Microwave-assisted extraction of peppermint polyphenols – Artificial neural networks approach

Journal Pre-proof Microwave-assisted extraction of peppermint polyphenols – Artificial neural networks approach Branimir Pavli´c, Muammer Kaplan, Oskar Bera, Elmas Oktem Olgun, Oltan Canli, Nemanja Milosavljevi´c, Boris Anti´c, Zoran Zekovi´c

PII:

S0960-3085(19)30570-X

DOI:

https://doi.org/10.1016/j.fbp.2019.09.016

Reference:

FBP 1153

To appear in:

Food and Bioproducts Processing

Received Date:

19 June 2019

Revised Date:

24 September 2019

Accepted Date:

29 September 2019

Please cite this article as: Pavli´c B, Kaplan M, Bera O, Oktem Olgun E, Canli O, Milosavljevi´c N, Anti´c B, Zekovi´c Z, Microwave-assisted extraction of peppermint polyphenols – Artificial neural networks approach, Food and Bioproducts Processing (2019), doi: https://doi.org/10.1016/j.fbp.2019.09.016

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Microwave-assisted extraction of peppermint polyphenols – Artificial neural networks approach

Branimir Pavlić1, Muammer Kaplan2, Oskar Bera1, Elmas Oktem Olgun3, Oltan Canli3,

1University

2TUBITAK

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Nemanja Milosavljević1, Boris Antić4, Zoran Zeković1,*

of Novi Sad, Faculty of Technology, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia

Marmara Research Centre, Institute of Chemical Technology, P.O.Box 21, 41470,

Marmara Research Centre, Environment and Cleaner Production Institute, P.O.Box

4University

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21, 41470, Gebze, Kocaeli, Turkey

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3TUBITAK

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Gebze, Kocaeli, Turkey

of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi

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Sad, Serbia

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Corresponding author: Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; Tel.: +381 64 138

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7583, Fax: +381 21 450 413, E-mail: [email protected]

Highlights 

Utilization of peppermint leaves for production of antioxidant-rich extracts.

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Evaluation of MAE parameters influence on polyphenols yield and antioxidant activity.

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ANN simulation provided adequate fit with experimental data.

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Multi-response optimization was used to improve polyphenols yield and maximize and antioxidant capacity. Polyphenols and terpenoid volatiles were characterized by LC-MS/MS and GC-MS.

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

Abstract

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Microwave-assisted extraction (MAE) of natural resources was the focus of investigation for

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great number of researches recently, however great potential of this technique is still not fully utilized. The aim of this work was evaluation of MAE parameters (ethanol

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concentration, extraction time and liquid-solid ratio) on total polyphenols yield (TP) and antioxidant activity (DPPH, FRAP and ABTS) of peppermint (Mentha piperita L.) extracts.

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Experiments were performed within face-centered central composite design and artificial neural network (ANN) approach was used for extraction system modeling. Good fit of

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applied model and experimental data was achieved for almost all responses which provided

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influence analysis and optimization. Ethanol concentration was the most influential MAE factor. TP, FRAP and ABTS were selected responses included in separate and multi-response optimization. Narrow range of ethanol concentration (49.75-51.07%) and lower L/S ratio (13.04-18.88 mL/g) provided different solutions for optimal conditions. LC-MS/MS analysis suggested that flavonoids, phenolic acids and stilbenes were the most dominant in polyphenolic fraction, while GC-MS results showed that menthomenthene, menthone, 1,82

cineole, d,l-limonene and p-cimene were the most abundant volatiles. Therefore, MAE was successfully applied for co-extraction of polyphenolics and terpenoid bioactives and production of liquid extracts with high antioxidant potential. Keywords: Mentha piperita L.; microwave-assisted extraction (MAE); polyphenols; terpenoids; antioxidant activity; artificial neural network (ANN) 1. Introduction

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Interest and market demands towards natural products and pharmaceutical formulations derived from them have increased recently. Conventional synthetic drugs were often

characterized by various adverse effects, while their production could provide negative

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impact on environment. Therefore, peppermint (Mentha piperita L.) has been recognized as

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one of the most important commercial medicinal plants used in pharmaceutical (official according to European Pharmacopoeia), food (condiment and soft beverages) and cosmetic

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(fragrance) industry (Gavahian et al., 2015). Peppermint leaves and pharmaceutical formulations obtained from them (essential oil, liquid and dry extracts) have exhibited wide

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spectrum of pharmacological activities such as anticarcinogenic, gastro protective, antiinflammatory, antimicrobial, antivirotic, antioxidant, fungicidal and spasmolytic activities

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(Shah and Mello, 2004; Mckay and Blumberg, 2006). Its biological potential could be

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attributed to the chemical profile and presence of two major groups of bioactive compounds. Polyphenols fraction consists of mainly phenolic acids and flavonoids, whereas terpenoids fraction consists of monoterpenes. Furthermore, these were recognized as the most important peppermint bioactives. These compounds have numerous differences in structure, physico-chemical properties and activity, and their recovery from dry peppermint leaves depends on applied extraction 3

technique and process conditions. Therefore, co-extraction of polyphenols and terpenoids from peppermint in order to obtain extracts with increased biological activity could be considered as a great challenge. Conventional solid-liquid extraction has been commonly used for isolation of polyphenols, however, various disadvantages in terms of low yield and increased resource consumption were attributed to it (Žuntar et al., 2019). Recently, novel extraction techniques using ultrasound, pulsed electric fields and microwaves have been successfully developed in order to overcome these disadvantages and improve yield and

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quality of the final product derived from the agro-industrial natural resources (Kumari et al., 2018). On the other hand, essential oils have been commonly produced by hydrodistillation, while supercritical fluid extraction (SFE) was utilized as an excellent alternative as a green

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technique which could overcome main limitations of conventional processes (Pourmortazavi

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and Hajimirsadeghi, 2007). Although, SFE has been recognized as green and efficient technique for isolation of non-polar compounds (de Melo et al., 2014), it has not been

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suitable for recovery of polar and moderately polar compounds, such as polyphenols. Polyphenolic compounds are widely spread in nature and they are present in plants as

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secondary metabolites with several functions. They have been considered as important class of bioactive compounds known for their antioxidant properties and beneficial effects on

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chronic diseases and ageing (Galanakis, 2018). Peppermint has been recognized as valuable

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source of potent polyphenols (Riachi and De Maria, 2015). However, besides high content of polyphenols in raw material, it is essential to adjust extraction technique and optimize its conditions in order to maximize yield of the target compounds. Economic and sustainable recovery of plant polyphenols is essential for expansion of their application in food products, dietary supplements, cosmetics and pharmaceutical formulations.

