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Phenolic profile and antioxidant activity of Serbian polyfloral honeys

Uroš Gašic a , Silvio Kec keš b,  Dragana Dabic  c, Jelena Trifkovic a , Dušanka Milojkovic-Opsenica a

Maja Natic a , Z ivoslav Tešic a ,⇑

a Faculty of Chemistry, University of Belgrade, P.O. Box 51, 11158 Belgrade, Serbiab Analysis, Gandijeva 76a, 11070 Belgrade, Serbiac Innovative Center, Faculty of Chemistry Ltd., Studentski trg 12-16, 11158 Belgrade, Serbia

a r t i c l e i n f o

 Article history:

Received 17 May 2013

Received in revised form 9 August 2013

Accepted 20 August 2013

Available online 2 September 2013

Keywords:

Polyfloral honey

Phenolic profile

UHPLC-LTQ OrbiTrap MS

Geographical origin

Pattern recognition methods

a b s t r a c t

A total of 58 polyfloral honey samples from different regions in Serbia were studied to determine their

phenolic profile, total phenolic content and antioxidant capacity. UHPLC-LTQ OrbiTrap MS made possible

the identification of 36 compounds: 24 flavonoids, two abscisic acids, and 10 phenolic acids and their

derivatives. Quantification was done using 14 available standards. Data on phenolics and abscisic acids

allowed the discrimination and classification of honeys in accordance to their geographical origin, using

pattern recognition techniques, principal component analysis and partial least squares discriminant anal-

ysis. Samples originated from Vojvodina and Zlatibor region were clearly distinguished from those from

the rest of Serbia because of the presence of dicaffeoylquinic acid, ellagic acid, caffeic acid phenethyl

ester, and chlorogenic acid, among others. A good correlation (r  = 0.865) was observed between total phe-

nolic content and radical-scavenging activity. Total phenolic content ranged from 0.03 to 1.39 mg GAE/g

and radical scavenging activity ranged from 1.31% to 25.61%.

 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Generally, honeys are classified as monofloral or polyfloral.

Monofloral honeys are produced by one plant species containing

predominantly its nectar with minor nectar contributions from

other botanical origins. Polyfloral honey has several plant sources,

none of which is predominant. In practical terms it can be consid-

ered as a blend of several monofloral honeys with significant nec-

tar or honeydew contributions from different plants.

Phenolic extracts from different plants have been shown to

have antioxidant as well as antimicrobial activity (Viuda-Martos,

Ruiz-Navajas, Fernandez-Lopez, & Perez-Alvarez, 2008). Similarly,

polyphenols, i.e. flavonoids and phenolic acids, are considered as

one of the important group of components identified in honey hav-

ing antioxidant activity. Available literature indicates that untilnow there have been only a fewstudies to determine the total phe-

nolic content and antioxidant activity of Serbian honeys (Gorjano-

vic et al., 2013; Savatovic et al., 2011; Tumbas et al., 2012).

The UHPLC-LTQ OrbiTrap MS technique is reliable for the

unambiguous detection of phenolic acids, their derivatives, and fla-

vonoid aglycones based on their molecular masses and fragmenta-

tion pattern. In our previous investigation (Kec keš, Gašic, Cirkovic

Velic kovic, Milojkovic-Opsenica, Natic, & Tešic, 2013), a total of 

43 polyphenols were identified in Serbian unifloral honeys.

Numerous flavonoids (such as apigenin, pinocembrin, pinobanksin,

kaempferol, quercetin, galangin, chrysin, and luteolin) and pheno-

lic acids (caffeic, gallic, cinnamic, protocatechuic,  p-coumaric, and

chlorogenic acids) were identified in samples. The samples were

classified according to their botanical origin using principal com-

ponent analysis (PCA). PCA resulted in two discriminant clusters

in the set of different monofloral honeys. Similarities between hon-

eys derived from perennial plants were observed, as well as among

honeys derived from annual plants.

Different methods for testing authenticity of geographical ori-

gin of honey exist. Classical pollen analysis is a reference method

used for routine control of honey authenticity, whereas physico-

chemical parameters, such as pH, acidity and electrical conductiv-

ity are the methods of quality control. Determination of sugars,

amino acids, minerals, and flavonoids in combination with chemo-metric data evaluation are methods used to identify geographical

origin (Bogdanov & Martin, 2002; Primorac et al., 2008; Lazarevic,

Andric, Trifkovic, Tešic, & Milojkovic-Opsenica, 2012; Wang & Li,

2011). Mineral and trace elements concentration in different honey

types depend largely on the elemental composition of flowers,

with regard to their geographical origin (Bilandz ic et al., 2011). Dif-

ferent sugars (Consonni, Cagliani, & Cogliati, 2013) and amino acids

(Belitz et al., 2009) have been used to detect the geographical ori-

gin of honey. Stable isotopes (Kropf et al., 2010) and Raman spec-

troscopy (Goodacre, Radovic, & Anklam, 2002) are among the most

reliable techniques for the detection of the geographical origin of 

honey. Wang et al., 2009 reported a new MALDI-TOF-MS method

0308-8146/$ - see front matter    2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.foodchem.2013.08.088

⇑ Corresponding author. Tel./fax: +381 11 2639 357.

E-mail address:  ztesic@chem.bg.ac.rs (Z . Tešic).

Food Chemistry 145 (2014) 599–607

Contents lists available at   ScienceDirect

Food Chemistry

j o u r n a l h o m e p a g e :  w w w . e l s e v i e r . c o m / l o c a t e / f o o d c h e m

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for determination of the geographical origin of honey by finger-

printing and bar-coding honey proteins in samples produces in

various regions in the USA.

