plants
Article
Chemical Characterization of Marrubium vulgare Volatiles
from Serbia
Milica Aćimović 1, * , Stefan Ivanović 2 , Katarina Simić 2 , Lato Pezo 3 , Tijana Zeremski 1 , Jelena Ovuka 1
and Vladimir Sikora 1
1
2
3
*
Citation: Aćimović, M.; Ivanović, S.;
Simić, K.; Pezo, L.; Zeremski, T.;
Ovuka, J.; Sikora, V. Chemical
Characterization of Marrubium vulgare
Volatiles from Serbia. Plants 2021, 10,
600. https://doi.org/10.3390/
plants10030600
Institute of Field and Vegetable Crops Novi Sad, Maksima Gorkog 30, 21000 Novi Sad, Serbia;
tijana.zeremski@ifvcns.ns.ac.rs (T.Z.); jelena.ovuka@ifvcns.ns.ac.rs (J.O.); vladimir.sikora@ifvcns.ns.ac.rs (V.S.)
Institute of Chemistry, Technology and Metallurgy, University of Belgrade, 11000 Belgrade, Serbia;
stefan.ivanovic@ihtm.bg.ac.rs (S.I.); katarina.simic@ihtm.bg.ac.rs (K.S.)
Institute of General and Physical Chemistry, University of Belgrade, 11000 Belgrade, Serbia;
latopezo@yahoo.co.uk
Correspondence: milica.acimovic@ifvcns.ns.ac.rs
Abstract: Marrubium vulgare is a cosmopolitan medicinal plant from the Lamiaceae family, which
produces structurally highly diverse groups of secondary metabolites. A total of 160 compounds
were determined in the volatiles from Serbia during two investigated years (2019 and 2020). The main
components were E-caryophyllene, followed by germacrene D, α-humulene and α-copaene. All these
compounds are from sesquiterpene hydrocarbons class which was dominant in both investigated
years. This variation in volatiles composition could be a consequence of weather conditions, as in the
case of other aromatic plants. According to the unrooted cluster tree with 37 samples of Marrubium
sp. volatiles from literature and average values from this study, it could be said that there are several
chemotypes: E-caryophyllene, β-bisabolene, α-pinene, β-farnesene, E-caryophyllene + caryophyllene
oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could
be consequence of adaptation to grow in different environment, especially ecological conditions such
as humidity, temperature and altitude, as well as hybridization strongly affected the chemotypes.
In addition, this paper aimed to obtain validated models for prediction of retention indices (RIs) of
compounds isolated from M. vulgare volatiles. A total of 160 experimentally obtained RIs of volatile
compounds was used to build the prediction models. The coefficients of determination were 0.956
and 0.964, demonstrating that these models could be used for predicting RIs, due to low prediction
error and high r2 .
Academic Editor: Jésus Palá-Pául
Keywords: horehound; GC–MS; retention indices; QSRR; boosted trees regression model
Received: 28 February 2021
Accepted: 16 March 2021
Published: 23 March 2021
1. Introduction
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Copyright: © 2021 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/).
Marrubium vulgare L., also known as white horehound, is a perennial species from
the Lamiaceae family. It is indigenous to the region between the Mediterranean Sea
and Central Asia; however, today it is found worldwide, apart from the coldest regions
and high altitudes [1]. This plant is highly resistant to drought and due to this it grows
well in semiarid areas [2]. Additionally, as it is a moderate salt-tolerant species this
medicinal plant could be grown on saline soil [3]. The surface of M. vulgare vegetative
and generative organs is densely clothed with glandular and nonglandular trichomes
which accumulate secondary metabolites [4]. M. vulgare produces structurally highly
diverse groups of secondary metabolites, thus represents a valuable source of bioactive
compounds and preparations with health-promoting effects: antioxidant, hepatoprotective,
antiproliferative, anti-inflammatory, antidiabetic, and antimicrobial [5]. The use of this
herb in traditional medicine is recorded worldwide for ameliorating chronic cough and
cold, numerous conditions related to skin, liver, gastric, heart, and immune system [6].
Generally, M. vulgare is poor in essential oil, and the major compounds are diverse [1,7–17].
Plants 2021, 10, 600. https://doi.org/10.3390/plants10030600
https://www.mdpi.com/journal/plants
Plants 2021, 10, 600
2 of 17
This proves that there are various chemotypes of M. vulgare. The lack of information in
this field is pointed out by Yabrir [1,18]. The studies about genus Marrubium are mainly
focused on taxonomical, morphological, and genetic diversity [4,19–26].
The main aim of this investigation was to determine volatiles composition of M. vulgare
grown in Serbia during two years and to compare its chemical composition with literature
data not only of M. vulgare but with other species from this genus as well (M. anisodon,
M. aschersonii, M. astracanicum, M. crassidens, M. deserti, M. duabense, M. parviflorum, M.
peregrinum, M. persicum, M. propinquum, M. velutinum). Another goal was to establish
the new quantitative structure retention relationship (QSRR) models for anticipating the
retention indices (RIs) of certain compounds in M. vulgare volatiles obtained by GC–MS
chromatography utilizing the genetic algorithm (GA) variable selection method and the
boosted trees regression. Furthermore, we gather information about the volatile compounds
of species from Marrubium genus in order to classify the chemotype of M. vulgare from this
study according to unrooted cluster tree.
