), and revealed that flavo-
noids and anthocyanins in particular had higher contents in purple PF. Additionally, their
results showed that the purple PF had more pronounced antioxidative activities than the
green PF.
In the present study, purple PF and green PF were compared and distinguished from
the aspect of chemical composition by the GC-MS-based metabolomic approach. In addi-
tion, multivariate statistical methods, including principal component analysis (PCA), or-
thogonal partial least-squares discrimination analysis (OPLS-DA) and clustering analysis
(CA) were used to screen the chemical markers between purple and green PF.
2. Results and Discussion
2.1. Compounds Identification
In this study, the chemical profiling of n-hexane extract in 12 batches of purple PF
and 10 batches of green PF (sample information see in Table 1) was achieved by GC-MS.
The representative total ion chromatogram (TIC) of the two varietal forms of PF is shown
in Figure 1. With reference to the NIST17 database, 54 compounds were identified by com-
paring their mass spectra. Most of the identified compounds belong to monoterpenes and
sesquiterpenes. The retention time, retention index, molecular weight and molecular for-
mula of the identified compounds are summarized in Table 2.
Table 1. The information of collected purple Perillae Folium (Z1–Z12) and green Perillae Folium
(B1–B10).
No.
Source
Specimen No.
No.
Source
Specimen No.
Z1
Hebei Province
PF201908Z01
Z12
Imported from Japan
PF201908Z12
Z2
Hebei Province
PF201908Z02
B1
Gansu Province
PF201908B01
Z3
Hebei Province
PF201908Z03
B2
Gansu Province
PF201908B02
Z4
Guizhou Province
PF201908Z04
B3
Gansu Province
PF201908B03
Z5
Hebei Province
PF201908Z05
B4
Hebei Province
PF201908B04
Z6
Hebei Province
PF201908Z06
B5
Gansu Province
PF201908B05
Z7
Hebei Province
PF201908Z07
B6
Hebei Province
PF201908B06
Molecules 2022, 27, 6792 3 of 10
Z8
Hebei Province
PF201908Z08
B7
Gansu Province
PF201908B07
Z9
Sichuan Province
PF201908Z09
B8
Gansu Province
PF201908B08
Z10
Shanxi Province
PF201908Z10
B9
Gansu Province
PF201908B09
Z11
Gansu Province
PF201908Z11
B10
Liaoning Province
PF201908B10
Figure 1. The typical total ion chromatograms of n-hexane extracts of (A) purple Perillae Folium
and (B) green Perillae Folium by GC-MS. The number of peaks was consistent with those of com-
pounds in Table 2.
Table 2. The information of the compounds in purple PF and green PF by GC-MS.
Peak
No.
Retention
Time (min)
Compounds
Molecular
Weight
Molecular
Formula
Retention
Index
VIP
p-Value
1
5.01
α-Pinene
136
C10H16
918
0.11
***
2
5.66
Pseudolimonene
136
C10H16
964
0.12
***
3
6.45
D-limonene
136
C10H16
1018
1.02
***
4
7.52
α-Terpinene
136
C10H16
1083
0.09
***
5
7.69
Linalool
154
C10H18O
1093
0.13
-
6
9.71
α-Terpineol
154
C10H18O
1193
0.24
***
7
10.01
Perilla alcohol
152
C10H16O
1207
0.06
***
8
10.1
Egomaketone
166
C10H14O2
1210
0.34
***
9
10.54
Nerol
154
C10H18O
1229
0.14
*
10
11.21
Perilla ketone
166
C10H14O2
1257
5.78
***
11
11.71
Shisool
154
C10H18O
1277
1.06
***
12
11.87
Perillaldehyde
150
C10H14O
1284
2.56
***
13
12.45
Isoegomaketone
164
C10H12O2
1307
2.03
***
14
13.28
Methyl perillate
