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Industrial Crops and Products 65 (2015) 7–13

Contents lists available at ScienceDirect

Industrial Crops and Products

jo ur nal home p age: www.elsev ier .com/ locate / indcrop

nhibitory effect of thyme and cinnamon essential oils on Aspergillusavus: Optimization and activity prediction model development

atarina Rajkovic a, Marina Pekmezovic b, Aleksandra Barac b, Jasmina Nikodinovic-Runic c

Valentina Arsic Arsenijevic b,∗

High Chemical and Technological School for Professional Studies, Kosanciceva 36, 37000 Krusevac, SerbiaNational Reference Medical Mycology Laboratory, Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, dr Subotica 1,1000 Belgrade, SerbiaInstitute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11010 Belgrade, Serbia

r t i c l e i n f o

rticle history:eceived 11 July 2014eceived in revised form5 November 2014ccepted 23 November 2014

eywords:rtificial neural network (ANN)spergillus flavus

a b s t r a c t

Antifungal effect of individual thyme (Thymus vulgaris L.) and cinnamon (Cinnamomum cassia L.) essentialoils (EOs) and mixture of thereof on Aspergillus flavus spores was investigated. In order to optimize theprocess variables (time of action, concentration of individual or mixture EOs and their mass ratio) for theantifungal effect of EO mixture, two models were developed: the response surface methodology (RSM)and artificial neural network (ANN) combined with genetic algorithm (GA). In RSM model, three factorswere involved in Box–Behnken design that was applied for the experiment. Based on the mean relativepercent deviation (MRPD), both models provided a good quality prediction for the antifungal effect interms of all three process variables. RSM and ANN–GA techniques predicted the 0.5% as an optimum

innamomum cassiaesponse surface methodology (RSM)hymus vulgaris

percentage concentration of EO mixture in EOs mass ratio T. vulgaris:C. cassia 1:1, ensuring the highestantifungal effect of 95.8% and 96.4% after 65 min. Both models were found useful for the optimization ofthe antifungal effect in vitro. ANN–GA was found more accurate in comparison to RSM due to its lowervalue of MRPD. Therefore, ANN–GA can be generally used for optimization and prediction of antimicrobialeffects of EOs and their mixtures.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

Fungus Aspergillus flavus has received much attention due to itsevere impact on agriculture and fermented products productionaused by aflatoxin secretion (Wu et al., 2014). In human medicine,. flavus is the second leading cause of invasive aspergillosis and onef the the most common causes of superficial infections (Hedayatit al., 2007). Current treatment is difficult due to limited selectivityf antifungals, fungal resistance to medications, and side effectshat usually occur in patients during long term treatment. The use ofatural products such as essential oils (EOs) and extracts, as well asombinations of their active substances with different mechanismsf action is one of optional approaches (Hemaiswarya et al., 2008).herefore, a search was made to find natural products that could

afely be used to inhibit the growth of fungi and their mycotoxinsroduction (Gorran et al., 2013; Zuzarte et al., 2013).

∗ Corresponding author. Tel.: +381 11 3643 242; fax: +381 11 2685 584.E-mail address: mikomedlab@yahoo.com (V. Arsic Arsenijevic).

ttp://dx.doi.org/10.1016/j.indcrop.2014.11.039926-6690/© 2014 Elsevier B.V. All rights reserved.

EOs have been widely used for bactericidal, virucidal, fungicidal,and other effects in medicinal and cosmetic applications in a rangeof industries including pharmaceutical, cosmetic, agricultural, andfood industries (Soliman and Badea, 2002; Bakkali et al., 2008). Nat-ural antifungals have been and are still commonly identified fromherbs and spices (da Cruz Cabral et al., 2013; Medimagh-Saidanaet al., 2014). In previous studies, EOs of Thymus and Cinnamomumspecies were tested against Aspergillus species (Rasooli et al., 2006;Tzortzakis, 2009), including A. flavus (Passone et al., 2012; Mansoet al., 2013). However, there is no data on antifungal activity of theirmixtures, despite the fact that the mixture of EOs and drugs mayreduce the minimum effective dose, as well as toxic side effects ofantifungals (Fadli et al., 2012). Thus, examination of the antifungaleffect of EO mixture needs a process optimization which have notyet been developed and standardized.

