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On track for a truly green propolis - fingerprinting propolis samples from seven countries by means of a fully green approach Cristiano Soleo Funari, Renato Lajarim Carneiro, Mari Jystad Egeness, Gabriel Mazzi Leme, Alberto José Cavalheiro, and Emily F. Hilder ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.6b02005 • Publication Date (Web): 06 Oct 2016 Downloaded from http://pubs.acs.org on October 10, 2016

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ACS Sustainable Chemistry & Engineering

On track for a truly green propolis - fingerprinting propolis samples from seven countries by means of a fully green approach Cristiano Soleo Funaria,b,*, Renato Lajarim Carneiroc, Mari Jystad Egenessb, Gabriel Mazzi Lemed, Alberto José Cavalheirod, Emily Frances Hilderb a

College of Agricultural Sciences, São Paulo State University (UNESP), R. José Barbosa de

Barros, 1780, Private Bag 237, Botucatu 18610-307, São Paulo, Brazil b

Australian Centre for Research on Separation Science (ACROSS), School of Physical

Sciences, University of Tasmania, Churchill Avenue, Private Bag 75, Hobart 7001, Australia c

Department of Chemistry, Federal University of São Carlos, Rodovia Washington Luis, Km

235, São Carlos 13565-905, São Paulo, Brazil d

Institute of Chemistry, São Paulo State University (UNESP), R. Prof. Francisco Degni, 55,

Private Bag 355, Araraquara 14800-900, São Paulo, Brazil

*Corresponding author.

E-mail address: [email protected] (C.S. Funari).

Tel.: +551438807514 (C.S. Funari). 1

ACS Paragon Plus Environment


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ABSTRACT. The production of quality bee products, as well as bee survival itself, depends

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on the health condition of environment but ironically, harmful solvents are often employed by

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scientists and traders to monitor the quality of these products. Many types of propolis have

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been recognized around the world, but specific biological activities can be expected for

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specific types of propolis. This work aimed to develop a new and green ultra-high

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performance liquid chromatography method for the identification of green propolis type. The

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method was able to discern this type of propolis in a set of samples from seven countries as

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well as to cluster these samples by fingerprint similarity based on Principal Component

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Analysis and Partial Least Squares – Discriminant Analysis. It proved to be efficient,

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reproducible and greener than methods previously reported in the literature for similar

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purposes and compatible with the cheap, largely available food grade ethanol produced from

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sugarcane

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KEYWORDS: green solvents, green chromatography, green chemistry, bioethanol,

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metabolite profiling 2


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INTRODUCTION

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Propolis is an overly generic designation for a resinous product prepared by honey

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bees (Apis mellifera) to act as both an antiseptic and a construction material for general use in

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their beehives.1,2 It has been widely utilized by humans since antiquity due to the many

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medicinal properties attributed to it.1

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Propolis’ chemical composition is flora-dependent since in different ecosystems the

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bees will access different plant species for raw materials to produce propolis.1–3 Whilst this

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potential chemical diversity explains why a large number of in vitro and in vivo biological

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activities have been reported in the literature for propolis, it also explains the inconsistencies

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observed in biological activities when propolis samples from different geographical regions

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are tested for the same activity.4,5 Nevertheless, recent studies have demonstrated common

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botanical sources and consistent chemical composition of propolis samples from specific and

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large geographical areas.1,4 For example, the main botanical sources of propolis from

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temperate zones were found to be Populus sp. and its hybrids or Betula sp.1,2 Flavonoids,

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notably galangin and pinocembrin, are the predominant compounds found in this type of

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propolis.2,4 On the other hand, the main botanical source of propolis from South-eastern

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Brazil was found to be Baccharis dracunculifolia DC.3,6,7 This type of propolis is mainly

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composed of hydroxycinnamic acids, most notably, their prenylated derivatives (chromenes

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and non-chromenes) and caffeoylquinic acids.2,4 It is often called, “Brazilian green propolis”

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or simply “green propolis” due to its greenish-yellow-grey to deep green colour.4,8,9 Other

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types of propolis have been also found in Brazil due to the high biodiversity and large

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territory of this country.9–11 However, none of them has attracted more attention than green

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propolis, which has led Brazil to be an important supplier of propolis in the global market.9,11

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Antibacterial, antitumor, immunomodulatory and anti-inflammatory activities, among others, 3



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have been reported for this type of propolis.3,5 Furthermore, more than 250 patents related to

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its applications have been deposited so far.11

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From the understanding that specific biological activities can only be expected for

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specific types of propolis, the need of analytical methods that are able to discern between

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types of propolis has become clear.9 High-performance Liquid chromatography (HPLC)

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coupled with a UV-absorbance-based detector is generally used to quality control propolis

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samples due to the high content of non-volatile compounds in propolis samples and UV

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absorbance of phenolics present in different types of propolis.9 Ironically, despite the

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production of quality bee products, as well as bee survival itself depending on the health