180
C11H16O2
1339
0.07
***
15
13.43
γ-Elemene
204
C15H24
1344
0.20
***
16
13.94
Eugenol
164
C10H12O2
1363
0.20
***
Molecules 2022, 27, 6792 4 of 10
17
14.51
α-Copaene
204
C15H24
1385
0.06
-
18
14.77
β-Bourbonene
204
C15H24
1395
0.18
***
19
14.94
β-Elemene
204
C15H24
1401
0.05
*
20
15.77
β-Caryophyllene
204
C15H24
1431
0.45
***
21
16.66
Perillic acid
166
C10H14O2
1464
0.22
***
22
17.44
β-Copaene
204
C15H24
1492
0.19
-
23
17.74
Cis-α-Bergamotene
204
C15H24
1503
0.63
-
24
17.88
Bicyclogermacrene
204
C15H24
1508
0.28
**
25
18.09
α-Farnesene
204
C15H24
1516
0.17
***
26
18.51
Myristicin
192
C11H12O3
1531
0.03
*
27
18.59
δ-Cadinene
204
C15H24
1532
0.05
*
28
19.43
Elemicin
208
C12H16O3
1565
0.05
-
29
19.64
Nerolidol
222
C15H26O
1572
0.15
-
30
20.12
Espatulenol
220
C15H24O
1590
0.10
***
31
20.27
β-Caryophyllene oxide
220
C15H24O
1595
0.11
-
32
20.59
α-Patchoulene
204
C15H24
1607
0.36
***
33
21.35
Apiol
222
C12H14O4
1636
0.07
-
34
22.16
Isoelemicin
208
C12H16O3
1666
0.03
-
35
22.62
Isoaromadendrene
epoxide
220
C15H24O
1683
0.03
**
36
26.89
Phytyl acetate
338
C22H42O2
1849
0.29
**
37
27.04
Pentadecanone
268
C18H36O
1855
0.05
***
38
29.91
Palmitic acid
256
C16H32O2
1973
0.38
***
39
30.67
Ethyl palmitate
284
C18H36O2
2005
0.03
*
40
33.39
Phytol
296
C20H40O
2119
0.24
*
41
34.89
α-Linolenic acid
278
C18H30O2
2181
0.06
-
42
37.32
Glycidyl palmitate
312
C19H36O3
2283
0.08
***
43
47.41
Squalene
410
C30H50
2705
1.60
***
44
48.56
Nonacosane
408
C29H60
2754
0.40
*
45
49.16
1-Heptatriacotanol
537
C37H76O
2779
0.38
***
46
51.91
Hentriacontane
436
C31H64
2894
0.98
***
47
52.61
Tocopheryl
430
C29H50O2
2923
1.01
***
48
54.44
Campesterol
400
C28H48O
3000
0.20
***
49
55.2
β-Stigmasterol
412
C29H48O
3031
0.12
*
50
56.33
Dotriacontane
450
C32H66
3079
0.87
***
51
56.68
γ-Sitosterol
414
C29H50O
3093
0.39
***
52
57.42
β-Amyrin
426
C30H50O
3124
0.17
-
53
57.98
β-Amyrone
424
C30H48O
3148
0.04
-
54
58.7
α-Amyrin
426
C30H50O
3178
0.27
-
“-” represent no significant difference. VIP, variable importance in projection. * p < 0.05; ** p < 0.01;
*** p < 0.001.
2.2. Chemical Comparison of Purple and Green PF
In this work, all 54 detected compounds were found in both purple and green PF,
with their contents varying. To further specify the difference of the n-hexane extract pro-
files of purple and green PF, multivariate statistical methods, including PCA, OPLS-DA
and CA, were used to analyze the data.
PCA is an unsupervised pattern recognition method to visualize grouping trends and
outliers. PCA was performed with 54 compounds used as independent variables. As
shown in the PCA scores plot (Figure 2A), all samples were clearly separated into two
groups corresponding to purple PF and green PF. The first two components explained
68.5% of the total variance. PCA results indicated that the purple PF and green PF samples
were indeed different in terms of the content of identified compounds.
OPLS-DA is a supervised pattern recognition method that can be used to analyze,
classify and reduce the dimensionality of complex datasets. To filter out the differential
components of the two varietal forms of PF, the GC-MS data were analyzed by OPLS-DA.
