Optimization of the Temperature and Oxygen Concentration … · 2016. 9. 13. · Beatrice Sordini, and Maurizio Servili Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Universitàdegli

Optimization of the Temperature and Oxygen ConcentrationConditions in the Malaxation during the Oil Mechanical ExtractionProcess of Four Italian Olive CultivarsRoberto Selvaggini,* Sonia Esposto, Agnese Taticchi, Stefania Urbani, Gianluca Veneziani, Ilona Di Maio,Beatrice Sordini, and Maurizio Servili

Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Universita ̀ degli Studi di Perugia, Via S. Costanzo, 06126 Perugia, Italy

*S Supporting Information

ABSTRACT: Response surface modeling (RSM) was used to optimize temperature and oxygen concentration duringmalaxation for obtaining high quality extra virgin olive oils (EVOOs). With this aim, those chemical variables closely related toEVOO quality, such as the phenolic and the volatile compounds, have been previously analyzed and selected. It is widely knownthat the presence of these substances in EVOOs is highly dependent on genetic, agronomic, and technological aspects. Based onthese data, the two parameters were optimized during malaxation of olive pastes of four important Italian cultivars using somephenols and volatile compounds as markers; the optimal temperatures and oxygen levels, obtained by RSM, were as follows foreach cultivar: 33.5 °C and 54 kPa of oxygen (Peranzana), 32 °C and 21.3 kPa (Ogliarola), 25 °C and 21.3 kPa (Coratina), and 33°C and 21.3 kPa (Itrana). These results indicate the necessity to optimize these malaxing parameters for other olive cultivars.KEYWORDS: extra virgin olive oil, quality, oil mechanical extraction process, phenols, volatile compounds, HS-SPME-GC/MS,optimization, response surface methodology

■ INTRODUCTIONSome extra virgin olive oil (EVOO) minor compounds highlyinfluence its quality: in particular, aroma and taste (bitter andpungent) sensory notes are given by volatile and phenoliccompounds, respectively.1,2

Furthermore, hydrophilic phenols are responsible for severalhealth properties associated with virgin olive oil consump-tion.1,3,4

Many studies have demonstrated that the main causesresponsible for the qualitative and quantitative presence of boththese groups of substances in the final product are strictlycorrelated with the genetic origin of the olives (cultivar) andthe agronomic and the technological production condi-tions.2,5−7

The wide variety of different cultivars used for themechanical extraction process of monovarietal and blendedEVOOs plays an important role in their sensory, nutritional,and health properties, even if the mean concentration ofphenolic and volatile compounds of a single cultivar isnecessarily influenced also by agronomic factors such as thegrowing area, fruit ripening, cultivation techniques, waterresources, fertilization, and soil management and by thetechnological extraction system.7,8

From a technological point of view, malaxation represents acritical step in the EVOO extraction process, where theselective control of the oxidoreductase enzymes such aspolyphenoloxidase (PPO), peroxidase (POD), and lipoxyge-nase (LPO) is very important.1,2,10−12 Indeed, all theseoxidoreductases remain active after crushing, but since PPOand POD are responsible for the degradation of polyphenols,they should certainly be inhibited; on the contrary, LPO activity

has a positive effect on EVOO quality, as it produces a goodaroma and should be favored in the malaxation process.10,13−15

The results of several studies in this field have led experts tobuild new machines that can control these enzymatic activitiesby controlling the water temperature in the malaxer chamberand reducing the concentration of O2 in contact with the olivepastes (closed or confined malaxer); this allows on the onehand the limitation of PPO and POD activity and on the otherthe usual production of the aroma by the LPO;10,14,16−18 hencethe choice of the optimal temperature and the amount of O2during mixing is a strategy for the production of a high qualityEVOO,19,20 even if these parameters must be correlated to theolive cultivar.RSM might be used for evaluating the relative importance of

different influencing factors even in the presence of complexinteractions. RSM is an empirical statistical modeling techniqueused for optimizing one or more responses (Y variables)influenced by several variables (X) using quantitative datagathered from properly designed experiments to solve multi-variable equations simultaneously by using the method of least-squares.21,22

When multiple variables might influence the outputs, RSM isan effective technique for exploring the relationships betweenthe responses and the independent variables.23,24 Centralcomposite design (CCD) is the most common form of designof experiments for minimizing the number of experimentsrequired and suitable to perform RSM.25 It has been widely

Received: January 8, 2014Revised: April 2, 2014Accepted: April 3, 2014Published: April 3, 2014

Article

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pubs.acs.org/JAFCcolettaEvidenziato

utilized by several researchers to optimize various foodprocessing methods such as fermentation,26 milling,27 extrac-tion from vegetable matrices28 and virgin olive oil mechanicalextraction;19 RSM has thus become one of the most popularoptimization techniques in the field of food science.29,30

The objectives of this study were to systematically investigatethe influence of temperature and oxygen concentration on theolive pastes belonging to four of the most important Italianolive cultivars during malaxation and to explore the bestoperative conditions for obtaining high quality EVOOs.

