Thermoinduced Lipid Oxidation of a Culinary Oil: A Kinetic Study of the

Aug 30, 2010 - Adrian L. Kerrihard , Ronald B. Pegg , Anwesha Sarkar , Brian D. Craft ... effect of materials used in common food processing on the ev...
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J. Phys. Chem. A 2010, 114, 10059–10065

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Thermoinduced Lipid Oxidation of a Culinary Oil: A Kinetic Study of the Oxidation Products by Magnetic Resonance Spectroscopies Adriano Silvagni, Lorenzo Franco, Alessandro Bagno, and Federico Rastrelli* Dipartimento di Scienze Chimiche, UniVersita` di PadoVa, Via Marzolo, 1 - 35131 PadoVa, Italy ReceiVed: May 11, 2010; ReVised Manuscript ReceiVed: August 11, 2010

1

H NMR and EPR spectroscopies were employed to detect the evolution of lipid peroxidation products resulting from thermal stress in a vegetable oil. The obtained concentration profiles indicate that the secondary oxidation products (saturated and unsaturated aldehydes) may form not only via a direct degradation of primary oxidation products (hydroperoxides), as assumed in the classic kinetic models. In order to explain the observed concentration profiles, an alternate kinetic model is proposed where the aldehydes are additionally generated from hydroperoxides through an independent pathway.

Introduction 1

It is well-known that thermal stress of edible oils caused by routine frying practices (typically 30-90 min at 180 °C in air) generates high levels of toxic aldehyde products (alkanals, alkenals, alkadienals, hydroxyalkenals, and malonic aldehyde) which may react with essential biomolecules such as free amino acids, low-density lipoproteins, and DNA. The most important reaction involved in the oxidative deterioration of polyunsaturated fatty acids is the so-called “lipid peroxidation”, which is a radical chain process that produces hydroperoxy alkenes (ROOH), as described in Scheme 1a.2 Hydroperoxide species are unstable at high temperatures and decompose to a variety of secondary oxidation products including the aforementioned aldehydes3 as well as epoxides, ketones, and esters. The accepted mechanism for aldehyde formation is the β-scission of an alkoxyl radical resulting from the homolytic cleavage of the RO-OH bond, as illustrated in Scheme 1b.4 Except for the initiation step, whose nature remains unclear, the first events of lipid peroxidation have been investigated in multiple contexts, from the elucidation of the role of pro- and antioxidants5 to the degradation process of cellular membranes.6 Furthermore, a mathematical analysis has been recently provided7 where the role of the radical termination reactions is explored in detail. It is perhaps worth noting that many such studies employ photo- or thermal radical initiators to provide a constant supply of radical species during the experiments.5c,8 As far as the secondary oxidation is concerned, dedicated kinetic studies are not commonplace in the literature, partly due to the difficulty of finding an accurate method to quantify the oxidation products, in particular aldehydes. In fact, even though several complementary techniques have been employed to detect lipid oxidation products (UV,9 IR,10 GC, or HPLC/MS11), each of them exhibits some drawbacks. IR spectroscopy is useful for monitoring the evolution of peroxides, since the hydroperoxide group absorbs at about 3444 cm-1, but secondary oxidation carbonyl compounds feature a band which overlaps with that of the ester groups. On the other hand, UV and MS techniques are valid tools to identify the aldehydes, yet they * To whom correspondence [email protected].

should

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addressed.

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cannot be employed on raw samples without preprocessing or derivatization of the target species. NMR spectroscopy, on the contrary, allows one to analyze samples with little or no pretreatment at all, and several works have appeared reporting on NMR studies of edible oils12 (often in association with other techniques, such as IR13 or MS14 spectrometry) to identify the molecular structure of oxidation products. 1H NMR is particularly useful to analyze the evolution of aldehydes,15 since aldehyde protons resonate in a characteristic region of the spectrum (9-10 ppm) which is generally free from other signals. Besides the well-known sensitivity issues of the method itself, a major problem in the NMR approach is that aldehyde signals have a much lower intensity relative to lipid signals and, when most of the instrumental dynamic range is used to digitize the strongest signals, the weak ones may lie close to (or below) detectability. However, this stumbling block can be effectively removed by cutting off the more intense resonances through selective excitation of restricted portions of the NMR spectrum.16 Finally, electron paramagnetic resonance spectroscopy (EPR) has also been successfully employed in food chemistry to investigate the auto-oxidation processes of beverages (wine, beer)17 and edible oils.18 Given its high sensitivity, EPR could in principle detect the evolution of radical intermediate species in reaction kinetics, but the radical stability is often too low to produce a quantifiable steady-state concentration. In order to circumvent this drawback, the spin-trapping technique was developed,19 which exploits a fast reaction between diamagnetic molecules (spin traps) and short-lived radicals to produce more stable, easily detectable secondary radicals (spin adducts). Moreover, since the EPR spectra of the spin adducts depend on the precursor radical, in favorable cases, the structure of the radical intermediates can also be identified. Starting from this survey, we decided to combine EPR and NMR spectroscopies to investigate in detail the lipid peroxidation kinetics of a complex substrate (peanut oil) under controlled conditions of thermal stress. We deliberately chose to investigate an edible vegetable oil rather than a pure synthetic triacylglycerol, in order to investigate the sought phenomena under conditions closest to actual usage, thereby emphasizing the connection of this study with food chemistry. However, the choice of peanut oil has no special significance.