Response surface methodology (RSM) is commonly used to opti-mize a variety of biological and chemical processes (Jin and Ning,

2013; Kostic et al., 2014; Milic et al., 2013) and was applied tooptimize the extraction of polysaccharides from mycelium of A.fumigatus (Jin and Ning, 2013). The artificial neural network (ANN)is method which also provides optimization and prediction of bio-

8 rops and Products 65 (2015) 7–13

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Table 1Experimental range and levels of independent variables.

Variables Symbols Range and levels

−1 0 1

Time of action (min) t 5 65 140

K. Rajkovic et al. / Industrial C

ogical process based on set of the experimental data. The ANN cane used simultaneously with the genetic algorithm (GA) which mayvercome possible limitations of ANN method in order to improvehe ability of artificial intelligence for optimization of experimentalrocesses and prediction of biological activities (Desai et al., 2008;ostic et al., 2014; Marchitan et al., 2010; Milic et al., 2013; Rajendrat al., 2009). Therefore, the objectives of the study were: (1) to testntifungal effect of individual Thymus vulgaris and Cinnamomumassia essential oils and their mixture on A. flavus spores; (2) toptimize in vitro process variables (time of action, concentration of

ndividual or EO mixture and T. vulgaris:C. cassia mass ratio) for anti-ungal effect of EO mixture using RSM and ANN–GA; (3) to compareata obtained by RSM and ANN–GA for optimization of antifungalctivity in vitro.

. Materials and methods

.1. EOs and their mixtures

T. vulgaris L. and C. cassia L. EOs (CityCHEM, Belgrade, Serbia)ere mixed in the following mass ratios: 0:1 (T. vulgaris only), 1:1

EO mixture with equal mass of T. vulgaris and C. cassia), and 1:0 (C.assia only).

EO dilutions were made in a standard cell culture medium RPMI640 (Sigma–Aldrich, St. Louis, Missouri, USA; supplemented with% (w/v) glucose) resulting in final EOs concentrations (v/v) of.25%, 1%, and 2.0% for each three EO solutions.

.2. Preparation of A. flavus spores

Tested strain A. flavus (PL-2/10) was taken from the collectionf National Reference Medical Mycology Laboratory (Institute oficrobiology and Immunology, Faculty of Medicine, University of

elgrade, Belgrade, Serbia) and subcultured on Sabouraud dextrosegar (SDA) (Oxoid, Basingstoke, United Kingdom) at 28 ◦C. After2 h, spore suspension was prepared using a sterile cotton swabrom the surface of the formed mycelium and resuspended in 0.9%w/v) NaCl with 0.1% (v/v) Tween 20 by mixing thoroughly usingortex mixer. After settling of the larger particles, suspensions weredjusted to 106 colony forming units per 1 ml (CFU/ml), and theniluted in RPMI 1640 (2% (w/v) glucose) in a ratio of 1:10 (105/ml)Arsic-Arsenijevic et al., 2010).

.3. Antifungal effect assessment of EOs using microdilutionethod

Antifungal effect of EOs on A. flavus was tested using EUCASTroth microdilution method (Rodriquez-Tudela et al., 2008). Thenal volume of the microtiter plate wells was 200 �l (100 �l/wellf EO solution and 100 �l/well A. flavus spore suspension). Finaloncentrations of individual and mixture EOs were: 0.12; 0.5 and% (v/v), and final inoculum size 104/well of A. flavus spores. Appro-riate controls including no addition of EOs (100 �l/well RPMI 1640nd 100 �l/well A. flavus) and known antifungal agent (100 �l/welloriconazole solution of 1 �g/ml and 100 �l/well A. flavus sporeuspension) were also included.

Microtiter plates were incubated at 28 ◦C for 3 h under aerobiconditions. A. flavus spores viability was tested at the beginning ofhe experiment (0 min) and in the following time points (t): 5, 65,nd 140 min for every concentration of individual and mixture EOs.pore viability was determined by seeding 5 �l from each well on

he SDA plate (90 mm), incubated at 28 ◦C under aerobic conditions.fter 24 h of incubation, colonies were counted and the number ofolony-forming unit (CFU) determined. CFU data were convertednto values relative to the no treatment control CFU, expressed as

Percentage of concentration of EOs (%) C 0.12 0.5 1T. vulgaris:C. cassia mass ratio (g/g) m 1:0 1:1 0:1

percentage of live spores of A. flavus (yv). Antifungal effect is calcu-lated according to Eq. (1) and represents the percentage of reducedspores (yr):

yr = y0 − yv (1)

where y0 is initial percentage of live spores. Each experiment wasrepeated three times.