■ MATERIALS AND METHODSOlives. In this research, olive drupes of Coratina, Peranzana, Itrana,

and Ogliarola cultivars (cvs) were tested. Coratina and Ogliarola olivetrees on a side and Peranzana by the other were planted in the ApuliaRegion, in the Province of Bari, and in the Province of Foggia,respectively. Instead, the growing area of Itrana olive trees was theProvince of Latina (Lazio). All the cvs were harvested on the periodOctober−November 2011 and the ripening stage of these olives,evaluated on the basis of the pigmentation index according to Pannelliet al.,31 was 0.95. The olives have been processed within 48 h afterharvesting.Reference Compounds. The (p-hydroxyphenyl)ethanol (p-

HPEA) was purchased from Fluka (Milan, Italy), while the 3,4-(dihydroxyphenyl)ethanol (3,4-DHPEA), produced by the CaymanChemical Co. (Ann Arbor, MI, USA), was obtained from Cabru s.a.s.(Arcore, Milan, Italy). The dialdehydic forms of elenolic acid linked to3,4-DHPEA and p-HPEA (3,4-DHPEA-EDA and p-HPEA-EDA,respectively), the isomer of oleuropein aglycon (3,4-DHPEA-EA),(+)-1-acetoxypinoresinol, and (+)-pinoresinol were extracted fromvirgin olive oil according to the method developed by Montedoro etal.32 In this method the phenolic compounds were extracted from theextra virgin olive oil using a mixture of methanol/water (80:20 (v/v));then, after solvent evaporation and a partial purification of the crudeextract obtained from EVOO, the phenols were separated bysemipreparative high performance liquid chromatography (HPLC)analysis. The HPLC separation was conducted using a WhatmanPartisil 10 ODS-2 column (500 mm × 9.4 mm i.d.). The mobile phasewas 0.2% acetic acid (pH 3.1) in water (A) and methanol (B), and theelution was performed at a flow rate of 6.5 mL/min. The total runningtime was 150 min, and the gradient changed as follows: the startingcomposition was 95% A/5% B, then the percentage of B was increasedto 74% A/26% B in 2.5 min, 64% A/36% B in 4.5 min, and thispercentage was maintained for 33 min, 61% A/39% B in 35 min, 0%A/100% B in 35 min, and this percentage was maintained for 20 min,returning at the end to the initial conditions (95% A/5% B) in 20 min.The phenols were detected using a diode array detector (DAD) at awavelength of 278 nm.The purity of these substances was tested by analytical HPLC,33 and

their chemical structures were verified by NMR using the sameoperating conditions reported in previous papers by recording 1H and13C spectra.32,34 Pure analytical standards of volatile compounds Flukaand Aldrich were purchased from Sigma-Aldrich (Milan, Italy).Experimental Procedure. Virgin Olive Oil Mechanical Extrac-

tion Process. The experiments were conducted with an industrial plantusing a TEM 200 system (Toscana Enologica Mori, Tavarnelle Val diPesa, Florence, Italy) composed of a hammer mill, a malaxer with a gascontroller system, and a working capacity of 200 kg of olives and atwo-phase decanter; for the separation of olive oil, a UVPX 305 AGT14 centrifuge (Alfa Laval S.p.A., Tavarnelle Val di Pesa, Florence, Italy)was used. The extraction was performed on a sample of 150 kg ofolives, and malaxation was carried out for 40 min, a period commonlyused in industrial plants, using a top-covered malaxing machineequipped with an O2 valve and a Mettler-Toledo O2 4100 sensor(Mettler-Toledo, Novate Milanese, Milan, Italy) for oxygen measure-ment.35 The trials were performed at malaxing temperatures (rangingfrom 20 to 40 °C) and initial oxygen partial pressures in the headspaceof the malaxer chamber (ranging from 21.3 to 101.3 kPa) according to

a central composite circumscribed design (CCC; see Table 1 for thetrial temperatures and oxygen partial pressures). The EVOO sampleswere filtered and stored in the dark at 13 °C until analyzed.