Although propolis-derived polyhenolics are not helpful for the

determination of botanical origin, these compounds can be used

together with other polyphenols as markers of geographical origin

of honey (Ferreres et al., 1992).  Tomás-Barberán, Ferreres, García-

Viguera, and Tomás-Lorente (1993)  have compared the flavonoid

profiles of different honey samples from various regions in the

world and reported flavonoid profiles characteristic for the pres-

ence of propolis-derived flavonoids in honeys from the Northern

Hemisphere, where poplars are native. Martos, Ferreres, and Tomá-

s-Barberán (2000)   have compared the geographical variations in

the flavonoid profiles between Australian and European  Eucalyptus

honeys. Australian   Eucalyptus   honeys contains the propolis-de-

rived flavonoids, such as pinobanksin, pinocembrin, and chrysin,

in very small concentrations and the European  Eucalyptus  honeys

are relatively rich in these propolis-derived flavonoids. Therefore,

studies of flavonoid profiles of honey could be related to its geo-

graphical origin.

The main goal of this study was to determine characteristic

phenolic profiles by identifying flavonoids and phenolic acids in

Serbian polyfloral honeys. The possibility of verifying the regional

origin based on specific phenolic compounds, using multivariate

statistical methods, was examined. The variables discriminating

honey samples from different regions were identified and success-

ful models for further prediction were developed. Also, total phe-

nolic content (TPC) and the radical-scavenging activity (RSA) of 

honey samples were determined, and correlation between these

parameters was evaluated.

2. Experimental

 2.1. Chemicals and materials

Acetonitrile and formic acid (both MS grade), methanol (HPLC

grade), sodium carbonate, hydrochloric acid, and Folin–Ciocalteu

reagent were purchased from Merck (Darmstadt, Germany). The

SPE cartridges used for extraction and concentration of samples

were Strata C18–E (500 mg/3 mL) obtained from Phenomenex

(Torrance, CA).

2,2-Diphenyl-1-picrylhydrazyl(DPPH) and flavonoid standards

(rutin (quercetin 3-O-rutinoside), luteolin, quercetin, apigenin,

kaempferol, chrysin, pinocembrin, and galangin) were purchased

from Fluka AG (Buchs, Switzerland), whereas   cis,   trans-abscisic,

protocatechuic, chlorogenic (3-O-caffeoylquinic), caffeic, p-couma-

ric, and ellagic acids were supplied by Sigma–Aldrich (Steinheim,

Germany).

Ultrapure water (ThermoFisher TKA MicroPure water purifica-

tion system, 0.055  lS/cm) was used to prepare standard solutions

and blanks. Syringe filters (13 mm, PTFE membrane 0.45lm) were

purchased from Supelco (Bellefonte, PA). Filter paper (Whatman

No. 1) was supplied by Merck (Darmstadt, Germany).

 2.2. Honey samples

A total of 58 polyfloral honey samples were collected from five

different regions of Serbia (Fig.  Supplementary1) during the 2009

harvesting season were provided by ‘‘The Association of the Bee-

keeping Organizations of Serbia’’ (SPOS) (www.spos.info). The geo-

graphical origin of the samples (Table 1) was specified by the SPOS

based on the information provided by beekeepers. The honeyswere stored at room temperature in the dark before analysis.

 2.3. Extraction of phenolics from honey samples

 2.3.1. Preparation of sample extracts for UHPLC-LTQ OrbiTrap MS 

analysis

The method used for extraction and isolation of phenolics from

the honey was a modification of the method described in the liter-

ature (Michalkiewicz, Biesaga, & Pyrzynska, 2008). Honey samples

(5 g) were mixed with 5 mL of ultrapure water, adjusted to pH 2

with 0.1% HCl and homogenised in an ultrasonic bath for 30 min

at room temperature. The samples were then filtered through filter

paper to remove solid particles. An SPE cartridge was conditioned

by washing with 3 mL of acetonitrile and 9 mL of ultrapure water.

The filtrate was passed through cartridge, which was then washed

with 6 mL of acidified water to remove all sugars and other polar

constituents of honey. The adsorbed compounds were eluted with

acetonitrile (1.5 mL). The extracts were filtered through a 0.45-lm

PTFE membrane filter to be analysed by UHPLC-HESI-MS/MS.

 2.3.2. Sample preparation for TPC and RSA

Samples were prepared according to the slightly modified

method proposed by  Meda, Lamien, Romito, Millogo, and Naco-

ulma (2005). Each honey sample (5 g) was mixed with 15 mL ultra-pure water, homogenized in ultrasonic bath for 15 min at room

temperature, transferred to 50 mL volumetric flask, and filled with

ultrapure water. The solution was then filtered through 0.45 lm

PTFE membrane and analyzed for determination of TPC and RSA.

 2.4. UHPLC-LTQ OrbiTrap MS 

A 1000 mg/L stock solution of a mixture of flavonoids (rutin,

luteolin, quercetin, apigenin, kaempferol, chrysin, pinocembrin,

and galangin), phenolic acids (protocatechuic, chlorogenic, caffeic,

 p-coumaric, and ellagic acid) and  cis ,   trans-abscisic acid was pre-

pared in methanol. Dilution of the stock solution with methanol

yielded working solutions at concentrations of 0.025, 0.050,

0.100, 0.250, 0.500, 0.750, and 1.000 mg/L. All stock and workingsolutions were storedin the dark at 4 C and were stable for at least

3 months. Calibration curves were obtained by plotting the peak

areas of the compounds identified relative to the peak area against

the concentration of the standard solution. Calibration curves re-

vealed good linearity, with   r 2 values exceeding 0.99 (peak areas

vs. concentration).