2.4. Models for optimization of antifungal effect of EO mixture onA. flavus

2.4.1. Optimization of antifungal effect of EO mixture on A. flavusspores using the RSM model

To optimize the antifungal effect of EO mixture three inde-pendent variables were used: the time of action, the percentageconcentration of individual or mixture EOs and the T. vulgaris:C. cas-sia mass ratio. RSM method utilizes the second-order polynomialpresented by the following equation:

yr = a0 + a1t + a2C + a3m + a12tC + a13tm + a23Cm + a11t2+a22C2 + a33m2

(2)

where yr is the percentage of reduced spores, t is the time of actionof individual or mixture EOs, C is the percentage concentration ofindividual or mixture EOs and m is the T. vulgaris:C. cassia massratio, ai i aij (i = 0, 1, 2 i j = 1, 2) are the parameters of Eq. (2) obtainedusing the multiple nonlinear regression method by computer pro-gram (Design Expert-Demo version). The coded and uncoded levelsof the independent variables are shown in Table 1. A Box–Behnkenfactorial design with three factors and three levels was used for fit-ting a second-order response surface. Statistical significance of theindependent variables and their interactions were estimated by theanalysis of variance (ANOVA).

2.4.2. Optimization of antifungal effect of EO mixture on A. flavususing the ANN–GA model

ANN was developed using MATLAB 7.1 (demo version). Theobtained experimental data (antifungal effect of EO mixture on A.flavus spores at different time points for different concentrations ofindividual or mixture EOs and T. vulgaris:C. cassia mass ratio) wereused to develop the ANN with Levenberg–Marquardt (LM) algo-rithm. ANN included three layers of neurons (information): input,hidden, output. Three input neurons were the input data vectors:time interval (t) and the concentration of individual EO or mix-ture EOs (C), and the T. vulgaris:C. cassia mass ratio (m). The outputlayer was a single neuron, the percentage of reduced spores (yr).The number of neurons in the hidden layer were determined by aheuristic method, testing different number of neurons to obtain theminimum mean square error (MSE), value of the output data. Theproperties of ANN methods are shown in Table 2.

ANN with LM algorithm consisted of three phases: training, val-idation, and testing. The use of the ANN with the LM comprisedthree phases based on a set of input and output data of the design

of experiments with replication, which was divided into three sub-sets: training (70% of the total data), testing (15% of the total data),and validating (15% of the total data). The same procedure wasapplied as in a previous work (Kostic et al., 2014; Milic et al.,

K. Rajkovic et al. / Industrial Crops a

Table 2Artificial neural network with the genetic algorithm (ANN–GA) parameters for pre-dicting of antifungal effect of essential oil (EO) mixture (yr ).

Model Property Value/comment

ANN Algorithm Levenberg-Marquardtback-propagation

Minimised error function MSELearning SupervisedInput layer No transfer function is usedHidden layer Hyperbolic tangent transfer functionOutput layer Hyperbolic tangent transfer functionNumber of date 74Number of training iterations 71Number of best iterations 65Number of input neurons 3a

Number of hidden neurons 1–20Number of output neurons 1b

Genetic algorithm Parameters Value/comment

Population Size 20Generations 100Crossover Rate 0.8Mutation Probability 0.01

2aoen(

2

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M

R

M

wtpm

Fa

a Time (t), concentration of EO mixture (C), and T. vulgaris:C. cassia mass ratio (m).b Percentage of reduced spores (yr ).

013). For the purpose of finding optimal conditions for antifungalctivity of EO mixture, optimization of the input space of the devel-ped ANN was performed using a genetic algorithm (GA) (Kostict al., 2014; Milic et al., 2013). GA parameters were population size,umber of generations, crossover rate, and mutation probabilityTable 2).

