Chemometric analysis of volatiles of propolis from different regions using static headspace GC-MS

1. IntroductionPropolis (also called “bee glue”) is a unique product produced by bees. Propolis is valuable product from ancient times to current days. Mostly it is used in folk medicine and pharmacy as a remedy with high antioxidant, antimicrobial, anti-inflammatory, antiviral, antitumor and other activities [1-5]. Propolis also is useful in food technology due to its antioxidant, antibacterial and antifungal activities. Compounds, found in propolis, are typical food ingredients and/or food additives and generally are recognized as safe (GRAS) substances

[6]. Chemical composition of propolis is dependent on its geographical origin [7-9], species or races of the bees collecting propolis [10-13], and propolis collection season in the regions where it can be collected all year [14-16].

Crude propolis is composed of 50% resin (consisting of flavonoids and related phenolic acids and regarded as the polyphenolic fraction), 30% wax, 10% essential and aromatic oils, 5% pollen, and other substances [6]. More than 400 different chemical compounds including aldehydes, organic acids, esters, hydrocarbons, cyclic compounds, terpenes, flavonoid aglycones, phenolic acids and their esters, phenolic aldehydes, alcohols

Central European Journal of Chemistry

Chemometric analysis of volatiles of propolis from different regions

using static headspace GC-MS

* E-mail: [email protected]

Received 5 September 2013; Accepted 12 January 2014

Abstract:

© Versita Sp. z o.o.Keywords: Propolis • Static headspace GC-MS • Data pre-processing • Hierarchical cluster analysis • K-Means cluster analysis

1Faculty of Natural Sciences, Vytautas Magnus University, LT-40444 Kaunas Lithuania

2Metrology Institute, Kaunas University of Technology,

LT-51368 Kaunas, Lithuania

3Department of Drug Technology and Social Pharmacy, Medical Academy, Lithuanian University of Health Science,

LT-44307 Kaunas, Lithuania

Vilma Kaškonienė1*, Paulius Kaškonas2, Audrius Maruška1, Loreta Kubilienė3

Research Article

Six samples of propolis were analyzed in the paper: a sample from Brazil, Estonia, China and three samples from different locations of Uruguay. Static headspace technique coupled with gas chromatography-mass spectrometry analysis has been applied for the determination of the characteristic volatile profile with the aim to differentiate the propolis from different regions. Monoterpenes (α- and β-pinenes) were predominant in all samples, except the sample from China. This sample separated itself by the alcohols 3-methyl-3-buten-1-ol and 3-methyl-2-buten-1-ol, (40.33% and 11.57%, respectively) and ester 4-penten-1-yl acetate (9.04%). α-Pinene and β-pinene composed 64.59-77.56% of volatiles in Brazilian and Uruguayan propolis, and 29.43% in Estonian propolis. Brazilian propolis was distinguished by a high amount of β-methyl crotonaldehyde (10.11%), one of Uruguayan samples – by limonene (15.58%), and the Estonian sample – by eucalyptol (25.95%). Statistical investigation of the samples was made applying principal component, hierarchical cluster and K-Means cluster analyses. Various data pre-processing techniques were proposed and used to study and obtain the important volatile compounds contributed to the differentiation of the propolis samples from different regions to separate clusters.

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Cent. Eur. J. Chem. • 12(6) • 2014 • 736-746DOI: 10.2478/s11532-014-0521-7

V. Kaškonienė et al.

and ketones, sesquiterpenes, quinones, coumarins, steroids, amino acids were identified in the volatile and non-volatile fractions of propolis of different botanical and geographical origin.

Despite the fact, that volatile compounds are found in low concentrations in propolis, their aroma and significant biological activity make them of importance for the characterisation of propolis [17]. The presented results in the papers [14,18] reveal that the antibacterial activity of propolis is influenced mainly by the volatile compounds. The reviewed studies show that spectra of volatile compounds identified in propolis is very wide (Table 1); the chemical composition depends on the specificity of the local flora at the location of collection and thus on its geographic and climatic characteristics [2].

Besides it should be kept in mind that the composition of volatile compounds is dependent on the extraction method [19]. The most studies of propolis volatiles/essential oil composition were performed using hydrodistillation, which is characterized by long extraction process and possible thermal degradation or oxidation of thermally labile compounds [20]. Therefore this technique is suitable for the extraction of less-volatile compounds. Newer methods, like headspace analysis, overcome disadvantages of hydrodistillation because not only is it simple, fast and suitable for solid materials, but it also needs no special sample preparation [21] and therefore could be used in routine analysis for fast characterization of volatile compounds. But there are little literature data about analysis of propolis volatiles applying this technique.

