Influence of chemical composition of olive oil on the development of volatile compounds during...

ORIGINAL PAPER

Influence of chemical composition of olive oil on the developmentof volatile compounds during frying

Giuseppe Procida • Angelo Cichelli •

Dario Compagnone • Ruben M. Maggio •

Lorenzo Cerretani • Michele Del Carlo

Received: 22 July 2009 / Revised: 21 September 2009 / Accepted: 28 September 2009 / Published online: 16 October 2009

� Springer-Verlag 2009

Abstract In this study, 15 virgin olive oils from an

industrial oil plant in the Abruzzo region were analyzed in

terms of the volatile compounds responsible for the char-

acteristic odor and olfactory perception of virgin olive oils

and its modification upon frying (up to 60 min of heat

treatment). Dynamic headspace–gas chromatography–mass

spectrometry analysis was used to evaluate the volatile

profile before and after each frying step and examine cor-

relations with qualitative characteristic of oil (fatty acid

composition, total phenolic compound content, tocopherols

and pigments). The chemometric approach (genetic algo-

rithms–partial least squares/multiple linear regression)

developed for this study is a novel model for data treatment

to select important variables in olive oil composition and

understand their influence on spoilage during frying. An

inverse correlation between oleic acid content and forma-

tion of toxic volatiles such as acrolein and crotonal during

frying was demonstrated. Moreover, it was also observed

that pigments such as chlorophylls, pheophytins, and

carotenoids may prevent the formation of some aldehydes

during frying.

Keywords Virgin olive oil � Volatile compounds �Frying � Degradation � Safety � Acrolein � Chemometric

Introduction

It is estimated that globally about 20 million tons of frying

fats and oils are used each year, with an estimated value of

$83 billion in the United States alone [1]. Frying is a fast,

convenient and energy-efficient cooking method that

increases palatability due to fat absorption, crust formation

and formation of pleasant flavors and odors [2]. However, it

is well known that heating oils for extended periods of time

at high temperatures in the presence of environmental oxy-

gen and water from food leads to severe thermal oxidation.

This causes a series of reactions, namely hydrolysis, isom-

erization and polymerization, whose decomposition prod-

ucts adversely affect both the flavor and color of oils [3–5].

These decomposition products, also known as polar

contaminant materials (PCM), tend to remain liquid and

cannot be filtered out of the oil. They are also deposited on

the surfaces of the fryer and absorbed by food. The PCM

fraction is thought to include most of the potential toxic

products produced in frying oils [6]. In biological tests

conducted by Billek [7], rats fed used frying oil displayed

hematological changes indicative of liver damage. Accord-

ing to Kahl and Hildebrandt [8], the presence of by-products

G. Procida

Dipartimento dei Materiali e delle Risorse Naturali,

Universita di Trieste, Via A. Valerio 6, 34127 Trieste, Italy

A. Cichelli

DASTA, Universita G. d’Annunzio Chieti-Pescara,

Viale Pindaro 42, 65127 Pescara, Italy

D. Compagnone � M. Del Carlo (&)

Dipartimento di Scienze degli Alimenti, Universita di Teramo,

Via C. Lerici 1, 64023 Mosciano Stazione (TE), Italy

e-mail: [email protected]

R. M. Maggio

Departamento de Quımica Analıtica,

Facultad de Ciencias Bioquımicas y Farmaceuticas,

Universidad Nacional de Rosario and Instituto de Quımica

Rosario, (CONICET-UNR), Suipacha 531,

Rosario (S2002LRK), Argentina

L. Cerretani (&)

Dipartimento di Scienze degli Alimenti, Universita di Bologna,

P.zza Goidanich, 60, 47521 Cesena (FC), Italy

e-mail: [email protected]

123

Eur Food Res Technol (2009) 230:217–229

DOI 10.1007/s00217-009-1160-7

of lipid peroxidation in food is undesirable, because the

nutritional value of food decreases with the destruction of

unsaturated fatty acids and other essential food constituents

with an unsaturated lipid structure. Nielsen et al. [9] also

reported that almost all amino acids react with primary and

secondary products of oxidized lipids, thereby decreasing

the digestive quality of proteins, amino acids and fats that

may affect weight gain. Another dangerous compound

formed from food carbohydrates and amino acids by heating

(primarily in fried potato products, bread and coffee) is

acrylamide [10]. Its exposure and toxic effects have been

evaluated by the IARC [11] and a joint FAO/WHO expert

committee on food additives [12]. Special studies have also

dealt with the general toxicity of acrylamide (Mutation

Research, 2005) and its nutritional risks.

The peroxide value (PV) is one of the most frequently

used parameters for assessing the quality of edible oils

during production, storage and marketing. PV shows the

degree of oxidation and measures the amount of total

peroxides as a primary product of oil oxidation [13]. Pro-

gressive lipid oxidation causes hydroperoxide decomposi-

tion (or evolution) yielding multiple secondary products

characterized by different molecular weight (MW) with

respect to starting triglycerides which include volatile

compounds (lower MW), oxidized triglycerides (medium

MW) and triacylglycerols polymers (higher MW).

Although numerous methods for the determination of

volatile compounds are known, the problem of accurate

measurement still exists, particularly for constituents of

low MW. Several methods require the isolation and, fre-

quently, pre-concentration of volatile compounds before

separation by gas chromatography (GC). The dynamic

headspace (DHS) technique, involving the collection of the

volatile fraction, has the advantage of minimal manipula-

tion of the sample and avoids contaminants or artifacts. In

the present study, DHS–GC–mass spectrometry (DHS–

GC–MS) optimized by Barcarolo et al. [14] was used for

the analysis of volatile compounds in olive oils.

Frying is influenced by a large number of variables: the

type of process (i.e. continuous or discontinuous), the

surface to oil volume ratio, the food itself (if it comes in

contact with the oil), the possible addition of fresh oil,

temperature and finally, and of particular relevance, the

kind of oil used [15]. Virgin olive oil is well known for its

fatty acid composition, minor constituents, including toc-

opherols, b-carotene and mainly phenolic compounds that

contribute to its high-oxidative stability [16]. The fatty acid

composition of virgin olive oils (in particular the low

amount of polyunsaturated fatty acids) has an important

effect on their stability during frying, but other factors,

such as the amount and types of naturally occurring minor

constituents can also affect oil stability and the formation

of undesirable fried food by-products [17].