Chromatographic separations were performed using an ultra-

high-performance liquid chromatography (UHPLC) system consist-

ing of a quaternary Accela 600 pump and Accela autosampler

(ThermoFisher Scientific, Bremen, Germany). The analytical col-

umn used for separations was a Hypersil gold C18 column

(50 2.1 mm, 1.9lm particle size) from ThermoFisher Scientific.

The mobile phase consisted of (A) water containing 1% formic acid

and (B) acetonitrile. The gradient program was as follows: 0.0–10.0 min, 5–95% B; 10.0–12.0 min, 95% B; 12.0–12.2 min, 95–5%

B; 12.2–15.0 min, 5% B. The injection volume for all samples was

5  lL, and the flow rate was 300  lL/min.

The UHPLC system was coupled to a linear ion trap-OrbiTrap

hybrid mass spectrometer (LTQ OrbiTrap MS) equipped with

heated electrospray ionisation probe (HESI-II, ThermoFisher Scien-

tific, Bremen, Germany). The mass spectrometer was operated in

negative ion mode. Parameters of the ion source were as follows:

source voltage 5 kV, capillary voltage   40 V, tube lens voltage

80 V, capillary temperature 275 C, sheath and auxiliary gas flow

(N2) 42 and 11 (arbitrary units). MS spectra were acquired by full

range acquisition covering m/z 100–900. For fragmentation studies,

a data-dependent scan was performed by deploying collision-in-

duced dissociation (CID). The normalised collision energy of thecollision-induced dissociation (CID) cell was set at 35 eV.

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Flavonoids, phenolic acids and abscisic acid were identified

and quantified according to their spectral characteristics: mass

spectra, accurate mass, characteristic fragmentation, and charac-

teristic retention time. Xcalibur software (Version 2.1) was used

for instrument control, data acquisition and data analysis. Frag-

mentation mechanism and characteristic fragments were con-

firmed using Mass Frontier software (Version 6.0). The

generated MS/MS spectra were processed by ToxID software (Ver-

sion 2.1.1). Internet database of accurate mass spectrometry data,

ChemSpider (www.chemspider.com), was used as a reference li-

brary to identify compounds of interest.

 2.5. Determination of the total phenolic content 

The TPC was spectrophotometricaly determined with a Folin–

Ciocalteu method reported by   Singleton and Rossi (1965),   with

some modification. Briefly, 0.3 mL of the sample extracts and

6 mL deionized water were mixed with 0.5 mL of Folin–Ciocalteu

reagent and the solution was incubated 6 min at room tempera-

ture. Next, 3 mL of 20% sodium carbonate were added. After

30 min at 40 C, absorbance was measured at 765 nm. Gallic acid

was used as standard, concentration 50–250 lg/mL. A mixture of 

water and reagent was used as a blank. The results were ex-

pressed as mg gallic acid equivalents (GAE) per gram of honey.

 2.6. Determination of radical-scavenging activity

The RSA of the extracts of honey samples was evaluated by a

modified method of  Li, Wang, Li, Li, and Wang (2008). An aliquot

of 1.0 mL of extract (some extracts were diluted ten times) was

mixed with 3 mL of methanol solution of DPPH   (71 mM). The

mixture was left for 60 min in the dark (until stable absorption

values were obtained). The reduction of the DPPH   radical was

measured by monitoring continuously the decrease of absorption

at 515 nm. RSA was calculated as a percentage of DPPH discolor-

ation using the equation:

RSA ð%Þ ¼  ð ADPPH     AsampleÞ

 ADPPH

  100

where ADPPH  is the absorbance of methanol solution of DPPHrad-

ical, Asample is the absorbance in the presence of honey extract. The

assays were carried out in triplicate and the results were ex-

pressed as mean values.

 2.7. Statistical analysis

Principal component analysis (PCA) and partial least squares

discriminant analysis (PLS-DA) were carried out by means of 

PLS ToolBox, V.6.2.1, for MATLAB 7.12.0 (R2011a). All data were

group-scaled, i.e. groups of variables were scaled to grand stan-

dard deviation, prior to any multivariate analysis. PCA was carriedout as an exploratory data analysis by using a singular value

decomposition algorithm (SVD) and a 0.95 confidence level for

Q  and T 2 Hotelling limits for outliers. PCA was performed in order

to reduce dimensionality of data space, visualise the structure of 

data, identify important variables, and confirm the presence of 

outliers. The PLS-DA was performed using the SIMPLS algorithm

without forcing orthogonal conditions to the model in order to

condense  Y -block variance into first latent variables. PLS-DA is a

classical PLS regression where the response variable is categorical,

i.e. it indicates the classes or categories of the samples. The aim of 

this supervised technique is to build a mathematical model that

could be used for further classification of unknown samples.