This study was focused on the screening of propolis volatiles from the headspace with the aim to differentiate the propolis from different geographic regions. The comparative analysis of the volatile compounds composition of propolis collected in different regions (South America, East Europe and Asia) was performed. Samples were analyzed using static headspace with direct injection gas chromatographic-mass spectrometric analysis, applying statistical data mining techniques, which include data pre-processing, principal component analysis and classification, afterwards.

2. Experimental procedure

2.1. Propolis samplesPropolis samples were obtained from Apiproduktai, Ltd. (Vilnius, Lithuania). Six samples of propolis were under test in this study: a sample from Brazil, Estonia and China and three samples from different locations of Uruguay (named Uruguayan No1, Uruguayan No2 and Uruguayan No3 in the text below).

2.2. Static headspace GC-MC conditionsThe analysis was carried out using GC-MS system (Shimadzu, Tokyo, Japan), consisting of GC2010 gas chromatograph and QP2010 single quadrupole mass spectrometer, and CTC Combi-Pal autosampler. 0.5 g of grinded propolis sample was added into 10 mL headspace glass vial (O.D. 22.6 mm × H 46 mm) and immediately sealed with a PTFE/silicone septum. It was placed in the Combi-Pal autosampler for heating at 50°C for 10 min in order to ensure the equilibration of volatiles between the gaseous and solid phases in the vial. An aliquot of the gas phase above the tested sample was injected to the injector of the gas chromatograph. The following headspace autosampler conditions were set: incubation oven temperature 50ºC; incubation time 10 min; headspace syringe temperature 90ºC; agitator speed 450 rpm; injection volume 500 μL; fill speed 50 μL s-1; syringe pull-up delay 1000 ms; injection speed 500 µL s-1; pre-injection delay 500 ms; post injection delay 500 ms; syringe flush 30 s with nitrogen.

A Restec (Bellefonte, USA) RTX-5MS (30 m × 0.25 mm i.d. × 0.25 μm film thickness) GC column was used. The oven temperature gradient started at 35ºC for 3 min and then raised to 180ºC at 6ºC min-1, then raised to 280ºC at 17ºC min-1 and held for 4 min. Helium was used as carrier gas with a constant flow rate of 1.2 mL min-1. The injector temperature was kept at 200ºC in split mode (1:1). The MS ionization mode was set as follows: electron ionization energy was 70 eV, the detector voltage was 1.0 kV and the temperatures of ion source and interface were set at 200ºC and 250ºC, respectively. MS scanning experiments were conducted using a solvent delay of 3.1 min and a mass range of 33–350 m/z, event 0.25 s and speed scan 1428. Identification of compounds was performed according to their mass spectra (NIST v1.7). Positive identification was assumed when good matches (90% and above) of mass spectra were achieved.

Quantification was based on the areas of total ion current (TIC) peaks. Relative area values (percentage of total volatile composition) were used for characterization purposes.

2.3. Statistical data analysisThere were studied 6 samples of propolis from different regions. For metrological repeatability of the measurements each propolis sample was analyzed 6 times, except the Uruguayan No3, which was under analysis 5 times. After applying GC-MS method there were found 24, 11, 30, 21, 33 and 53 components in Brazilian, Estonian, Uruguayan No1, Uruguayan No2, Uruguayan No3 and Chinese propolis samples,

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Chemometric analysis of volatiles of propolis from different regions using static headspace GC-MS

Table 1. The predominant volatile compounds identified in propolis.

Propolis origin

Region (propolis collecting bee specie)

Predominant volatile compounds

Method of analysis

Literature

Brazil Rio de Janeiro (Apis mellifera)β-Caryophyllene (12.7%), acetophenone (12.3%),

linalool (6.47%), γ-elemene (6.25%), γ-cadinene (5.86%), γ-muurolene (3.61%)

Steam distillation [18]

Brazil Botucatu (Apis mellifera)

Benzoic acid (2.4–11.5%), spathulenol (9.0–13.9%), (2Z,6E)-farnesol (8.3–11.6%), benzyl benzoate

(0.3–7.6%), prenyl acetophenone (6.5–8.7%), diprenyl acetophenone (0.2–8.9%) Likens-Nickerson

hydrodistillation [14]

Botucatu (Apis mellifera lingustica)