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condition of the environment,12 the potentially harmful methanol (MeOH) and acetonitrile

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(MeCN)13,14 are the organic solvents of choice in reported HPLC methods employed by

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scientists and traders to monitor the quality of propolis. Although analytical chemistry has

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been recognized as a target for green principles13–16 and the strong evidence that potential

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drawbacks of the greener EtOH, compared with MeOH and MeCN, can be circumvented,13,16

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no chromatographic methods guided clearly by Green Analytical Chemistry (GAC)

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principles15 have been reported in the literature to analyze samples of propolis. Briefly, the

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12 principles of GAC proposed by Gałuszka et al.15 state that (1) sample pre-treatment should

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be prevented whenever feasible, (2) small samples as well as a reduced number of them

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should be pursued, (3) in situ analysis should be prioritized whenever feasible, (4) a solvent

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and energy saving approach, through process integration, should be adopted, (5) machine-

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driven and miniaturized methods should be selected whenever available, (6) sample

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derivation should be prevented, (7) the generation of analytical waste should be preferentially

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eliminated. If it is not feasible, the generation of chemical waste should be minimized and the

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produced waste must be treated, (8) multi-analyte and multivariate approaches should be 4


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preferred, (9) the overall energy consumption should be minimized, (10) chemicals produced

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from renewable sources should be prioritized, (11) potentially harmful chemicals should be

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eliminated or replaced and (12) the safety of the operator should be enhanced.15

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The goal of this work is to develop a new method which efficiently distinguishes

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green propolis from other types of propolis but, at the same time, one that embraces as many

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principles of GAC as possible. For that, (i) the combination of an automated Ultra-high

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performance liquid chromatography (UHPLC) with a sub-2-µm column was selected, (ii)

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EtOH was the organic modifier of choice, (iii) a multiparameter, multi-analyte and solvent

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and energy-saving experimental design for the method development process itself was

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prioritized, (iv) the ability of the method to discern green propolis in a set of samples of

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propolis from Australia, Norway, Denmark, Ukraine, England, and United States of America

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was verified, (v) the greenness of the proposed method was compared to those previously

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reported in the literature for similar purposes by using two different metrics and (vi) the

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compatibility of the method with the cheaper food grade EtOH obtained from a renewable

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source (sugarcane) by fermentation, which is generally used to extract propolis by bee-based

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companies, was evaluated.

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EXPERIMENTAL

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Chemicals. The absolute EtOH (Scharlau, Spain, J.T. Baker, USA and Merck, Germany) was

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HPLC grade. The EtOH 96°GL obtained from sugarcane (Santa Elisa, Brazil) was food

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grade. The anhydrous acetic acid (AcOH) (Merck, Germany) was AR grade.

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Samples. An authentic green propolis sample (P) was used for the UHPLC method

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development. Commercial propolis extracts produced by local companies were purchased in

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Brazil (A, B, C, D, E, H, L, M, N, O, Q, R, S and T), United States (F, G and I), England (J) 5



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and Australia (W and WW). Crude propolis samples were kindly provided by beekeepers

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from Australia (Y), Norway (U and X), Denmark (V) and Ukraine (Z).

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Extraction and concentration. Approximately 0.5 g of crude dry propolis samples was

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extracted with 10 mL of EtOH-H2O (7:3 v/v) solution for 2 days with stirring. The resulting

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fluid extracts, as well as the commercially available extracts, were filtered and dried at 35°C

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by vacuum concentrator (Speed Vac, Thermo), equipped with a RVT4104 refrigerator

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(Refrigerated Vapour Trap) and vacuum pump.

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Solid phase extraction. All samples were submitted to solid phase extraction (C18

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cartridges, 500 mg loading, and 3.0 mL) prior to analysis to eliminate the beeswax materials,

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chlorophylls and other low-polarity compounds which could reduce the column lifetime.13

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Ultra-high performance liquid chromatography analyses. The method was developed

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using a UHPLC-DAD system (all Agilent Technologies, USA) comprising of a 1290 Infinity

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binary pump, an autosampler, a thermostatted column compartment with mobile phase pre-

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heater, and a diode array detector. All other analyses were performed using a UHPLC-DAD

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system (Ultimate3000, Dionex®, Sunnyvale, USA), equipped with two ternary pumps (DGP-

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3600RS), thermostatted column compartment (TCC-3000RS), a diode array detector (DAD-

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3000RS), and an autosampler (WPS-3000RS). Separations were achieved in a Zorbax Eclipse

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Plus RRHD C18 column 2.1 × 150 mm, 1.8 µm particle size (Agilent Technologies, USA). A

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7 µL mobile phase pre-column heater was installed inside the column compartment. For the

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optimization of the method, only peaks with area above 8.5 mAU*min were taken into

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consideration, since this value is slightly higher than the maximum area observed in blank

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runs (8.2 mAU*min).13 For the final method, the mobile-phase was composed of 1.0 %

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AcOH in H2O and HPLC grade EtOH at the following gradient elution: from 5 to 84% of 6


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HPLC grade EtOH in 50 min. Flow rate and temperature of analysis were set at 0.3 mL/min

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and 60°C, respectively. An aliquot of 3 µL of a 60 mg/mL propolis solution was injected for

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each analysis. The chromatographic data were processed with OpenLab CDS ChemStation

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(Agilent Technologies, USA) and Chromeleon 6.80 (Dionex®, USA). Chromatograms were

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extracted at 280 nm.