10.1021/jp104295c  2010 American Chemical Society Published on Web 08/30/2010

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SCHEME 1: Peroxidation of Unsaturated Fatty Acids: Reaction Pathways Leading to Primary (a) and Secondary (b) Oxidation Productsa

a

Additional pathways3 leading to geometric and position isomers among the oxidation products are omitted for clarity.

As far as the first reaction steps are concerned, calculations with models and rate constants available in the literature7 suggest that the concentration of the alkyl radical species should reach a steady state within a few minutes after an initial, rapid buildup. In order to verify this anticipation, in the first place, we performed spin-trapping EPR experiments to trace the evolution of the alkyl radical species and to estimate the initiation constant k1. NMR spectroscopy was then employed to detect the primary (hydroperoxides) and secondary (saturated and unsaturated aldehydes) oxidation products generated under the same conditions of thermal stress. Quite surprisingly, the concentration profiles obtained for primary and secondary oxidation products indicate that these species may form not only via the mechanism highlighted in Scheme 1b and that an alternate pathway is likely to be involved. Experimental Methods Materials and Samples Processing. Commercial peanut oil was obtained from a local supermarket. In order to eliminate any initial amount of peroxides, the raw oil was passed through an alumina plug (4 cm, Alumina basic, Sigma) and the obtained sample was stored at 4 °C under a nitrogen atmosphere prior to thermal stress. The removal of peroxides was monitored via UV spectroscopy, whereby the efficiency of this method proved to be comparable to that attained by an alumina column eluted with hexane20 (see the Supporting Information). The spin trap N-tert-butyl-R-phenylnitrone (PBN, Sigma) was used as received. All the following experimental procedures were repeated at least in duplicate on freshly prepared samples. Thermal Stress. Preliminary experiments on the thermal stress of peanut oil highlighted a high variability of the generated oxidation products depending on the experimental conditions. To ensure the highest possible reproducibility, the following procedure was adopted. Compressed air (previously dried in a CaCl2 trap) was forced into a gas line with two outlets. The first outlet was connected to the reaction vessel itself, that is, a sealed three-neck round-bottomed flask containing 40 mL of the pretreated oil. The second outlet was connected to the external environment allowing a steady air flow of 140 mL/ min. Samples were withdrawn at fixed time intervals (generally 20 min) by stopping the second outlet in such a way that the overpressure pushed the oil outside the flask through an immersed stem. Thermal stress was carried out by heating the flask at 180 ( 1 °C with a thermostatic bath operated by an electronic feedback temperature controller.

NMR Analysis. 200 µL of each oil sample was dissolved in a mixture of 900 µL of CDCl3 and 100 µL of DMSO-d6. 500 µL of the resulting mixtures was transferred into 5 mm NMR tubes. A standard solution of pyrazine (10-2 M in CDCl3) was prepared separately and its concentration accurately determined by UV-vis spectroscopy (ε312 ) 767 ( 5 cm-1 M-1). 100 µL of this standard solution was then transferred into a coaxial insert. Before any NMR measurement, the same coaxial insert was arranged into each of the 5 mm NMR tubes containing the samples. NMR spectra were acquired at 25 °C on a Bruker DMX 600 spectrometer equipped with a 5 mm TXI xyz gradient inverse probe. The pulse scheme adopted for the detection of oxidation products was the same as that described in ref 16, with an 8 ms adiabatic Gaussian pulse as the refocusing element (providing a flat excitation bandwidth of about 2 kHz). The first hard pulse was reduced to a π/4 flip angle, and 16k pointss corresponding to 0.98 s acquisition timeswere collected for each transient. After 8 equilibration scans, 96 scans separated by a 5 s relaxation delay were averaged for each sample. The total experimental time resulting from this setup was about 10 min per sample. EPR Analysis. 22 mg of PBN was dissolved into pretreated peanut oil (1 mL) under mild sonication. About 100 µL of the resulting solution (0.125 M) was transferred into quartz tubes of 4 mm outer diameter, and these were inserted inside the resonant cavity preheated at the selected temperature. The equilibration time of the small-volume samples was estimated to be about 1 min. EPR spectra were recorded on an X-band (9.5 GHz) Bruker ER200D spectrometer equipped with a nitrogen-flow Bruker BVT-1000 variable temperature system. Typical experimental parameters were as follows: sweep width 100 G, modulation amplitude 0.2 G, microwave power 1 mW, sweep time 20 s, time constant 5 ms. When required, samples of oil/PBN were deareated directly inside the quartz tubes by repeated pump-freeze-thaw cycles and sealed under a 10-3 mmHg vacuum. The analysis of EPR spectra was carried out by means of EasySpin routines21 running in a Matlab22 environment. The intensities of the EPR spectra were measured as the double integral of simulated spectra. Absolute concentrations of the radical species were calculated by means of a calibration curve obtained from EPR spectra of 2,2,6,6-tetramethylpiperidine-1oxyl (TEMPO) solutions in toluene. Mathematical Modeling. The systems of differential equations derived from every kinetic model were first integrated