The objectives of this investigation were to study the

chemical changes produced during frying with extra virgin

olive oil (EVOO) and to evaluate the influence of the

diverse classes of compounds naturally present (fatty acids,

phenols, tocopherols and pigments). For this purpose, a

model frying system at 180 �C up to 60 min was used, and

modifications in volatile compounds were analyzed by

DHS–GC–MS. The use of chemometric techniques, such

as genetic algorithms (GA), capable of screening the rel-

evant variables [18], and studies of multiple linear

regression (MLR), able to build efficient mathematical

models and even simple understanding [19], may be crucial

for in-depth studies and analysis of cost–efficiency

relationships.

Materials and methods

Reagents and standards

The standards used for spectrophotometric evaluation of

phenols (gallic acid), antioxidant activity (a-tocopherol)

and ABTS [2,20-azinobis(3-ethylbenzothiazoline)-6-sul-

fonic acid, diammonium salt], and ethyl propionate were

obtained from Sigma-Aldrich (St. Louis, MO, USA). All

solvents were of analytical or HPLC grade (Merck & Co.

Inc., Darmstadt, Germany).

Samples

Sampling of 15 virgin olive oils was carried out directly in

an industrial mill over a period of 3 months from 2006 to

2007: November (7 samples), December (6 samples) and

January (2 samples). Samples were stored in the dark at

room temperature until analysis and frying treatment.

Sample preparation

A 150 mL of each EVOO sample were used for the frying

experiments using a 250 mL black glass beaker with a

surface to volume ratio of 50.2 cm2/150 mL. Two con-

secutive 30-min frying cycles were carried out using gel-

atinized maize starch. During the frying process, the oil

was heated to 180� (the temperature was controlled during

the entire process, and was maintained between 175 and

185 �C). At the end of first frying cycle, the oil was cooled

for 15 min at room temperature.

Gelatinized maize starch was prepared to obtain 3-g

cubes. These cubes had a volume of 1 cm3, and were

prepared using starch maize and distilled water (1:3, w/

w). For each frying cycle, 20 mL of oil was collected

immediately after the frying process and analyzed within

24 h.

218 Eur Food Res Technol (2009) 230:217–229

123

Free acidity and PV

Evaluation of primary auto-oxidation products was carried

out by the determination of the PV according to the official

method described in Regulation EEC 2568/91 [20]. Free

acidity (FA) was also determined according to EU official

method [20]. The mean values are the average of three

replicates per sample.

Extraction and analysis of total phenolic compound

content

Extraction and analysis of total phenolic compounds were

carried out according to Del Carlo et al. [21]. Briefly, C18

cartridges (1 g, 6 mL) (International Sorbent Technology,

UK) were used for the extraction of the phenolic fraction

according to the following protocol: 1 g of olive oil was

dissolved in 10 mL of n-hexane, and the solution was loaded

onto a column previously conditioned with 2 9 10 mL of

methanol and 2 9 10 mL of n-hexane. The column was

eluted with 4 9 10 mL of n-hexane to eliminate the lipo-

philic fraction, and retained phenolic compounds were

recovered by eluting with 4 9 10 mL of methanol.

The total phenolic compound content of the extract was

evaluated colorimetrically using the Folin–Ciocalteau

reagent. The method was adapted as described by Singleton

and Rossi [22]. A diluted extract (0.5 mL of 1:10, v/v) or

phenolic standard was mixed with Folin–Ciocalteau

reagent (5 mL, 1:10 diluted with Nanopure water) and

aqueous Na2CO3 (4 mL, 1 M). Solutions were maintained

at room temperature for 60 min and total polyphenols were

determined colorimetrically at 725 nm. Gallic acid stan-

dard solutions were used to calibrate the method, and

phenolic content was expressed as gallic acid equivalents

(GAE). The mean values were the average of two repli-

cates per sample.

Chlorophylls analysis by HPLC

For determination of chlorophylls a and b and the respec-

tive products of transformation, the pheophytins a and b,

the analytical procedure reported by Minguez Mosquera

et al. [23] was used. This technique is characterized by

preliminary extraction of the pigments by liquid-phase

distribution (LPD). LPD is realized using n-hexane and

dimethylformamide (DMF) as solvents; the hexanic frac-

tion retains lipids and carotenoids, while the DMF fraction

retains chlorophylls and chlorophyllic derivatives such as

pheophytins and xanthophylls. The former fraction was

used for spectrophotometric determination of carotenoids

(vide infra). The latter fraction was then treated with a 2%

Na2SO4 and re-extracted with n-hexane–diethyl ether (1:1);

organic and aqueous phases were obtained, and the first

was discarded removing polyphenols and other hydrophilic

compounds, while the second was dried and re-suspended

in acetone before HPLC injection. The mean values are the

average of two replicates per sample.

Spectrophotometric analysis of carotenoids

The hexanic fraction obtained during chlorophyll extrac-

tion was used for spectrophotometric determination of

carotenoids. 3 mL of the solution were placed into a cuv-

ette and the absorbance measured at k = 454 nm with a

LambdaBio 20 Spectrophotometer (Perkin Elmer, Monza,

Italy). The b-carotene concentration was determined

according to the following equation:

Cðmg� kg�1Þ ¼ E � 10; 000

2; 592

The mean values are the average of two replicates per

sample.

a-Tocopherol determination

A solution of oil (1 g) in acetone (10 mL) was analyzed by

HPLC on a RP-C18 column (particle size 4 lm, 250, 4 mm

i.d.). 20 lL were injected and eluted with 0.5% H3PO4 water

solution and acetonitrile/methanol 1:1 (v/v) at a flow rate of

1.3 mL min-1. A UV detector at k = 295 nm was used. The

mean values are the average of two replicates per sample.