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Design of experiments (DoE). Initially, a screening with five variables – % EtOH in the

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initial mobile phase (x1), injection volume (x2), mobile phase flow rate (x3), gradient time (x4)

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and percentage of acetic acid (x5) – was performed by means of a 2V5-1 fractional factorial

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design with four replicates in the central point (Table S1, supporting information).17 The

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following parameters were fixed: column (ZORBAX Eclipse Plus C18, 2.1 x 150mm, 1.8

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µm), final EtOH percentage in the gradient elution (100%), column temperature (60°C, since

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it is the maximum working temperature according to the manufacturer), detection wavelength

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(280 nm) and sample concentration (60 mg/mL). Number of peaks (y1) and sample peak

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capacity (y2, Eq. 1) were monitored as dependent variables (responses).

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y2 = 1 + (∆tR / W)

(Eq. 1)

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Where ∆tR is the difference in retention time between the last and the first eluted compound

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detected and W is the average baseline peak-width.18

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In the optimization step, a Doehlert design was performed using the four most

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significant variables found by the fractional factorial design in the previous step (x1 to x4,

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with 3 to 7 levels for each variable – Table 1).17 In this step, the percentage of acetic acid (x5)

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was fixed at 1% (v/v), since this variable didn’t show significance compared to the others in

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the previous fractional factorial design.

7



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Multivariate analysis. After the optimization of the analytical method, a principal

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component analysis (PCA) was performed using the chromatographic data of all the 26

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propolis samples. In this context, the PCA was used as an unsupervised clustering method,

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which just seeks identify the most similar (or dissimilar) samples. The data pre-treatment was

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kept as simple as possible: selection of the data at 280 nm; selection of retention time range

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from 3.25 to 50 min; decreasing of the time resolution from 8.3×10-4 to 8.3×10-3 min; and all

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chromatograms were normalized by the intensity of the most intense peak in each

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chromatogram, in another words, the intensity of the highest peak in each chromatogram was

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an unit (1.0). Treatments such as mean center, first derivative and auto scaling were not

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employed, since they could amplify the noise or increase the effect of small shifts in retention

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times. Later, a partial least squares for discriminant analysis (PLS-DA) was performed using

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the same data used for PCA analysis, including the normalization procedure, in order to

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classify the samples as “green propolis” based on the similarity of chromatographic profile of

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authentic green propolis samples. PLS-DA is a regression method performed for supervised

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clustering or classification of samples. It is a multivariate PLS regression which uses classes

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of samples, instead numbers, in the y vector (or matrix, when working with more than one

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response).19

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RESULTS AND DISCUSSION

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UHPLC Method development. The optimization process described here was fully guided by

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DoE. The multivariate approach was preferred over an univariate approach to avoid

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neglecting potential interactions among variables, thus preventing obtaining a local optimum

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instead a global one.20 In the univariate approach only one variable at a time is evaluated,

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requiring a high number of experiments (and resources, such as solvents and energy) to get

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acceptable results, which clearly does not fit Principles 7, 8 and 9 of GAC, whereas the 8


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multivariate approach does.15,20 The outputs of the 2V5-1 fractional factorial design employed

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during the screening step are shown in Table S1 (supporting information). Normal probability

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plots21 (Fig. S1, supporting information) evidenced that in the range of levels evaluated the

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variable x1 and the interaction x1x2 were more significant for the response number of peaks,

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which can be verified in the experiments 3, 7, 11 and 15, all of them in the levels x1 = -1 and

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x2 = +1, generating 149, 165, 162 and 165 peaks, respectively (Table S1). The variable x3

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and the interaction x1x2 were more significant for sample peak capacity response, the best

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average results being obtained when +1 level is used for x3. As the variable x5 (% AcOH in

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A) did not present significant influence over both responses, its column could be removed

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from the DoE to simulate a 2-levels full factorial design with central point. A multivariate

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linear regression was then performed in order to evaluate the significance of variables x1 to x4

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at 95% of confidence. For the response number of peaks it was found that the variables x1, x3,

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x4, and the interactions x1x2 and x2x4 were significant, explaining 89% of the variance of the

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data (R2 = 0.89). For the response sample peak capacity, the significant variables were x1, x3,

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x4, together with the interaction x1x2, explaining 74% of the variance of the data (R2 = 0.74).