2. Results
The main components in M. vulgare volatiles were E-caryophyllene with 24.6% and
23.0%, followed by germacrene D with 9.6% and 17.0%, α-humulene with 5.2% and 5.3% as
well as α-copaene with 3.3% and 6.1% in 2019 and 2020, respectively. All these compounds
are from the sesquiterpene hydrocarbons class which was dominant in both years of the
investigation, 52.0% in 2019 and 67.8% in 2020. This variation in volatiles composition
could be a consequence of weather conditions, as in case of other aromatic plants [27–33].
However, some of the components detected in M. vulgare volatiles during the twoyear research have not been detected yet in this species, while other components have
not been detected in other species of this genus. ScienceDirect Elsevier, SpringerLink,
PubMed, Scopus, Scifnder, Web of Science, Wiley Online, and Google Scholar databases
were reviewed and scientific publications from 1990 until 2020 that deal with chemical
composition of volatiles species from genus Marrubium were summarized and shown
in Table 1.
Table 1. Chemical composition of Marrubium vulgare during two years (2019 and 2020).
No
Compound/Class
Cycle
RIpred.
1
2E-Hexenal O
Train
2
Furan,
2,5-diethyltetrahydro O
Validation
2019
2020
RIa
%
RIa
%
892.915
-
-
847
0.2
853.684
-
-
897
0.1
Reference
M. aschersonii [34], M. deserti [35],
M. peregrinum [36], M. vulgare [10,12,15,16,34]
M. anisodon [37], M. astracanicum [38],
M. crassidens [39], M. deserti [35], M. duabense [40],
M. parviflorum [41,42], M. peregrinum [43,44],
M. persicum [45], M. propinquum [41],
M. velutinum [44], M. vulgare [7,8,10,15]
3
1-Octen-3-ol O
Validation
965.818
976
0.2
974
0.6
4
2-Pentyl furan O
Train
1059.803
-
-
989
0.1
5
3-Octanol O
Test
962.233
-
-
992
0.1
M. anisodon [37], M. astracanicum [46],
M. duabense [40], M. peregrinum [36,44],
M. velutinum [44]
6
Linalool OMN
Train
1106.041
1102
0.1
1098
0.1
M. aschersonii [34], M. astracanicum [46],
M. parviflorum [41,42,47,48],
M. peregrinum [36,43,44], M. persicum [45],
M. velutinum [44], M. vulgare [8,10,12,17,34,36,47,49]
7
n-Nonanal O
Train
1078.484
-
-
1102
0.1
M. aschersonii [34], M. deserti [35], M. duabense [40],
M. peregrinum [43,44], M. persicum [45],
M. velutinum [44], M. vulgare [34]
8
E-Thujone OMN
Train
1118.307
-
-
1114
0.1
M. peregrinum [43], M. vulgare [8,15]
9
NI-1
-
-
-
-
1132
0.1
-
Plants 2021, 10, 600
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Table 1. Cont.
No
Compound/Class
Cycle
RIpred.
10
Geijerene O
Train
11
2E-Nonen-1-al O
12
β-Cyclocitral O
13
2019
2020
Reference
RIa
%
RIa
%
1192.301
1143
0.1
1139
0.6
Validation
1097.602
-
-
1156
0.1
Train
1216.889
-
-
1219
0.1
Cogeijerene O
Train
1203.235
-
-
1283
0.1
14
Pregeijerene O
Train
1149.857
1290
0.1
1287
0.2
M. astracanicum [38], M. crassidens [38],
M. parviflorum [42,47], M. peregrinum [43]
15
Thymol AR
Test
1209.017
1292
0.3
-
-
M. deserti [52], M. vulgare [7,8,10,15,50]
16
2-Undecanone O
Train
1269.228
1295
0.1
1292
Trace
M. vulgare [15]
-
M. duabense [40], M. incanum [50], M. parviflorum [42],
M. peregrinum [43], M. vulgare [7,8,10,49,50]
17
Carvacrol AR
Validation
1179.072
1302
0.1
-
M. incanum [50,51], M. parviflorum [42,47],
M. peregrinum [43], M. vulgare [50]
M. peregrinum [44], M. velutinum [44], M. vulgare [10]
18
δ-Elemene ST
Test
1512.436
-
-
1336
0.1
M. anisodon [37], M. astracanicum [38],
M. crassidens [38], M. deserti [35,40],
M. duabense [40], M. incanum [50,51],
M. parviflorum [47], M. peregrinum [44],
M. persicum [53], M. thessalum [54],
M. velutinum [44], M. vulgare [47,50]
19
α-Cubebene ST
Train
1491.202
-
-
1348
0.1
M. astracanicum [38], M. crassidens [39],
M. deserti [35,40], M. duabense [40],
M. parviflorum [47], M. peregrinum [44],
M. persicum [45], M. vulgare [8,47]
20
Eugenol AR
Train
1372.624
-
-
1357
0.4
M. aschersonii [34], M. peregrinum [36,43,44],
M. persicum [53], M. velutinum [44],
M. vulgare [10,12,34,36,47]
6.1
M. anisodon [37], M. aschersonii [34],
M. astracanicum [38], M. crassidens [38],
M. deserti [35], M. duabense [40], M. incanum [50,51],
M. parviflorum [42,47,48], M. peregrinum [36,43,44],
M. persicum [53], M. thessalum [54],
M. velutinum [44], M. vulgare [8–13,36,47,50]