Estimation of TEAC activity using the ABTS�? assay

The radical scavenging activity was measured using the

TEAC (a-tocopherol equivalent antioxidant activity)

method modified for lipophilic samples. The ABTS�?

solution was prepared by reaction of a 7 mM aqueous

ABTS solution with a potassium persulfate (K2S2O8)

solution, final concentration 2.45 mM, as proposed by Re

et al. [24]. Following the storage in the dark for 16 h, the

radical cation solution was further diluted in n-hexane/

ethanol 1:9 v/v until the initial absorbance value of

0.7 ± 0.05 at 734 nm was reached. Each phenolic extract

under study was directly analyzed at three different dilution

ratios (1:5, 1:10, 1:20) in n-hexane/ethanol: a constant

volume (30 lL) of the prepared solutions was added to the

ABTS�? solution and the decrease in absorbance was

recorded after 3 min under stirring. In the same experi-

mental conditions, a dose–response curve for a-tocopherol

as the reference compound was constructed in the range of

0.15–2.40 lM using the percent absorbance reduction as

response signal. For each sample, lmoL g-1 of a-tocoph-

erol equivalent was calculated using the linear regression

equation, yielding the RSA–TTP value. The mean values

are the average of two replicates per sample.

Eur Food Res Technol (2009) 230:217–229 219

123

Fatty acid composition

The fatty acid composition of oil samples, before frying,

was determined as methyl esters by capillary GC (Clarus

500 GC Perkin Elmer Inc., Shelton, CT) according to

Bendini et al. [13] after alkaline treatment of samples. The

alkaline treatment was carried out by mixing 0.05 g of oil

dissolved in 2 mL of n-hexane with 1 mL of 2 N KOH in

methanol according to Christie [25]. The mean values are

the average of two replicates per sample.

DHS–GC–MS analysis of volatile compounds

Olive oil (about 7 g) was weighed in a 10 mL vial and

mixed with an internal standard (ethyl propionate,

35.6 lg). The vials were sealed with an aluminum rubber

septum and conditioned at 35 �C for 15 min before the

analysis. The stripping into a heated block (70 �C) was

carried out for 120 s with helium at a rate of 8 mL min-1.

Volatile compounds were driven into a capillary tube that

was inside a cryogenic trap (liquid nitrogen) maintained at

-100 �C, and connected in on-column mode to the capil-

lary gas chromatograph. The connection to the analytical

column was not direct, as a Y press was inserted and

connected to a vapor exit valve. During the sampling step,

helium was back-flushed through the analytical column

with an outlet in the vapor exit device to avoid any con-

tamination of the column. At the end of sampling (purging)

time, the trap was heated to 240 �C for 5 s and volatile

compounds were desorbed and transferred to the analytical

column. The electronic eight-port valve switched, so that

helium came back in the original direction. The analytical

column used was a capillary fused silica column, 50-m

length 0.32-mm i.d., coated with PS 264, 3-lm film

thickness. The capillary GC was coupled directly to an MD

800 mass spectrometer. Gas chromatographic conditions

were an initial oven temperature of 40 �C for 6 min, then

raised by 5 �C min-1 to 180 �C and held for 5 min; sub-

sequently, the temperature was raised by 7 �C min-1 to

200 �C and held for 2 min, and finally raised by 7 �C

min-1 to 240 �C with 10 min of isotherm. The transfer line

temperature was kept at 250 �C. Mass spectrometer was

scanning from m/z 29 to 300 at 0.5-s cycle time. The mean

values are the average of two replicates per sample.

Chemometric methods

Genetic algorithms–partial least squares (GA-PLS)

Genetic algorithms are general purpose search algorithms

inspired by Charles Darwin’s principle of the ‘survival of

the fittest’ to solve complex optimization problems [26,

27]. GA-PLS [28] is hybrid approach that combines GA as

a powerful optimization method with PLS [29–32] as a

robust statistical method for variable selection. The com-

bination of variables and the internal predictivity of the

derived PLS model in GA-PLS correspond to a chromo-

some (binary bit string, by which the existence of a vari-

able is represented) and its fitness (explained variance and

the root mean square error in prediction, RMSEP) in GA,

respectively.

GA-PLS consists of three basic steps:

1. An initial population of chromosomes is created.

2. Fitness of each chromosome in the population is

evaluated by the internal predictivity of PLS.

3. The population of chromosomes in the next generation

is reproduced (selection, crossover and mutation of

chromosomes)

Steps 2 and 3 are continued until the number of the

repetitions is reached at the designated number of

generations.

In this study, crossover was set at 50% probability.

Mutation changes the value of a gene of an individual with

a given probability to introduce a degree of random noise

into the procedure. This helps to avoid local optima. In this

experiment, mutation occurs with a probability of 10%.

The initial population consisted of 30 chromosomes that

were evolved through minimal 200 generations. As a cri-

terion for accepting a solution, it was assumed that a

solution was already present in the population during the

last five generations [33]. This procedure was repeated for

10 different initial populations by applying different

‘seeds’ to find the optimal input variable subsets for neo-

formation compounds (NFCs).

In this study, it was important to obtain a model con-

taining as few variables as possible because this will pro-

vide a simple and interpretable model. Therefore, the

quality of a chromosome is determined by both its internal

predictivity and the number of variables it uses.

Data were processed employing GA-PLS routines [34]

running in Matlab (Mathworks Inc., Natick, MA, USA).