Analytical Methods. Extraction and HPLC Analysis of thePhenolic Compounds of Virgin Olive Oils. The extraction of EVOOphenolic compounds was performed in accordance with Montedoro etal.36 The HPLC analyses of the phenolic extracts were conductedaccording to Selvaggini et al.33 with a reversed-phase column using anAgilent Technologies system Model 1100 (Agilent Technologies,Santa Clara, CA, USA) which was composed of a vacuum degasser, aquaternary pump, an autosampler, a thermostated column compart-ment, a DAD, and a fluorescence detector (FLD). The C18 columnused in this study was a Spherisorb ODS-1 250 mm × 4.6 mm with aparticle size of 5 μm (Waters, Milford, MA, USA); the injected samplevolume was 20 μL. The mobile phase was composed of 0.2% aceticacid (pH 3.1) in water (solvent A)/methanol (solvent B) at a flow rateof 1 mL/min and the gradient changed as follows: 95% A/5% B for 2min, 75% A/25% B in 8 min, 60% A/40% B in 10 min, 50% A/50% Bin 16 min, and 0% A/100% B in 14 min; this composition wasmaintained for 10 min and then returned to the initial conditions andequilibration in 13 min; the total running time was 73 min. Lignanswere detected by using the FLD operated at an excitation wavelengthof 280 nm and an emission of 339 nm; for the detection of all theother phenolic compounds a DAD was employed with the wavelengthset at 278 nm.

Volatile Compounds Analysis. The evaluation and quantification ofvolatile compounds in EVOOs were done by headspace−solid phasemicroextraction (HS-SPME) followed by gas chromatography−massspectrometry analysis (HS-SPME-GC/MS) according to Servili et al.37

with few modifications.For sampling the headspace volatile compounds, SPME was applied

as follows: 3 g of EVOO were placed in a 10 mL vial and thermostatedat 35 °C, then the SPME fiber (a 50/30 μm divinylbenzene/Carboxen/poly(dimethylsiloxane) (DVB/CAR/PDMS) with a lengthof 1 cm; StableFlex, Supelco, Inc., Bellefonte, PA, USA) was exposedto the vapor phase for 30 min to sample the volatile compounds.Afterward, the fiber was inserted into the GC injector set in splitlessmode, using a splitless inlet liner of 0.75 mm i.d. for thermaldesorption, where it was held for 10 min. All of the SPME operationswere automated by using a Varian CP 8410 AutoInjector (Varian,Walnut Creek, CA, USA).

GC/MS Analysis. The analysis of the volatile compound sampledwith SPME was conducted as reported in Servili et al.37 with fewmodifications. A Varian 4000 GC/MS equipped with a 1079 universalcapillary injector (Varian) was used. An Agilent J&W fused-silicacapillary column was employed (DB-WAXetr, 50 m, 0.32 mm i.d., 1μm film thickness; Agilent). The column was operated with helium ata constant flow rate of 1.7 mL/min maintained with an electronic flowcontroller (EFC). The GC oven heating program started at 35 °C.This temperature was maintained for 8 min, then increased to 45 °C at

Table 1. Temperature and Oxygen Content According to aCentral Composite Circumscribed Design Used in theOptimization Study of the Malaxation Process

trial temp (°C) oxygen content (kPa)

1 20 61.32 24 37.23 24 85.44 30 21.35 30 101.36 30 61.37 30 61.38 30 61.39 36 37.210 36 85.411 40 61.3

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a rate of 1.5 °C/min, increased to 150 °C at a rate of 3 °C/min,increased to 180 °C at a rate of 4 °C/min, and finally increased to 210°C at a rate of 3.6 °C/min; this temperature was then held for 14.5min. The total time of analysis was 80 min. The injector temperaturewas maintained at 250 °C; the temperature of the transfer line wasfixed at 170 °C. The mass spectrometer was operated in the electronionization (EI) mode at an ionization energy of 70 eV, with scanningin the mass range of m/z 25−350 amu at a scan rate of 0.79 s/scan anda trap set point temperature of 150 °C. The GC-MS was operated withthe Varian MS Workstation Software, Version 6.6 (Varian). Thevolatile compounds were identified by comparison of their massspectra and retention times with those of authentic referencecompounds. Integration of all the chromatographic peaks wasperformed by choosing the three masses with the highest intensitiesamong those specific for each compound, to selectively discriminatethem from their nearest neighbors. The volatile compound resultswere calculated on the basis of the calibration curves for eachcompound and expressed in micrograms per kilogram of oil.37