M. anisodon [37], M. astracanicum [38],
M. crassidens [38], M. deserti [35,52],
M. incanum [50,51], M. parviflorum [41,42,47,48],
M. peregrinum [43,44], M. persicum [45,53],
M. thessalum [54], M. velutinum [44],
M. vulgare [9,10,13,50]
21
α-Copaene ST
Train
1475.878
1377
3.3
1377
22
β-Bourbonene ST
Train
1487.976
1385
0.8
1384
1.2
23
NI-2
-
-
-
-
1388
0.1
-
0.2
M. aschersonii [34], M. deserti [35],
M. peregrinum [43,44], M. parviflorum [42],
M. velutinum [44], M. vulgare [12,13,34,47]
M. anisodon [37], M. astracanicum [38],
M. crassidens [38], M. deserti [35,52],
M. duabense [40], M. incanum [50,51],
M. parviflorum [42,47], M. peregrinum [44],
M. persicum [53], M. thessalum [54],
M. velutinum [44], M. vulgare [47,50]
24
25
β-Cubebene ST
Test
β-Elemene ST
Train
26
Z-Caryophyllene ST
27
α-Z-Bergamotene ST
1475.610
1390
1475.506
1392
Train
1463.161
Train
1428.215
0.1
1389
0.4
1391
1.0
1407
0.1
1406
0.2
1416
0.2
-
-
28
E-Caryophyllene ST
Validation
1463.161
1422
24.6
1423
23.0
M. anisodon [37], M. aschersonii [34],
M. astracanicum [38,46], M. crassidens [38,39],
M. deserti [35,52], M. duabense [40],
M. incanum [50,51], M. parviflorum [41,42,47,48],
M. peregrinum [36,43,44], M. persicum [45,53],
M. propinquum [41], M. thessalum [54],
M. velutinum [44],
M. vulgare [7–13,16,17,34,36,47,49,50]
29
β-Copaene ST
Test
1459.623
1430
0.4
1430
1.3
M. incanum [50], M. vulgare [50]
Plants 2021, 10, 600
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Table 1. Cont.
No
Compound/Class
Cycle
RIpred.
30
α-E-Bergamotene ST
Train
31
NI-3
-
2019
2020
Reference
RIa
%
RIa
%
1428.215
1436
0.1
1435
0.1
M. anisodon [37], M. astracanicum [46],
M. crassidens [38], M. parviflorum [42,47],
M. peregrinum [44], M. velutinum [44], M. vulgare [47]
-
1445
0.2
1444
0.6
M. anisodon [37], M. aschersonii [34],
M. astracanicum [38,46], M. crassidens [38,39],
M. duabense [40], M. incanum [50,51],
M. parviflorum [42,47], M. peregrinum [36,43],
M. persicum [45], M. thessalum [54], M. velutinum [44],
M. vulgare [8–10,12,13,15,34,36,47,50]
32
α-Humulene ST
Validation
1503.999
1454
5.2
1455
5.3
33
Sesquisabinene ST
Train
1442.740
-
-
1457
0.9
34
E-β-Farnesene ST
Test
1431.419
1457
35
C16H34 A
Train
1573.436
36
NI-4
-
-
37
Z-Muurola-4(14),5diene
ST
Train
38
NI-5
39
1.3
-
1462
1.5
1462
0.2
-
Trace
-
0.2
1482.433
-
-
1466
0.1
-
-
1469
0.1
-
-
-
NI-6
-
-
1472
0.1
-
-
-
40
E-Cadina-1(6),4-diene
ST
Train
1481.465
-
-
1475
Trace
M. vulgare [15]
41
γ-Muurolene ST
Test
1450.203
1479
0.1
-
-
M. incanum [50], M. peregrinum [43,44],
M. parviflorum [42], M. velutinum [44]
17.0
M. anisodon [37], M. aschersonii [34],
M. astracanicum [38], M. crassidens [38,39],
M. deserti [35,52], M. incanum [50,51],
M. parviflorum [41,42,47,48],
M. peregrinum [36,43,44], M. persicum [45,53],
M. propinquum [41], M. thessalum [54],
M. velutinum [44],
M. vulgare [9–13,15–17,34,36,47,50]
42
43
Germacrene D ST
E-β-Ionone O
Test
Test
1450.188
1483
9.6
1487
-
M. anisodon [37], M. aschersonii [34], M. crassidens [39],
M. parviflorum [41,42,47], M. peregrinum [43,44],
M. persicum [45], M. propinquum [41],
M. thessalum [54], M. velutinum [44],
M. vulgare [8,10,12,16,17,34,36,47]
-
1471.735
1486
0.4
1489
Trace
M. anisodon [37], M. aschersonii [34],
M. duabense [40], M. incanum [51],
M. parviflorum [42], M. peregrinum [43,44],
M. thessalum [54], M. vulgare [12]
-
44
NI-7
-
-
-
-
1489
0.1
45
epi-Cubebol OST
Train
1622.285
-
-
1495
0.2
46
Viridiflorene ST
Validation
1507.447
1497
0.1
-
-
47
Bicyclogermacrene ST
Validation
1493.697
1498
0.2
1498
0.2
M. astracanicum [38], M. crassidens [38,39],
M. deserti [35,52], M. duabense [40],
M. incanum [50,51], M. parviflorum [41,42,47,48],
M. peregrinum [36,43,44], M. persicum [45],
M. propinquum [41], M. thessalum [54],
M. velutinum [44], M. vulgare [10,11,17,50]
48
NI-8
-
-
-
-
1499
0.7
-
49
Pentadecane A
Test
1486.884
1500
0.2
1500
Trace
50
α-Muurolene ST
Train
1465.650
1501
0.1
1501
0.2
M. aschersonii [34], M. deserti [35,52], M. incanum [51],
M. peregrinum [43], M. velutinum [44],
M. vulgare [12,13,34]
51
Germacrene A ST
Train
1450.188
1508
0.1
1506
0.1
M. incanum [50], M. parviflorum [47,48]
Plants 2021, 10, 600
5 of 17
Table 1. Cont.