Multiple linear regression (MLR)

Multiple linear regression is one of the simplest methods of

producing a multivariate calibration model [19], and is still

one of the most commonly used. The advantage of MLR is

its simple form and easily interpretable mathematical

expression. A fixed regressor model is used, which of the

form:

Y ¼ XBþ E ð1Þ

where B are the unknown parameters and Y are the vectors,

the measured calibration data for regressor variables X and

response variable Y, respectively. The error vector, E,


123

consists of the systematic modeling errors and random

measurement errors assumed to have normal distribution

and expected value E(E) = 0. Estimated of the parameter

values are determined by minimizing E. This is simply

done by solving the equation:

B0 ¼ XXT� ��1

XTY ð2Þ

where B0 is the least squares estimate of B. This produces a

model:

Z ¼ XB0 ð3Þ

where Z is the predicted response vector given the cali-

bration matrix X [19]. Although utilized to a large effect,

MLR is vulnerable to descriptors (variables) which are

correlated to one another, making it impossible to dis-

criminate among correlated sets that are significant to the

model. To overcome this problem, new methodologies

based on the MLR have been developed which include best

MLC [35], Heuristic method [36], GA, stepwise MLR [37]

and factor analysis MLR [38].

The MLR models with sequential variable selection

were constructed using the Design-Expert� 7.0.0 software

(Stat-Easy Inc., Minneapolis, MN, USA). For sequential

variable selection, a stepwise regression with type II error

associated with the F-in = 0.10 (a-in) and type I error

associated with the F-out = 0.30 (a-out) was carried out.

In addition, forward regression was carried out with a-

in = 0.20, when the first did not yield satisfactory results.

Results and discussion

Evaluation of FA and PV

The FA values (reported in Table 1) of the oils studied

ranged from 0.14 to 1.03%. Taking into account, the FA of

oil samples, there were 14 oils that could be classified as

‘‘extra virgin olive oil’’ with low free FA values (B0.8%),

while one samples (S15) had an FA between 0.8 and 2%

that defined it as ‘‘virgin olive oil’’ according to European

regulations [39]. Regarding PV, the samples showed values

from 5.5 to 11.2 meq O2 kg-1 oil and an average value of

7.5 (Table 1). This confirms that the samples were all

freshly produced. PV data of all samples were lower than

their legal limit of 20 meq O2 kg-1 [39], respectively.

Phenols, tocopherols and carotenoid content

Untreated EVOO samples were analyzed to quantify phe-

nols, tocopherols and carotenoids, which are known for their

antioxidant activity [16, 40], to evaluate their effects on oil

changes after frying treatment. Data are reported in Table 1.

With respect to the content of phenolic compounds, two

analyses were carried out. In the first, total phenolic com-

pound content (TTP) was evaluated by spectrophotometric

analysis with Folin–Ciocalteau reagent. The TTP reported in

Table 1 presented a high variability depending on the vari-

ety of olives, different typologies of transformation and

production systems [41]. TTP ranged from 266.6 to

803.5 mg GAE kg-1 for S15 and S8, respectively. Fifteen

samples showed a medium average value of 408.8

(mg GAE kg-1 of oil), which demonstrates the virgin olive

oils used in this study were of good quality. In particular, 4

samples showed a TTP lower than 400 mg GAE kg-1; 8

ranged between 401 and 500 mg GAE kg-1 and 3 higher

than 501 mg GAE kg-1. These data are in good agreement

with other quality parameters such as FA and PV and con-

firm the good quality and freshness of the samples.

RSA–TTP values showed a low correlation with TTP

analysis (r = 0.48 for P \ 0.1). However, the outside

values showed an agreement between two analyses, and, in

fact, S8 showed the highest TTP and RSA–TTP values.

S15 exhibited the lowest values for TTP and RSA–TTP.

In EVOO, tocopherols present were found as: a-, b- and

c-tocopherols. a-tocopherol was by far the most abundant

isoform of vitamin E [42]. Table 1 reports the tocopherol

content of the 15 oils before frying treatment. In five

samples tested, the total tocopherol was not below

100 mg kg-1, and only one sample (S7) exceeded

150 mg kg-1 of oil. The average tocopherol concentration

was 115.7 mg kg-1 of oil, which is medium low and is

related to the olive variety and degree of fruit ripening [43].

Spectrophotometric analysis of total carotenoid showed

minimum value of 1.3 and maximum of 2.9, expressed as

mg kg-1 of oil (Table 1). Although low, these values are in

agreement with the previous results reported [44] for olive

oil produced in the Molise region close to the area of

production of this sample oil (Abruzzo region).

Fatty acid composition

Differences in fatty acid composition between the 15 oil

samples were more pronounced for palmitic (C16:0),

stearic (C18:0), oleic (C18:1) and linoleic (C18:2) acids

(Table 1). With regards to the ratio between oleic and

linoleic acid (C18:1/C18:2), the average value of 14.1 is

high and represents an important contribute to virgin olive

oil oxidative stability [16]. In particular, this ratio ranged

from 9.8 to 17.2 and the highest values were observed in

samples S4 and S10.

Volatile profile of the analyzed samples at frying time

zero (t0)