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Application of microwave-assisted extraction (MAE) for recovery of bioactive compounds from medicinal plants, food industry by-products and agricultural waste streams has recently emerged. Acceleration mechanism in MAE has been attributed to penetration of microwaves into natural matrices and interaction with polar molecules (Wijngaard et al., 2012). The main effects of absorbed microwaves are ionic conduction and dipole rotation, which further cause friction, heating and disruption of cell walls compact structure and diffusion from solid to liquid phase (Chemat and Cravotto, 2012). Temperature, extraction time, irradiation

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power, matrix properties and solvent (type, concentration and polarity) have been identified as the main factors influencing MAE (Routray and Orsat, 2012). Therefore, these variables, as well as properties of target compounds should be taken into account for optimization of

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MAE process. Flexibility of this technique is highlighted by the possibility of recovery of

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various classes of bioactive compounds (quinones, polyphenols, terpenoids, alkaloids, saponins, etc.) with different polarity (Zhang et al., 2011). It could be assumed that MAE

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could be adapted for simultaneous recovery of polyphenols and terpenoids from peppermint leaves, while application of green solvents, sustainable natural resources and

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optimization of MAE could adapt it according to the main principles of green extraction

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(Chemat et al., 2012).

Therefore, the main goal of this research was one-step MAE of peppermint bioactives

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(polyphenols and terpenoids) and production of antioxidant-rich liquid extracts. Another goal was to evaluate effects of investigated MAE parameters (ethanol concentration, extraction time and liquid-solid ratio) on total extraction yield, polyphenols (total phenols and total flavonoids) yield and antioxidant activity determined by DPPH, FRAP and ABTS assays. Finally, multi-response process optimization by artificial neural network (ANN) was

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performed for prediction of MAE conditions which could provide a high yield of bioactive compounds and improved antioxidant potential. 2. Materials and methods 2.1. Plant sample Peppermint (Mentha piperita L.) was produced by the Institute of Field and Vegetable Crops, Novi Sad, Serbia (year 2015). Collected plant material (Menthae piperitae folium) was air-

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dried distributed uniformly as in thin layer onto the stainless steel trays dried protected from direct sunlight at temperatures between 20 and 30 °C for 24 h in July in Vojvodina region

(Serbia). Dried material was packed in paper bags and stored at room temperature. After

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that, peppermint leaves were milled in blender and mean particle size was determined by

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sieve set (CISA Cedaceria Industrial, Spain). Moisture content was determined by drying the samples at 105 °C until constant weight according to official procedure from European

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Pharmacopoeia (Council of Europe, 2007). Experiments were performed in triplicates and

2.2. Chemicals

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results were expressed as mean value ± standard deviation.

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Folin-Ciocalteu reagent, (±)-catechin, 1,1-diphenyl-2-picryl-hydrazyl-hydrate (DPPH) TPZT

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(2,4,6-tris (2-pyridil)-s triazine), iron (III)-chloride and Iron (II)-sulfatheptahydrate and potassium persulfate were purchased from Sigma (Sigma-Aldrich GmbH, Steinheim, Germany), while gallic acid and a standard mixture of n-alkanes (C7-C25) was obtained from Sigma-Aldrich (St. Louis, MO, USA). Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2carboxylic acid) was purchased from Sigma–Aldrich (Milano, Italy). ABTS (2,2’-azino-bis-(-3ethylbenzothiazoline-6-sulfonic acid) diammonium salt) was purchased from J&K Scientific

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GmbH (Pforzheim, Germany). 1-Bromo-2-fluorobenzene was purchased from Absolute Standards, Inc. Hamden, CT USA. The ultra-pure water was obtained by a Milli-Q Plus system (EMD Millipore, Billerica, MA, USA). All other chemicals used were of analytical reagent grade. 2.3. Microwave-assisted extraction (MAE) protocol Microwave-assisted extraction (MAE) was performed in mono-mode at fixed frequency.

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Home-made MAE setup consisted of microwave oven (MM817ASM, Bosch, Germany) and appropriate glass apparatus with round flask (500 mL) and condenser. MAE experiments

were performed within face-centered central composite design with three numeric factors

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at three levels which consisted of nineteen randomized runs with five replicates at the

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central point. Investigated independent MAE factors were ethanol concentration (40, 60 and 80%), extraction time (3, 10 and 17 min) and liquid to solid ratio (10, 20 and 30 mL/g), while

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irradiation power (600 W) was held constant. Flasks were always positioned at the same position of microwave extractor and no additional agitation was applied. After the

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extraction, crude extracts were immediately filtered through filter paper (4-12 μm pore size, Schleicher & Schuell, Germany) under vacuum (V-700, Büchi, Switzerland). Extracts were

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collected into glass flasks and stored at 4 °C until further analysis.

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2.4. Total extraction yield (Y)

Total extraction yield (Y) was determined by vacuum evaporation and further drying of certain volume (10 mL) of crude liquid extract. Results were expressed as percentage of total extractable solids per 100 g of dry peppermint leaves (%, w/w). 2.5. Polyphenols content

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Total phenols content (TP) Total phenolics content (TP) in obtained liquid extracts was determined using Folin-Ciocalteu procedure (Kähkönen et al., 1999; Singleton and Rossi, 1965). Gallic acid was used as the standard for preparation of standard curve (0-7 µg/mL, R2=0.999) and absorbance of the samples was measured at 750 nm (6300 Spectrophotometer, Jenway, UK). Content of phenolic compounds was expressed as mg of gallic acid equivalents (GAE) per 100 g dry

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weight (DW) of peppermint leaves. All experiments were performed in triplicate, and results were expressed as mean values. Total flavonoids content (TF)

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Total flavonoids content was determined using aluminum chloride colorimetric assay

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(Harborne, 1984). Catechin was used for preparation of standard curve (0-30 µg/mL, R2=0.999) and absorbance was measured at 510 nm. Results were expressed as mg of

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catechin equivalents (CE) per 100 g DW. All experiments were performed in triplicate, and

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results were expressed as mean values. 2.6. Antioxidant activity