Molecules 2022, 27, 6792 5 of 10
The OPLS-DA scores plot (Figure 2B) shows that purple PF and green PF can also be
clearly classified into two groups. Further to validate the model of OPLS-DA, a permuta-
tion test (n = 200) was conducted. The results of R2Y (cum) = 0.962 and Q2 (cum) = 0.870
(Figure 2C), indicated good classification and predictability of the OPLS-DA model. By
using the metabolite features with VIP > 1 and p < 0.05, 7 compounds, including D-limo-
nene (3), perilla ketone (10), shisool (11), perillaldehyde (12), isoegomaketone (13), squa-
lene (43) and tocopheryl (47), were screened out as potential chemical markers for distin-
guishing purple PF and green PF (Figure 2D). The relative peak areas (%) of potential
chemical markers in purple and green PF were calculated (Table 3). The results indicated
that perilla ketone (10) was the most abundant compound in green PF, with relative peak
areas of 27.50 ± 3.01%, while perillaldehyde (12) was the most abundant compound in
purple PF, with relative peak areas of 31.72 ± 3.12%.
Figure 2. Determination of differential compounds from two PF varieties. (A) Unsupervised PCA
score plot of purple and green PF samples. PC1 occupies 49.0% and PC2 19.5% of total variance. (B)
Supervised OPLS-DA score plot of purple and green PF samples. PC1 occupies 81.5% and PC2 6.73%
of total variance. (C) Permutation test at 200 times used for the discrimination between the two PF
varieties. (D) Scatter plot of p-value and VIP value. The green points show differential compounds
with VIP > 1, p < 0.05.
Table 3. The relative peak areas (%) of the potential chemical markers in purple PF and green PF.
No.
Retention
Time (min)
Retention
Index
Compounds
Purple PF (𝑿
±
SD, n = 12, %)
Green PF (𝑿
±
SD, n = 10, %)
3
6.45
1018
D-limonene
5.12 ± 1.23
0.20 ± 0.04
10
11.21
1257
Perilla ketone
2.15 ± 0.97
27.50 ± 3.01
11
11.71
1277
Shisool
5.41 ± 0.86
0.05 ± 0.02
12
11.87
1284
Perillaldehyde
31.72 ± 3.12
0.60 ± 0.21
13
12.45
1307
Isoegomaketone
0.13 ± 0.07
5.71 ± 0.80
43
47.41
2705
Squalene
4.44 ± 0.88
7.32 ± 0.76
47
52.61
2923
Tocopheryl
4.81 ± 0.67
7.00 ± 0.68
Molecules 2022, 27, 6792 6 of 10
CA is a multivariate statistical method to classify samples or indicators, and a
heatmap was used to show the relative concentration trends of compounds across all sam-
ples. In order to visualize the differences in metabolic profiles between the two varieties
of PF, the peak areas of 54 compounds were used to construct a heatmap. The heatmap
(Figure 3) showed that the two PF varieties could be clearly distinguished on the basis of
the clustering relationships of the identified compounds, consistent with the results of
PCA and OPLS-DA. Among the 54 compounds, the content of perillaldehyde (12), shisool
(11), D-limonene (3), perillic acid (21), α-terpineol (6), perilla alcohol (7), terpinene (4) and
α-pinene (1) in purple PF was significantly higher than that of green PF, while
isoegomaketone (13), squalene (43), dotriacontane (50), tocopheryl (47), hentriacontane
(46), perilla ketone (10) and perilla ketone (8) had higher content in green PF. Specifically,
the main identified components in purple and green PF were perillaldehyde (12) and
perilla ketone (10), respectively. According to the classification principles of volatile oil
chemotypes of PF in previous studies [30,31], all purple PF samples of volatile oil chemo-
types were PA (perillaldehyde), and all green PF samples were PK (perilla ketone).
Figure 3. The relative concentration trends of identified compounds in purple PF and green PF.