Benzoic acid (0.2–5.2%), spathulenol (3.0–12.2%), (2Z,6E)-farnesol (1.6–14.9%), benzyl benzoate

(0.9–18.3%) prenyl acetophenone (0.2–6.7%), diprenyl acetophenone (1.1–8.0%)

Brazil Santiago (n/aa) α-Pinene (62.0%), β-pinene (29.2%), limonene (1.5%)

Hydrodistillation [22] São Francisco de Assis (n/a) α-Pinene (57.0%), β-pinene (30.8%), sphatulenol (1.2%) Jaguari (n/a) α-Pinene (63.0%), β-pinene (12.5%), limonene (11.2%)

Brazilian green propolis

Minas Gerais (Apis mellifera)β-Caryophyllene (13.4%), (E)-nerolidol (17.1%),

selina-3,7(11) diene (10.4%), aromadendrene (8.3%), germacrene A (7.6%)

Hydrodistillation [23]


Southeastern Brazil (Apis mellifera)

Nerolidol (6.64%), linalool (4.99 %), 1-phenylethanone (4.85%), trans-caryophyllene (4.05%) Hydrodistillation [24]


Commercial from Zhejiang Jiangshan Hengliang Bee

Products Co., Ltd

Longipinene (24.89%), α-eudesmol (6.94%), β-eudesmol (6.08%),β-caryophyllene (5.27%), guaiol

(5.06%) Microwave assistant

extraction [25]

Greece

Chalkidiki (n/a) Junipene (11.7%), α-pinene (7.9%), manoyl oxide (7.1%).

Hydrodistillation [28]

Andros (n/a) α-Pinene (45.8%), trans-β-terpineol (6.6%)

Agrinio (n/a) α-Pinene (17.7%), α-eudesmol (12.1%), n-decanal (6.2%), guaiol (5.0%)

Arta (n/a) α-Pinene (18.2%), δ-cadinene (8.4%), α-muurolene (5.0%).

Preveza (n/a) α-Pinene (10.9%), n-decanal (10.3%), cedrol (6.3%), n-nonanal (5.4%), manool (5.2%)

Turkey

Hatay (Apis mellifera)Cinnamyl cinnamate (28.0%), β-maaliene (12.8%),

β-eudesmol (19.4%), α-eudesmol (15.4%), α-bisabolol (12.3%), 17-pentatriacontene (11.7%)

Ethanol extraction [30]Mersin (Apis mellifera) α-Cedrol (33.0%), 10-methylnonadecane (13.2%), 17-pentatriacontene (23.4%), tetracosanoic acid (15.8%)

Adana (Apis mellifera)Cembrene (13.3%), 10-methylnonadecane (11.5%),

n-hekzatriakontane (11.6%), tetracosanoic acid methyl ester (10.3%), n-hentriacontanol-1 (10.5%)

Mexico

Yucatán (Apis mellifera) α-Pinene (11.9%), hexadecanoic acid (10.9%), trans-verbenol (7.0%)

Likens-Nickerson hydrodistillation [29]

Yucatán (Melipona beecheii)

α-Pinene (17.6%), β-caryophyllene (11.8%), spathulenol (9.7%), caryophyllene oxide (9.5%), β-bourbonene (9.2%), β-pinene (6.7%), α-copaene (4.8%), trans-

verbenol (4.0%)

CroatiaDalmatia (n/a) Limonene (10.5%), naphthalene (17.8%), β-pinene

(6.5%), γ-terpinene (5.6%)Hydrodistillation [26]

Slavonia (n/a) Limonene (6.4%), benzyl alcohol (18.2%), benzoic acid (27.0%)

Ethiopia

Haramaya (Apis mellifera jementica)

Calamenene (13.82%), 4-terpineol (8.57%), epi-bicyclosesquiphellandrene (8.37%), 4-(2-acetyl-5,5-dimethylcyclopent-2-enylidene) butan-2-one (7.83%)

and 3-isopropyl-6-methyl-2-oxo-1-(3-oxobutyl)-cyclohexanecarbaldehyde (5.90%). Hydrodistillation [27]

Assela (Apis mellifera monticola)

5,6,7,8-Tetramethylbicyclo[4,1,0]hept-4-en-3-one (15.01%), acoradien (13.77%), epicedrol

(6.80%), (6E,6E)-3,7,11,15-tetramethyl-1,6,10,14-hexadecatetraen-3-ol (6.14%)

a data not available, i.e., authors did not indicate precise bee specie.