No
Compound/Class
Cycle
RIpred.
2019
RIa
2020
%
RIa
%
Reference
52
β-Bisabolene ST
Validation
1425.139
1511
0.2
1507
0.2
M. anisodon [37], M. aschersonii [34],
M. crassidens [38], M. parviflorum [47],
M. peregrinum [44], M. persicum [45],
M. propinquum [41], M. thessalum [54],
M. velutinum [44], M. vulgare [11–13,17,34,47,49]
53
γ-Cadinene ST
Test
1450.203
1513
0.2
1515
0.4
M. deserti [52], M. incanum [50],
M. parviflorum [47,48], M. peregrinum [43,44],
M. persicum [53], M. velutinum [44],
M. vulgare [7,10,15,47]
54
δ-Cadinene ST
Test
1475.070
1523
4.7
1528
9.7
M. deserti [52], M. incanum [50],
M. parviflorum [42,47], M. peregrinum [43,44],
M. persicum [53], M. velutinum [44],
M. vulgare [7,10,15,47]
55
E-Cadina-1,4-diene ST
Train
1471.521
1533
0.1
1533
0.1
M. vulgare [15]
56
α-Cadinene ST
Train
1465.650
-
-
1537
0.1
M. peregrinum [43,44], M. velutinum [44],
M. vulgare [47]
57
α-Calacorene ST
Train
1540.123
-
-
1543
0.1
M. deserti [52], M. vulgare [12,15]
58
NI-9
-
-
1555
0.2
1552
0.2
-
59
E-Nerolidol OST
Validation
1567.136
1561
3.5
1564
1.5
M. anisodon [37], M. deserti [52], M. parviflorum [42],
M. peregrinum [43,44], M. thessalum [54],
M. velutinum [44], M. vulgare [9,36]
60
NI-10
-
-
-
-
1571
0.1
-
61
NI-11
-
-
1577
0.2
1575
0.9
-
62
NI-12
-
-
-
-
1582
0.3
M. anisodon [37], M. astracanicum [46],
M. crassidens [38,39], M. deserti [52],
M. duabense [40], M. incanum [50,51],
M. parviflorum [41,42,47,48], M. peregrinum [36,43],
M. persicum [45,53], M. propinquum [41],
M. thessalum [54], M. velutinum [44],
M. vulgare [8–10,12,36,47,50]
63
Caryophyllene
oxide OST
Test
1636.612
1580
1.0
1583
1.8
64
NI-13
-
-
-
-
1587
0.1
-
65
Viridiflorol OST
Validation
1573.436
1597
0.1
-
-
M. aschersonii [34], M. astracanicum [38],
M. crassidens [38], M. incanum [51], M. parviflorum [47],
M. peregrinum [43], M. vulgare [10,12,34,47]
66
Hexadecane A
Train
1594.576
1602
0.1
-
-
M. duabense [40], M. velutinum [44]
67
Humulene epoxide
II OST
Train
1626.959
1607
0.2
1607
0.2
M. anisodon [37], M. incanum [51], M. thessalum [54],
M. vulgare [10]
68
Muurola-4,10(14)dien-1-β-ol OST
Train
1605.330
-
-
1627
0.3
69
NI-14
-
-
1628
0.1
-
-
70
4,4-dimethylTetracyclo
[6.3.2.0(2,5).0(1,8)]
tridecan-9-ol O
Validation
1605.030
-
-
1631
0.2
71
NI-15
-
-
1632
0.1
-
-
72
Caryophylla-4(12),
8(13)-dien-5-α-ol OST
Train
1605.030
1636
0.1
1635
0.3
73
epi-α-Muurolol
(=tau-muurolol) OST
Test
1605.030
1642
0.2
1641
0.6
74
α-Muurolol
(=Torreyol) OST
Train
1652.148
-
-
1645
0.1
-
-
M. astracanicum [38], M. deserti [35],
M. incanum [51], M. parviflorum [42],
M. peregrinum [44], M. velutinum [44]
Plants 2021, 10, 600
6 of 17
Table 1. Cont.
No
Compound/Class
Cycle
RIpred.