The models were validated using a Venetian blinds validation

procedure. The quality of the models was monitored with the fol-lowing parameters: (1) R2

cal, the cumulative sum of squares of the       T     a       b       l     e

       1

              (      c      o      n       t        i      n      u      e        d              )

     N

    o

     G    e    o    g    r    a    p     h     i    c    a     l    r    e    g     i    o    n

     P    r     A ,

     C     h     A

 ,

     C    a     A ,

     C    o     A ,

     E     l     A ,

     R    u    t ,

     L    u    t ,

     Q    u    e ,

     A     b     A ,

     A    p     i ,

     K    a    e ,

     C     h    r ,

     P     i    n ,

     G    a     l ,

     R     S     A ,

     T     P     C ,

    m    g     /     k    g

    m    g     /

     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k

    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

    m    g     /     k    g

     %

     (    m    g     /    g     )

     5

     5

     C    e    n    t    r    a     l

     0 .     1

     7

     0 .     0     5

     0 .     2

     6

     3 .     8

     8

     1 .     1

     4

     N     D

     N     D

     0 .     1

     4

     2 .     8

     3

     0 .     1

     6

     0 .     1

     3

     0 .     2

     3

     0 .     3

     0

     0 .     1

     1

     3 .     6

     8

     0 .     4

     2

     5

     6

     C    e    n    t    r    a     l

     0 .     2

     1

     0 .     0     8

     0 .     2

     9

     1 .     9

     5

     2 .     7

     5

     0 .     0     5

     0 .     0

     9

     0 .     2

     3

     0 .     7

     1

     0 .     4

     0

     0 .     2

     5

     0 .     7

     2

     0 .     3

     6

     0 .     1

     8

     7 .     9

     7

     0 .     7

     6

     5

     7

     C    e    n    t    r    a     l

     0 .     1

     7

     0 .     0     3

     0 .     8

     6

     1 .     1

     1

     2 .     6

     3

     0 .     0     4

     0 .     0

     5

     0 .     2

     1

     2 .     6

     3

     0 .     4

     6

     0 .     3

     7

     1 .     0

     3

     2 .     0

     9

     0 .     7

     7

     3 .     4

     3

     0 .     4

     0

     5

     8

     C    e    n    t    r    a     l

     0 .     1

     0

     N     D

     0 .     6

     7

     3 .     4

     4

     3 .     0

     9

     N     D

     0 .     0

     4

     0 .     1

     6

     3 .     0

     9

     0 .     3

     3

     0 .     2

     3

     1 .     8

     8

     1 .     7

     7

     0 .     6

     6

     1 .     4

     5

     0 .     3

     7

     N     D ,

    n    o    t     d    e    t    e    c    t    e     d    ;     P    r     A ,

    p    r    o    t    o    c    a    t    e    c     h    u     i    c    a    c     i     d    ;     C     h     A ,

    c     h     l    o    r    o    g    e    n     i    c    a    c     i     d    ;     C    o     A ,

     p  -    c    o    u    m    a    r     i    c    a    c     i     d    ;     E     l     A ,

    e     l     l    a    g     i    c    a    c     i     d    ;     R    u    t ,    r    u    t     i    n    ;     L    u    t ,     l    u    t    e    o     l     i    n    ;     Q    u    e ,

    q    u    e    r    c    e    t     i    n    ;     A     b     A ,

     c       i     s ,

      t     r     a     n     s  -    a

     b    s    c     i    s     i    c    a    c     i     d    ;     A    p     i ,    a    p     i    g    e    n     i    n    ;     K    a    e ,

     k    a    e    m    p     f    e    r    o     l    ;     C     h    r ,    c     h    r   y    s     i    n    ;     P     i    n ,

    p     i    n    o

    c    e    m     b    r     i    n    ;     G    a     l ,    g    a     l    a    n    g     i    n    ;     T     P     C ,

    t    o    t    a     l    p     h    e    n    o     l     i    c    c    o    n    t    e

    n    t    ;     R     S     A ,

    r    a     d     i    c    a     l  -    s    c    a   v    e    n    g     i    n    g    a    c    t     i   v     i    t   y .

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Y s explained by all extracted components,   R2CV, the cumulative

fraction of the total variation of the  Y s that can be predicted by

all extracted components, and   R2pred, the cumulative fraction of 

the total variation of the  Y s that can be predicted by test compo-

nents (these three values should be as high as possible), and (2)

RMSEC   (root mean square errors of calibration),   RMSECV   (root

mean square errors of cross-validation), and   RMSEP   (root mean

square errors of prediction); these three values should be as low

as possible, and with the lowest difference between them. Descrip-

tive statistics and Kruskal–Wallis one-way analysis of variance by

ranks test was performed using a demo version of NCSS statistical

software (Hintze, 2001, Number Cruncher Statistical Systems,

Kaysville, UT; www.ncss.com).

Limits of detection (LOD) and limits of quantification (LOQ)

were calculated using standard deviations of the responses (SD)

and the slopes of the calibration curves (S) according to the formu-

las: LOD = 3(SD/S) and LOQ = 10(SD/S). The values of standard

deviations and slopes were obtained from the calibration curves

created in MS Excel. LOD, LOQ, and the recoveries at three concen-

tration levels were determined according to a previously described

method (Kec keš et al., 2013). These results are given in Supplemen-

tary Data (Table S1).

3. Results and discussion

Generally, there is a growing interest on the effects of natural

antioxidants in food. Different honey types subjected to antioxi-

dant activity tests have demonstrated significant potential, compa-

rable to other foodstuffs (Schramm et al., 2003). Antioxidants

protect cell defence systems against oxidative damage, contribut-

ing to the prevention of certain illnesses, including cardiovascular

diseases, cancer, and diabetes (Viuda-Martos et al., 2008). Antiox-

idant activity of honey partly depends on the phenolics and is clo-

sely related to the floral source of honey. Phenolics’ structural

features and substituents (such as hydroxyl, acyl, methyl, and gly-

cosyl groups) are basic factors influencing the final activity.