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respectively. According to these results, data matrix of [35×104] was formed for further analysis.

Measured data were processed using MATLAB v7.6 software. Before statistical analysis, two data treatment procedures were applied: 1) centering and scaling procedure, which resulted in data standardization by subtracting the mean and dividing to the standard deviation; 2) filtering procedure, which rejected all small compounds contributing less than 1%. These procedures let reduce the data matrix to the size of [35×41].

To investigate the effect of different groups of compounds on samples similarity (difference) analysis, the following data pre-processing techniques were used:

1) no data pre-processing – the results of the analysis will show the real similarity (classification) situation;

2) data pre-processing using inversion – linear data pre-processing intended for investigation of the impact of the compounds contributing least, which are shaded during normal analysis

==≠−=

,0,0,0,%100

ii

iii

xwhenyxwhenxy

(1)

3) data pre-processing using logarithm – nonlinear data pre-processing intended for equalization the difference between amounts of the compounds

(2)

where xi is the measured value of the i-th compound, yi is the new value of the i-th compound after pre-processing, i is the compound index in the data matrix,

.After data preparation including pre-processing

stage, the following statistical methods were applied:

principal component analysis (PCA), for reduction of the space of the variables; hierarchical cluster analysis (HCA) using average linkage, in order to estimate the similarity (or distance) between any pair of the clusters applying average distance between all pairs of objects in any two clusters calculation; K-Means cluster analysis (KMCA), which is the iterative classification technique that minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid Euclidean distances.

3. Results and discussion

3.1. Chemical analysisThe composition of volatiles of the propolis samples under test was analyzed. First of all, it should be mentioned that the color difference of all propolis samples was evident: this property varied from opaque green – Brazilian sample to glossy dark brown (almost black) – sample from China. The color of propolis is dependent on its botanical source [23,31]. In the review paper of Bankova et al. [17] is stated, that the main botanical source of the propolis in Europe, non-tropical regions of Asia and North America is Populus spp. (poplar). Besides the main source bees also use pine, linden, willow, cherry, apple and other trees [31]. In tropical regions there are no poplars; therefore bees use other plant sources for bee glue. A botanical source of green Brazilian propolis is Baccaris dracunculifolia (Asteraceae), a native Brazilian plant, called “alecrim” [23,24]. Other popular plant sources of Brazilian propolis are Araucaria angustifolia and Eucalyptus citriodora [5].Applied gas chromatographic analysis showed significant differences of the volatiles composition of propolis, collected in different regions (Fig. 1). A total of 104 different compounds were found in the headspace of samples. Unfortunately, 37 compounds

Figure 1. GC-MS chromatograms obtained from the headspace of tested propolis samples.

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were not identified, however these compounds were not predominant (except one in Estonian propolis) – amount of 22 out of 37 unidentified compounds was lower than 0.50%, while amount of 9 compounds was in the range of 1.11-4.59%. The highest amount and diversity of volatile compounds obtained from the headspace of Chinese and Uruguayan No3 samples – 53 and 33 compounds, respectively, while the lowest amount obtained was from Estonian propolis sample – only 11 (Table 2).

Identified compounds mainly belonged to the class of alcohols, aldehydes, monoterpenes and sesquiterpenes. Monoterpenes, like α- and β-pinenes were predominant in all samples, except Chinese propolis, where these compounds were not found. Melliou et al. used hydrodistillation method for analysis and identified α-pinene as the major constituent, which amount in four of the five samples of Greece propolis fell in range 7.9–45.8% [28], while among the volatiles of tropical propolis this compound was reported in small amounts – up to 1.6% in the studies [18,24], noting that steam distillation technique and hydrodistillation method was used, respectively, or was not found at all [14,23], when Likens-Nickerson hydrodistillation method and hydrodistillation method was applied, respectively. However, Nunes and Guerreiro performed headspace analysis and attributed higher amounts of α-pinene, β-pinene, sulcatone, carene, limonene, eucalyptol, α-ocimene, β-ocimene, acetophenone and nonanal to low-quality green Brazilian propolis [32]. α-Pinene composed more than a half of all volatiles in studied Brazilian and Uruguayan No1 propolis, 52.50±2.91 and 53.41±0.81%, respectively; slightly lower amounts were found in Uruguayan No2 propolis (40.45±2.38%) and almost twofold lower in Uruguayan No3 (22.96±2.80%) and Estonian (20.57±8.39%). Uruguayan No3 propolis was the only having higher amount of β-pinene (27.44±1.07%) than α-pinene (22.96±2.80%). This sample also distinguished itself by high amount of limonene (15.58±0.57%) and cis-β-ocimene (7.42±0.41%), which was found only in this propolis sample. High content of limonene, 10.5% and 11.2%, employing hydrodistillation method was found in Croatia propolis [26] and Brazil propolis from Jaguari region [22], respectively. Our tested sample of Brazilian propolis contained 1.13±0.14% of limonene. High amounts of α- and β-pinenes, 57.0-62.0% and 12.5-29.2%, respectively in Brazilian propolis were also reported in the above mentioned study [22] (see Table 1). According to our investigation results, despite the high amounts of α- and β-pinenes, analyzed Brazilian propolis distinguished by a high amount of β-methyl crotonaldehyde, 10.1±1.45%, which was not determined in tested samples from other regions.