DHS-GC–MS analysis of volatile fractions of virgin olive

oil samples at t0, just prior to the frying process, showed


123

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sam

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PC

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C1

6:0

C1

6:1

C1

8:0

C1

8:1

C1

8:2

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8:3

C2

0:0

C2

0:3

C2

2:0

C2

4:0

C1

8:1

/

C1

8:2

S1

0.1

65

.12

82

.20

.77

62

.13

10

8.7

10

.81

0.9

51

.88

79

.14

5.8

00

.29

0.3

20

.52

0.1

10

.04

13

.64

S2

0.2

75

.53

77

.50

.62

52

.70

13

3.7

11

.44

0.8

81

.95

78

.81

5.4

10

.31

0.3

50

.52

0.1

20

.05

14

.57

S3

0.1

74

.82

73

.60

.49

92

.14

11

5.4

12

.36

0.9

61

.77

78

.52

4.9

50

.28

0.3

10

.56

0.0

90

.03

15

.86

S4

0.4

29

.22

89

.20

.29

71

.47

95

.51

2.4

21

.01

1.9

07

8.6

74

.61

0.2

70

.32

0.5

40

.09

0.0

31

7.0

7

S5

0.2

58

.53

36

.80

.61

21

.56

92

.61

3.3

71

.06

1.6

37

7.3

15

.22

0.2

80

.28

0.5

70

.09

0.0

31

4.8

1

S6

0.1

45

.83

14

.30

.77

82

.69

90

.91

1.2

70

.80

1.7

87

9.4

35

.27

0.3

00

.31

0.5

40

.10

0.0

41

5.0

7

S7

0.2

26

.85

18

.70

.33

72

.76

18

8.7

10

.68

0.8

11

.82

79

.71

5.4

80

.31

0.3

20

.56

0.1

10

.04

14

.55

S8

0.2

28

.08

03

.51

.13

71

.85

13

6.5

11

.65

0.8

01

.86

77

.17

6.8

70

.34

0.3

60

.65

0.1

10

.05

11

.23

S9

0.3

97

.06

16

.40

.57

32

.11

13

2.3

12

.91

1.0

21

.78

76

.63

6.1

70

.29

0.3

00

.61

0.1

00

.04

12

.42

S1

00

.28

6.0

43

3.8

0.5

73

2.8

51

24

.41

1.7

70

.91

1.7

87

9.5

14

.62

0.2

90

.30

0.5

40

.09

0.0

31

7.2

1

S1

10

.20

6.0

37

8.4

0.6

04

2.9

31

10

.61

0.8

80

.90

1.8

08

0.0

34

.94

0.2

90

.31

0.5

50

.10

0.0

41

6.2

0

S1

20

.22

6.4

33

1.6

0.8

34

2.2

61

18

.81

1.5

30

.94

1.6

87

8.5

95

.78

0.3

10

.30

0.5

70

.10

0.0

41

3.6

0

S1

30

.31

7.4

47

7.1

1.0

85

1.6

37

9.8

11

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123

Table 2 Volatile compounds (expressed in lg kg-1) in olive oil samples before frying treatment

Rt Compound name S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

4.04 Acetaldehyde 24.7 42.9 28.6 17.0 14.8 28.4 19.5 60.9 23.3 31.8 29.8 27.6 83.5 14.8 92.4

4.69 Methylformate 10.8 145.4 131.2 6.5 21.7 31.9 51.4 13.6 24.3 21.2 40.9 17.6 30.0 8.0 147.4

5.41 Ethanol 6.4 2.1 2.3 4.6 1.0 1.1 2.5 1.1 1.2 1.4 2.7 0.7 5.6 2.8 6.0

6.32 2-Propenal (acrolein) n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

6.64 Propanal n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

7.63 Ethylformate 29.1 228.9 60.4 47.1 102.7 129.9 163.3 15.2 91.3 208.6 188.6 93.3 130.8 n.d. 343.1

7.81 Methylthiomethane 680.1 753.5 493.3 n.d. 342.1 600.5 488.5 246.2 372.6 212.6 411.5 365.3 555.9 683.8 842.7

8.15 Methylacetate 133.7 722.2 686.5 47.8 382.3 296.3 463.9 72.3 594.7 385.3 449.7 323.3 148.1 96.5 1,005.4

8.75 Dimethylsulfone 5.1 6.6 5.1 n.d. 4.4 8.6 n.d. 9.4 6.1 n.d. 7.6 3.9 n.d. 15.7 5.2

9.48 1-Propanol 29.6 53.1 30.5 11.5 33.0 70.5 42.5 45.6 54.2 24.7 62.8 42.0 173.8 39.3 295.1

10.04 2-Butenal (crotonal) n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

10.89 3-Buten-2-one n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

10.96 2-Butenone 3.9 n.d. n.d. 25.56 n.d. n.d. n.d. 4.99 3.3 n.d. n.d. n.d. n.d. n.d. 26.09

11.18 Butanal n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

11.42 2-Butanone 192.7 237.4 131.4 227.1 89.7 174.2 170.7 56.4 97.5 123.2 98.1 118.6 202.9 249.1 75.5

12.17 2-Methyl-3-buten-2-ol 10.3 3.9 8.9 n.d. 4.3 7.1 6.5 1.6 4.8 3.8 4.7 3.1 8.5 13.9 11.3

12.36 Ethylacetate 118.6 878.4 607.1 229.9 507.9 542.5 793.3 233.2 690.1 702.8 698.9 564.3 543.4 113.4 1,792.8

13.01 Isobutanol 125.1 462.2 398.5 28.0 237.2 457.1 518.4 178.5 327.2 262.1 457.2 311.4 607.2 224.1 1,067.3

13.76 2-Methyl-2-butanol 44.7 45.3 36.7 24.1 20.8 52.6 54.7 12.2 24.5 19.4 26.8 24.8 86.8 58.9 75.6

14.57 3-Methylbutanal 12.9 24.2 5.0 9.1 11.4 6.7 10.9 2.2 21.4 8.3 7.2 6.9 12.9 22.7 19.1

14.86 Diacetyl 1.5 1.7 0.8 1.2 0.5 1.3 1.1 0.3 0.4 n.d. 0.4 0.6 2.3 0.9 2.6

14.96 Butylformate 5.3 2.3 2.3 1.1 1.2 4.2 1.7 3.3 1.9 3.7 0.9 2.8 11.0 2.8 6.5

15.12 2-Methylbutanal 13.8 39.8 21.1 23.3 10.2 4.5 3.7 13.1 24.9 210.7 19.0 20.0 58.1 35.4 73.0

15.57 2-Methyltetrahydrofuran n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

15.98 1-Penten-3-ol 46.3 61.4 35.5 16.6 24.5 60.2 43.5 11.5 47.2 22.7 34.0 34.6 94.9 52.4 37.9

16.8 Pentanal n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

17.51 Ethylpropionate n.d. 6.6 6.9 2.3 5.1 7.5 11.3 4.0 7.9 10.2 12.1 11.1 5.9 1.7 49.6

17.64 3-Ethyl-2,2-

dimethyloxirane

n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

18.68 3-Methyl-1-butanol 73.8 676.1 535.1 35.5 287.5 659.7 579.8 138.3 388.7 452.8 488.4 396.5 677.3 190.1 1,079.8

18.88 2-Methyl-1-butanol 41.6 284.7 316.3 28.5 122.8 247.8 232.9 90.8 184.8 171.1 209.9 163.8 247.4 104.7 490.4