 3.1. Phenolic profiles of polyfloral honeys

Solid-phase extraction (SPE) combined with ultra-high-perfor-

mance liquid chromatography-OrbiTrap mass spectrometry

(UHPLC-LTQ OrbiTrap MS) was used to analyse the contents of 

flavonoids, phenolic acids and abscisic acid in honeys. A total of 

36 compounds were identified and 14 of them were quantified

by comparing retention times and MS spectra with available stan-

dards. Selected base peak chromatogram of honey extract from

Vojvodina region is shown in Fig. 1.

In the absence of standards, identification of another 22 peaks

in the chromatograms was based on the search for the [M–H]-

deprotonated molecule and its fragmentation. The exact mass

search and the study of the fragmentation pathways described inthe literature enabled us to obtain as much structural information

as possible. In this way, it was possible to identify two abscisic

acids, different phenolic acids, and many flavonoids.   Table 2

summarises the data obtained for all 36 identified compounds.cis,

trans-Abscisic acid was quantified using an available standard

and the other isomer,   trans,  trans-abscisic acid, was confirmed by

the exact mass search of deprotonated molecule and MS/MS

fragmentation.

A common fragmentation pathway based on the loss of the CO2

group was observed for all phenolic acids identified in samples.

Hydroxybenzoic acids (gallic, protocatechuic, and ellagic acid)

and hydroxycinnamic acids (caffeic,  p-coumaric, and ferulic acid),

with a characteristic deprotonated [M–H]molecule and [M–

H44] fragment, were identified. Four caffeic acid derivatives

were detected: chlorogenic acid (identified and quantified using

available standard 3-O-caffeoylquinic acid), dicaffeoylquinic acid,

prenyl caffeate, and caffeic acid phenylethyl ester (identified using

exact mass search of deprotonated molecule [M–H] and its frag-

mentation). All of the caffeic acid derivatives showed negative

product ion at  m/z  179 due to the loss of the deprotonated mole-

cule of caffeic acid.

Four subclasses of flavonoids were detected in all honey sam-

ples: flavonols, flavanonols, flavanones, and flavones. In the nega-

tive mode, the loss of groups such as H2O (18 amu), CO

(28 amu), C2H2O (42 amu) and CO2   are common for all flavo-

noids and these characteristic fragments were found (Kec keš

et al., 2013). Methylated or methoxylated flavonoids were charac-

terised by the loss of CH3  (15 amu) in the negative mode. Also,

complementary fragments from the retro-Diels–Alder (RDA) reac-

tion, which is major fragmentation reaction for flavonoids, were

found in the MS spectra. The fragmentation pattern of flavonoids

is given in detail in our previous paper (Kec keš et al., 2013).

In most samples flavones luteolin, apigenin, chrysin, and flavo-

nols quercetin, kaempferol, galangin, and glycoside rutin were

identified. As for flavanonols, it was possible to characterise taxif-

olin, pinobanksin, and three derivatives of pinobanksin (pinobank-

sin 5-methylether-3-O-acetate, pinobanksin 5-methylether, and

pinobanksin 3-O-acetate). The fragmentation step observed for

pinobanksin esters was the loss of the acyl group, yielding frag-

ments at 271 and 253   m/z  corresponding to [M–acyl] and [M–acyl–H2O] ions, respectively. Flavanones (eriodictyol, sakuranetin,

and pinostrobin) were identified using exact mass search, and

pinocembrin was quantified using a standard.

The chromatograms of the investigated samples showed similar

profiles. All honey extracts contained somewhat large amount of 

flavonoids that are known to originate from propolis, namely chry-

sin, pinocembrin, and galangin (Tomás-Barberán, Martos, Ferreres,

Radovic, & Anklam, 2001). Similar phenolic profiles exist in Slove-

nian honeys (Bertoncelj, Polak, Kropf, Korošec, & Golob, 2011).

The summarised parameters of descriptive statistics for total

intensities obtained from the MS/MS spectra and content of quan-

tified compounds are presented in Table S2 (Supplementary Data)

for each region separately.

Normality of distribution of data was analysed by three tests:D’Agostino  K-squared (skewness normality of residuals), Bonnet-

Seier’s (kurtosis normality of residuals) and Shapiro–Wilk’s (nor-

mal scores correlation) test. A significant deviation from normal

Fig. 1.  Base peak chromatogram of a honey sample: (1) gallic acid, (2) protocatechuic acid, (4) caffeic acid, (5)  p-coumaric acid, (6) ellagic acid, (7) rutin, (8) taxifolin, (9)

dicaffeoylquinic acid, (10) ferulic acid, (11) eriodictyol, (12)  trans, trans-abscisic acid, (13) luteolin, (14) quercetin, (15)  cis, trans-abscisic acid, (18) apigenin, (20) kaempferol,(21) pinobanksin, (23) bis-methylated quercetin, (26) chrysin, (30) pinocembrin, (32) galangin, and (34) caffeic acid phenethyl ester (CAPE).

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distribution for each of the studied variables was observed. There-

fore, for statistical evaluation of differences in the total intensities

of detected compounds, the Kruskal–Wallis test was employed for

each flavonoid, phenolic acid and abscisic acid separately, as vari-

ables, taking the appropriate region as a single factor. Kruskal–

Wallis one-way analysis of variance tests if samples originate from

the same distribution. When the Kruskal–Wallis test led to signif-

icant results, multiple-comparison Z -value test was performed to

identify where the differences occurred or how many differences

actually occurred. Results are presented in Table S2 (Supplemen-

tary Data), with separated regions denoted in parentheses.