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DPPH assay

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Scavenging capacity of peppermint leaves extracts towards 2,2-diphenyl-1-picrylhydrazyl free radicals (DPPH∙) was measured using a slightly modified method originally presented by Brand-Williams et al. (1995). For that purpose, methanolic solution of the DPPH reagent (65 µM) was freshly prepared and adjusted with methanol to reach absorbance of 0.70 (±0.02). DPPH reagent and properly diluted liquid extracts were mixed (2.9 mL + 0.1 mL) in the 10 mm plastic cuvettes and incubated at room temperature for 60 min. Absorbance was further 8

measured at 517 nm in triplicates with UV-VIS spectrophotometer (6300 Spectrophotometer, Jenway, UK). Calibration curve was obtained by measuring free radical scavenging of freshly prepared Trolox aqueous solutions (0-0.8 mM, R2=0.984). The obtained results were reported as mM of Trolox equivalents per g of dry M. piperita leaves (mM TE/g). FRAP assay Reduction capacity of extracts towards Fe3+ was measured using slightly modified method

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firstly presented by Benzie and Strain (1996). The FRAP reagent was freshly prepared from 300 mM acetate buffer (pH=3.6), 10 mM 2,4,6-tris(2-pyridil)-s triazine (TPZT), 40 mM HCl

solution and 20 mM FeCl3 aqueous solution. Solutions were mixed in the ratio 10:1:1 (v/v/v).

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Properly diluted extracts and FRAP reagent were mixed (0.1 mL + 1.9 mL) and incubated in

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the dark at 37 °C for 10 min. Absorbance measurements were performed at 593 nm (in triplicates) with UV–VIS spectrophotometer (6300 Spectrophotometer, Jenway, UK).

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Calibration was performed using freshly prepared Fe2+ (Fe2SO4) aqueous solutions (0-0.23 mM, R2=0.999). Results were expressed as mM of Fe2+ equivalents per g of dry plant material

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ABTS assay

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(mM Fe2+/g).

The ability of extracts towards scavenging of ABTS free radicals was measured using a

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modified method previously reported by Re et al. (1999). ABTS stock solution was freshly prepared from a mixture (1:1, v/v) of 2.45 mM potassium persulfate aqueous solution and 7 mM ABTS (2,2’-azino-bis-(-3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt) aqueous solution and left in the dark at room temperature for 16 h. A stock solution was diluted using 300 mM acetate buffer (pH=3.6) to an absorbance of 0.70 (±0.02). Properly diluted extracts and ABTS reagent were mixed (0.1 mL + 2.9 mL) and stored in the dark at 9

room temperature for 5 h. Absorbance was further measured at 734 nm in triplicates with UV–VIS spectrophotometer VIS spectrophotometer (6300 Spectrophotometer, Jenway, UK). Freshly prepared Trolox aqueous solutions (0-0.8 mM, R2=0.987) were used to obtain the calibration curve. Results were expressed as mM of Trolox equivalents per g of dry plant material (mM TE/g). 2.7. Artificial neural network (ANN) modeling

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Artificial neural network (ANN) is very useful closed-form model approach for obtaining the mathematical dependence where theoretical models are not well defined and the influence of the independent variables cannot be clearly determined. ANN model generation and

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fitting were performed in MATLAB software using Bayesian regularization based on

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beckpropagation algorithm with variable initial values of weight, biases and number of hidden neurons in one hidden layer. Values of biases and weights from fitted ANN were used

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for determination of three independent variables (ethanol concentration, extraction time, L/S ratio) relative influence, separately on six outputs (Y, TP, TF, DPPH, FRAP, ABTS).

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Networks with the best fitting results were used for extraction optimization. General structure of used ANNs is shown in Figure 1.

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The accordance between experimentally obtained values and ANN model was established by

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the sum of squared errors (SSer) and coefficient of determination (R2). Better accordance between experimental data and model was achieved when SSer was minimal, while R2 was higher.

2.8. Identification of phenolic compounds

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Free, ester-linked and glycoside-linked phenolic compounds for each sample were tentatively analyzed using Q-Exactive LC-MS/MS – Orbitrap (Thermo Scientific, Hemel Hempstead, UK). Chromatographic separation of compounds was achieved on a Poroshell 120 EC-C18 column (3.0 x 100 mm, 2.7 µm, Agilent) with a 0.6 mL/min gradient flow (mobile phase A: 0.1% formic acid-water, mobile phase B: methanol; 0-5 min, mobile phase B concentration changed as 0-9% B; 5-9 min, 9-2% B; 16-35 min, 2-18% B 35-50 min, 18-20% B; 50-65 min, 20-30% B and 65-80 min, 30% B). The injection volume was 10 µL. Q Exactive

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hybrid quadrupole-Orbitrap mass spectrometer equipped with an ESI source working in both negative and positive ionization mode was used for accurate mass measurements. The

operation parameters set were: ion spray voltage, 2.8 kV; capillary temperature, 300 oC;

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capillary voltage, 35 V and tube lens voltage, 95 V; sheath gas, 19 (arbitrary units); auxiliary

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gas, 7 (arbitrary units). Mass spectra were recorded covering the m/z range of 55-1000 da. Default values were used for most other acquisition parameters (Automatic gain control

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(AGC) target 3x106 ions). The data processing was achieved using XCalibur 2.2 software (Thermo Fisher Scientific, Waltham, MA, USA). An external calibration for mass accuracy was

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performed before the analysis.

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2.9. Identification of volatile compounds

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GC-MS system: Volatile compound analyses were conducted using an Agilent gas chromatograph-mass spectrometer equipped with a headspace sampler. The system consists of an Agilent 7697A Headspace Sampler, a 6890N Gas Chromatograph (GC) and a 5975C Mass Selective Detector. Chromatographic separation was performed on a DB-5MS (60 m × 0.25 mm ID, 0.25 µm) capillary column. After the vials had been pressurized with carrier gas (helium and 15 psi) for 15 min, some volatiles were filled into the 1 mL loop for

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0.5 min and injected into a capillary column in split mode (16.2:1) for 0.5 min with a 110 oC oven temperature. The transfer line was set at 150 oC. Helium with a constant flow rate at 1 mL/min was used as the carrier gas. Initially, the oven temperature was held at 50 oC for 2 min and then programmed to 300 oC at a rate of 10 oC/min and finally kept at 300 oC for 5 min. The mass spectrometer was operated in the electron ionization mode (70 eV), in the mass scan range of m/z 45-550 da. The ion source temperature was set at 300 oC.