20.32 Amylformate 44.7 n.d. 25.5 4.6 3.3 20.4 n.d. 2.1 n.d. 5.5 n.d. 33.5 44.2 9.9 11.7

20.33 1-Pentanol n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

21.55 2-Hexanone 2.9 5.7 3.0 1.6 1.4 2.8 5.4 1.5 3.1 6.4 1.7 2.2 5.4 6.9 3.3

21.98 3-Hexenal 11.4 5.0 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

22.13 Octane 512.4 444.2 430.6 376.7 265.8 440.7 334.3 137.0 370.1 390.6 283.5 308.2 608.8 478.5 911.8

22.41 2-Octene 1.8 n.d. 2.1 1.6 1.1 1.8 1.5 0.7 1.7 1.7 1.1 1.4 2.0 2.2 4.9

24.23 1-Octene 98.3 121.7 98.1 96.9 54.8 117.9 111.1 44.9 81.2 115.3 75.0 75.9 155.2 128.5 179.9

24.71 2-Hexenal 165.6 166.4 81.8 70.4 81.4 213.4 54.2 55.6 99.0 80.6 119.3 89.3 295.1 114.9 137.3

24.81 3-Hexenol 25.8 39.3 22.5 n.d. 2.2 29.8 20.6 4.1 9.3 18.4 20.9 13.7 45.9 12.8 27.0

25.04 2-Hexenol 22.3 54.5 48.9 7.9 17.3 104.4 27.9 4.9 27.9 36.2 55.8 50.6 97.0 24.0 70.7

25.19 1-Hexanol 28.5 153.2 116.1 10.8 31.0 176.5 52.5 10.5 46.1 59.9 122.8 62.5 97.9 41.1 186.6

26.34 2-Heptanone 3.4 3.8 2.7 n.d. n.d. 1.8 n.d. 0.2 1.0 n.d. 0.8 1.7 1.1 n.d. 0.7

26.86 Heptanal 30.1 6.0 22.2 n.d. 4.2 16.1 n.d. 3.1 10.5 6.6 8.2 13.2 20.0 9.5 11.6

29.39 2-Heptenal 11.8 n.d. 17.1 n.d. n.d. 14.4 n.d. n.d. 4.0 0.9 4.6 13.7 16.0 1.2 1.8

29.67 Heptanol 0.8 1.0 n.d. 0.3 n.d. 1.8 n.d. n.d. n.d. 0.7 n.d. 1.1 1.3 n.d. n.d.

30.51 6-Methyl-5-hepten-2-one 2.1 0.9 2.0 0.7 1.1 1.7 0.9 0.4 0.6 0.7 1.8 1.2 1.7 1.5 2.1

31.22 Octanal 1.2 n.d. 2.1 n.d. 0.5 0.6 n.d. 0.4 1.3 0.6 3.8 5.9 6.8 1.8 3.0

35.26 Nonanal 5.5 1.0 11.1 1.5 0.6 8.6 1.1 0.9 1.5 0.7 2.7 7.8 7.6 0.7 0.8

Internal standard isooctane at 0.02 lL = 13.8 lg

n.d. not detected


123

Table 3 Neoformated volatile compounds (expressed in lg kg-1) on olive oil samples after frying

Sample 2-Propenal

(acrolein)

Propanal 2-Butenal

(crotonal)

3-Buten-2-one Butanal 2-Methyltetrahydrofuran Pentanal

S1

30 min 266.1 129.4 177.9 37.7 57.0 1.1 125.1

60 min 274.3 251.1 198.6 136.1 153.9 1.9 164.5

S2

30 min 491.2 287.3 129.8 87.0 149.5 3.4 239.1

60 min 203.7 91.2 140.9 46.1 96.6 2.2 94.3

S3

30 min 216.3 270.6 186.9 26.6 84.3 1.4 192.3

60 min 427.1 409.2 225.9 51.2 117.3 1.8 190.5

S4

30 min 316.3 212.5 168.2 39.0 100.4 2.4 138.5

60 min 269.5 152.9 58.1 16.3 41.6 1.3 105.8

S5

30 min 450.9 283.3 221.7 66.6 160.8 3.4 27.0

60 min 264.8 419.1 318.6 39.7 19.9 n.d. 70.6

S6

30 min 138.9 68.8 67.5 17.9 42.7 0.6 61.1

60 min 212.6 125.5 84.4 23.8 73.2 1.6 68.1

S7

30 min 288.1 129.3 158.7 60.0 92.0 2.0 135.2

60 min 316.5 161.5 187.1 28.6 64.3 1.3 76.3

S8

30 min 338.1 181.1 114.2 25.3 83.8 1.8 147.9

60 min 142.7 78.9 46.3 12.3 39.5 0.6 55.3

S9

30 min 255.6 118.1 94.1 18.4 54.2 1.1 85.1

60 min 339.8 169.3 147.1 36.0 79.2 1.4 100.9

S10

30 min 187.1 22.4 197.2 31.0 43.6 1.7 153.6

60 min 177.0 79.6 107.0 27.8 44.5 1.1 71.4

S11

30 min 72.3 73.6 104.0 17.2 29.9 0.4 150.7

60 min 123.2 206.2 154.4 59.6 109.1 1.9 127.6

S12

30 min 288.3 186.1 103.7 49.0 106.6 2.0 191.4

60 min 248.8 152.6 86.6 23.2 41.5 0.6 74.6

S13

30 min 264.7 230.7 192.8 55.1 102.3 1.1 174.7

60 min 409.6 298.4 243.1 132.4 167.6 1.5 164.1

S14

30 min 469.2 277.5 255.5 55.9 111.5 3.1 206.1

60 min 305.9 221.8 269.5 61.4 134.2 2.8 146.2

S15

30 min 285.4 135.8 154.7 36.5 57.9 0.9 197.1

60 min 350.2 407.5 239.3 59.0 126.7 1.7 178.9

Time 0 concentration was below the detectable limit

Internal standard isooctane at 0.02 lL = 13.8 lg


123

that the sample aroma consisted of a complex mixture of

more than 38 compounds, representing 97.8–99.9% of the

total GC area (data not shown). Table 2 reports basic

information on the volatile composition of virgin olive oils

before frying. The total volatile content (TVs) of all sam-

ples ranged from 1.4 to 9.1 mg kg-1. Furthermore, in the

headspace of all oil samples, the identified compounds

were mainly alcohols (0.5–3.3 mg kg-1), ketones (0.1–

0.3 mg kg-1), esters (\3.4 mg kg-1), hydrocarbons

(\1.9 mg kg-1) and aldehydes (\0.3 mg kg-1), while no

organic acids or furans were found.