75
α-Cadinol OST
Train
76
NI-16
77
2019
2020
Reference
RIa
%
RIa
%
1682.934
1654
0.3
1654
0.9
M. crassidens [38], M. deserti [35,52],
M. incanum [50,51], M. parviflorum [42],
M. persicum [45], M. vulgare [12,50]
-
-
1658
0.2
1656
0.2
-
NI-17
-
-
1662
0.1
1662
0.1
-
78
E-Calamenen- 10-ol
OST
Train
1608.844
-
-
1669
0.1
79
NI-18
-
-
1668
0.2
-
-
-
80
NI-19
-
-
-
-
1670
0.2
81
8-Heptadecene O
Train
1607.164
-
-
1673
0.2
82
1-Tetradecanol O
Train
1702.771
1675
0.1
-
-
83
Germacra-4(15),5,
10(14)-trien-1-α-ol OST
Train
1700.003
1682
0.1
1685
0.2
84
Heptadecane A
Validation
1726.886
1696
0.3
1696
0.2
M. anisodon [37], M. parviflorum [42,47],
M. vulgare [10,47]
85
Pentadecanal O
Validation
1581.928
1710
0.1
1711
0.1
M. anisodon [37]
86
Mint sulfide ST
Train
1778.777
1733
0.1
1736
0.1
87
NI-20
-
-
1734
0.1
-
-
-
88
NI-21
-
-
1742
0.1
-
-
-
89
NI-22
-
-
1743
0.4
1744
0.1
90
E-3-Octadecene O
Train
1722.391
-
-
1777
0.1
91
n-Pentadecanol O
Train
1787.022
1778
0.1
-
-
M. parviflorum [42]
92
NI-23
-
-
-
-
1782
0.1
-
93
Octadecane A
Validation
1950.093
1796
0.1
-
-
M. parviflorum [47], M. peregrinum [43],
M. vulgare [47]
94
NI-24
-
-
1819
0.1
-
-
-
95
6,10,14-trimethyl2-Pentadecanone O
Train
1915.818
1844
4.8
1842
0.5
M. peregrinum [44], M. velutinum [44],
M. vulgare [10]
96
NI-25
-
-
1849
0.1
-
-
-
97
NI-26
-
-
1853
0.2
-
-
-
98
NI-27
-
-
1888
0.1
-
-
-
99
NI-28
-
-
1891
0.8
1891
0.1
-
100
Nonadecane A
Test
1869.346
1897
0.2
1897
0.2
M. duabense [40], M. parviflorum [47],
M. peregrinum [43], M. vulgare [10,15,47]
101
NI-29
-
-
1904
0.1
1906
Trace
-
102
5E,9E-Farnesyl
acetone OST
Train
1956.289
1916
0.3
1915
Trace
M. thessalum [54], M. vulgare [15]
103
NI-30
-
-
1918
Trace
1917
Trace
-
104
NI-31
-
-
1924
0.1
-
-
-
105
NI-32
-
-
-
-
1926
Trace
-
106
NI-33
-
-
1925
0.1
-
-
-
107
NI-34
-
-
1929
0.1
-
-
-
108
NI
1938
0.1
1940
Trace
109
Hexadecanoic acid O
Validation
1995.491
1960
3.9
-
-
M. parviflorum [42], M. peregrinum [36],
M. vulgare [36,47]
110
NI-35
-
-
1973
0.1
1974
Trace
-
111
Eicosane A
Train
2034.560
1997
0.2
1994
0.1
M. parviflorum [48]
112
NI-36
-
-
2001
0.1
-
-
-
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Table 1. Cont.
No
Compound/Class
Cycle
RIpred.
113
E,E-Geranyl
linalool OD
Train
2028.645
114
3,7,11,15-tetramethyl(E,E)-1,6,10,14Hexadecatetraen3-ol OD
115
Manool OD
Train
116
NI-37
117
NI-38
118
119
2019
2020
Reference
RIa
%
RIa
%
2027
1.6
-
-
-
-
2028
0.9
2064.196
2057
0.3
-
-
-
-
2061
0.1
-
-
-
-
-
2067
0.1
-
-
-
NI-39
-
-
2084
0.1
-
-
-
NI-40
-
-
2096
0.1
-
-
-
M. aschersonii [34], M. parviflorum [42],
M. vulgare [12,34]
120
Heneicosane A
Train
2120.284
2101
1.6
2100
1.3
M. parviflorum [42,47], M. peregrinum [43],
M. propinquum [41], M. vulgare [10]
121
NI-41
-
-
2108
0.3
2105
0.2
-
122
NI-42
-
-
2112
0.2
2110
0.3
-
0.4
M. anisodon [37], M. incanum [51],
M. parviflorum [41,42], M. peregrinum [36],
M. propinquum [41], M. vulgare [10,15]
123
Phytol OD
Test
2124.818
2116
1.4
2113
124
NI-43
-
-
2131
0.2
-
-
-
125
NI-44
-
-
2143
0.2
2143
0.1
-
126
NI-45
-
-
2147
0.2
-
-
-
127
NI-46
-
-
2164
0.2
2160
0.2
-
128
NI-47
-
-
2167
0.1
2172
0.3
-
129
NI-48
-
-
2175
0.6
2176
0.4
-
130
NI-49
-
-
2181
0.9
2179
0.2
-
131
NI-50
-
-
2183
0.4
-
-
-
132
NI-51
-
-
2198
2.4
2195
2.4
-
133
Docosane A
Validation
2194.421
2205
0.9
2198
0.6
M. crassidens [39], M. parviflorum [47]
134
NI-52
-
-
-
-
2201
0.1
-
135
NI-53
-
-
-
-
2209
0.3
-
136
NI-54
-
-
2215
0.3
-
-
-
137
NI-55
-
-
2225
0.3
2221
0.1
-
138
NI-56
-
-
2246
0.3
-
-
-
139
NI-57
-
-
2258
0.2
2253
0.3
-
140
NI-58
-
-
2270
0.1
2265
0.1
-
141
NI-59
-
-
2277
0.2
2274
0.2
-
142
NI-60
-
-
2293
3.8
2288
1.7
143
Tricontane A
Train
2381.642
2305
3.6
2302
2.6
144
NI-61
-
-
2309
0.2
2305
0.2
-
145
NI-62
-
-
2344
0.2
2341
0.3
-
146
NI-63
-
-
2380
0.1
2377
0.1
-
147
NI-64
-
-
2383
0.1
2382
0.1
-
148
Tetracosane A
Train
2493.491
2401
0.3
2395
0.2
M. deserti [52], M. parviflorum [41,42,47],
M. propinquum [41]
149
NI-65
-
-
2454
0.4
2447
0.2
-
150
NI-66
-
-
2488
0.2
2483
0.2
-
151
Pentacosane A
Test
2510.087
2503
0.8
2497
0.6
M. anisodon [37], M. parviflorum [42,47]
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Table 1. Cont.