Considering the biosynthetic information of potential chemical markers, perilla alco-
hol (7), shisool (11) and perillaldehyde (12) that metabolized from limonene (3) all had
Molecules 2022, 27, 6792 7 of 10
higher contents in purple PF samples, whereas egomaketone (8), perilla ketone (10) and
isoegomaketone (13) that derived from geranial together with squalene (43) and to-
copheryl (16) had higher contents in green PF (Figure 4).
Figure 4. Putative biosynthetic pathways of the main terpenoids in perilla. Metabolites are written
in black letters, whereas enzymes are written in red letters. DMD, diphosphomevalonate decarbox-
ylase; FDPS, farnesyl diphosphate synthase; LS, limonene synthase; LHS, limonene hydroxylase;
PAD, perillylalcohol dehydrogenase; GDD, geranyl diphosphate diphosphohydrolase; FDS, farne-
syl diphosphate synthase; SQS, squalene synthase; GGR, geranylgeranyl reductase; TPC, tocopherol
C-methyltransferase. *** p < 0.001.
Generally, the potential mechanisms of differences in chemical composition are re-
lated with genes encoding biosynthetic enzymes and regulatory proteins [32]. Zheng et
al. [29] reported that the conserved gene sequences of ITS2 (internal transcribed spacer 2)
are consistent in green and purple PF, which suggests that it is reasonable to classify them
as the same species of P. frutescens (L.) Britt from the perspective of plant taxonomy. There-
fore, the obvious differences in the chemical composition between the two varieties of PF
may relate with nonconserved gene regions and downstream regulatory proteins. Previ-
ous research had found quite different levels of the PFLC1 gene encoding limonene syn-
thase in different perilla chemotypes [33]. The content difference of identified terpenoids
in purple and green PF might be related with the expression of key genes encoding limo-
nene synthase (LS), geranyl diphosphate diphosphohydrolase (GDD) and farnesyl di-
phosphate synthase (FDS).
3. Materials and Methods
3.1. Plant Material
A total of 12 batches of purple PF (P. frutescens var. arguta) and 10 batches of green
PF (P. frutescens var. frutescens), were collected from Hebei Academy of Agriculture and
Forestry Sciences in Shijiazhuang (China 38°06′41.7′′ N, 114°45′35.8′′ E) on 30 August 2019
and identified by Yuguang Zheng, professor in the field of identification of Chinese Med-
icine. The origins of the 22 samples are listed in Table 1. The harvested leaves were air-
dried in the dark at room temperature for 2 weeks to acquire consistently low water
Molecules 2022, 27, 6792 8 of 10
content. All voucher specimens were deposited in dry, dark room of Traditional Chinese
Medicine Processing Technology Innovation Center of Hebei Province, Hebei University
of Chinese Medicine with their specimen number (see Table 1).
3.2. Metabolite Extraction
Plant materials of each batch were pulverized and screened through 60-mesh sieves.
The powdered sample was extracted according to an ultrasonic extraction protocol [34]
with some modification. A total of 0.1 g of the powdered sample was extracted with 1 mL
of n-hexane by means of sonication (power, 300 W; frequency, 40 kHz) for 15 min at room
temperature. The extract was then centrifuged at 13,000 rpm for 10 min at room tempera-
ture. A total of 1μL of supernatant was injected into the GC-MS for analysis.
3.3. GC-MS Analysis
The GC-MS analysis was performed with an Agilent 7890B GC coupled with 5977B
MSD mass detector (Agilent Technologies, Santa Clara, CA, USA). The GC-MS instrument
coupled with an Agilent HP-5MS 5% phenyl methyl siloxane capillary column (30 m ×
0.25 mm, 0.25 μm film thickness, Agilent, Santa Clara, CA, USA). Helium (≥99.999%) was
used as carrier gas at a constant flow rate of 1.0 mL·min−1. A total of 1 μL of the prepared
supernatant solution was injected in split mode with the split ratio set to 2:1 at a temper-
ature of 250°C. The oven temperature program was initially set at 45°C, then raised to
100°C at a rate of 10°C·min−1 and subsequently raised to 280°C at a rate of 4°C·min−1, then
finally held for 10 min. The quadrupole mass detector was operated in electron impact
(EI) mode at 70 eVwith a mass range of 50–500 m/z. A total of 22 batches of samples were
randomly analyzed with three replicates to ensure system stability throughout the analy-
sis. n-Alkane standard solution (C8–C20, 40 mg·L−1, Sigma-Aldrich, Buchs, Switzerland)
was analyzed under the same condition for retention index (RI) calculation.