As expected, all fresh olive oil samples contained C6

compounds, also called ‘‘green volatiles’’ which are of

great importance for the green perception in virgin olive oil

flavor [45].

These compounds are derived from a cascade of enzy-

matic reactions starting with the formation, by the action of

lipoxygenase (LOX), of 13-hydroperoxides from linoleic

and linolenic acid [46]. The percentage of C6 aldehydes

ranged between 30 and 79% of total LOX products, with

the exception of one sample having a mean value of 50%.

These values are in accordance with the recent findings in

the analysis of volatile compounds of EVOO [47].

The mean total C6 content was 0.3 mg kg-1, whereas

the mean total C5 content was 1.2 mg kg-1 of which 49

and 58% were C6 and C5 alcohols, respectively.

Neoformation compounds

The analysis of the 15 samples collected, respectively, at

30 min (t30) and 60 min (t60) during the frying process

showed that seven compounds that were not detectable in

the samples at t0 were present after the thermal treatment;

namely, five aldehydes (2-propenal, 2-butenal, propanal,

butanal and pentanal), 1 ketone (3-buten-2-one), 1 furan (2-

methyltetrahydrofuran) are reported in Table 3. Figure 1

shows the mean content of these compounds expressed as a

natural logarithm for a better evaluation of the different

trends during frying in the same scale.

Quantitatively, the five aldehydes represent the most

abundant compounds formed during the frying process and

are general markers of oxidation. As a mean value over the

15 samples, they represent 95% of the total area of NFCs

both for t30 and t60 of frying process. Among aldehydes, 2-

propenal (acrolein) was the most abundant representing 34

and 32% of total aldehydes formed at t30 and t60 frying

process, respectively. Acrolein concentration ranged

between 72.3 and 491.2 lg kg-1 at 30 min and 123.2 and

427.1 lg kg-1 at 60 min. No significant differences in

acrolein concentrations were observed after t30 and t60.

Propanal was the second most abundant aldehyde produced

during the frying process. This compound represented 20

and 25% of total aldehyde formed at t30 and t60, respectively.

Propanal concentrations ranged between 22.4 and

287.3 lg kg-1 at t30 and 78.9 and 419.1 lg kg-1 at t60.

Significant differences in propanal concentration (mean

value) were observed: 173.8 and 215.0 lg kg-1 after 30 and

60 min, respectively. 2-butenal was the third most abundant

aldehyde produced during frying. This compound repre-

sented 18 and 19% of total aldehyde formed at t30 and t60,

respectively. 2-butenal concentrations ranged between 67.5

and 255.5 lg kg-1 at 30 min and 46.3 and 318.6 lg kg-1 at

60 min. No significant differences in 2-butenal concentra-

tions were observed after 30 and 60 min. Pentanal was also

produced during frying, and represented 17 and 13% of total

aldehyde formed at t30 and t60 min, respectively. Pentanal

concentrations ranged between 27.0 and 239.1 lg kg at t30

min and 71.4 and 190.5 lg kg-1 at t60 min. As a mean value,

no significant differences in pentanal concentration were

observed after 30 and 60 min. Finally, butanal was the fifth

most abundant aldehyde detected in fried olive oil. This

compound accounted for the 10% of total aldehyde at both

t30 and t60. Butanal concentrations ranged between 29.9 and

149.5 lg kg-1 at t30 and from 19.9 and 167.6 lg kg-1 at t60.

As a mean value, no significant differences in butanal con-

centrations were observed after 30 and 60 min.

Among the aldehydes detected, only pentanal is reported

to be responsible for a woody, bitter and oily aroma [48].

Acrolein and crotonal have been reported to be toxic

compounds [49, 50]. The ketone 3-buten-2-one and 2-

methyltetrahydrofuran constituted the remaining 5% of

total neoformation products, and none of these compounds

was relevant in terms of off-flavor and toxicity. Nonethe-

less, all these compounds can be used singularly, or as a

group, as a marker of high-temperature thermal oxidation.

Chemometric analysis

The aim of this analysis was to identify compounds present

in olive oil that were statistically related to the develop-

ment of compounds during frying. In the first instance, a

screening step for relevant variables using a GA coupled

0.0

0.5

1.0

1.5

2.0

2.5

0 min 30 min 60 min

ln (

µg k

g-1

)

Frying time

acrolein

propanal

crotonal

pentanal

butanal

3-buten-2-one

2-methyltetrahydrofuran

Fig. 1 Plot showing the trends of neoformated volatile compounds

on olive oil samples during frying. Mean values of data of Table 3 are

reported in the figure


123

q

q

qqq

q

q

A B

C D

E F

G H

Fig. 2 Histograms of cumulative frequency of compounds results of

GA-PLS screening for acrolein (a), propanal (b), crotonal (c), 3-

buten-2-one (d), butanal (e), 2-methyltetrahydrofuran (MTHF) (f) and

pentanal (g). Variance versus number of compounds (h) contributing

to the PLS model. Gene frequency versus number of compounds

(h) contributing to the PLS model


123

with partial least squares (GA-PLS) was carried out

(Fig. 2).