No
Compound/Class
Cycle
RIpred.
152
Heptacosane A
Train
153
NI-67
-
154
Octacosane A
155
Squalene T
Train
2019
2020
Reference
RIa
%
RIa
%
2730.537
2702
0.6
2696
0.5
-
-
-
2766
0.1
M. crassidens [39], M. parviflorum [42],
M. persicum [45]
2801
0.1
2791
Trace
2870.673
2835
0.1
2823
0.1
M. anisodon [37], M. aschersonii [34],
M. incanum [51], M. parviflorum [42], M. vulgare [34]
156
NI-68
-
-
2868
0.1
2855
0.1
-
157
Nonacosane A
Test
2930.732
2905
0.7
2892
0.6
M. anisodon [37], M. crassidens [39], M. persicum [45]
158
Untriacontane A
Validation
3150.673
3105
0.4
3095
0.3
159
NI-69
-
-
-
-
3212
0.1
160
Tritriacontane A
Train
3319.753
3300
0.1
3301
Trace
Oxygenated
monoterpenes OMN
Sesquiterpene
hydrocarbons ST
Oxygenated
sesquiterpenes OST
Oxygenated
diterpenes OD
Triterpene T
Aromatics AR
Alkanes A
Other O
NI
Total
0.1
0.2
52.0
67.8
5.8
6.2
3.3
1.3
0.1
0.4
11.7
9.9
16.7
100
0.1
0.4
7.4
3.4
12.5
99.3
-
RIpred. —BRT calculated retention index; RIa —retention index experimentally obtained on a HP-5MS column; Other—aliphatic hydrocarbons,
aliphatic aldehydes and alcohols, aliphatic acids, their esters and aldehydes, aromatic esters with aliphatic acids, alkyl-aromatic alcohols, or
aryl esters of aromatic acids; NI—not identified compound.
The predicted RIs are shown in Table 1, and confirm the good quality of the constructed BRT model by showing the relationship between the predicted and experimental
RI values. Graphical comparison between experimentally obtained RIs of M. vulgare
volatiles composition (RIa ), the retention time indices found in NIST database (RIb ) and the
retention time indices predicted by the two BRT models (RIpred. ) are presented in Figure 1.
Figure 1. Retention indices (RIs) of the M. vulgare volatiles composition, from experimentally obtained
GC–MS data on a HP-5MS column (RIa ) and NIST database (RIb ).
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In order to calculate the molecular descriptors, the PaDel-descriptor was used in this
investigation. Due to a great amount of data that was obtained, it was required to select
the most important set of descriptors to build the adequate model which would be able to
predict the RIs [55]. The factor analysis was done before the GA calculation, and only ca. 320
uncorrelated descriptors remained in the GA calculation [56,57]. The seven most significant
molecular descriptors chosen by GA are as follows: four 2D autocorrelation descriptors
(AATSC4e, AATSC2p, GATS6v and MATS5v), two Barysz matrix descriptors (VR1_Dzs
and SM1_Dzv) and Vertex adjacency information (magnitude) descriptor (VAdjMat).
The predicted RIs and molecular descriptors are presented in Table 1. Seven molecular
descriptors were utilized for predictions of RIs in the two BRT models. The predicted RIs
are presented in Figure 2, and visually confirm the adequate prediction capabilities of the
constructed BRT by showing the relationship between the predicted and experimental
retention values.
Figure 2. Comparison of experimentally obtained RIs on a HP-5MS column (RIa) with BRT pre-dicted values (RIpred.) in
2019 (a) and 2020 (b).
Separation of compounds in GC–MS and their RIs is linked to their affinity towards
mobile and stationary phases. Affinity and solubility of separated molecules directly
depend on their chemical structure and physico-chemical properties, which could be
expressed by molecular descriptors. According to Pearson’s correlation coefficients, there
was a rather poor correlation between all 3D autocorrelation descriptors (Table 2). Therefore,
utilized molecular descriptors were appropriate to predict RIs of compounds in M. vulgare
by the two multivariate BRT models [58].
Table 2. The correlation coefficient matrix for the selected descriptors by GA.
AATSC4e
AATSC2p
MATS5v
GATS6v
SM1 Dzv
VR1 Dzs
AATSC2p
MATS5v
GATS6v
SM1_Dzv
VR1_Dzs
VAdjMat
0.031
−0.138
−0.265
−0.135
−0.131
0.212
0.030
0.036
−0.008
0.072
0.205
−0.231
0.066
0.131
0.058
0.224
−0.010
0.109
0.214
0.084
2.339
Detailed explanations about the descriptors were found in the Handbook of Molecular
Descriptors [59]. These descriptors encode different aspects of the molecular structure and
were applied to develop the QSRR model. According to Pearson’s correlation, there was
a rather poor correlation between all molecular descriptors. Hence, utilized descriptors
were appropriate to predict RIs of compounds isolated from M. vulgare volatiles by the
two multivariate BRT models. The calibration and predictive capability of a QSRR model
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should be tested through model validation. The most widely used squared correlation
coefficient (r2 ) can provide a reliable indication of the fit of the model, thus, it was employed
to validate the calibration capability of a QSRR model.