β-Methyl crotonaldehyde was also detected in some propolis samples of Chinese origin by Cheng et al. [31], using headspace analysis. Estonian propolis differed in high amounts of eucalyptol (25.95±10.64%), benzaldehyde (10.85±0.92%) and unidentified compound (12.20±0.60%). Eucalyptol was not found in other tested propolis samples (see Table 2). We could not find published data about Estonian and Uruguay propolis volatiles.

Propolis sample from China was totally different than samples from Europe and South America. 44 out of 53 compounds were found only in Chinese propolis. This sample can be characterized by high amounts of alcohols 3-methyl-3-buten-1-ol and 3-methyl-2-buten-1-ol, (40.33±0.44% and 11.57±0.26%, respectively), ester 4-penten-1-yl acetate (9.04±0.36%) and sesquiterpene α-longipinene (9.41±0.25%). The last-mentioned was found only in propolis from China (see Table 2). Microwave assistant extraction technique was applied in the study of Li et al. [25]; there was reported high amount of longipinene, 24.89%, in analysed Brazilian green propolis. The volatile compounds 3-methyl-3-buten-1-ol, 3-methyl-2-buten-1-ol and 4-penten-1-yl acetate were identified in some China propolis samples employing headspace analysis method by Cheng et al. [31]. Authors of the study tested 12 propolis samples from four China regions and found 3-methyl-3-buten-1-ol and 3-methyl-2-buten-1-ol in the samples from all regions, while 4-penten-1-yl acetate was constituent of one sample only. Nevertheless acetic acid was predominant in tested samples and amounted from 9.13 to 62.67% of propolis volatiles, this compound was not identified in our study.

The headspace of Chinese propolis was the richest in volatiles, while Estonian samples – the poorest. According to the GC peak area, the tested propolis samples aligned in the following order: Chinese > Uruguayan No3 > Uruguayan No1 > Brazilian > Uruguayan No2 > Estonian (see Table 2). Reason for the low quantity of volatile compounds in Estonian propolis could be waxes and other ballast material, which presence were visibly noticeable in the sample.

The evaluation of the biological activity of propolis was not under the scope of the present study, however, the identified compounds such as 3-carene, β-pinene, limonene, α-pinene, γ-terpinene, myrcene, β-caryophyllene, α-bisabolol, α-phellandrene, β-ocimene, aromadendrene are known to possess antimicrobial activity [33]. Melliou et al. analyzed the antibacterial activity of five samples of propolis from Greece and determined that propolis containing higher content of α-pinene, 45.8%, possessed higher antibacterial activity than propolis containing lower

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Table 2. The percentage composition of identified volatile compounds in tested propolis samples (the values represent average±standard deviation of six replicates).

RTa Compoundsb MS match

(%)