Based on Kruskal–Wallis test several compounds, such as chlor-ogenic acid, ellagic acid, quercetin, dicaffeoylquinic acid and pino-

banksin 5-methylether-3-O-acetate, were identified as parameters

governing a separation of Vojvodina honey samples from those

originating from all other regions. Also, bis-methylated quercetin

and pinobanksin 3-O-propionate differentiate Vojvodina samples

from those from Zlatibor, and Central and East regions. Separation

of Zlatibor honey samples from the rest of Serbia was achieved by

the intensities of ellagic acid, pinocembrin, galangin, eriodictyol,

sacuranetin, pinobanksin, acacetin, caffeic acid phenethyl ester

and methoxychrysin. The rest of the factors are responsible for dif-

ferentiation of only limited numbers of regions, whereas cis, trans-

abscisic acid, gallic acid, pinostrobin and pinobanksin 5-methyle-

ther do not differ among any of the considered regions.

Among the quantified compounds, p-coumaric acid was presentin the highest amount, while the lowest contents were recorded for

luteolin and rutin. Also, median value for chlorogenic acid of honey

samples from Vojvodina was somewhat higher, and for ellagic acid

lower, compared to values from the rest of Serbia (Table S2).

 3.2. Geographical origin of polyfloral honey

The identification of geographical origin of honey is a hard task

often requiring the use of several criteria and chemical markers as

well as a combination of several analytical methods with appropri-

ate statistical analyses. The amount of polyphenols in honeys is

closely related not only to the floral variety but also to specific

parameters, such as soil composition and meteorological condi-tions. Thus, it is possible to relate the phenolic profiles of honeys

of the same botanical source with their geographical origin.

Although polyfloral honeys are produced from nectars (various

meadow flowers and fruit trees) and honeydew, several papers

indicated the possibility of discrimination between samples col-

lected from different regions (Bilandz ic  et al., 2011; Kropf et al.,

2009).

Multivariate statistical analysis, PCA and PLS-DA were applied

in order to distinguish the honey samples from Serbia according

to their regional origin. Total intensities obtained from the MS/

MS spectra processed through ToxID software served as an input

data matrix.

A PCA resulted in a five-component model which explains

68.99% of total variance. The first principal component, PC1, ac-counted for 34.76% of the overall data variance, the second one,

 Table 2

Phenolic profiles of Serbian polyfloral honeys; number of identified compound, target compounds, mean expected retention times, calculated mass, exact mass, mean mass

accuracy (mDa), and MS/MS fragments.

Peak noa Compounds Retention time, min Calculated massb, [M–H]– Exact mass, [M–H]– mDa Mass fragments

1 Gallic acidd 0.55 169.01473 169.01360 1.13 125

2 Protocatechuic acidc 1.13 153.01972 153.01877 0.95 109

3 Chlorogenic acidc 2.03 353.08782 353.08612 1.70 191, 179, 146

4 Caffeic acidc 2.21 179.03498 179.03427 0.71 135, 161

5   p-Coumaric acidc 2.81 163.04007 163.03949 0.58 1196 Ellagic acidc 2.91 300.99898 300.99762 1.36 283, 200, 175

7 Rutinc 3.00 609.14610 609.14404 2.06 301

8 Taxifolinb 3.05 303.05101 303.05042 0.59 285, 269, 255, 217

9 Dicaffeoylquinic acidd 3.33 515.11949 515.11725 2.24 191, 179

10 Ferulic acidd 3.41 193.05063 193.05009 0.54 175, 139

11 Eriodictyold 3.53 287.05611 287.05499 1.12 125

12   trans,  trans-Abscisic acidd 3.91 263.12889 263.12808 0.81 191, 179

13 Luteolinc 4.15 285.04045 285.03915 1.30 213, 151

14 Quercetinc 4.15 301.03539 301.03400 1.39 151, 179, 121

15   cis,  trans-Abscisic acidc 4.20 263.12889 263.12790 0.99 191, 179

16 Sakuranetind 4.36 285.07686 285.07559 1.27 133

17 Rhamnetind 4.36 315.05101 315.04980 1.21 300, 165, 121

18 Apigeninc 4.61 269.04555 269.04437 1.18 149, 151, 173, 183

19 Isorhamnetind 4.67 315.05101 315.04999 1.02 300, 151, 107

20 Kaempferolc 4.70 285.04045 285.03918 1.27 199, 161, 151, 135

21 Pinobanksind 4.71 271.06120 271.06006 1.14 253, 243, 165, 151, 107

22 Methyl quercetin

d

4.81 315.05101 315.04974 1.27 300, 271, 207, 15123 Bis-methylated quercetind 5.03 329.06667 329.06589 0.78 315, 165

24 Pinobanksin 5-methylether-3-O-acetated 5.33 327.08699 327.08652 0.47 271, 253

25 Quercetin tetramethyl etherd 5.43 357.09740 357.09537 2.03 300, 207, 179, 161

26 Chrysinc 5.82 253.05063 253.04951 1.12 101, 151, 181, 209, 143

27 Prenyl caffeated 5.83 247.09758 247.09639 1.19 135, 179

28 Pinostrobind 5.87 269.08193 269.08081 1.12 135, 179

29 Pinobanksin 5-methyletherd 5.87 285.07672 285.07532 1.40 271, 165

30 Pinocembrinc 5.92 255.06627 255.06523 1.04 213, 211, 151

31 Acacetind 5.95 283.06120 283.05984 1.36 268, 133, 151, 107

32 Galanginc 5.98 269.04555 269.04443 1.12 151, 183

33 Pinobanksin 3-O-acetated 6.08 313.07120 313.07043 0.77 271, 253

34 Caffeic acid phenethyl ester (CAPE)d 6.12 283.09758 283.09653 1.05 135, 179

35 Methoxychrysind 6.19 283.06120 283.06003 1.17 268, 239, 211

36 Pinobanksin 3-O-propionated 6.66 327.08686 327.08670 0.16 285, 165

a Peak numbers corresponding to Fig. 1b.b Calculated mass of the parent ion using free chemical database, ChemSpider.

c Confirmed by standard.d Confirmed by reference.