Statistical Analysis. Descriptive Statistics. To show the variabilityof data were employed box and whisker plots, in which the lower andthe upper edges of the box indicate the 25th and the 75th percentile,respectively; the line within the box shows the median while thewhiskers designate the 10th and 90th percentiles. In addition, on thegraphs, are reported two points representing the fifth and 95thpercentiles. These plots were elaborated using the statistical softwareSigmaPlot version 12.3 (Systat Software, Inc., San Jose, CA, USA).Principal Components Analysis. Principal component analysis

(PCA) models were built to analyze the influence of processingparameters on the analytical data of EVOO. The SIMCA 13.0chemometric package was used (Umetrics AB, Umea,̊ Sweden).To perform multivariate statistical analysis, the analytical data were

put in a matrix with the samples (n objects) in rows and the analyticalparameters (k variables) in columns. The raw data were normalized,with the subtraction of the mean, and autoscaled, dividing these resultsby the standard deviation. The number of significant components wasfound by applying cross-validation. The results of PCA modeling arepresented in graphical form.38,39 First a PCA model with all data wasbuilt; then four SIMCA models, one for each cultivar studied in thiswork, were made for selecting a small number of variables (volatile andphenolic compounds), from among those with the highest absolutevalues of loadings, successively employed in the optimization study.When the distributions of these data were not normal (or Gaussian),the data were log-transformed.Optimization by Response Surface Modeling. Response surface

model (RSM) was obtained with the chemometric package MODDE.9.1 (Umetrics AB).To optimize the malaxation parameters in the mechanical extraction

process, the original data (Y), phenols and volatile compounds chosenby previous PCA, were transformed into desirability functions (di),successfully used in RSM-based optimization when several responsevariables (several Y variables) are considered, so reducing the problemto the optimization of only one Y variable. di are dimensionless values,calculated using a linear transformation according to Derringer andSuich40 with a small modification, so as to obtain desirabilities rangingbetween 0.1 and 1 using the following equation:

for phenolic and volatile compounds of EVOO that must bemaximized:

=+ −

−d

Y Y YY Y

0.9 0.1i

max min

max min

for phenolic compounds of EVOO that must be minimized:

=− + −

−d

Y Y YY Y

0.9 0.1i

max min

max min

Ymin and Ymax correspond to the minimum and the maximumvariable values, respectively.The overall desirability (D) was calculated as the geometric mean of

the individual di values:

= × ×D d d d... n1 2n

The partial least-squares analysis (PLS) was used to estimate thecoefficients of the terms in the models.41

■ RESULTS AND DISCUSSIONThe phenolic and volatile compounds of the EVOOs obtainedin this study were determined according to the list given inTable 2.

The results from the combinations of temperature and O2concentration applied during malaxation are shown, withrespect to the phenolic compounds, as distributions in thebox and whisker plots in Figure 1, while for volatile compoundsthe range and the average values contents are given in Table 3.From Figure 1 can be evidenced a strong variation of the

phenolic concentration among the cultivars and, in addition,the distribution of the values obtained with different malaxationconditions is cultivar-dependent (cvs Peranzana and Coratinaby a side and cvs Ogliarola and Itrana by the other show similartrends). The main phenolic compounds in all the cultivars werethe oleuropein and the ligstroside derivatives, while the lignansshowed the lowest concentrations and the lowest variabilitywith the operating extraction conditions. It is important to note

Table 2. List of the Variables Evaluated in Extra Virgin OliveOils

phenolic compounds alcoholshydroxytyrosol (3,4-DHPEA) 2-methyl-1-butanoltyrosol (p-HPEA) 1-pentanol3,4-DHPEA-EDA 1-hexanolp-HPEA-EDA 1-heptanol(+)-1-acetoxypinoresinol 1-octanol(+)-pinoresinol 1-penten-3-ol3,4-DHPEA-EA (E)-2-penten-1-oltotal phenols (Z)-2-penten-1-ol

(E)-3-hexen-1-olvolatile compounds (Z)-3-hexen-1-ol

ketones (E)-2-hexen-1-ol3-pentanone1-penten-3-one esters

ethyl acetatealdehydes hexyl acetate

pentanal (Z)-3-hexenyl acetatehexanaloctanal aromatic alcoholsnonanal benzyl alcohol(E)-2-pentenal phenylethyl alcohol(E,E)-2,4-hexadienal2,4-hexadienal (i) sum of classes of volatile

compounds(Z)-3-hexenal sum of ketones(E)-2-hexenal sum of saturated

aldehydes(E)-2-heptenal sum of C6 unsaturated

aldehydes(E)-2-octenal sum of saturated

alcoholssum of C5 unsaturatedalcohols

phenols sum of C6 unsaturatedalcohols

phenol sum of esters



that the main contribution to the total phenols content was due

to the oleuropein derivatives.