Brazilian Estonian Uruguayan No1

Uruguayan No2

Uruguayan No3

Chinese

3.458 Heptane 92 0.31±0.22 4.52±2.88

3.505 m/z = 100, 85, 71, 60, 55, 45 (100), 43, 41

1.18±0.50

3.650 m/z = 114, 99, 74, 73, 60, 57, 55, 45 (100), 43, 41

1.18±0.08

3.756 Propanoic acid ethyl ester 96 0.33±0.12

4.192 3-Methyl-3-buten-1-ol 97 0.91±0.23 4.02±0.76 0.16±0.11 40.33±0.44

4.363 2-Methyl-1-butanol 93 0.82±0.41 0.41±0.13

4.450 2-Methyl-2-butenal 91 0.55±0.45 0.59±0.22

4.987 2-Methyl-propanoic acid 91 0.23±0.19 0.08±0.01 0.99±0.05

5.301 3-Methyl-2-buten-1-ol 94 0.55±0.11 2.00±1.12 0.10±0.03 11.57±0.26

5.493 β-Methyl crotonaldehyde 97 10.11±1.45

5.883 Hexanal 96 2.87±0.41

5.963 m/z = 112, 88, 71, 68 (100), 67, 53, 43, 41

1.11±0.13

7.346 2-Methylbutanoic acid ethyl ester

90 0.06±0.00

8.384 4-Penten-1-yl acetate 94 1.02±0.27 9.04±0.36

8.480 Styrene 98 2.33±0.65 4.00±0.06 4.84±2.10 0.11±0.03

9.361 Tricyclene 95 0.58±0.05 1.13±0.18 1.66±0.55 1.54±0.26

9.486 3-Methyl-2-buten-1-ol acetate 94 3.56±0.06

9.534 α-Thujene 96 0.38±0.03 0.66±0.02 1.77±0.18 2.13±0.47

9.710 α-Pinene 97 52.50±2.91 20.57±8.39 53.41±0.81 40.45±2.38 22.96±2.80

9.871 m/z = 169, 99, 84, 70, 56, 46 (100), 45

2.24±0.24

10.141 Camphene 97 1.20±0.05 3.98±0.13 5.03±1.10 5.14±0.61

10.324 1-Isopropyl-4-methylene-bicyclo[3.1.0] hex-2-ene

92 0.45±0.04 1.16±0.05 0.97±0.24 0.62±0.10

10.538 Benzaldehyde 96 2.01±0.50 10.85±0.92 0.61±0.04 0.90±0.14 0.19±0.03

10.774 m-Cymene 92 0.21±0.01 0.14±0.07

10.879 β-Phellandrene 93 1.38±0.20

10.956 β-Pinene 97 20.85±1.16 8.86±3.91 24.15±0.24 24.14±3.53 27.44±1.07

11.059 4-Pentenyl propionate 94 0.83±0.02

11.112 m/z = 159, 93, 90, 87, 74, 62, 63, 53, 44, 36 (100)

1.08±0.11

11.314 6-Methyl-5-hepten-2-one 92 0.35±0.17

11.414 β-Myrcene 93 0.17±0.04 0.26±0.03 3.91±=0.30

11.622 m/z = 142, 113, 99, 85, 71, 57, 43(100), 41

2.64±0.47 0.34±0.03

11.656 Hexanoic acid ethyl ester 94 0.27±0.01

11.906 3-Carene 96 0.95±0.05 0.98±0.09

11.942 3-Hexenoic acid ethyl ester 90 0.22±0.01

11.956 1,3-Dichloro-benzene 97 6.25±3.28

12.089 4-Carene 90 0.09±0.02

12.330 o-Cymene 93 0.30±0.03 1.01±0.02 0.80±0.08 2.05±0.13

12.369 o-Methylstyrene 96 0.99±0.14

12.439 Limonene 96 1.13±0.14 2.43±0.34 2.08±0.06 2.38±0.45 15.58±0.57

12.507 Eucalyptol 91 25.95±10.64

12.699 trans-β-Ocimene 98 1.70±0.11

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12.712 Benzyl alcohol 95 3.02±0.72 0.34±0.08 0.24±0.07