3.4. Data Processing and Statistical Analysis
The identification of metabolites in purple and green PF were achieved by comparing
the obtained mass spectra with reference mass spectra from the National Institute of
Standards and Technology 17 (NIST17) library. The peaks in all the samples were aligned
and matched by using Agilent MassHunter analysis program (Agilent, Santa Clara, CA,
USA). The RI of all the identified compounds was calculated by comparing their corre-
sponding peak retention time to that of n-alkanes (C8–C20) [35,36]. Finally, the resulting
data matrix consisting of sample codes, variables and peak areas was extracted and used
for statistical analysis.
The obtained data matrix was imported into SIMCA P13 software (Umetrics, Umea,
Sweden) for principal component analysis (PCA) and orthogonal partial least-squares dis-
crimination analysis (OPLS-DA). Cluster analysis (CA) was performed with Origin Pro
2020 (OriginLab Corporation, Northampton, MA, USA) software. p-value was calculated
by independent-samples t-test with IBM SPSS Statistics 23.0 (IBM, Armonk, NY, USA)
software.
4. Conclusions
In this study, a GC-MS-based metabolomics method for rapid discrimination of dif-
ferential metabolites between purple and green PF was established. The chemical compo-
sitions of n-hexane extracts of purple and green PF were investigated and a total of 54
compounds were identified by comparison of their mass spectra with NIST17 library.
Among them, 7 differential compounds between the two varieties of PF were screened
and characterized using multivariate statistical methods and heatmap visualization anal-
ysis. The results indicated that purple PF and green PF samples could be distinguished
from each other according to the relative content of these marker compounds. This study
Molecules 2022, 27, 6792 9 of 10
may offer data support for research and exploitation of purple and green PF, and provide
a feasible method for the authentication of purple and green PF.
Author Contributions: J.C.: methodology, software, validation, formal analysis, writing—original
draft preparation, writing—review and editing; D.Z.: investigation, supervision, writing—review
and editing; Q.W.: writing—review and editing; A.Y.: validation, formal analysis; Y.Z.: conceptual-
ization, writing—review and editing, funding acquisition; L.W.: conceptualization, supervision,
writing—review and editing, funding acquisition; project administration. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by Natural Science Foundation of Hebei Province, grant number
C2020423047; Research Foundation of Hebei Provincial Administration of Traditional Chinese Med-
icine, grant number 2019083; The Innovation Team of Hebei Province Modern Agricultural Industry
Technology System, grant number HBCT2018060205.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: We would like to thank Chunxiu Wen and her team for their careful cultivation
of perilla and kindly providing us the plant materials. We would like to thank all the members in
Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province for fruit-
ful discussions.
Conflicts of Interest: The authors declare no conflicts of interest.
Sample Availability: Samples of the compounds are available from the authors.
References
1. Tang, W.F.; Tsai, H.P.; Chang, Y.H.; Chang, T.Y.; Hsieh, C.F.; Lin, C.Y.; Lin, G.H.; Chen, Y.L.; Jheng, J.R.; Liu, P.C.; et al. Perilla
(Perilla frutescens) leaf extract inhibits SARS-CoV-2 via direct virus inactivation. Biomed. J. 2021, 44, 293–303.
https://doi.org/10.1016/j.bj.2021.01.005.
2. Zhang, Y.; Shen, Q.; Leng, L.; Zhang, D.; Chen, S.; Shi, Y.; Ning, Z.; Chen, S. Incipient diploidization of the medicinal plant
Perilla within 10,000 years. Nat. Commun. 2021, 12, 5508. https://doi.org/10.1038/s41467-021-25681-6.