.4.3. Statistical evaluation of mathematical modelsMathematical methods (Section 2.4.1 and 2.4.2) were statisti-

ally evaluated by determination of the mean square error (MSE),oefficient of determination (R) and the mean relative error per-entage (MRPD), calculated according to the following equations:

SE = 1n

∑n

i=1(ya,i − yp,i)

2 (3)

2 =∑n

i=1(yp,i − ya,i)2

∑ni=1(yp,i − ym)2

(4)

RPD = 100n

∑n

i=1

∣∣∣∣∣yp,i − ya,iya,i

∣∣∣∣∣ (5)

here yp,j i ya,i are the calculated and experimental values ofhe percentage of reduced spores, ym is the mean value of theercentage of reduced spores, and n is number of experimentaleasurements.

ig. 1. Response surface methodology(RSM): the response surface (left) and contour (righction and the percentage concentration of individual or mixture EOs at T. vulgaris:C. cass

nd Products 65 (2015) 7–13 9

3. Results and discussion

3.1. Optimization of antifungal effect of the EO mixture using theRSM model

Food decay and contamination by fungi represents a health riskfor consumers due to the potential for fungi to produce myco-toxins and causes considerable economic losses (da Cruz Cabralet al., 2013). Numerous studies have demonstrated that plantextracts contain diverse bioactive components that can controlmould growth (Nguefack et al., 2004; Soliman and Badea, 2002).

T. vulgaris (common thyme) a plant native to southern Europehas been used as ingredient in cooking and as an herbal medicine(Figueiredo et al., 2008; Figueiredo et al., 2010). C. cassia (Chinesecinnamon), is an evergreen tree originating in southern China, withits aromatic bark used as one of the most important spices daily bypeople all over the world (Rao and Gan, 2014). A strong antimicro-bial activity has been demonstrated for both cinnamon and thymeEOs (Passone et al., 2012; Manso et al., 2013).

After establishing the potent antifungal activity against A. flavus,the RSM was used for the prediction and optimization of the anti-fungal activity of the T. vulgaris and C. cassia EOs mixture on A. flavusspores. The results of the antifungal activity using Box–Behnkenexperimental design are depicted in Table 3. The quadratic regres-sion model that provided the best description of the antifungaleffect is given by Eq. (6). The second-order polynomial equationbased on the values of the coded levels of the independent variableswas:

yr = 95.81 + 30.09t + 8.06C − 27.60m + 1.50tC + 10.58tm−3.13Cm − 15.76t2 + 0.042C2 + 3.70m2

(6)

The statistical significance of the model equation, the individualfactors, their interactions and squares analyzed by the ANOVA areshown in Table 3. The p-value of this model was very low (<0.0001),indicating that the model was significant at the 95% confidencelevel. The significant effect on the percentage of reduced sporeshad: individual independent variables (time of action, concentra-tion of individual or mixture EOs, T. vulgaris:C. cassia mass ratio),interaction of the time of action and T. vulgaris:C. cassia mass ratio,interaction of the percentage EO concentration and the T. vulgaris:C.cassia mass ratio as well as the square of the time. Other multipleinteractions and squares had minor importance.

A coefficient R2 was 0.988, showing a strong correlation betweenthe percentage of reduced spores and independent variables (time,

EO concentration, and T. vulgaris:C. cassia mass ratio). The value ofAdeq Precision, which measures the signal to noise ratio, was muchhigher than 4, so the model could be used to navigate the designspace.

t) plots for the predicted percentage of reduced spores as a function of the time ofia mass ratio of 1:1.

10 K. Rajkovic et al. / Industrial Crops and Products 65 (2015) 7–13

Table 3Analysis of variance of response surface methodology (RSM) method of antifungal effect the essential oil (EO) mixture on A. flavus spores.