By the data analysis, the highest average value of totalphenols was found in cv. Coratina (992 mg/kg) and the lowestin cv. Peranzana (337 mg/kg); the EVOOs obtained from cv.

Figure 1. Box and whisker plots of total phenols (A), derivatives of oleuropein (B), derivatives of ligustroside (C), and lignans (D) evaluated onEVOOs obtained from four Italian olive cultivars in different malaxing conditions. (Limits in percentile: box = lower 25th, upper 75th; whiskers =lower 10th, upper 90th; points = lower fifth, upper 95th. The line within the box represents the median.)

Table 3. Range and Average Values (μg/kg) of Volatile Compounds Evaluated on EVOOs Obtained from Peranzana, Ogliarola,Coratina, and Itrana Cultivars in Different Malaxation Conditions

cv. Peranzana cv. Ogliarola cv. Coratina cv. Itrana

range mean range mean range mean range mean

aldehydes

pentanal 20−33 25 22−39 29 11−34 24 22−45 29(E)-2-pentenal 120−186 149 66−114 92 66−134 102 116−352 254hexanal 354−929 554 516−894 694 243−412 304 274−1,213 780(E,E)-2,4-hexadienal 380−595 497 356−463 410 362−569 438 391−1,124 8142,4-hexadienal (i) 164−258 215 182−230 207 180−286 219 176−461 347(Z)-3-hexenal 236−303 275 301−501 413 322−439 377 302−891 595(E)-2-hexenal 24,985−37,615 30,772 38,580−54,625 47,985 35,415−56,950 45,329 31,270−55,515 43,381sum of C6 unsaturated aldehydes 26,092−38,524 31,760 39,565−55,772 49,015 36,315−58,206 46,362 32,484−57,350 45,137

alcohols

1-pentanol 13−26 19 11−19 15 11−20 15 28−78 44(E)-2-penten-1-ol 29−47 37 16−43 31 28−47 38 27−68 49(Z)-2-penten-1-ol 124−224 174 75−169 131 148−300 231 107−292 1931-penten-3-ol 205−362 287 130−271 212 237−459 345 162−388 2831-hexanol 659−1,891 1,025 779−1,211 934 607−1,925 1,000 1,543−4,340 2,859(E)-3-hexen-1-ol 4−10 6 4−7 5 2−6 4 14−30 22(Z)-3-hexen-1-ol 271−1,149 563 101−190 137 115−231 167 1,195−2,334 1,816(E)-2-hexen-1-ol 1,028−3,667 1,899 2,053−3,463 2,585 1,425−2,557 2,031 2,556−5,487 3,743sum of C6 unsaturated alcohols 1,614−4,825 2,468 2,158−3,642 2,726 1,564−2,760 2,202 4,007−7,791 5,581

esters

hexyl acetate 874−1,697 1,239 37−99 64 4−21 9 80−123 95(Z)-3-hexenyl acetate 1,856−3,535 2,650 29−89 62 17−82 56 208−691 488sum of esters 2,744−5,231 3,889 66−188 127 22−94 65 289−791 583



Itrana contained an average total phenols amount of 364 mg/kg, while that of cv. Ogliarola was 480 mg/kg. The phenolicvariation among the cultivars, in agreement with the literature,indicates that genetic biodiversity is one of the most importantparameters that affects the phenolic concentration inEVOOs.1,2,42 So far, however, the impact on the bioactivephenols of the operative conditions of malaxation, such as O2concentration in the olive pastes and the processing temper-ature, was very strong in all the cultivars studied. This isdemonstrated by the ranges of variability between the extremevalues of the total phenols, corresponding to 1,241 mg/kg in cv.Coratina, 488 mg/kg in cv. Ogliarola, 387 mg/kg in cv. Itrana,and 351 mg/kg in cv. Peranzana.High variability was observed also for the volatile compounds

according to the cultivar and the operative conditions ofmalaxation, with the main variations found in the compounds,related to the lipoxygenase pathway, that include hexanal, (E)-2-hexenal, 1-hexanol, (E)-3-hexen-1-ol, (Z)-3-hexen-1-ol, and(E)-2-hexen-1-ol, together with esters such as hexyl acetate and(Z)-3-hexenyl acetate.The significant differences in terms of cultivar impact in the