3. Ma, S.J.; Sa, K.J.; Hong, T.K.; Lee, J.K. Genetic diversity and population structure analysis in Perilla frutescens from Northern
areas of China based on simple sequence repeats. Genet. Mol. Res. GMR 2017, 16, gmr16039746.
https://doi.org/10.4238/gmr16039746.
4. Igarashi, M.; Miyazaki, Y. A review on bioactivities of perilla: Progress in research on the functions of perilla as medicine and
food. Evidence-based complementary and alternative medicine: eCAM 2013, 2013, 925342. https://doi.org/10.1155/2013/925342.
5. Hou, T.; Netala, V.R.; Zhang, H.; Xing, Y.; Li, H.; Zhang, Z. Perilla frutescens: A Rich Source of Pharmacological Active Com-
pounds. Molecules 2022, 27, 3578. https://doi.org/10.3390/molecules27113578.
6. Lee, J.K.; Ohnishi, O. Genetic relationships among cultivated types of perilla frutescens and their weedy types in East Asia re-
vealed by AFLP markers. Genet. Resour. Crop Evol. 2003, 50, 65–74. https://doi.org/10.1023/A:1022951002271.
7. Ahmed, H.M. Ethnomedicinal, Phytochemical and pharmacological investigations of Perilla frutescens (L.) Britt. Molecules 2018,
24, 102. https://doi.org/10.3390/molecules24010102.
8. Makino, T.; Furuta, Y.; Wakushima, H.; Fujii, H.; Saito, K.; Kano, Y. Anti-allergic effect of Perilla frutescens and its active constit-
uents. Phytother. Res. PTR 2003, 17, 240–243. https://doi.org/10.1002/ptr.1115.
9. Yu, H.; Qiu, J.F.; Ma, L.J.; Hu, Y.J.; Li, P.; Wan, J.B. Phytochemical and phytopharmacological review of Perilla frutescens L.
(Labiatae), a traditional edible-medicinal herb in China. Food Chem. Toxicol. 2017, 108 Pt B, 375–391.
https://doi.org/10.1016/j.fct.2016.11.023.
10. Ghimire, B.K.; Yoo, J.H.; Yu, C.Y.; Chung, I.M. GC-MS analysis of volatile compounds of Perilla frutescens Britton var. Japonica
accessions: Morphological and seasonal variability. Asian Pac. J. Trop. Med. 2017, 10, 643–651.
https://doi.org/10.1016/j.apjtm.2017.07.004.
11. Seo, W.H.; Baek, H.H. Characteristic aroma-active compounds of Korean perilla (Perilla frutescens Britton) leaf. J. Agric. Food
Chem. 2009, 57, 11537–11542. https://doi.org/10.1021/jf902669d.
12. Ahmed, H.M.; Tavaszi-Sarosi, S. Identification and quantification of essential oil content and composition, total polyphenols and an-
tioxidant capacity of Perilla frutescens (L.) Britt. Food Chem. 2019, 275, 730–738. https://doi.org/10.1016/j.foodchem.2018.09.155.
13. Lee, Y.H.; Kim, B.; Kim, S.; Kim, M.S.; Kim, H.; Hwang, S.R.; Kim, K.; Lee, J.H. Characterization of metabolite profiles from the
leaves of green perilla (Perilla frutescens) by ultra high performance liquid chromatography coupled with electrospray ionization
quadrupole time-of-flight mass spectrometry and screening for their antioxidant properties. J. Food Drug Anal. 2017, 25, 776–
788. https://doi.org/10.1016/j.jfda.2016.09.003.
Molecules 2022, 27, 6792 10 of 10
14. Nakajima, A.; Yamamoto, Y.; Yoshinaka, N.; Namba, M.; Matsuo, H.; Okuyama, T.; Yoshigai, E.; Okumura, T.; Nishizawa, M.;
Ikeya, Y. A new flavanone and other flavonoids from green perilla leaf extract inhibit nitric oxide production in interleukin 1β-
treated hepatocytes. Biosci. Biotechnol. Biochem. 2015, 79, 138–146. https://doi.org/10.1080/09168451.2014.962474.