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Sample introduction: A 2 mL aliquot of sample extracts spiked with internal standard (1bromo-2-fluorobenzene) at a final concentration of 1.25 mg/L were added into a 20 mL headspace vials. The vials were sealed with PTFE lined silicone septa immediately after

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addition of the spiked extracts.

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Identification and quantification of compounds: Volatile compounds released from the sample extract were tentatively identified by comparing their spectra to those of the

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reference mass spectra libraries (NIST98 and Wiley7N), and also by comparing of their GC Kovats indices that were determined on the basis of the retention times of n-alkanes C7-C25

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(Sigma-Aldrich, St. Louis). Non-isothermal linear retention indices (Kovats type) were calculated after the analysis of n-alkane under the same conditions. Semi-quantitative

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GC/MS analysis of volatiles in M. piperita extracts was achieved using 1-bromo-2-

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fluorobenzene as an internal standard and results were expressed as mg per 100 g of dry plant material (mg/100 g). 3. Results and discussion 3.1. Yield of target compounds

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Total extraction yield (Y), total phenols (TP) and total flavonoids (TF) yield were observed responses in peppermint extracts obtained at a different set of microwave-assisted extraction (MAE) conditions and results are given in Table 1. Observed range of Y was from 25.14 to 43.11% and the highest Y was observed at following conditions: 40% ethanol, 17 min and 30 mL/g of L/S ratio. Results indicated that MAE procedure provided pronouncedly high Y, while Tušek et al. (2018) reported that higher Y could be obtained from peppermint comparing to other Lamiaceae species. Polyphenols were studied as the most dominant

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group of peppermint bioactives responsible for antioxidant activity (Riachi and De Maria, 2015) and achieved TP in M. piperita extracts were 6.3703 - 11.3087 g GAE/100 g, which

suggested that TP depends on the applied set of MAE conditions. Results were in accordance

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with Tušek et al., (2018) since the reported TP was approx. 2 – 12.5 g GAE/100 g and it was

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significantly influenced by applied extraction procedure. Similarly, Moldovan et al., (2014) reported that TP in peppermint leaves extract was 9.67 ± 0.62 g GAE/100 g. Petkova et al.

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(2017) investigated MAE of peppermint and obtained 37.7 ± 0.5 mg GAE/g. Same authors also reported that MAE provided slight advantages in TP yield comparing to conventional

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extraction procedure. Flavonoids have been highlighted as particularly important subgroup of polyphenols. TF obtained in peppermint extracts ranged from 7.0235 to 11.6222 g CE/100

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g. Since flavonoids are subgroup of polyphenols, it is unexpected that their yield was in

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certain cases higher than TP (Table 1). It should be highlighted that applied colorimetric procedure for TF determination is rather non-selective and interferences could act in reactions similarly as flavonoids.

3.2. Antioxidant activity of peppermint extracts

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The imbalance with the generation of free radicals could induce oxidative stress in different medium. Elevated concentration of free radicals is responsible for degradation of biomolecules which could lead to different pathophysiological conditions in human body, or accelerated deterioration of different products and reduction of shelf life. Plant extracts are being thoroughly investigated as natural agents with antioxidant activity in order to use them as substitutes for potentially toxic conventional synthetic antioxidants such as propyl galate, butylated hydroxytoluene and butylated hydroxyanisole. Therefore, plant

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polyphenols with high antioxidant capacity could scavenge free radicals by different

mechanisms such as electron or hydrogen transfer reactions, chelating and reduction of pro-

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oxidant metals and inhibition of oxidizing enzymes (Sharma, 2017).

Antioxidant activity of peppermint extracts obtained by different extraction techniques using

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mostly polar solvents (water, aqueous ethanol and aqueous methanol) has been attributed

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mostly to flavonoids and phenolic acids and it has been evaluated by DPPH, FRAP, ABTS, ferric-reducing power (FRP), ferrous ion chelating (FIC), oxygen radical absorbance capacity

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(ORAC) and hydroxyl radical scavenging capacity (HRSC) assays which have been reviewed by Riachi and De Maria (2015). Radical scavenging capacity towards DPPH and ABTS+ radicals

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and reduction capacity of extracts towards Fe3+ were chosen as model systems to evaluate

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antioxidant potential of peppermint extracts. The range of antioxidant activity of M. piperita extracts obtained by MAE was from 0.1559 to 0.5037 mM TE/g. Gorjanović et al. (2012) reported that antioxidant activity of peppermint herbal infusion towards DPPH radicals was 0.0042 mM TE/g, therefore, it is suggested that solvent type (aqueous ethanol) and applied MAE procedure would improve antioxidant properties. Similarly, Oh et al. (2013) obtained 30.56 ± 1.67 mg ascorbic acid equivalents

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(AAE) per g of sample in peppermint leaves ethanolic extract, which is considerably lower comparing to results obtained in this work. It has been suggested that polyphenolic compounds are often responsible for antioxidant properties of peppermint extracts (Riachi and De Maria, 2015), therefore, linear regression was applied in order to determine correlation of polyphenols content (TP and TF) with antioxidant activity parameters (DPPH, FRAP and ABTS). It could be observed that poor linear regression was observed with DPPH antioxidant activity and TP and TF, since calculated R2 were 0.201 and 0.475 (Figure S1,

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supplementary data), respectively. It could be suggested that some coextracted compounds such as essential oil volatiles might be also responsible for antioxidant effects.

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Radical scavenging capacity of peppermint extracts towards ABTS+ radicals was in range from 0.6500 to 1.3687 mM TE/g. Extract with the most potent antioxidant activity was obtained

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using 40% ethanol, 17 min of extraction time and 30 mL/g of L/S ratio. Considerably high

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values of TP and TF were observed in the same sample (Table 1), which indicated correlation between antioxidant activity and polyphenols content. This was confirmed by linear

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regression between antioxidant activity and polyphenols content due to moderately high R2 (0.7376 and 0.6987, respectively). The antioxidant activity of aqueous and ethanolic

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peppermint extracts reported by Oh et al. (2013) were 50.08 ± 1.70 and 40.70 ± 0.42 mg AAE/g, which are lower than the results obtained in this work. Similarly, Tušek et al. (2018)

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reported that range of antioxidant activity of peppermint extracts towards ABTS + radicals was approx. 0.2 to 0.6 mM TE/g. Reduction of transition metals which could act as pro-oxidants could decrease oxidative stress. Therefore, reduction power of plant extracts should be also considered and evaluated as antioxidant activity indicator. Reducing capacity of peppermint extracts determined by

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FRAP assay was from 0.2070 to 0.3492 mM Fe2+/g. It could be observed that the highest value was approx. twice higher comparing to the lowest value of antioxidant activity parameter (DPPH, FRAP and ABTS), therefore, influence of MAE parameters on these responses should be evaluated. Results of reduction power of peppermint extracts obtained in this work could not be compared with literature data since FRAP of aqueous and ethanolic peppermint extracts was expressed by different equivalents (GAE and AAE) (Riachi and De

3.3. ANN simulation and influence of MAE parameters

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Maria, 2015).