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These results corroborate those found by the normal probability plot method. Although a 2-

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levels full factorial design with central point might be used to find a mathematical model, it is

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more recommended for an initial evaluation of the system under development. Thus, the

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original aim of this stage which was to recognize the variables most statistically relevant to

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the separation process by means of a time and resource-saving approach (fractional factorial

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design) was maintained.13

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The outcomes of the Doehlert design employed during the optimization step are

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shown in Table 1. This type of DoE was selected because it allows the modeling of the

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surface response and its curvature using a minimum number of experiments. In other words, 9



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it is also a resource saving approach for optimization purposes. For those variables where

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importance became clear according to the previous fractional factorial design performed

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during the screening step (% EtOH in the initial mobile phase and mobile phase flow rate, x1

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and x2, respectively), seven levels were tested, whereas five and three levels were tested to

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sample injection volume (x4) and gradient time (x3), respectively (Table 1). The variable x5

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(the percentage of acetic acid) was fixed at 1% (v/v) in the Doehlert design, since it was not

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significant in the previous fractional factorial design. In addition, the upper level of the

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injection volume (x4) was decreased from 10 µL to 5 µL in this new step since this was the

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maximum concentration without exceeding 5000 mAU in a given chromatogram.

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From the experimental setup shown in Table 1, the mathematical models for y1 and y2 (at

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95% of confidence level) were found to be as follow (Eq. 2 and Eq. 3, respectively):

2

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y1 = 106 .2 − 28.4 x1 + 7.7 x2 + 7.8 x3 + 10.8 x4 + 10.3 x1

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y2 = 267.3− 58.9 x1 + 29.3 x2 + 25.0 x3 + 14.7 x4 + 16.8 x1 − 12.7 x3

±1.2

±1.9

±1.8

±1.7

± 2 .2

(Eq. 2)

± 2 .7

2

± 3.7

± 4.7

± 4.3

± 4.3

± 5.5

± 7.1

± 5.3

2

(Eq. 3)

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with R2 values of 0.94 and 0.93 for y1 and y2, respectively, and 94.2 and 93.3% of the

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explained variation. In addition, the analysis of variance (ANOVA) was performed where the

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experimental F-test for y1 were 41.84 (model/error) and 7.40 (lack of fit/pure error), with

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critical values of 2.66 (F6,18,95%) and 5.91 (F14,4,95%), respectively. The experimental F’s for y2

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were 61.55 (model/error) and 1.17 (lack of fit/pure error), with critical values of 2.74

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(F5,19,95%) and 5.86 (F15,4,95%). These F-tests showed that the regression is statistically

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significant, since the experimental Fmodel/error is around 10 times the critical value for both

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responses. In addition, the observed lack of fit of the models are acceptable, since they are 10


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close to or lower than the critical values, showing that the pure error (random errors from

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replicates) is the source of the lack of fit.22

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From the mathematical models, response surfaces were built (Fig. S2, supporting

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information) and the predicted optimal points were found to be as follow: % EtOH initial

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˂5%, injection volume >5 µL, flow rate >0.3 mL/min for both responses, and gradient time >

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60 min for y1 and 60 min for sample peak capacity (y2). Due to practical limitations, the

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feasible combination which is closer to the predicted optimal point mentioned above should

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be: 5% for x1 (minimum percentage of organic solvent recommended by the column

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manufacturer), 5 µL for x4 (maximum concentration without exceeding 5000 mAU in a given

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chromatogram), 0.3 mL/min for x2 (maximum flow rate without exceeding 600 bar of

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backpressure, thus allowing the method to be compatible with less potent LC instruments

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compared to those used in this work in terms of maximum tolerated backpressure) and 60

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min for X3 (maximum analysis time fixed by us). This combination was tested in

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quintuplicate, leading to 189 ± 1.7 peaks (y1) and 570 ± 1.4 of sample peak capacity (y2),

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respectively. These values surpassed the predicted maximum value (171 and 399,

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respectively) and all original experiments, which proved the efficacy of the optimization

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procedure.

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Finally, the method could be further greened just by stopping the gradient elution at

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50 min. with no loss of fingerprint quality. A representative chromatogram with the final

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chromatographic conditions is shown in Figure 1a.

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The selection of UHPLC as an analytical technique avoided derivatization of the

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sample (fitting Principle 6 of GAC), whilst a sub-2-µm column and an autosampler led to the

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observation of Principles 2 and 5, respectively, together with Principle 7.15 On the other hand, 11



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the selection of EtOH as the organic modifier allowed the direct observation of Principles 7,

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11 and 12 of GAC.15

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Assessment of the greenness of the proposed method. Although the 12 principles of GAC15

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were used as a guideline during all method development, the environmental performance of

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such method would not be completely understood without using appropriate metrics designed

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for such purposes. Initially, the greenness of our method was checked using a metric called

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Analytical Eco-Scale proposed by Gałuszka et al.23 According to this, an analysis performed

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using our method is classified as excellent green analysis (the obtained total score was 76 per

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analysis - see supporting information, Table S2). This metric is a comprehensive and generic

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tool useful to classify an analytical procedure regarding its greenness since it considers all

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steps involved on this. However, for comparisons between HPLC methods, a more specific

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metric called HPLC-Environmental Assessment Tool (HPLC-EAT)24 can be more

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appropriated.13 Thus, the HPLC-EAT was selected to assess the environmental impact of the

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method proposed here in comparison with seven methods also developed to get comparative

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HPLC-DAD/UV fingerprints of propolis samples from different sources or geographical

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origins.7,25–30 Results are summarized in Table 2. The lower the score calculated by HPLC-

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EAT, the greener the method.