15. Fujiwara, Y.; Kono, M.; Ito, A.; Ito, M. Anthocyanins in perilla plants and dried leaves. Phytochemistry 2018, 147, 158–166.
https://doi.org/10.1016/j.phytochem.2018.01.003.
16. Kim, J.K.; Park, S.Y.; Na, J.K.; Seong, E.S.; Yu, C.Y. Metabolite profiling based on lipophilic compounds for quality assessment
of perilla (Perilla frutescens) cultivars. J. Agric. Food Chem. 2012, 60, 2257–2263. https://doi.org/10.1021/jf204977x.
17. Asif, M. Health effects of omega-3,6,9 fatty acids: Perilla frutescens is a good example of plant oils. Orient. Pharm. Exp. Med. 2011,
11, 51–59. https://doi.org/10.1007/s13596-011-0002-x.
18. Peng, Y.; Ye, J.; Kong, J. Determination of phenolic compounds in Perilla frutescens L. by capillary electrophoresis with electro-
chemical detection. J. Agric. Food Chem. 2005, 53, 8141–8147. https://doi.org/10.1021/jf051360e.
19. Assefa, A.D.; Jeong, Y.J.; Kim, D.J.; Jeon, Y.A.; Ok, H.C.; Baek, H.J.; Sung, J.S. Characterization, identification, and quantification
of phenolic compounds using UPLC-Q-TOF-MS and evaluation of antioxidant activity of 73 Perilla frutescens accessions. Food
Res. Int. (Ott. Ont.) 2018, 111, 153–167. https://doi.org/10.1016/j.foodres.2018.05.017.
20. Wang, Z.; Tu, Z.; Xie, X.; Cui, H.; Kong, K.W.; Zhang, L. Perilla frutescens Leaf Extract and Fractions: Polyphenol Composition,
Antioxidant, Enzymes (α-Glucosidase, Acetylcholinesterase, and Tyrosinase) Inhibitory, Anticancer, and Antidiabetic Activi-
ties. Foods 2021, 10, 315. https://doi.org/10.3390/foods10020315.
21. Shin, T.Y.; Kim, S.H.; Kim, S.H.; Kim, Y.K.; Park, H.J.; Chae, B.S.; Jung, H.J.; Kim, H.M. Inhibitory effect of mast cell-mediated
immediate-type allergic reactions in rats by Perilla frutescens. Immunopharmacol. Immunotoxicol. 2000, 22, 489–500.
https://doi.org/10.3109/08923970009026007.
22. Li, Y.; Yang, X.; Chen, S.; Wu, L.; Zhou, J.; Jia, K.; Ju, W. Integrated Network Pharmacology and GC-MS-Based Metabolomics to
Investigate the Effect of Xiang-Su Volatile Oil Against Menopausal Depression. Front. Pharmacol. 2021, 12, 765638.
https://doi.org/10.3389/fphar.2021.765638.
23. Yang, J.H.; Yoo, J.M.; Lee, E.; Lee, B.; Cho, W.K.; Park, K.I.; Yeul Ma, J. Anti-inflammatory effects of Perillae Herba ethanolic
extract against TNF-α/IFN-γ-stimulated human keratinocyte HaCaT cells. J. Ethnopharmacol. 2018, 211, 217–223.
https://doi.org/10.1016/j.jep.2017.09.041.
24. Kagawa, N.; Iguchi, H.; Henzan, M.; Hanaoka, M. Drying the leaves of Perilla frutescens increases their content of anticancer
nutraceuticals. Food Sci. Nutr. 2019, 7, 1494–1501. https://doi.org/10.1002/fsn3.993.