C6 unsaturated aldehydes show the lowest average value in cv.Peranzana (31,760 μg/kg) and the highest concentration in cv.Ogliarola (49,015 μg/kg); with regard to the C6 unsaturatedalcohols, these range between 2,202 and 5,581 μg/kg for theCoratina and the Itrana cvs, respectively. The most importantvariation in the volatile composition related to cultivar origin is,however, shown by the ester mean concentrations having thelowest value in cv. Coratina (65 μg/kg) and the highest in cv.Peranzana (3,889 μg/kg). All the classes of volatile compounds

related to the EVOO flavor evaluated in this work are highlyaffected by the operative conditions. The maximum range ofvariability for the C6 unsaturated aldehydes corresponding to24,866 μg/kg was found in cv. Itrana; on the contrary, cv.Peranzana shows the minimum value with 12,432 μg/kg. Themean amounts of the C6 unsaturated alcohols have the highestcontent in cv. Itrana (5,581 μg/kg), about twice the averageamount of the other three cultivars; with respect to the range ofvariability for this class of compounds, cv. Itrana has themaximum value, 3,784 μg/kg, and cv. Coratina shows theminimum, with 1,196 μg/kg. Regarding the esters concen-tration, cv. Peranzana has the highest average value (3,889 μg/kg), about seven times the nearest value found in cv. Itrana, andeven in this case the operative conditions of malaxation greatlyaffect their content: the maximum range of variability wasfound in cv. Peranzana, with 2,487 μg/kg, while the minimumvalue was found in cv. Coratina, with 72 μg/kg.Multivariate statistical methods were used to better interpret

all the data collected from the EVOO samples obtained byvarying the combination of two malaxing parameters, the O2concentration in the olive pastes and the processing temper-ature, and to optimize these two variables. A PCA model wasfirst built with the whole data set, which included the EVOOsevaluated for all four Italian cultivars studied using the list ofcompounds given in Table 2. The model, built with somevariables log-transformed so as to obtain a distribution closer toa Gaussian, explains 93% of the total variance with sevensignificant principal components (37%, 22%, 15%, 9%, 5%, 3%,and 2%, respectively) and the results for the first twocomponents are shown in Figure 2. A clear discrimination of

Figure 2. Score plot and loading plot of the first two principal components (a) and of the first and third components (b) of the PCA of EVOOsobtained from four Italian olive cultivars in different malaxation conditions. Abbreviations used: P = Peranzana; O = Ogliarola; C = Coratina; I =Itrana.



the EVOOs in four clusters, corresponding to the olive cvs, isobserved in the first three principal components, and inparticular in the first component the oils most differentiated areItrana (on the left side) and Coratina (on the right side), whilein the second component there are the Coratina and ItranaEVOOs (on the upper side) opposite to the Peranzana andOgliarola oils (Figure 2a). The loading plot in Figure 2a showsthat the Itrana oils are characterized by higher concentration ofketones, C6 saturated and unsaturated alcohols, (E)-2-pentenal,hexanal, and (Z)-3-hexenal; on the contrary, the CoratinaEVOOs have a higher secoiridoids content, while Peranzanaand Ogliarola EVOOs have lower contents of C5 unsaturatedalcohols and secoiridoids (in comparison with cv. Coratina)and higher concentrations of some saturated alcohols. In thescore plot of the third component vs the first, there is a verygood separation of Ogliarola (on the upper side) compared toPeranzana EVOOs and the former has higher levels of C6unsaturated aldehydes; on the other hand, Peranzana is veryrich in esters (Figure 2b).For the data set for each cultivar without the two lignans, due

to very little differences in the samples, preliminary PCAmodels were built to establish the variables (with regard to thevolatile compounds, those substances originated by thelipoxygenase pathway were chosen, significant in defining theflavor of EVOOs) that play an important role in the quality ofthis food product and that undergo the most variations underthe different mechanical oil extraction conditions, temperatureand oxygen concentration during malaxation, fixed with theCCC design (for a detailed description of these PCA modelssee the Supporting Information). For the RSM optimization,only a few analytical variables must be selected in order todefine the overall desirability starting with the partialdesirability functions (for the list of the variables chosen foreach olive cultivar see Table 4).The RSM models explain with two PLS components for all

the cultivars analyzed 98%, 97%, 94%, and 85% of the totalvariance for Peranzana, Ogliarola, Coratina, and Itrana,respectively.The results for Peranzana, given in Figure 3, show a response