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According to this metric, the method proposed here was 2.6-14.9-fold greener than

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the reference methods (Table 2), which again evidenced the environmental advantages of

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using the method proposed in this work. Although the greenness of an analytical process is

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strongly impacted by sample preparation procedures, these were not taken into consideration

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in the calculations using HPLC-EAT tool which resulted in the scores presented in Table 2.

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That was because the exact amount of solvents used during sample preparation was not

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always clear for all compared methods. Nevertheless, even the most optimistic assumptions 12


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for the missing information in the methods under comparison would rank the method

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proposed here as the greenest one (data not shown).

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Method application – comparison of propolis samples from different origins. From the

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understanding that information about the greenness of an analytical method that is not

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associated with information about its figure of merit lacks practical sense, the ability of the

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method developed here to recognize green propolis type in a set of samples from different

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origins was tested. Twenty six samples (see experimental section) from seven countries were

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analyzed.

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Principal component analysis. The first four PCs explained 90.03% of the total variance of

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the data. Figure 2 shows the score and loading plots obtained from this PCA analysis. This

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unsupervised analysis by PCA showed the presence of three clusters of similar samples,

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sample WW was found to be out of any cluster and sample F seems to be between two

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groups, as discussed in detail hereafter. Figure 3 shows representative chromatograms of

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samples belonging to the different clusters, plus the sample WW.

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As can be seen in Figure 2a, all samples from Brazil present positive scores in PC2,

269

whereas all the samples acquired outside Brazil present negative scores in PC2, thus allowing

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separation into two major clusters. Since the data was not mean centered, the PC1 can be

271

understood as an average of all chromatograms. The samples with high PC1 values are those

272

which showed high baseline and/or high number of peaks presenting high intensity, according

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the loadings from PC1. The visual analyses of all chromatograms (Fig. S3, supporting

274

information) confirmed the separation observed from PC1xPC2. Indeed, all samples from

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Brazil clearly presented very similar qualitative metabolite profiling (Fig. S3, supporting

276

information), which evidenced that even those commercial propolis extracts commercialized 13



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Page 14 of 30

277

by companies based in North-eastern (Ceará state, samples H and L) and Northern Brazil

278

(Pará state, sample O) were produced with propolis of the B. dracunculifolia type. This was

279

not a surprise since it is well known that the major producers and traders of crude propolis in

280

Brazil are based in Minas Gerais and São Paulo states (both in South-eastern Brazil), where

281

honey bees take advantage of the native B. dracunculifolia plant as a botanical source to

282

produce propolis.

283

Whereas the samples from Brazil did not present influence on PC3 and PC4, since

284

these samples present score values around zero for both, the PC3xPC4 plot was useful to

285

distinguish samples acquired outside Brazil (Figure 2b). Two major clusters were observed

286

for these samples. One cluster was composed of samples U, X and Z (the two samples

287

produced in Norway and the only sample produced in Ukraine - all supplied as crude propolis

288

by a beekeeper association), whereas the second cluster was composed of samples F, G, I (all

289

acquired from USA makers as commercial extracts), J (produced by an English maker as

290

commercial extracts), V (produced in Denmark and supplied as crude propolis by a beekeeper

291

association), plus samples W and Y (the former was as a commercial extract from an

292

Australian maker, whereas the latter was supplied by an Australian beekeeper from Tasmania

293

as crude propolis). Sample WW (also a commercial extract from an Australia maker) is

294

clearly out of any cluster.

295

A visual inspection of the chromatograms confirmed these results (Fig. S3, supporting

296

information). The chromatograms of Brazilian samples were highly similar among

297

themselves; the chromatograms of the group F, G, I, J, V, Y and W present a satisfactory

298

similarity, whereas samples U, X and Z present a good similarity taking into account just the

299

most intense peaks. The sample WW really exhibits a very particular chromatographic

300

profile, especially from 20 to 40 min (Fig. 3 and Fig. S3, supporting information). 14


Page 15 of 30

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301

Figure 2c shown the loadings of the PCA analysis. As stated before, the PC1 (main

302

peaks at 7.94, 24.77, 25.73, 34.01 and 37.13 min.) present a high number of peaks and some

303

baseline shift. It is not a trivial task to state which samples present, or not, specific peaks