25. Huang, B.; Lei, Y.; Tang, Y.; Zhang, J.; Qin, L.; Liu, J. Comparison of HS-SPME with hydrodistillation and SFE for the analysis
of the volatile compounds of zisu and baisu, two varietal species of Perilla frutescens of Chinese origin. Food Chem. 2011, 125,
268–275. https://doi.org/10.1016/j.foodchem.2010.08.043.
26. Tabanca, N.; Demirci, B.; Ali, A.; Ali, Z.; Khan, I.A. Essential oils of green and red perilla frutescens as potential sources of com-
pounds for mosquito management. Ind. Crops Prod. 2015, 65, 36–44. https://doi.org/10.1016/j.indcrop.2014.11.043.
27. Fan, Y.; Cao, X.; Zhang, M.; Wei, S.; Zhu, Y.; Ouyang, H.; He, J. Quantitative Comparison and Chemical Profile Analysis of
Different Medicinal Parts of Perilla frutescens (L.) Britt. from Different Varieties and Harvest Periods. J. Agric. Food Chem. 2022,
70, 8838–8853. https://doi.org/10.1021/acs.jafc.2c03104.
28. Deguchi, Y.; Ito, M. Rosmarinic acid in Perilla frutescens and perilla herb analyzed by HPLC. J. Nat. Med. 2020, 74, 341–352.
https://doi.org/10.1007/s11418-019-01367-8.
29. Zheng, Y.F.; Li, D.Y.; Sun, J.; Cheng, J.M.; Chai, C.; Zhang, L.; Peng, G.P. Comprehensive Comparison of Two Color Varieties
of Perillae Folium Using Rapid Resolution Liquid Chromatography Coupled with Quadruple-Time-of-Flight Mass Spectrome-
try (RRLC-Q/TOF-MS)-Based Metabolic Profile and in Vivo/in Vitro Anti-Oxidative Activity. J. Agric. Food Chem. 2020, 68, 14684–
14697. https://doi.org/10.1021/acs.jafc.0c05407.
30. Nitta, M.; Kobayashi, H.; Ohnishi-Kameyama, M.; Nagamine, T.; Yoshida, M. Essential oil variation of cultivated and wild
perilla analyzed by GC/MS. Biochem. Syst. Ecol. 2006, 34, 25–37. https://doi.org/10.1016/j.bse.2005.05.006.
31. Xu, Z.; Wu, W.; Zheng, Y.; Chen, L.; Cai, Q. Essential oil variations in different Perilla L. accessions: Chemotaxonomic implica-
tions. Plant Syst. Evol. 2009, 281, 1–10. https://doi.org/10.1007/s00606-009-0152-1.
32. Vogt, T. Phenylpropanoid biosynthesis. Mol. Plant 2010, 3, 2–20. https://doi.org/10.1093/mp/ssp106.
33. Yuba, A.; Yazaki, K.; Tabata, M.; Honda, G.; Croteau, R. cDNA cloning, characterization, and functional expression of 4S-(-)-
limonene synthase from Perilla frutescens. Arch. Biochem. Biophys. 1996, 332, 280–287. https://doi.org/10.1006/abbi.1996.0343.
34. Guo, L.; Zhang, D.; Wang, L.; Xue, Z.; Guo, M.; Duan, L.; Zheng, Y. Comparison and Discrimination of Artemisia argyi and
Artemisia lavandulifolia by Gas Chromatography-Mass Spectrometry-Based Metabolomic Approach. J. AOAC Int. 2019, 102,
1814–1821. https://doi.org/10.5740/jaoacint.19-0080.
35. Cebi, N.; Arici, M.; Sagdic, O. The famous Turkish rose essential oil: Characterization and authenticity monitoring by FTIR,
Raman and GC-MS techniques combined with chemometrics. Food Chem. 2021, 354, 129495. https://doi.org/10.1016/j.food-
chem.2021.129495.
36. Avci, A.B.; Korkmaz, M.; Özçelik, H. Essential oil composition of Cymbocarpum erythraeum (DC.) Boiss. from Turkey. Nat. Prod.
Res. 2014, 28, 636–640. https://doi.org/10.1080/14786419.2014.891116.