According to the literature, ANN simulation has been previously applied for optimization of

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MAE process aimed for recovery of volatiles (Rorke et al., 2017), essential oil (Thakker et al.,

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2016), pigments (Sinha et al., 2013), natural sweeteners (Ameer et al., 2017) and polyphenols (Simic et al., 2016). Target compounds yield (Y, TP and TF) and antioxidant

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activity parameters (DPPH, FRAP and ABTS) were used as response variables for ANN simulation in this work. Results obtained by ANN (weights and biases) depend on the initial

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values of parameters necessary for its development and fitting, as well as on number of neurons in the hidden layer. The number of neurons in hidden layer was varied from 1 to 20

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and the training process for each response was repeated 10 times with random initial values

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of weights and biases in order to minimalize aforementioned effects on ANN results. This provided construction of 200 ANNs in total for each. Influences of hidden neurons number on R2 mean value obtained from 10 repeated trainings has been given in Figure S2. It could be observed that R2 increased with higher number neurons in hidden layer. However, it should be highlighted that the best ANNs with ≤10 hidden neurons were further used in calculations in order to avoid or prevent “overfitting” with high number of hidden neurons.

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It could be observed that excellent fit was observed in case of Y, TF, FRAP and ABTS since calculated R2 was 0.9938, 0.9925, 0.9615 and 0.9999, respectively. ANN developed for TP also provided satisfactory fitting with experimental data since R2 was 0.8826, while poor fit was achieved in case of DPPH (R2=0.6646). Calculated SSer suggested similar results since its values for Y, TP, TF, DPPH, FRAP and ABTS were 3.653, 4.242, 0.232, 0.0619, 1.471 10-3, 5.501 10-5, while the numbers of neurons in the hidden layer which provided the best were

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3, 7, 8, 2, 8 and 10, respectively.

Since MAE of peppermint was performed at different set of ethanol concentration,

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extraction time and L/S ratio, it was essential to evaluate variable influence on each

response using the connection weights partitioning methodology (Yoon et al., 1993) and

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results were presented in Figure 3. It could be observed that influence of ethanol

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concentration was the most prominent within applied experimental domain since its variable influence was in the range from 46.42% (ABTS) to 93.01% (Y). This is rather expected

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since ethanol concentration directly affects polarity of the solvent, while absorption of the microwaves depends on the dielectric constant of the solvent and increase with water

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content in aqueous ethanol. Furthermore, negative influence of ethanol concentration was achieved in all cases (Figure 3), suggesting that optimal ethanol concentration for each

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response would be below 60%. Similar results were obtained by Nabet et al. (2019) in case of MAE of Thymus fontanesii where 50% ethanol provided the highest Y, TP, DPPH and ABTS. Aqueous ethanol has often been recommended as the most suitable solvent for extraction of polyphenols, and it has been considered safe for human consumption. This effect was also observed in MAE of sage herbal dust since 46.2% ethanol provided the highest TP and TF

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(Zeković et al., 2017). Extraction time commonly exhibits positive influence of polyphenols extraction. However, it has to be studied as important process parameters particularly in techniques using elevated temperatures such as MAE in order to prevent degradation. Extraction time and L/S ratio provided positive influence on TP and TF, while the pattern of variable influences was almost the same (Figure 3b and 3c). Similar results were obtained in previous study investigating MAE of sage herbal dust within similar experimental domain (Zeković et al., 2017). Increase of L/S ratio directly increases concentration gradient and

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improves extraction rate, however, it should be rationally optimized in order prevent

dilution of liquid extracts increase cost of their processing into powder form (Pavlić et al.,

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2017).

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3.4. GA-ANN separate and multi-response optimization

The primary objective of present paper was optimization of polyphenols recovery from

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peppermint using ANN approach. Since ANN modeling provided adequate fit of experimental data for TP, FRAP and ABTS, these responses were included in separate and

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multi-response optimization. Genetic algorithm (GA) method was used for determination of optimal conditions using obtained ANN models. Y was excluded from optimization since

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ethanol concentration exhibited substantially higher influence (93.01%) compared to other

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MAE factors (Figure 3). TF was excluded from optimization since measured values were higher than TP due to rather poor selectivity of applied colorimetric assay (Table 1), while DPPH was excluded due to poor fit ANN modeling and experimental data (Figure 2d, supplementary data). The calculated MAE conditions aimed for maximization of separate (TP, FRAP and ABTS) and simultaneous optimization were given in Table 2. Rather narrow range of ethanol concentration could be observed for all three responses (47.29-56.94%).

18

The highest extraction time and L/S ratio were optimal for TP and ABTS, while these values were significantly lower in case of FRAP. Calculated solutions for multi-response optimization (TP, FRAP and ABTS) were given in Table S3 (supplementary data). ANN optimization provided 18 possible solutions aimed to maximize aforementioned responses. Further processing of peppermint extracts obtained by MAE should be also considered in order to fulfill practical aspects of optimization. Positive

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influence of L/S ratio was achieved and explained. However, application of high L/S could excessively dilute extracts which is not cost-effective for their further processing in dry

powders. Therefore, optimized solutions with L/S<20 mL/g were further considered. Narrow

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range of ethanol concentration was observed for multi-response optimization (49.75-

51.07%), while range of L/S was broader (13.04-18.88 mL/g). On the other hand, optimized

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extraction time could be in whole range of experimental domain (3-17 min) depending on

ABTS).