surface with a maximum located at about 33.5 °C and 54 kPa ofoxygen. The most important effect is due to temperature,although oxygen also plays a significant role. For cv. Ogliarola,the surface shows a saddle shape and the best operativeconditions can be found at the temperature of 32 °C and thelowest oxygen concentration (Figure 3); in this case, bothtemperature and oxygen are important in the optimizationmodel. In the case of cv. Coratina, when transformed intodesirability functions, due to very high concentration of thephenolic substances (for total phenols up to 1,690 mg/kg), amaximum total phenols value of 1,000 mg/kg was chosen forthese compounds, and for secoiridoids proportional values totheir original concentrations were calculated, above which theequation for the minimization of the desirability was used,because EVOOs with higher phenolic contents are unpleasantdue to their bitter and pungent sensory notes.43,44 The RSMmodel gave the best results with a temperature of 25 °C and anoxygen concentration of 21.3 kPa, even if in this case a highdesirability function value can be obtained at a temperature of36 °C and an oxygen concentration of 101.3 kPa with the twofactors both influencing the model (Figure 4). The bestmalaxing conditions for cv. Itrana are a temperature of 33 °Cand the lowest oxygen concentration, and as for cv. Coratina,very high response values were obtained at a temperature of 37

°C with the highest oxygen content; between the two factorsnow the temperature plays an important role in the modelingprocess (Figure 4).The results of the optimization models show that the optimal

malaxing conditions are strongly affected by genetic biodiver-sity. These results confirm, as found in previous works,1,2,7,8,42

that cultivars have a significant impact on the chemical andbiochemical characteristics of olive fruits and the EVOOsobtained from them. The total amount of phenols in the fruitand the activities of several endogenous enzymes that areinvolved in phenolic oxidation and aroma generation, such aspolyphenoloxidases, peroxidases, and lipoxygenases, are influ-enced by the genetic origins of the cultivars. As a consequence,to obtain high quality EVOOs, the operative conditions ofmalaxation, such as the O2 concentration in the pastes and theprocessing temperature, which both affect the activities ofendogenous enzymes, must be optimized according to thecultivar. In this context it is very important to stress that theEVOOs obtained from Itrana and Peranzana among thecultivars studied, rich mainly in esters among the aromaticcompounds, formed in the lipoxygenase pathway, but relativelypoor in phenolic substances, show the highest malaxationtemperatures for obtaining the maximum response values in theoptimization models. The high temperatures (near 40 °C)reduce the activity of hydroperoxide lyase responsible for theformation of C6 aldehydes and, consequently, of the otherclasses of compounds originated by these, but, at the same time,improving the cell wall degradation, due to the endogenousdepolymerizing enzymes, and enhancing the solubility of thesesubstances in the oils, they increase the release of phenols in theEVOOs and in the vegetation waters.45,46 On the basis of theseresults, to improve the health and the sensory properties ofEVOOs for cultivars poor in phenols and characterized by high

Table 4. List of the Phenolic and Volatile CompoundsTransformed into Desirability Function for Each Cultivar forthe RSM

Peranzana Ogliarola Coratina Itrana

3,4-DHPEA-EDA 3,4-DHPEA 3,4-DHPEA 3,4-DHPEA-EDA

p-HPEA-EDA p-HPEA p-HPEA p-HPEA-EDA3,4-DHPEA-EA total phenols 3,4-DHPEA-

EDAa3,4-DHPEA-EA

(Z)-3-hexen-1-ol hexanal p-HPEA-EDAa total phenolshexanal (E)-2-pentenal 3,4-DHPEA-

EAahexanal

(E)-2-pentenal (E)-2-hexenal total phenolsa (E)-2-pentenal(E)-2-hexenal 1-pentanol hexanal (Z)-3-hexenalhexyl acetate 1-hexanol (E)-2-pentenal (E)-2-hexenal(E)-2-hexen-1-ol sum of C5

unsaturatedalcohols

(E)-2-hexenal (Z)-3-hexen-1-ol

(Z)-3-hexenyl acetate sum of C6unsaturatedalcohols

(Z)-3-hexen-1-ol

(E)-2-hexen-1-ol

sum of saturatedalcohols

sum of esters (E)-2-hexen-1-ol

sum of C5unsaturatedalcohols

sum of C5 unsaturatedalcohols

(Z)-3-hexenylacetate

sum of C5unsaturatedalcohols

aCompounds maximized and minimized in the cv. Coratina (fordetails see the text).