12.978 cis-β-Ocimene 98 7.42±0.41

13.265 γ-Terpinene 93 0.10±0.01

13.521 Acetophenone 97 0.88±0.18 0.34±0.01

14.342 m/z = 136, 121, 105, 93 (100), 71, 69, 55, 43, 41

4.37±1.14 0.09±0.01

14.771 Phenylethyl alcohol 95 0.62±0.04

15.086 α-Campholenic aldehyde 93 0.18±0.03

15.414 trans-Pinocarveol 94 0.40±0.04 0.56±0.05

15.730 1,4-Dihydronaphthalene 96 1.14±0.07 0.40±0.07

16.048 2(10)-Pinen-3-one 93 0.21±0.01 0.30±0.02

16.074 Acetic acid phenylethyl ester 95 0.20±0.01

16.238 Benzoic acid ethyl ester 95 0.09±0.05

16.334 3-Pinanone 93 0.09±0.01

16.893 (1R)-(-)-Myrtenal 94 0.33±0.03 0.51±0.02

17.238 2-Pinen-4-one 91 0.08±0.00

18.337 Acetic acid 2-phenylethyl ester

96 0.57±0.03

19.373 6,7-Dimethyl-1,2,3,5,8,8a –hexahydronaphthalene

91 0.14±0.01

19.969 1-(2-Butenyl)-2,3-dimethyl-benzene

91 0.14±0.00

21.039 Longicyclene 96 0.26±0.02

21.121 Copaene 95 0.43±0.06 0.08±0.01

21.272 β-Patchoulene 96 0.70±0.05

21.723 Cedr-8-ene 94 0.16±0.01

21.822 d-Longifolene 96 0.44±0.00

21.946 Di-epi-α-cedrene 93 0.95±0.03

22.109 β-Caryophyllene 95 0.72±0.10

22.367 Zingiberene 91 1.36±0.02

22.458 Guaia-1(5),11-diene 96 0.84±0.02

22.513 m/z = 177, 163, 135, 121, 107, 93, 79, 69, 43, 41 (100)

12.20±0.60

22.528 Aromadendrene 94 0.49±0.06

23.017 trans-α-Bergamotene 91 0.93±0.04

23.272 α-Longipinene 91 9.41±0.25

23.500 Eudesma-4(14),11-diene 95 1.35±0.04

23.534 m/z = 281, 207, 147, 133, 107, 85, 73 (100), 57, 43

0.27±0.02 4.59±1.73 0.14±0.01 0.45±0.02 0.11±0.01

23.696 m/z = 204, 202, 189, 161, 147, 145, 133, 119, 107, 105, 93 (100), 91, 81, 79, 67, 55,

41, 39

2.43±0.06

23.884 α-Bisabolol 90 1.85±0.24

Total GC peak area ×106 65.30±8.62 4.33±0.66 118.75±12.56 31.87±1.15 134.49±3.38 178.16±3.99

Total number of compounds 24 11 30 21 33 53

a retention time.b unidentified compounds, which amount was less than 1.0% in all samples are not presented in the table.

ContinuedTable 2.

RTa Compoundsb MS match

(%)

Brazilian Estonian Uruguayan No1

Uruguayan No2

Uruguayan No3

Chinese

The percentage composition of identified volatile compounds in tested propolis samples (the values represent average±standard deviation of six replicates).

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than 18.2% of this constituent against Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa and Klebsiella pneumonia [28].

3.2. Chemometric analysisFor the statistical analysis standardized, filtered and pre-processed data matrix of [35×41] was prepared as described above. Firstly the PCA was applied to reduce the space of variables and therefore the correlation between them. As a result the orthogonal space of 41 principal components was formed. The number of principal components that will represent the data by explaining the corresponding part of the total variance of the initial variables and will be used in further statistical analysis was chosen according to Kaiser’s eigenvalue criterion. After applying this decision rule, 5 principal components were left for 1st and 2nd case and 8 principal components – for 3rd case of the data pre-processing, explaining 96.3%, 99.9% and 97.1% of the total variance of the initial variables, respectively. As a result new data

matrices of [35×5], [35×5] and [35×8] were formed for the following clustering analysis.

To cluster the samples under investigation two techniques were used: hierarchical cluster analysis and K-Means clustering analysis. The calculated distances, the results of the HCA, are presented as dendrograms in Fig. 2. To form clusters the dendrogram have to be cut at chosen distance value. There are two dendrogram cutting points shown in Fig. 2: 70% of the maximum distance value as reference and visually chosen percentage of the maximum distance value – 51%, 59% and 67% for the data pre-processing cases that are being analyzed, respectively.

Before classification using KMCA, it is required to specify the number of clusters and the number of iterations to the algorithm of this analysis. The first parameter was calculated using a simple rule of thumb

= 2

nk , (3)

where operator means rounding the result to the nearest larger integer number, n is the number of the initial variables, n=41. The number of iterations was not constant as it depends on more critical parameter – minimum sum of Euclidean distances of particular classification result. In order to find the global minimum, KMCA was programmed to run 5000 times for each data pre-processing case. The minimum value of the calculated minimums of the sum of Euclidean distances of each run was chosen as the best classification result. The results of KMCA, when k=5, are presented as data scatter plots in three-dimensional space of first three PCA components in Fig. 3.

The case showed in dendrogram Fig. 2a and in scatter plot Fig. 3a represents clustering results when no data pre-processing was used. It is clearly seen that Chinese, Uruguayan No3 and Estonian samples of propolis fall into separate clusters, meaning that their volatile composition is very different. Uruguayan No1 and Uruguayan No2 samples are very similar and the Brazilian propolis is not very far from this cluster, despite the fact, that we chose such dendrogram cutting point (0.51∙max(Distance)) which separates it from the two.