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3.5. Polyphenols profile

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the combination of other MAE factors and priority in targeting certain response (TP, FRAP or

HPLC-MS/MS technique was used for chemical screening of polyphenols in peppermint

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extracts obtained by MAE. Analyses were performed in the samples obtained at central

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point (Run 2) and sample where the particularly high polyphenols content and antioxidant activity parameters were observed (Run 4). Tentative identification was achieved by molecular (MS) and tandem mass spectra (MS/MS) measurements and comparison with database and retention times and results were given in Table 3. A total of 41 and 42 compounds were identified in samples 2 and 4. Results suggested that the main compounds in peppermint extracts were flavonoids, phenolic acids and stilbenes. This was in accordance 19

with literature data since Riachi and De Maria (2015) reported that 53% of TP are flavonoids, followed by phenolic acids (42%) and lignans and stilbenes (2.5%), while the most abundant compounds were eriocitrin, rosmarinic acid, eriodictyol-glycopyranosyl-rhamnopyranoside and luteolin- 7-O-rutinoside. The main identified flavonoid subgroups were flavan-3-ols (epicatechin), flavanones (naringenin and eriodictyol), flavonols (kaempferol, quercetin, rutin and isorhamnetin) and flavones (luteolin) (Table 3). Flavonols were either free or in form of their respective 3-O-gycosides with glucose, galactose and rutinose as the

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carbohydrate compounds, while flavanones such as naringenin were free or in form of their 7-O-glucoside. On the other hand, epicatechin and luteolin were identified in free form only. Phenolic acids were also considered as the major class of peppermint polyphenols and

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vanillic acid, protocatechuic acid, caffeoyltartaric acid, fertaric acid, caffeic acid, p-coumaric

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acid and coutaric acid were identified in M. piperita extracts. Similarly, Moldovan et al. (2014) reported that the major polyphenols in peppermint extracts were phenolic acids (p-

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coumaric and ferulic) and flavonoids (isoquercitrin, rutin, luteolin and apigenin). According to Lv et al. (2012), caffeic acid was the most abundant phenolic acid in conventional and

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organic peppermint, however, there was not in-depth data about quantitative content of flavonoid compounds. Stilbenes identified in present samples were trans-piceatannol and

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trans-resveratrol (Table 3).

3.6. Profile of volatile compounds Microwave-assisted hydrodistillation (MWHD) has been extensively used for recovery of peppermint essential oil (Gavahian et al., 2015; Orio et al., 2012). Dai et al. (2010) evaluated effects of MAE parameters on recovery of major peppermint terpenoids (menthone, 20

menthofuran and menthol) using ethanol, hexane and their mixtures as extraction solvents. However, chemical profile of coextracted peppermint volatiles in liquid extracts obtained by MAE has not been investigated. Semi-quantitative GC-MS technique was used for evaluation of volatile compounds profile in peppermint extracts obtained by MAE (Run 2 and Run 4). It could be observed that menthomenthene (10.908 mg/100 g), menthone (7.752 mg/100 g), 1,8-cineole (6.351 mg/100 g), d,l-limonene (5.400 mg/100 g) and p-cimene (9.171) were the most abundant volatile compounds in an evaluated sample obtained at central point (Run 2).

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According to Orio et al. (2012), menthol and menthone were the most abundant compounds in peppermint essential oil, while 1,8-cineole and limonene were also present in certain amount. On the other hand, Gavahian et al. (2015) reported that neoiso-menthol, iso-

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menthone and menthofuran were the most abundant compounds in peppermint essential

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oil obtained by MWHD. Carvone (67.9%) was the most dominant compound in peppermint essential oil from Saudi Arabia and its content was followed by limonene (10.4%) and 1,8-

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cineole (5.33%) (Abdel-Hameed et al., 2018). This suggested that applied procedure, set of conditions, as well as origin and properties of raw material could significantly affect chemical

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profile of extracted volatiles. Results suggested that MAE provided coextraction of low molecular volatile compounds, mostly monoterpene hydrocarbons and their respective

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oxygenated derivatives (Table 3). Contrary to that, Orio et al. (2012) suggested that

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sesquiterpenes were also recovered in peppermint essential oil obtained by MWHD, which is attributed to the mechanism of MWHD aimed for the recovery of volatile compounds. It could be observed that total identified volatile compounds content (TVC) was higher in Run 2 (64.833 mg/100 g) comparing to Run 4 (55.220 mg/100 g), which could be explained by application of less polar solvent (60% ethanol). Dai et al. (2010) reported that MAE provided higher yield of peppermint volatiles compared to other solid-liquid extraction techniques, 21

however, content of potentially extracted polyphenols was not evaluated in their research. It has been reported that aqueous ethanol could cause oxidation and hydrolysis of various bioactive compounds during MAE (Zeković et al., 2017; Zeković et al., 2016). Similar phenomena were observed in this research since various oxygenated menthene derivatives (menthone, isomenthone, menthene, menthomenthene, L-(-)-menthol and 5-methyl-2-(1-

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methylethyl)-2-cyclohexen-1-one) were identified in peppermint extracts.

4. Conclusions

Microwave-assisted extraction (MAE) was applied as novel green method for simultaneous

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recovery of peppermint bioactives (polyphenols and terpenoids). Designed experiments

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were performed with ethanol concentration, extraction time and L/S ratio as independent variables, while yield of target compounds (Y, TP and TF) and antioxidant activity parameters

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(DPPH, FRAP and ABTS) were responses. Artificial neural network (ANN) approach was successfully used for fitting the most of experimental data according to satisfactory

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statistical parameters (R2 and SSer), while inappropriate fit was observed in case of DPPH. Ethanol concentration was the most influential MAE parameter for all investigated

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responses. The primary objective of present paper was optimization of polyphenols recovery

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from peppermint using GA approach. TP, FRAP and ABTS were selected responses included in separate and multi-response optimization. Eighteen solutions were generated by simultaneous optimization. Narrow range of ethanol concentration was observed for multiresponse optimization (49.75-51.07%), while lower L/S ratio (13.04-18.88 mL/g) was preferred in order to prevent excessive dilution of liquid extracts and decrease cost of their further processing. The range of optimized extraction time could be in whole experimental 22

domain (3-17 min) depending on the combination of other MAE factors and priority in targeting certain response (TP, FRAP or ABTS) in multi-response optimization. Tentative identification of polyphenolics fraction was achieved by LC-MS/MS and results suggested that the main compounds in peppermint extracts were flavonoids, phenolic acids and stilbenes. Semi-quantitative GC-MS results suggested that coextraction of terpenoid compounds occurred by MAE since the main detected volatiles were menthomenthene, menthone, 1,8-cineole, d,l-limonene and p-cimene. It could be concluded that MAE could be

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suitable technique for simultaneous recovery of two major groups of bioactive compounds

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and production of liquid extracts with particularly high antioxidant potential.