Figure 3. RSM and contour plots obtained in the PLS model built to optimize the temperature and oxygen concentration during malaxation in thePeranzana and Ogliarola cultivars.

Figure 4. RSM and contour plots obtained using the PLS model built to optimize the temperature and oxygen concentration during malaxation inthe Coratina and Itrana cultivars.



LPO activity, the optimal malaxing conditions are obtained attemperatures ranging between 30 and 36 °C and low to mid O2concentrations. In this context the esters production seems tobe increased by medium O2 levels in the pastes, which isparticularly apparent in cv. Peranzana, probably due to intensealcohol acetyltransferase activity, the last step in the lip-oxygenase pathway formation of the volatile compounds. Cv.Coratina, on the contrary, which was characterized by thehighest amount of phenols in the EVOOs,33,47,48 shows that theoperative conditions for obtaining the highest response value islocated to quite low malaxation temperature of (25 °C) amongthe cultivars studied. Moreover, the results showed that the lowO2 amount in cv. Coratina seems to be in contrast with thereduction of the phenolic compounds content, which should bepromoted in all cultivars extremely rich in these substances toimprove the oil consumer acceptability by reducing thebitterness and the pungent sensory notes.43,49 It is importantto note, however, that this result has been obtained with thelimit set at 1,000 mg/kg of total phenols content for obtainingpleasant EVOOs. Moreover, cv. Coratina is not onlycharacterized by a high amount of phenols but, at the sametime, shows a low polyphenoloxidase and peroxidase activity.13

The low activity of these enzymes can explain why an increaseof O2 in the pastes produces a small reduction of the phenoliccontent in the oils, lower than that connected with the malaxingtemperature; as a consequence, in cv. Coratina optimizationmodel, low processing temperatures seem to be moreimportant than high O2 concentrations in the reduction ofthe phenolic compounds in the oils. This phenomenon couldbe explained by reduced activity of the depolymerizing enzymesin the pastes that decrease the release of phenols from the cellwall into the olive oil.The optimization of the malaxing parameters of the four

Italian cultivars conducted in this study pointed out thedifferent results of the operative conditions, thus requiringspecific investigations of the optimal setting for the oilmechanical extraction process for each individual cultivar dueto the different influence of the temperature and oxygenconcentration during malaxation. However, it is important tonote that the malaxing parameters here investigated heavilymodify the concentrations of both the phenolic and volatilecompounds independently by olive cultivar.10,14,15,19,50

■ ASSOCIATED CONTENT*S Supporting InformationText describing PCA models, one for each cultivar, utilized toselect the few analytical variables for the RSM optimization andfigures showing score and loading plots of the first two principalcomponents of the PCA models of EVOOs. This material isavailable free of charge via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATIONCorresponding Author*Phone: +39 075 5857903. Fax: +39 075 5857916. E-mail:[email protected].

FundingThis study was kindly supported by the Consorzio OlivicoloItaliano UNAPROLItaly (Projects Reg. UE 1220/2011).NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSWe thank Michele Giglioni and Roberto Santibacci for theirtechnical assistance during this study.

■ ABBREVIATIONS USEDRSM, response surface modeling (or response surface method-ology); EVOOs, extra virgin olive oils; PPO, polyphenolox-idase; POD, peroxidase; LPO, lipoxygenase; CCD, centralcomposite design; p-HPEA, (p-hydroxyphenyl)ethanol; 3,4-DHPEA, (3,4-dihydroxyphenyl)ethanol; 3,4-DHPEA-EDA,dialdehydic form of decarboxymethyl elenolic acid linked to(3,4-dihydroxyphenyl) ethanol; p-HPEA-EDA, dialdehydicform of decarboxymethyl elenolic acid linked to (p-hydroxypheny1)ethanol; 3,4-DHPEA-EA, isomer of the oleur-opein aglycon; HPLC, high-performance liquid chromatog-raphy; DAD, diode array detector; NMR, nuclear magneticresonance; CCC, central composite circumscribed design; FLD,fluorescence detector; HS-SPME-GC/MS, headspace−solidphase microextraction−gas chromatography−mass spectrome-try; DVB/CAR/PDMS, divinylbenzene/carboxen/poly-(dimethylsiloxane); EFC, electronic flow controller; PCA,principal component analysis; PLS, partial least-squares analysis

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Optimization of the Temperature and Oxygen Concentration … · 2016. 9. 13. · Beatrice Sordini, and Maurizio Servili Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Universitàdegli