Source of variance Sum of squares Degrees of freedom Mean square F-value p-value

Model 41587.40 9 4620.82 305.86 <0.0001*

t 893.98 1 893.98 59.17 <0.0001*

C 1004.56 1 1004.56 66.49 <0.0001*

m 752.06 1 752.06 49.78 <0.0001*

tC 29.40 1 29.40 1.95 0.173tm 104.69 1 104.69 6.93 0.013*

Cm 135.70 1 135.70 8.98 0.005*

t2 1489.43 1 1489.43 98.59 <0.0001*

m2 0.01 1 0.01 0.00 0.979c2 24.07 1 24.07 1.59 0.216Residual 468.34 31 15.11Lack of fit 461.85 7 65.98 244.05 <0.0001Pure error 6.49 24 0.27Core total 42055.73 40

Adeq Precision 47.82R2 0.988

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t-time of action; C-concentration of EO mixture; m-T. vulgaris:C. cassia mass ratio.)* Statistically significant at the confidence level of 95%.

Response surface profiles and percentage of reduced spores as function of two variables, while the third was held at a con-tant level are shown in Figs. 1–3 . These graphs were suitable toepresent optimization process, since they allow defining the opti-

al conditions for achieving the maximum percentage of reducedpores. From response surfaces it can be observed that the antifun-al effect rapidly increased with the increase of the time and theoncentration of EOs mixture (Fig. 1). The percentage of reducedpores increased with the increase of the concentration of mixtureO which could be attributed to the positive effect of the concen-ration of mixture EO on the antifungal effect. The shape of theontours confirmed the significant effect of interaction of the timend the concentration of EO mixture on the percentage of reducedpores (Fig. 1). The effect of the time and the T. vulgaris:C. cassiaass ratio on the percentage of reduced spores is shown in Fig. 2.

he percentage of reduced spores generally increased, reached alateau and then declined slightly with increasing mass fractionf individual EO in mixture. The slight decrease of percentage ofeduced spores at high the mass fraction of individual EO could bettributed to the synergistic interaction between EOs. The shape ofhe contours confirmed the significance of the interaction betweenhe time and the T. vulgaris:C. cassia mass ratio (Fig. 2). Fig. 3 showshat percentage of reduced spores decreased with increase of massraction of C. cassia independently of percentage concentration ofO mixture at the time of 65 min. The shape of the contours con-rmed the significance of the interaction between the percentageoncentration of EO mixture and the T. vulgaris:C. cassia mass ratio

Fig. 3).

The RSM model predicted that the percentage of reduced sporesf 95.8% can be obtained within 65 min under the following condi-ions of action: the percentage concentration in EO mixture of 0.5%

ig. 2. Response surface methodology (RSM): the response surface (left) and contour (rigction and the T. vulgaris:C. cassia mass ratio at the percentage concentration of EO mixtu

and the T. vulgaris:C. cassia mass ratio of 1:1. The predicted valueof the percentage of reduced spores was close to the experimentalvalue of the percentage of reduced spores (100%) under the sameconditions of action. RSM has been successfully applied to optimizethe extraction of polysaccharides from the mycelia of A. fumigatus(Jin and Ning, 2013), and now this method was successfully appliedfor prediction and optimization of antifungal activity of EO mixtureon A. flavus spores.

3.2. Optimization of antifungal effect of the EO mixture using theANN–GA model

The antifungal effect of the EO mixture was predicted by theANN with the LM algorithm consisted of one output and three inputlayer neurons. The optimum number of hidden neurons was foundto be 10 when the lowest values of MSE for training, testing, and val-idation were determined (0.87; 2.69; 2.56). Fig. 4 shows the changeof the value of MSE with the number of neurons in hidden layer.The network predictive ability was measured by R2 and MSE. TheANN with the 3-10-1 (Fig. 5) topology was found to have the high-est R-values (0.998). The excellent fitting of experimental data wasachieved with a rather simple back ANN propagation performed byusing the LM algorithm. This implies that empirical models derivedfrom ANN can be used adequately. The plots obtained by the ANNmodel (not shown here) are very similar to those obtained by theRSM model.

The optimum set of the independent variables, namely: the time,

the concentration of EO mixture and the T. vulgaris:C. cassia massratio to the maximum the percentage of reduced spores, was deter-mined by applying the GA over the ANN model. The GA parameterswere optimized by the sensitivity analysis (Table 2) (Kostic et al.,

ht) plots for the predicted percentage of reduced spores as a function of the time ofre of 0.5%.