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Declaration of interests

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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Acknowledgments

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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The research is part of the project entitled “Development of system for precise control of microwave-assisted extraction parameters in order to increase yield and prevent degradation of target compounds” (Project No. 114-451-2800/2016-02) and is financially supported by the Provincial secretariat for science and technological development, Autonomous Province of Vojvodina, Serbia.

23

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Foods, 8(7), 248.

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Figure captions

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Figure 1. General structure of used ANN

31

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Figure 2. Regression plot for the ANNs with best performances for a) Y, b) TP, c) TF, d) DPPH,

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e) FRAP and f) ABTS

32

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Figure 3. Relative influence of MAE process parameters on investigated responses: a) Y, b)

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TP, c) TF, d) DPPH, e) FRAP and f) ABTS

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Table 1. Three-level, three-variable experimental design applied for MAE and experimentally observed values of investigated responses: total extraction yield (Y), total phenols (TP) yield, total flavonoids (TF) yield and antioxidant potential determined by DPPH, FRAP and ABTS assays Ethanol concentrat ion [%] cod natu ed ral

Extraction time [min]

L/S ratio [mL/g]

Responses

cod ed

natu ral

cod ed

natu ral

Y [%]

1

0

60

0

10

0

20

2

0

60

0

10

0

20

3

0

60

0

10

0

20

4

-1

40

0

10

0

20

5

1

80

0

10

0

20

6

-1

40

-1

3

-1

10

7

-1

40

-1

3

1

30

8

0

60

-1

9

1

80

1

10

1

35. 44 37. 14 37. 04 42. 70 27. 30 38. 04 41. 16 38. 34 26. 57 28. 65 37. 76 39. 78 43. 11 38. 09 38.

DPPH [mM TE/g]

FRAP [mM Fe2+/g]

ABTS [mM TE/g]

0.2741

0.2831

1.1644

9.9461

9.7568

0.3201

0.3136

1.2170

20

17

-1

10

0

60

1

17

0

20

12

-1

40

1

17

-1

10

13

-1

40

1

17

1

30

14

0

60

0

10

-1

10

15

0

60

0

10

1

30

17

30

Jo 11

1

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TF [g CE/100 g] 9.3173

9.1376

9.9383

0.3192

0.3295

1.2134

10.1707

10.0434 0.3090

0.3492

1.1980

re

0

1

80

TP [g GAE/100 g] 9.3772

7.7123

0.1559

0.2491

0.9543

9.2901

9.7344

0.4985

0.3226

1.0313

9.6750

11.6222 0.4851

0.3028

1.2193

9.1675

9.4129

0.3413

0.3416

1.1771

6.3703

7.2504

0.2825

0.2149

0.7424

7.5792

8.7690

0.3090

0.2334

0.9815

11.3087

10.7791 0.3490

0.3265

1.2722

10.0987

10.4092 0.5037

0.3269

1.0050

10.1009

11.2432 0.4404

0.2815

1.3687

9.2677

10.4808 0.4939

0.3270

1.0032

9.9888

10.9357 0.3760

0.3135

1.1812

8.6435

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na 3

ur

Ru n

34

1

80

-1

3

-1

10

17

1

80

-1

3

1

30

18

0

60

0

10

0

20

19

0

60

0

10

0

20

6.4265

7.0235

0.2961

0.2175

0.6500

6.6938

7.8751

0.2094

0.2070

0.9014

9.6766

9.9097

0.3005

0.3214

1.1961

9.5718

10.1198 0.3218

0.3090

1.2233

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na

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16

61 27. 01 25. 14 39. 00 38. 48

35

Table 2. Separate and multi-response optimization of peppermint MAE using ANNs Separate optimization Target

Ethanol

Extraction

L/S ratio

Optimal-maximal value

concentration

time

[mL/g]

[%]

[min]

TP

56.94

17.00

30.00

10.989 g GAE/100 g

FRAP

50.46

3.000

18.60

0.3493 mM Fe2+/g

ABTS

47.29

17.00

30.00

1.386 mM TE/g

TP

FRAP

ABTS

[mM Fe2+/g]

[mM TE/g]

0.349

1.228

0.341

1.137

Extraction

L/S ratio [mL/g]

concentration

time

[%]

[min]

50.54

3.04

18.71

9.50

50.11

16.98

13.04

10.74

50.53

8.63

17.42

9.95

0.344

1.188

51.07

3.04

18.88

9.49

0.349

1.229

50.11

11.52

16.26

10.22

0.342

1.176

50.15

13.75

17.49

10.44

0.341

1.226

50.35

6.89

17.55

9.80

0.345

1.191

50.35

16.35

17.50

10.71

0.341

1.259

51.03

5.26

18.42

9.66

0.347

1.209

50.23

16.98

13.04

10.74

0.341

1.137

49.75

16.21

18.61

10.67

0.339

1.281

Jo

ur

na

lP

-p

[g GAE/100 g]

ro of

Ethanol

re

Selected multi-response optimization solutions

36

Table 3. Tentative identification of phenolic compounds in peppermint samples using HPLC–

Compound

Formula

Accurate mass

Measured mass [m/z] / Error [mDa] Sample 2

Sample 4

10.21

Vanillic acid

C8H8O4

167.03498

167.03485 / -0.77

167.03491 / -0.40

10.79

Giffonin Q

C19H18O3

293.11832

ND

293.11716 / -3.96

10.94

Protocatechuic acid

C7H6O4

153.01933

153.01923 / -0.67

153.01929 / -0.28

12.01

Epicatechin (ESI -)

C15H14O6

289.07176

289.07132 / -1.52

289.07166 / -0.36

13.87

Caffeoyltartaric acid

C13H12O9

311.04086

311.04071 / -0.48

311.04092 / 0.20

15.58

Giffonin I

C30H36O12

587.2134

587.21252 / -1.49

587.21283 / -0.97

17.99

Glucocapparin (1-)