304

(compounds) using only the results of the first PC because it does not provide enough

305

discriminant power when it is individually analyzed. However, analyzing also the PC2

306

scores, we can infer that the peaks which present positive values in the PC2 loadings (e.g. at

307

7.95, 11,32, 13.04, 17.73, 24.77, 25.64, 25.74, 26.68, 33.98, 37.11 and 37.90 min.) come

308

from those compounds which are present in a high relative concentration in the samples from

309

Brazil. Indeed, the on-line UV spectra recorded for compounds with these retention times in

310

Brazilian samples are compatible with the main classes/compounds typically reported for

311

green propolis.8 E.g. for sample A, a representative sample of the cluster composed

312

exclusively by Brazilian samples, the peak at 7.95 min. could correspond to p-coummaric

313

acid, peaks at 24.77, 33.98 and 37.11 min. could correspond to prenylated free cinnamic

314

acids, peaks at 11.32, 13.04 and 25.64 min. could correspond to caffeoylquinic acids, peak at

315

17.73 min. could correspond to a flavanone or to a dihydroflavonol, the peak at 25.74 min.

316

could correspond to a flavonol, whereas peaks at 26.68 and 37.90 min. might correspond to

317

benzofurans.31,32 To see these UV spectra, please refer to the supporting information (Fig.

318

S4).

319

Similarly, peaks that present negative values in the PC2 (mainly 17.01, 24.95 and

320

27.91 min.) loadings probably are related to those compounds which present a high relative

321

concentration in samples acquired outside Brazil. On the other hand, the loadings of PC3 and

322

PC4 evidenced those peaks which are responsible for discerning the samples acquired outside

323

Brazil. Peaks with positive loadings in the PC3 (mainly at 17.00, 24.32, 24.89 and 30.31

324

min.) are related to compounds in higher concentration in samples F,I,J,Y,G,V and W. Peaks 15



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Page 16 of 30

325

with negative loading values at PC3 (mainly at 7.88, 9.41, 23.98, 27.94 and 31.21 min.) are

326

related to the compounds in higher concentration in the samples U, X and Z. PC4 is important

327

to explain the sample WW, which has the most intense peak at 7.36 minutes, showing yet

328

intense peaks at 7.86, 16.97, 23.57 and 31.52 minutes, and a lower peak at 24.97 minutes

329

(Fig. 3, bottom chromatogram).

330

Although our method was first developed to be able to discern green propolis from

331

other types, it was also able to differentiate propolis from outside Brazil. This suggests that

332

the method might be used for assessing the identity of propolis types other than green

333

propolis.

334

Partial Least Squares – Discriminant Analysis (PLS-DA). The chromatograms of samples A,

335

B, C, Q and N, which are the only ones that claimed to be “green propolis” on their label,

336

were used to perform supervised analysis by PLS-DA using 4 latent variables. According to

337

this, all propolis extracts produced by Brazilian companies, including those based outside

338

South-eastern Brazil (samples H and L, from a company located in Ceará state, and sample

339

O, from a company located in Pará state) were classified as green propolis by the PLS-DA

340

model. If the “green propolis” claim has secured a premium to these products, it did not affect

341

the overall qualitative metabolite profile compared to Brazilian commercial extracts for

342

which this claim was not used. However, it is important to highlight that chlorophyll is not

343

detected by the method proposed here, which might hinder possible discernment based on

344

these very non-polar compounds. All samples acquired outside Brazil were classified as non-

345

green propolis.

346

These results corroborate the PCA analysis and the visual inspection of the

347

chromatograms (Fig. S3, supporting information). They are also coherent with the fact that 16


Page 17 of 30

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348

the major producers and traders of crude propolis in Brazil are based in South-eastern Brazil.

349

Furthermore, it evidenced that although Brazil exports crude green propolis to many

350

countries, the extracts acquired outside Brazil were not produced from this type of propolis.

351

Method validation. Once the developed method was able to assess the identity of green

352

propolis samples, the RSD values of the retention times of ten peaks distributed over the

353

whole chromatogram of the authentic green propolis (sample P) were used to double check

354

the quality of the method.

355

Instrumental precision. Instrumental precision estimation was performed for nine consecutive

356

injections of the reference sample.33 The highest RSD observed was 0.05%.

357

Repeatability. Repeatability was calculated based on nine experiments, by varying sample

358

concentrations in three levels and three replicates of each level.33 The highest RSD observed

359

was 1.43%.

360

Intermediate precision. Intermediate precision was calculated by random combinations of the

361

following variables: sample (two samples prepared on two different days), sample

362

concentration (60, 30 and 20 mg/mL), injection volume (1.5, 3.0 and 5.0 µL) and day of

363

analysis (three different days), EtOH from two different makers and three replicates of each

364

level.33 The highest observed RSD was 1.49%.

365

Further greening the method - testing 96°GL bioethanol as the organic modifier.