In the algorithm, each variable is called a gene and a set

of variables is called a chromosome. A weighted random

selection is applied to the original population where the

probability of a particular subset (chromosome) to be

selected is function of its fittest function response (variance

explained for a PLS model). Thus, chromosomes with a

best fittest function response will have a greater chance of

selection. The chromosome that can explain the highest

percentage of variance in the PLS model was considered

the winner chromosome. For each NFC, the GA-PLS

procedure was applied 10 times to achieve consistent

results. The most favored genes have the highest frequency

of apparition in the winner chromosome; this implies that

they influence the development of NFC (Figs. 2, 3). The

genes with higher frequencies in the winner chromosome

were utilized for further calculations. These relevant

compounds found in the GA-PLS screening phase are listed

in the Table 4.

The second phase consisted in a correlation study among

relevant variables and NFCs using MLR. The nature of the

relationships between the NFCs and the relevant com-

pounds was investigated in this step. MLR established

correlations among a dependent variable and regressor

variables; an analysis of variance (ANOVA) procedure was

utilized to determine the significance of the model. If the

correlation model with all relevant compounds resulted not

significant, a stepwise or forward variable selection pro-

cedure was carried out. The results of the MLR model can

be seen in the final equation in terms of coded factors. This

model responds to equation like:

y ¼ h0 þ b1x1 þ b2 x2 þ � � � þ bn xn: ð4Þ

where y is dependent variable (NFC), xn are the factors

(regressor variables) which vary among -1 and ?1 corre-

sponding to the minimum and maximum value of the ori-

ginal variable, respectively. Calculations made in this

manner are not influenced by the different range of varia-

tions on regressor variables and allow running a global

ANOVA test for significance of the variable correlation.

The bn corresponded to coefficient correlation of xn coded

factors. A negative bn means a preventive or negative effect

of the corresponding factor on y (NFC). On the other hand,

positive values demonstrate a correspondence with NFC

formation during frying. h0 in the final equation correspond

to a mean value of y found in the data. Table 5 shows the

final equation in terms of coded factors for the NFCs.

The equations reported in Table 5 for the two com-

pounds acrolein and crotonal demonstrate that oleic acid is

involved in the prevention of the formation of such com-

pounds, and that no major promoting effect was exerted by

any particular olive oil component. A small positive

influence by a volatile compound (2-methylbutanal) was

observed only for crotonal formation.

3-Buten-2-one

Fig. 3 Variance (%) for the PLS model versus number of the

calibration variables for acroleine, propanal, crotonal, 3-buten-2-one,

butanal, MTHF and pentanal. Variance versus number of compounds

(Fig. 2h) contributing to the PLS model

Table 4 Results of GA-PLS screening for each NFC

NFC Influencing compounds

Acrolein ChloA, diacetyl, 2-methyl-1-butanol, 2-hexanone, 2-heptenal, nonanal, palmitic acid, oleic acid

Propanal 2-Octene, 3-ethyl-2,2-dimethyl, oxirane, palmitic acid, oleic acid

Crotonal ChloB, PheoA, PheoB, ChloHPLC, 2-methylbutanal, 3-methyl-1-butanol, 2-hexanone, 1-hexanol, 2-methyl-3-buten- 2-ol,

heptanol, palmitic acid, eptadecanoic ecid, oleic acid

3-Buten-2-

one

TPP(CAE), ChloB, PheoA, PheoB, ChloHPLC, 3-methylbutanal, 2-ethyl-1-butanol, amylformate, 2-hexanone, 2-heptenal,

nonanal, dimethylsulfone, heptanal

Butanal Tocopherol, ethylpropionate, 2-methyl-1-butanol, amylformate, 3-hexenol, 2-heptanone, 2-heptenal, 6-methyl-5-hepten-2-one,

nonanal, 3-ethyl-2,2-dimethyloxirane, heptanal, palmitic acid, oleic acid

MTHF ChloB, 1-propanol, isobutanol, 2-heptenal, 6-methyl-5-hepten-2-one oleic acid

Pentanal TPP(CAE), TPP(GAE), carotenoids, ethanol, methylthiomethane, 2-methyl-1-butanol, 6-methyl-5-hepten-2-one, 2-methyl-3-

buten-2-ol, octane, heptanal, stearic acid, linolenic acid, eicosenoic

acid, lignoceric acid


123

No influence on NFCs was reported for TPP, RSA and

tocopherols, thus, confirming that high-temperature treat-

ment spoilage cannot be prevented by antioxidants.

Moreover, the protective effect of oleic acid was observed

for all the NFCs with the exception of 3-buten-2-one and

pentanal. Palmitic acid had a positive influence on the

formation of two short chain aldehydes: butanal and

propanal. No effect was observed on pentanal formation,

which was negatively correlated to linolenic acid concen-

tration. Pigments (chlorophylls and carotenoids) did not

influence strongly the production of NFCs during frying,

even though a protective effect was observed for the for-

mation of acrolein (ChloA), crotonal (PheoA), 2-MTHF

(ChloB), pentanal (carotenoids). No positive effect was

observed for pigments, confirming that when high tem-

perature was applied, both the antioxidant and pro-oxidant

effects of thermolabile molecules were suppressed.

Conclusions

This study demonstrated that, despite the composition both

in terms of non-volatile and volatile compounds, during

frying EVOOs undergo reactions that lead to the devel-

opment of NFCs, some of which have recognized toxicity

such as acrolein and crotonal. Other non-toxic compounds

are also formed, the extent of which also depends on the

EVOO composition. The use of a chemiometric approach

based on a GA coupled with partial least squares (GA-PLS)

followed by MLR with ANOVA procedure was utilized to

determine the significance of the model, and evaluated the

influence of chemical composition on the extent of NFCs.

Concerning the formation of toxic volatiles such as acro-

lein and crotonal, the study showed the non-promoting

influence of oleic acid; moreover, we also observed, as

expected, that pigments and the labile fraction composition

had a limited effect on NCFs production during frying.

This result indirectly confirmed that the developed model is

valid. We can further consider that in domestic frying

where the length of time is not extensive and the temper-

ature is limited, EVOOs represent valid frying oil. The

successive step of this work will compare different frying

conditions (i.e. temperature, time, etc.) and other com-

mercial categories of olive oil and/or seed oils with com-

pare with EVOO.

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