In order to explore the nonlinear relationship between RIs and the descriptors selected
by GA, BRT technique was used to build the two predictive models. Two BRT models were
constructed to predict the retention time of compounds isolated from M. vulgare volatiles,
respectively. The coefficients of determination were 0.956 and 0.964, respectively, indicating
that these models could be used for prediction of RIs, due to low prediction error and
high r2 . The tests of the two BRT models fit (2019 and 2020) are shown in Table 3, with the
higher r2 values and lower χ2 , MBE, RMSE, and MPE values showing the better fit to the
experimental results [60,61].
Table 3. The “goodness of fit” tests for the developed BRT model.
Boosted Tree Model
χ2
RMSE
MBE
MPE
r2
2019
2020
4455.272
3975.751
66.160
62.581
−13.063
−7.698
3.285
3.241
0.956
0.964
χ2 —reduced chi-square, MBE—mean bias error, RMSE—root mean square error, MPE—mean percentage error.
Obtained results reveal the reliability of the BRT models for predicting the RIs of
compounds in M. vulgare volatiles obtained by GC–MS analysis. The influence of the seven
most important molecular descriptors, identified by using genetic algorithm on the RIs
was studied in this section. According to the Figure 3, VAdjMat was the most important
molecular descriptor for chemical compounds’ RIs calculation in M. vulgare, during 2019,
while VR1 Dzs was the most important variable during 2020.
Figure 3. Predictor importance of the molecular descriptors on RI in 2019 (a) and 2020 (b).
3. Discussion
According to the cluster analysis (unrooted cluster tree) with 37 samples of Marrubium
sp. volatiles from literature and average values from this study (Figure 4), it could be said
that there are several chemotypes, but only E-caryophyllene chemotype [9,12,36,38,47,50,51] is
clearly segregated. However, these are samples of M. vulgare, M. incanum, M. parviflorum,
M. peregrinum, and M. crassidens grown in Serbia, Poland, Slovakia, Egypt, and Iran. This
indicated that genus Marrubiumis very diverse in the case of volatiles composition.
α
β
β
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Figure 4. Unrooted cluster tree for different Marrubium samples.
In addition, E-caryophyllene is a compound which is occurring in all samples
(except M. vulgare from Eastern Algeria [15]), but in E-caryophyllene chemotype its
content ranges between 15.6% and 45.8%. Other chemotypes can be classified as
β-bisabolene (13.1–28.3%) [11,17,34,47], α-pinene (21.5–28.9%) [16,53], β-farnesene
(20.2–24.2%) [37,44], E-caryophyllene + caryophyllene oxide chemotype [44,46,54], and
diverse (unclassified) chemotypes [7,8,10,13,15,35,38,40,41,43,45,49,52].
Occurring polymorphism could be a consequence of adaptation to grow in different
environments [19,62], especially ecological conditions such as humidity, temperature and
altitude [22] as well as hybridization [20] strongly affected the chemotypes, as well as biotic
and abiotic stresses (including temperature, light, water, salt, and oxidative stresses) [63].
Detected compounds in M. vulgare volatiles obtained by GC–MS analysis were used
for QSRR analysis. The following seven molecular descriptors that characterize the RIs
of obtained compounds were suggested by the genetic algorithm. Selected molecular
descriptors were not autocorrelated which was suggested by a correlation coefficient
matrix; thus, descriptors were suitable for QSRR analysis. These descriptors were utilized
as inputs for the boosted trees regression models, for estimating the RIs using a set of GC–
MS data from a series of 160 compounds found in M. vulgare volatiles. Statistical models
that quantify the relation between the structure of molecules and their chromatographic RIs
were represented by the quantitative structure retention relationship (QSRR) model [58,64].
č
Numerous publications related to the QSRR analysis in plants from Lamiaceae family
could be found in the literature: Thymus vulgaris [65], T. serpyllum [66], Satureja kitaibelii [55],
Salvia officinalis [67], as well as Stachys sp. [68]. The connection between the molecular
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descriptors and the retention time can be established by artificial neural network, machine
learning algorithms [69–73] or by boosted trees regression (BRT) [74].
4. Materials and Methods
4.1. Plant Material
M. vulgare was grown in the Institute of Field and Vegetable Crops (IFVCNS) collection
garden of medicinal and aromatic plants in Bački Petrovac (45◦ 21′ N; 19◦ 35′ E), confirmed
by Milica Rat, PhD, and deposited at the Herbarium BUNS (Department of Biology and
Ecology, Faculty of Sciences, University of Novi Sad) under the voucher number 2-1409.
After seed maturation (August 2018), it was collected and sown in field conditions in
September 2018 and 2019. The experimental plot was 10 m long and 5 m wide, with a
70 cm spacing between rows. From the seven rows, only three central rows were used for
collecting plant material to avoid edge effects (one row one sample).
4.2. Volatiles Isolation and Analysis
Flowering aerial parts of M. vulgare (Marrubii herba) were collected during July 2019
and 2020, dried in a solar dryer at a temperature of 40◦ with air circulation. After drying,
plant material was fragmented, and volatiles was extracted by Clevenger apparatus. Taking
in account that M. vulgare produces trace amounts of essential oil, it was trapped in nhexane. This process was performed in tree repetition for both years, as well as analysis of
chemical composition.