C8H14NO9S2

332.01155

332.01007 / -4.45

332.01080 / -2.24

20.84

Fertaric acid

C14H14O9

325.05651

325.05615 / -1.10

325.05634 / -0.53

20.93

Dihydroxycoumarin

C9H6O4

177.01933

177.01912 / -1.19

177.01924 / -0.50

21.64

Caffeic acid

C9H8O4

179.03498

179.03481 / -0.97

179.0349 / -0.46

25.39

Naringenin

C15H12O5

271.0612

271.06097 / 0.83

271.06104 / -0.60

26.16

Kaempferol-3galactoside

C21H20O11

447.09328

447.09314 / -0.31

447.09308 / -0.45

26.16

C21H20O11

447.09328

27.71

Kaempferol-3glucoside p-Coumaric acid

C9H8O3

163.04007

163.04004 / -0.18

163.03998 / -0.56

27.94

Coutaric acid

C13H12O8

295.04594

295.04572 / -0.76

295.04565 / -0.96

28.55

C27H30O16

609.14611

609.14606 / -0.08

609.14600 / -0.18

28.55

Quercetin 3-Orutinoside Rutin (ESI-)

C27H30O16

609.14611

609.14606 / -0.08

609.14600 / -0.18

29.59

Biochanin A

C16H12O5

283.0612

283.06119 / -0.04

283.06143 / 0.81

30.91

trans-Piceatannol

C14H12O4

243.06628

243.06612 / -0.67

243.06607 / -0.86

31.15

Kaempferol-3rutinoside Naringenin-7-Oglucoside Isorhamnetin 3-Ogalactoside Isorhamnetin-3-Orutinoside trans-Resveratrol

C27H30O15

593.15119

593.15131 / 0.19

C21H22O10

433.11402

593.15100 / 0.31 433.11374 / -0.64

C22H22O12

477.10385

477.10315 / -1.46

477.10376 / -0.18

C28H32O16

623.16176

623.16248 / 1.14

623.16174 / -0.02

C14H12O3

227.07137

227.07147 / 0.44

227.07124 / -0.55

32.61

Quercetin glucuronide

C21H18O13

477.06746

477.06750 / 0.09

477.06778 / 0.66

32.62

Quercetin (ESI-)

C15H10O7

301.03538

301.03543 / 0.16

301.03522 / -0.54

32.71

Eriodictyol

C15H12O6

287.05611

287.05576 / -1.23

287.05603 / -0.27

32.76

Isorhamnetin

C16H12O7

315.05103

315.05099 / -0.11

315.05099 / -0.11

32.89

Kaempferol

C15H10O6

285.04046

285.03989 / -2.01

285.04037 / -0.29

31.46

32.36

-p

lP

na

Jo

31.61

ur

31.27

ro of

Rt* [min]

re

ESI-LTQ-Orbitrap

447.09314 / -0.31

447.09308 / -0.45

433.11356 / -1.07

37

32.89

Luteolin

C15H10O6

285.04046

285.03989 / -2.01

285.04037 / -0.29

32.91

Quercetin-3-Ogalactoside

C21H20O12

463.0882

463.08807 / -0.27

463.08911 / 1.96

32.91

Quercetin-3-Oglucoside

C21H20O12

463.0882

463.08807 / -0.27

463.08911 / 1.96

retention time [min]

Jo

ur

na

lP

re

-p

ro of

*

38

Table 4. Chemical profile of peppermint volatiles determined by GC-MS Content Compound

[mg/100 g] Run 2

Run 4

615-609c

RI, MS

1.556

0.420

7.63

2-Methylbutanal

660-664c

RI, MS

0.184

0.223

8.87

Propanoic acid ethyl ester

710-716c

RI, MS

0.280

0.063

9.85

Isoamyl alcohol

748-756

RI, MS

0.407

0.279

11.88

Butanoic acid ethyl ester

810-804c

RI, MS

0.385

0.099

15.09

Styrene

-

MS

0.448

1.234

15.21

2,5-Diethyltetrahydrofuran

-

MS

0.089

0.099

15.27

Nonane

900-900c

RI, MS

3.473

3.556

16.18

Tricyclene

923-926c

RI, MS

0.308

nd**

16.51

α-Pinene

935-939c

RI, MS

0.135

nd

16.72

α -Fenchene

949-952c

RI, MS

0.133

nd

17.01

(+)-Camphene

-

MS

0.165

nd

17.08

Camphene

958-954c

RI, MS

2.001

0.076

17.41

Benzaldehyde

969-960c

RI, MS

0.150

0.062

18.07

p-Menth-3-ene

985-987c

RI, MS

2.531

3.574

18.72

α-Phellandrene

1010-1002c

RI, MS

0.119

0.134

19.02

α-Terpinene

1018-1017c

RI, MS

2.035

2.930

19.24

p-Cymene

1025-1024d

RI, MS

9.171

9.620

19.38

d,l-Limonene

1035-1029d

RI, MS

5.400

2.150

19.53

1,8-Cineole

1035-1031c

RI, MS

6.351

6.604

19.71

cis-Ocimene

1046-1037c

RI, MS

0.054

nd

20.16

γ-Terpinene

1069-1059c

RI, MS

2.713

3.655

na

ur

ro of

Acetic acid ethyl ester

lP

6.37

-p

Identification

re

RIexp - RIlit

Jo

RT*

20.94

α-Terpinolene

1090-1088c

RI, MS

1.537

1.518

21.05

p-Cymenene

1095-1091d

RI, MS

1.514

1.440

21.21

2-Methyl butyl 2-methyl butyrate

1105-1100c

RI, MS

0.097

0.072

21.33

Amyl isovalerate

-

MS

0.287

0.214

22.86

Menthone

1148-1152c

RI, MS

7.752

nd

23.11

Isomenthone

1160-1162c

RI, MS

3.638

nd

23.26

5-Methyl-2-(1-methylethyl)-2-

-

MS

0.161

nd 39

cyclohexen-1-one 23,35

(+)-Menthol

1175-1171c

RI, MS

0.600

nd

23.36

L-(-)-Menthol

1175-1171c

RI, MS

nd

0.996

25.90

Menthomenthene

-

MS

10.908

15.876

26.35

(1α, 3α , 6α)-3,7,7-Trimethyl-

1295-1302e

RI, MS

0.139

0.294

1381-1388c

RI, MS

0.112

0.032

64.833

55.220

bicyclo[4.1.0]heptane 28.11

β-Bourbonene

∑TVC*** retention time [min],

**

not detected,

***

Total identified volatile compounds

ro of

*

RIexp, Kovat’s retention index calculated. RIlit, retention index reported in the literature. MS,

comparison with mass spectra library. c-eReferences c(Adams, 2007), d(Babushok et al., 2011), (Nikolaou et al., 2017).

Jo

ur

na

lP

re

-p

e

40