K. Rajkovic et al. / Industrial Crops and Products 65 (2015) 7–13 11

Fig. 3. Response surface methodology (RSM): the response surface (left) and contour (right) plots for the predicted percentage of reduced spores as a function of the T.vulgaris:C. cassia mass ratio and the percentage concentration of individual or mixture EOs at the time of action of 65 min.

Fig. 4. Changing the mean square error (MSE) values with changing the number ofn

2vacmiv

3.3. Comparison of RSM and ANN models performance

Fn

eurons in the hidden layer (� – training, © – validation, �-test).

014; Milic et al., 2013). Within the ranges of the independentariables applied in developing the model, the highest predictedntifungal effect of 96.4% corresponded to the time of 65 min, theoncentration of EO mixture of 0.5% and the T. vulgaris:C. cassiaass ratio of 1:1. This value agreed with the antifungal effect exper-

mentally obtained under the same conditions of action (100%),erifying the developed model.

ig. 6. The comparison of experimental and calculated values of the percentage of reduetwork (ANN) model (right).

Fig. 5. Multilayer artificial neural network (ANN) (t-time of action; C-concentrationof individual or mixture EOs; m – T. vulgaris:C. cassia mass ratio; yr – percentage ofreduced spores).

In order to check the accuracy of the models obtained from RSMand ANN, both R2 and MRPD were evaluated. The results showed

ced spores: response surface methodology (RSM) model (left) and artificial neural

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2 K. Rajkovic et al. / Industrial C

hat both optimization tools gave good predictions due to the valuesf R2 (0.988 and 0.998 for RSM and ANN, respectively), which arepproximately 1.0. However, ANN showed a clear superiority overSM due to smaller value of MRPD obtained (±10.7% and ±0.1% forSM and ANN, respectively).

Data fitting of the models were tested and ANN demonstrated better fitting than RSM (Fig. 6). Randomly scattering of pointsround the diagonal could be observed by comparing the pre-icted and actual values of the antifungal effect (Fig. 6A). Anxcellent agreement between the predicted and experimental val-es of the antifungal effect of the developed ANN is shown inig. 6B. Additionally, the relative deviations between the predictednd experimental of the percentage of reduced spores at the opti-al conditions of the models are ±4.8% and ±3.6% for RSM and

NN, respectively. Conclusively, ANN was better than RSM in theodeling and optimization studies of antifungal effect. ANN model

uperiority in relation to the RSM model has been shown in somerevious studies (Basri et al., 2007; Betiku and Ajala, 2014; Rajkovict al., 2013; Stamenkovic et al., 2013).

. Conclusion

Plant metabolites are a promising alternative to antifungalreatment and control because plants generate a wide variety ofompounds, either as part of their development or in response totress or pathogen attack (Bakkali et al., 2008). In this study, anti-ungal effect of individual T. vulgaris and C. cassia EOs and their

ixture on important human and food pathogen A. flavus wasnvestigated. RSM and ANN models were developed for predictionnd optimization of the antifugal effect of EOs mixture. Both modelsrovided a good quality prediction for the antifungal effect in termsf the three independent variables. The RSM demonstrated that theime of action, the percentage concentration of individual or mix-ure EOs and the T. vulgaris:C. cassia mass ratio played the importantole for antifungal effect of EOs. However, based on MRPD-valuesRSM: ±10.1%; and ANN: ±0.1%), the ANN model was found to be

ore accurate in predicting the antifungal effect of EO mixturehan the RSM model. Using RSM and ANN–GA techniques, the opti-

um percentage of the concentration mixture EO was found to be.5%, with T. vulgaris:C. cassia mass ratio of 1:1. Both RSM and ANNnsured the highest antifungal effect of 95.8% and 96.4% in 65 min,espectively. Both models are useful for optimization the antifun-al effect in vitro but ANN–GA applied for experimental design isore accurate than RSM due to its lower value of MRPD. Therefore,

NN–GA can be generally used for optimization and prediction ofntimicrobial in vitro effects of EO mixtures. Our data confirmedhat proper EOs mixtures may reduce the minimum effective dosef individual EOs. The applied model could be suitable for the deter-ination of the most efficient application of EOs not only in terms

f antifungal but their other activities important for agriculture andedical applications.

cknowledgments

This work has been funded by the Ministry of Education, Sciencend Technological Development of the Republic of Serbia (Grant No.I175034).

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