GC–MS analysis was carried out using an Agilent 7890A apparatus equipped with a
5975 C MSD, FID and a nonpolar HP-5MS fused-silica capillary column (30 m × 0.25 mm,
film thickness 0.25 µm). The carrier gas was helium, and its inlet pressure was 19.6 psi
and linear velocity of 1 mL/min at 210 ◦ C. The injector temperature was 250 ◦ C, injection
volume was 1 µL, split ratio, 10:1. Mass detection was carried out under source temperature
conditions of 230 ◦ C and interface temperature of 315 ◦ C. The EI mode was set at electron
energy, 70 eV with mass scan range of 40–600 amu. Temperature was programmed from
60 to 300 ◦ C at a rate of 3 ◦ C/min. The components were identified based on their linear
retention index relative to C8-C32 n-alkanes, compared with data reported in the literature
(Adams4 and NIST11 databases). The relative percentage of the oil constituents was
expressed as percentages by FID peak area normalization.
4.3. QSRR Analysis
PaDel-descriptor software was used to calculate specified molecular descriptors [75],
as described in our previous investigation [66]. Factor analysis and genetic algorithm (GA)
were used to determine the most important descriptors [76,77]. The relationship between
the chosen descriptors was examined and collinear descriptors were excluded. Statistica 10
software was used for the statistical investigation of the data [78].
4.4. BRT Model
In order to relate and to predict categorical or continuous dependent variables the BRT
model could be used [79,80], as it does not require transformation or outliers [81]. The BRT
method calculation is connected to the boosting methods enforced to regression trees [82].
The main idea is to calculate a set of simple trees, where each successive tree is built for
the prediction residuals of the preceding tree [83]. This method builds binary trees such as
partition the data into two samples at each split node [78].
The decision trees are combined through a cross-validation or “boosting” procedure
in order to acquire the single computational model [84]. BRT modeling consists of the
following steps: (a) an initial regression tree is defined according to a minimum loss
function; (b) the other trees are engaged in the iterative process in which several new
regression trees were developed and selected to the subsequent according to the StatSoft
Statistica’s recommendation—the least square error (LSE); (c) step (b) is repeated until a
stopping criterion is reached (for instance, the value of LSE).
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In this study, several regularization parameters were set in order to optimize the fit
between experimental results and computing model: the number of trees (between 100 and
1000), learning rate (between 0.0005 and 0.1), random test data proportion (0.1–0.9) and
subsample proportion (0.1–0.9). According to Statistica’s manual, prior to computation, a
subsample of data is created, according to random test data proportion of the cases, and
these data are treated as test samples used to evaluate the appropriate fit of the model.
The remaining set of data is used for the analyses via stochastic gradient boosting (for the
selection of samples for consecutive boosting steps).
4.5. Cluster Analysis
The cluster analysis (CA) was used to evaluate intra- and interpopulation variability
and differentiation of volatile constituents of Marrubium samples collected in different
locations and/or taken from literature reports. The phylogenetic tree diagram for Marrubium
samples was calculated and plotted using R software 4.0.3 (64-bit version). The R package
“ape” (Analysis of Phylogentics and Evolution) was used for calculation, applied as a
graphical tool to represent the arrangements of similar volatiles concentration (evaluated
in the cluster analysis). The obtained experimental results were collected in the matrix,
after which the hierarchical cluster analysis was performed. The distance matrix was
determined using the Euclidean method, while the cluster analysis was performed using
the “complete” method.
5. Conclusions
The main components in M. vulgare volatiles were E-caryophyllene (24.6% and 23.0%),
followed by germacrene D (9.6% and 17.0%), α-humulene (5.2% and 5.3%) and α-copaene
(3.3% and 6.1%) in 2019 and 2020, respectively. All these compounds are from sesquiterpene
hydrocarbons class, which was dominant in both years of the investigation, 52.0% in 2019
and 67.8% in 2020.
The results demonstrated that the boosted trees regression models were adequate in
predicting the RIs of the compounds in M. vulgare volatiles obtained by GC–MS analysis on
a HP-5MS column. The coefficients of determination were 0.956 and 0.964 (for compounds
found in M. vulgare volatiles, during the years 2019 and 2020, respectively), which is a good
indication that these models could be used as a fast mathematical tool for prediction of RIs,
due to low prediction error and moderately high r2 . Suitable models with high statistical
quality and low prediction errors were derived, and it could be further used for estimation
of RIs of newly detected compounds.
According to the unrooted cluster tree with 37 samples of Marrubium sp. volatiles
from literature and average values from this study, it could be said that there are several
chemotypes: E-caryophyllene, β-bisabolene, α-pinene, β-farnesene, E-caryophyllene +
caryophyllene oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could be a consequence of adaptation to grow in different environments,
especially ecological conditions such as humidity, temperature and altitude, as well as
hybridization which strongly affected the chemotypes. Further research on M. vulgare
chemotypes needs to be focused on genetic markers, because evaluation of genetic diversity
has key importance in improving the quality of raw material used for industrial purposes.
Author Contributions: Conceptualization, M.A. and J.O.; methodology, S.I. and K.S.; software, L.P.;
validation, S.I., K.S. and T.Z.; formal analysis, L.P.; investigation, M.A.; resources, M.A. and V.S.; data
curation, L.P.; writing—original draft preparation, M.A.; writing—review and editing, T.Z. and V.S.;
visualization, L.P.; supervision, J.O.; project administration, T.Z.; funding acquisition, M.A., T.Z., J.O.
and V.S. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by the Ministry of Education, Science and Technological
Development of the Republic of Serbia, grant number: 451-03-9/2021-14/200032.
Data Availability Statement: Not applicable.
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Acknowledgments: We thank Vele Tešević, Marina Todosijević, Jovana Stanković Jeremić, Jovana
Ljujić, and Mirjana Cvetković for participating in this research.
Conflicts of Interest: The authors declare no conflict of interest.
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