The second dendrogram in Fig. 2b and the second scatter plot in Fig. 3b show the classification of the propolis samples after inversion of the data using Eq. 1. In this case the common to most sample compounds with the highest contributions take values close to zero and the smallest and unique compounds are analyzed having the biggest impact on the classification results. The Chinese and Estonian samples of propolis again fall into separate clusters. A clear view about the situation that concerns the remaining samples could be

Figure 2. Dendrograms of hierarchical cluster analysis using different data pre-processing techniques (a – no data pre-processing, b – inverse data pre-processing, c – logarithmic data pre-processing).

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understood by looking at Fig. 3b: all three Uruguayan samples are near each other and the Brazilian sample is removed from this group. On the other hand even Uruguayan No1 and Uruguayan No2, falling to the same cluster, could be visually distinguished. This situation

could be concluded as follows: despite the obvious similarity of the Uruguayan No1 and Uruguayan No2 samples, the analysis of smallest compounds showed the existence of uniqueness.

The classification analysis results after logarithmic data pre-processing using Eq. 2 are presented in the dendrogram Fig. 2c and in the scatter plot Fig. 3c. This case clearly shows five different clusters, except the Uruguayan No1 and Uruguayan No2 samples falling to the same group. Comparing the results with the first case (see Fig. 2a and Fig. 3a) the conclusion about Brazilian propolis sample separateness from Uruguayan No1 and Uruguayan No2 is obvious.

Summarizing the statistical analysis, the following conclusions could be made: the volatile compounds composition of the Chinese, Estonian, Uruguayan No3 and Brazilian propolis samples is different, main compounds of Brazilian sample being closer to the Uruguayan No1 and Uruguayan No2 samples. These two last mentioned samples are not statistically different, except the difference (observed visually from the scatter plots) of the compounds with the smallest contributions.

4. ConclusionsSix samples of propolis from China, Estonia, Brazil and three regions of Uruguay were analyzed in the study using static headspace technique coupled with gas chromatography-mass spectrometry analysis. There were 66 out of 104 found different compounds identified in the headspace of the samples; 70 of all compounds were found in one of the samples only.

Monoterpenes α- and β-pinenes composed from 50.4 to 77.6% of volatile compounds in propolis collected in temperate zone, i.e., Brazil and Uruguay. Eucalyptol and α-pinene were the main compounds in the headspace of Estonian propolis (25.9 and 20.6%, respectively). Chinese propolis distinguished by high amount of alcohol 3-methyl-3-buten-1-ol (40.3%) and absence of α- and β-pinenes. The highest amount and diversity of volatile compounds were obtained from the headspace of propolis of China origin and one sample from Uruguay, while the lowest amount obtained from Estonian propolis.

The performed statistical analysis using different data pre-processing techniques revealed differences between compositions of the volatiles and let classify investigated samples of propolis to the clusters. The results showed high variation in the volatile compounds composition of the Chinese, Estonian, Uruguayan No3 and Brazilian propolis samples falling into separate clusters despite the data pre-processing used. It also was

Figure 3. Data scatter plots of K-Means cluster analysis, projected in the principal components space, using different data pre-processing techniques (a – no data pre-processing, b – inverse data pre-processing, c – logarithmic data pre-processing).

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observed that the composition of the main compounds of the Brazilian sample is closer to the Uruguayan No1 and Uruguayan No2 samples. The Uruguayan No1 and Uruguayan No2 samples, forming a separate cluster, are not statistically different; however the differences of the compounds with the smallest contributions were exposed from the scatter plots using inverse data pre-processing.

Author contribution V. Kaškonienė contributed to the experimental process, GC/MS and data analysis and results interpretation as well as preparation of paper part concerning chemical

analysis. P. Kaškonas performed all types of statistical analysis, interpretation and graphical presentation of the obtained results and prepared the paper part about chemometric analysis. A. Maruška and L. Kubilienė helped in experimental design and interpretation of the results.

AcknowledgementsAuthors would like to thank K. Barčauskaitė for preparation of the propolis samples for GC-MS analysis. The samples of propolis were obtained from Apiproduktai, Ltd. This study was supported by Vytautas Magnus University project BF-11-04.

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Chemometric analysis of volatiles of propolis from different regions using static headspace GC-MS

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Transcript of Chemometric analysis of volatiles of propolis from different regions using static headspace GC-MS