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PC2, for 11.56% and the third principal component, PC3, for 10.39%.

Mutual projections of factor scores and their loadings for the first

three principal components (PCs) are presented in   Fig. 2   and

Fig. S2. Score plot reveals four samples lying outside the Hotteling

T 2 ellipse (one sample from Vojvodina, two samples from the South

region and one sample from the Eastern region), suggesting that

they were outliers. They were excluded from further analysis. Tak-

ing into account PC1 score values, two distinctive groups of honeys

belonging to Vojvodina and Zlatibor regions are obtained. Samplesfrom Zlatibor are firmly clustered, exhibiting small internal vari-

ability. There was some overlapping among honey samples from

the South region and Vojvodina, and Central region and Zlatibor.

The samples from the East region are dissipated in a broader range

of score plots. The loading plot reveals that the most influential

compounds discriminating between honey from Vojvodina and

Zlatibor are methoxychrysin, bis-methylated quercetin, rhamnetin,

sakuranetin, galangin, pinobanksin, pinocembrin, eriodictyol and

caffeic acid.

Honey samples from different geographical areas were mod-

elled simultaneously using PLS-DA. The first model used all of 

the 36 identified phenolic compounds to predict the geographical

origin of the samples. According to the variable importance in pro-

 jection (VIP) scores, variables that most significantly affected thePLS-DA calculation were identified. Data from 18 selected parame-

ters were remodelled. The number of the latent variables (LVs) was

selected on the basis of the minimum value of  RMSECV , which was

achieved with seven latent variables. Classification and validation

results, expressed as R2cal, R

2CV, RMSEC  and RMSECV  values, for five

new models are presented in Table 3.

Statistical results of PLS-DA models for Zlatibor and Vojvodina

regions were statistically significant, with relatively high values

of  R2cal  and R2

CV, and low difference between  RMSEC  and  RMSECV 

values. In contrast, for the three other regions PLS-DA models were

not statistically significant. Hence, data fromthe phenolic profile of 

Serbian polyfloral honeys allow accurate prediction of geographical

origin of honey from Vojvodina and Zlatibor and provides a useful

tool for determination of sample authenticity. The scores plot of data for these regions also confirmed good predictive ability of 

PLS-DA modelling (Fig. 3a and b). The contribution of particular

variables with the strongest influence on differentiation according

to geographical origin was analysed using the VIP value (Fig. 3c and

d). The variables with a VIP score higher than one were considered

as the most relevant for explaining certain classes of samples. Thestandardised regression coefficient that reveals the significance of 

an individual variable in the regression model is shown in Fig. 3e

and f.

The descriptors included in two models are different and a few

mutual parameters are of opposite significance. The compounds

determining the samples originating from Vojvodina are dica-

ffeoylquinic acid, ellagic acid, quercetin, bis-methylated-quercetin,

methoxychrysin, and pinocembrin. Ellagic acid was found in only

two samples originating from Vojvodina; thus the absence of this

compound distinguishes the samples. The compounds that dis-

criminate Zlatibor region from the rest of Serbia are caffeic acid

phenethyl ester, chlorogenic acid, dicaffeoylquinic acid, caffeic

acid, methoxychrysin, isorhamnetin, and bis-methylated querce-

tin. These results are in accordance with the described results of the Kruskal–Wallis test and PCA.

The compound which gave a highest contribution to the PLS-DA

model proposed for data from Vojvodina is dicaffeoylquinic acid.

This compound was found in all polyfloral honey extracts from

Vojvodina, and in 5 out of 23 samples from Zlatibor. Dicaffeoylqui-

nic acid was previously detected in monofloral Serbian honey sam-

ples originating from plants characteristic for Vojvodina region:

sunflower, basil, and buckwheat (Kec keš et al., 2013). Dicaffeoyl-

quinic acid appears with the highest positive coefficient in the

model for the data from Vojvodina, contrary to a small negative

coefficient in the model for Zlatibor region. Specific soil composi-

tion, climate, and presence of regional endemic flora could be the

factors that distinguish phenolic profiles of samples from Zlatibor

from those from the rest of Serbia.In addition to the cross-validation procedure, and in order to

determine the degree of accuracy and sensitivity of PLS-DA models,

theentire data set for Vojvodina and Zlatibor region was divided into

two subsets, calibrationset andtestingset composedof randomly se-

lected samples. The calibration set consisted of 16 out of 23 samples

fromZlatibor, and 6 out of 9 samples fromVojvodina, while the test-

ing set was composed of the rest of the studied samples. The use of 

such subsets of samples resulted in the one LV model including

60.78% variability in the X -variable domain, demonstrating that the

variables are effectively used for building up the model. The ex-

plained calibration variance for the categorical variable was 90.09%

with the following statistical parameters:   R2cal = 0.9009,

R2CV = 0.8747,   R2

pred = 0.5005,   RMSEC = 0.1402,   RMSECV = 0.1583,

and RMSEP = 0.3623. Good predictive ability of PLS-DA model couldbe confirmed by scores plot of data for calibration and test set for

Fig. 2.  PCA scores plot of Serbian polyfloral honeys of different geographical origin.