366

Although the HPLC grade EtOH used to develop our method fits Principles 7, 11 and 12 of

367

GAC, it does not fit Principle 10, which states that reagents obtained from renewable sources

368

should be preferred.15 That is because the HPLC grade EtOH used was produced by synthesis

369

from ethylene. A reagent/solvent which is obtained from a renewable source by a 17



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Page 18 of 30

370

fermentation process is even more desirable from a sustainability perspective.14,34,35 Thus,

371

food grade EtOH from sugarcane (bioethanol) was tested as an alternative to the HPLC grade

372

EtOH. Since the former was not absolute EtOH, but 96°GL, the following gradient elution

373

was necessary to keep the same mobile phase composition during the separation: 5.21 to

374

87.5% of food grade bioethanol (96°GL) in 50 min (Figure 1). The chromatograms obtained

375

with HPLC grade absolute EtOH and those acquired with 96°GL bioethanol were very

376

similar (Figure 1). Nine consecutive injections using 96°GL bioethanol led to RSD values of

377

0.07%, which is similar to those observed when HPLC grade EtOH was employed (0.05%,

378

instrumental precision) (Fig. S5, supporting information). This is good news for propolis

379

extract producers, since 96°GL EtOH is the organic solvent mostly used to extract propolis,

380

thus being largely available in bee product based companies. In Brazil, it is produced from

381

sugarcane by fermentation process and costs c.a. US$ .60 cents per litter, whereas HPLC

382

grade costs c.a. US$ 14.0 per liter. It becomes more significant if one consider that all

383

organic chemicals present in the efluent generated by the method proposed here are

384

biodegradable (EtOH, AcOH and propolis itself), with potentially no costs associated to

385

chemical waste treatment. On the other hand, if MeOH and MeCN are employed, the

386

treatment of the efluent is mandatory. These findings corroborate those of Welch et al.16

387

These authors reported separations of prepared mixtures of up to 11 components employing

388

grain EtOH (95°GL, obtained from corn) with performances comparable to those acquired

389

when HPLC grade EtOH was used.

390

A heuristic approach was successfully applied in this work to the development of a new,

391

efficient, reproducible and green UHPLC method for green propolis identity assessment.

392

Additionally, this method was able to recognize chemical similarities and dissimilarities

393

among samples other than the green propolis type, which suggests that it might be useful for 18


Page 19 of 30

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394

quality controlling other types of propolis as well. A total of 9 of 12 principles of Green

395

Analytical Chemistry were observed in this work, evidencing that the full research process

396

deserves attention from a sustainable point of view, not only the final product/method. Whilst

397

it obviously includes the careful selection of the technique, technology and chemicals, it also

398

should include the selection of experimental designs optimized to reach a global optimization

399

while reducing solvent, energy and time consumptions during the method development itself.

400

It should be especially important for propolis investigators and producers, since the safety of

401

the environment is the first step to the production of quality propolis.

402

ACKNOWLEDGEMENTS

403

The authors acknowledge the São Paulo Research Foundation (FAPESP, grants

404

#012/15844-7, #010/18840-7, #10/16520-5 and #13/07600-3) and Australian Research

405

Council (ARC). Thanks also go to the Norwegian Beekeepers’ Association, Tasman Gold,

406

and ApisFlora Ltda. and Maria Goretti Vasconcelos Silva for the donation of propolis

407

samples.

408

ASSOCIATED CONTENT

409

Supporting Information

410

Table S1. Fractional factorial design

411

Table S2. Analytical Eco-Scale calculation for the UHPLC method developed in this work.

412

Figure S1. Normal probability plots.

413

Figure S2. Surface responses.

19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

414

Figure S3. Chromatograms of all samples analyzed.

415

Figure S4. On-line UV spectra of selected peaks of sample A.

416

Figure S5. Consecutive injections of an authentic green propolis using bioethanol.

417

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Table 1. Doehlert design with four variables normalized to 1 and the results for each experiment1 Variable Experiment

%EtOH initial (x1)1

Flow rate

Gradient

Inj.

(mL/min)

time (min)

Volume

(x2)1

(x3)1

(µL) (x4)1

Number of

Sample peak

peaks (y1)

capacity (y2)

1

0 (22.5)

0 (0.2)

0 (45)

0 (3)

107.2±4.5

270.6±5.0

2

0 (22.5)

0 (0.2)

0 (45)

1 (5)

122

301.5

3

1 (40.0)

0 (0.2)

0 (45)

0.5 (4)

93

223.0

4

0.33 (28.3)

1 (0.3)

0 (45)

0.5 (4)

116

281.0

5

0.33 (28.3) 0.25 (0.22)

1 (60)

0.5 (4)

109

272.3

6

0 (22.5)

0 (0.2)

0 (45)

-1 (1)

92

244.1

7

-1 (5.0)

0 (0.2)

0 (45)

-0.5 (2)

149

354.0

8

-0.33 (16.7)

-1 (0.1)

0 (45)