 Table 3

Statistical performances of the PLS-DA models.

Zlatibor South

region

East

region

Vojvodina Central

region

R2cal   0 .7139 0.5387 0.4360 0.8440 0.3738

R2CV   0 .5371 0.3076 0.1252 0.6781 0.0430

RMSEC    0.2656 0.2318 0.2709 0.1494 0.2332

RMSECV    0.3465 0.2956 0.3786 0.2185 0.3195

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Vojvodina andZlatibor region (Fig. S3, Supplementary Data). The rel-

atively low value of R2pred was, probably, a result of deviation of one

honey sample from Zlatibor from all others (Fig. S3), which could be

also observed on the score plot of the PCA. The mentioned sample

originates from near the border of Zlatibor.

 3.3. Antioxidant activity of polyfloral honeys

Antioxidant capacity of honey samples was determined by total

phenolic content and the radical-scavenging activity. Polyfloral

honey samples were characterised with TPC values ranging be-

tween 0.03 and 1.39 mg of gallic acid per g of honey, i.e. 0.05 to

1.39 mg GAE/g for Zlatibor region, 0.03–0.83 mg GAE/g for South

region, 0.08–1.36 mg GAE/g for East region, 0.23–0.70 mg GAE/g

for Vojvodina, and 0.36–0.76 mg GAE/g for Central region. Polyfl-

oral honeys collected from the South, Central, and Vojvodina re-

gions showed similar TPC mean values. The mean value of 

0.68 mg GAE/g for honey from the Zlatibor region is somewhat

higher than for other regions. The average content of total pheno-

lics was in good agreement with the values given in the literaturefor the polyfloral honeys of surrounding regions (Bertoncelj, Do-

beršek, Jamnik, & Golob, 2007; Dobre, Gâdei, Patrascu, Elisei, & Se-

gal, 2010; Piljac-Z egarac, Stipc evic, & Belšc ak, 2009).

The results of RSA ranged from 1.31% to 25.61%. Some differ-

ences between the mean values of antioxidant activity for samples

of specific regions are noted. The RSA mean value of Zlatibor region

(9.37%) was somewhat higher compared to the East region (8.44%).Also, RSA mean value for South and Vojvodina regions were similar

(5.65% and 5.21%, respectively), and the lowest value was found for

the Central region (3.48%). The concentration causing 50% inhibi-

tion expressed as micromoles of Trolox equivalents per gram of 

honey had a value of 0.58 lmol TE/g. Results expressed as Trolox

equivalents were comparable and consistent to the results found

in the paper published by Gorjanovic et al. (2013).

The correlation between the TPC and RSA resulted in a linear

model RSA¼ 1:55ð0:79Þ þ 16:01ð1:24ÞTPC with the following

statistical parameters: r  = 0.8646,  s = 3.01, t  = 12.88 (t cr(56) = 2.00),

F  = 165.82, P  = 0.0558. Significant correlation between TPC and RSA

indicated that amongall active phytochemicals,flavonoids andphe-

nolic acids could be identified as chemicals that account for theanti-

oxidant potential of honey. Our findings are in accordance withprevious reports (Bertoncelj et al., 2007; Piljac-Z egarac et al., 2009).

Fig. 3.  PLS-DA, (a and b) scores plots of data for Zlatibor and Vojvodina; (c and d) plots of the variables versus VIP scores in model for Zlatibor and Vojvodina; (e and f) plot of 

the coefficients of parameters in model for Zlatibor and Vojvodina, respectively.

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4. Conclusion

Polyfloral honey samples from different regions in Serbia were

studied to determine their phenolic profile, total phenolic content

and antioxidant capacity. UHPLC-LTQ OrbiTrap MS analysis pro-

vided information about numerous phenolic compounds specific

for the studied polyfloral honeys. The results of this study suggest

that the phenolic profiles varied considerably within samples. Mul-tivariate statistical analysis, PCA and PLS-DA were performed, in

order to delineate differences between polyfloral honeys according

to their regional origin. The results demonstrated that comprehen-

sive spectral data collected by the accurate mass search in combi-

nation with statistical methods could be successfully applied to

detect the geographical origin of Serbian honeys. Samples obtained

from Vojvodina and Zlatibor region were clearly distinguished

from the rest of Serbia due to the presence of dicaffeoylquinic acid,

ellagic acid, caffeic acid phenethyl ester, and chlorogenic acid.

Among numerous antioxidant constituents, flavonoids and phe-

nolic acids could be identified as components that account for the

total antioxidant activity in samples. Samples collected from the

Zlatibor region showed somewhat higher TPC and RSA mean values

when compared to the other regions. Radical-scavenging activity

correlated well with the total phenolic content.

 Acknowledgments

This work was performed within the framework of the research

projects No. 172017 and supported by the Ministry of Education,

Science and Technological Development, Republic of Serbia. The

authors acknowledge the support of the FP7 RegPot project FCUB

ERA GA No. 256716. The EC does not share responsibility for the

content of the article.

 Appendix A. Supplementary data

Supplementary data associated with this article can be found, in

the online version, at   http://dx.doi.org/10.1016/j.foodchem.2013.

08.088.

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