-0.5 (2)

106

241.4

9

-0.33 (16.7) -0.25 (0.17)

-1 (30)

-0.5 (2)

99

237.6

10

-1 (5.0)

0 (0.2)

0 (45)

0.5 (4)

147

347.4

11

-0.33 (16.7)

-1 (0.1)

0 (45)

0.5 (4)

111

251.7

12

-0.33 (16.7) -0.25 (0.17)

-1 (30)

0.5 (4)

108

237.1

13

1 (40.0)

0 (0.2)

0 (45)

-0.5 (2)

79

216.3

14

0.67 (34.2)

-1 (0.1)

0 (45)

0 (3)

82

203.7

15

0.67 (34.2) -0.25 (0.17)

-1 (30)

0 (3)

88

207.7

16

0.33 (28.3)

1 (0.3)

0 (45)

-0.5 (2)

104

281.4

17

-0.67 (10.8)

1 (0.3)

0 (45)

0 (3)

132

335.1

18

0 (22.5)

0.75 (0.27)

-1 (30)

0 (3)

101

247.3

19

0.33 (28.3) 0.25 (0.22)

1 (60)

-0.5 (2)

99

249.3

20

-0.67 (10.8) 0.25 (0.22)

1 (60)

0 (3)

138

329.9

1 (60)

0 (3)

112

277.9

21 1

Result

0 (22.5)

-0.75 (0.12)

the codified values are given without brackets, whereas the corresponding real values are

indicated in brackets

530

25



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Page 26 of 30

Table 2. Comparison of environmental performances of the method proposed here with seven methods previously reported in the literature to fingerprint propolis samples by HPLCDAD/UV

1 2

Method/reference

HPLC-EAT score1,2

Proposed in this work

13.4

Piccinelli et al.25

34.5

Yang et al.26

50.7

Pellati et al.27

60.3

Machado et al.28

76.6

Zhou et al.29

77.1

Park et al.7

136.4

Gardana et al.30

200.4

Sample preparation procedures were not taken into consideration for calculations The lower the score, the greener the method

531

26


Page 27 of 30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


532

533

Fig. 1. Representative optimized UHPLC-UV fingerprints of green propolis at 280 nm;

534

Column: ZORBAX Eclipse Plus C18, 2.1 x 150mm, 1.8 µm. Mobile-phase components: 1.0

535

% AcOH in H2O and EtOH at the following gradient elutions: from 5 to 84% of HPLC grade

536

EtOH in 50 min (upper chromatogram, A) or from 5.21 to 87.5% of food grade bioethanol

537

(96°GL) in 50 min (bottom chromatogram, B). Flow rate: 0.3 mL/min; analysis temperature:

538

60°C; sample concentration: 60 mg/mL. Injection volume: 3 µL.

539

540

541

27



542

4

O M H DQ R B A L N C P E T

PC2 (17.76%)

544

S

0 -2

I

Y J WG

-4

-8

0.1 0.08 0.06 0.04 0.02

V F

U

X

4

546

10

15

20

25 Tr

30

35

40

45

50

5

10

15

20

25 Tr

30

35

40

45

50

5

10

15

20

25 Tr

30

35

40

45

50

5

10

15

20

25 30 Tr (min)

35

40

45

50

0.1

Z 2

5

WW

-6

545

PC1

2 543

c)

6 PC1 (62.82%)

PC2

a)

8

10

0.05 0 -0.05

0.1

b)

6

WW

0.05 PC3

547

4 2 0

H J E C ST R O V B I MD G L Y W P ANQ F

U X

-2

0.1 PC4

549

0 -0.05

548

PC4 (2.85%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 30

Z

550

0.05 0 -0.05

-4 -6

-4

-2 0 PC3 (6.60%)

2

4

551

552

Fig. 2. Score plots from PCA at 280 nm. a) PC1 x PC2 and; b) PC3 x PC4; c) loadings of the

553

first four PCs. The explained variance by each PC is indicated in brackets.

554

555

556

557

558

28


Page 29 of 30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


559

560

Fig. 3. Representative chromatograms of samples belonging to the three different clusters

561

showed in Fig. 2, plus sample WW, which was out of any cluster. Refer to Fig. 1a for

562

experimental conditions. The most intense peaks which were found to be related with sample

563

clustering are indicated by stars over the chromatograms.

564

565

566

567

568

29



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Page 30 of 30

TOC/Abstract Graphic

569

On track for a truly green propolis - fingerprinting propolis samples from seven countries by means of a fully green approach Cristiano Soleo Funari, Renato Lajarim Carneiro, Mari Jystad Egeness, Gabriel Mazzi Leme, Alberto José Cavalheiro, Emily Frances Hilder

570

Synopsis

571

This work contributes to the replacement of harmful solvents and polluting analytical

572

procedures with greener alternatives for the metabolite profiling of complex samples

573

30


On Track for a Truly Green Propolis ... - ACS Publications

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