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SCHEME 2: Spin Adducts Resulting from the Reaction of PBN with Different Radicalsa

a

R indicates a fatty acid chain bound by an allylic position.

TABLE 1: EPR Parameters of Radical Adducts 1 and 2 (See Text)a radical spin adduct

aN (G)

aH (G)

1 2

14.95 14.80

2.10 3.20

a An example of the best-fit and the corresponding experimental EPR spectrum is given in the Supporting Information (Figure S2).

Figure 1. EPR spectra of PBN in peanut oil after 10 min of heating at 180 °C. Dashed, sample open to air; solid, deareated and sealed sample.

numerically by means of a dedicated Mathematica23 notebook using literature values of the kinetic constants (where available). The missing values were then estimated by visually matching the calculated curves and the experimental points. These estimates were then used as starting points for a least-squares fitting in a dedicated software.24 Results and Discussion Primary Oxidation. As mentioned in the Introduction, radical species evolving during the lipid oxidation processes are extremely short-lived, but the same species can react with suitable spin traps to give stable adducts. In particular, fatty acid esters heated in the presence of PBN have been reported to form three adducts,18a notably a first one between the spin trap and the peroxyl radical (ROO•) (A1), a second one formed by R• and methylnitrosopropane (a degradation product of the first adduct) (A2), and a third one between the spin trap and the alkyl radical (R•) (A3) (see Scheme 2). EPR spectra of peanut oil with added PBN were collected at 180 °C both in the presence and in the absence of oxygen. The resulting spectra feature three doublets originating from the hyperfine coupling of the unpaired electron with one 14N and one 1H nucleus (Figure 1). All EPR spectra can be fitted with a weighted sum of two subspectra originating from two radical adducts 1 and 2, whose hyperfine coupling constants with 14N (aN) and 1H (aH) are reported in Table 1. Although the hyperfine coupling constants aN and aH of 1 and 2 do not differ much, the two adducts can be identified by comparison with literature data.18a In such a way, we assign 1 and 2 to A2 and A3 of Scheme 2, respectively. The EPR spectra of oil samples heated at 180 °C and open to air (i.e., in the presence of O2) show a large excess of 1, in agreement with the assignment of this species to a peroxyl

Figure 2. Time evolution of the EPR spectrum during thermal stress at 180 °C carried out on a sample of peanut oil with added PBN and open to air.

radical derived adduct, whereas the EPR spectra of deareated and sealed samples are dominated by the presence of 2. In order to follow the kinetics of radical formation, we have monitored the evolution of the EPR spectra during the thermal stress up to 80 min (Figure 2). By means of a best fit procedure, we were able to separate the contributions of the species 1 and 2 at all time points of the thermal stress episode, thus obtaining the kinetic curves of each adduct. The contributions of 1 and 2 to the total EPR intensities, both on an aerated and on a deaerated sample, are reported in Figure 3. As expected, at high temperatures, the concentration of alkyl radicals reaches a steady state in a few minutes. More precisely, the signal of radical 1 reaches a plateau within the first 10 min of heating in the aerated sample. The kinetic curves obtained from the deaerated sample can be used to estimate the rate constant k1: in doing so, we assume a kinetic model where the only relevant events are the initiation, the radical spin trapping, the radical termination, and the degradation of the spin adduct (propagation does not vary the total number of free radicals). By means of a fitting procedure (reported in the Supporting Information), we estimated k1 ) 1.06 × 10-5 s-1.

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Figure 3. Contributions of radical 1 (circles) and 2 (squares) to the total EPR signal intensity during an episode of thermal stress at 180 °C carried out on an aerated sample (left) and on a deareated sample (right) of peanut oil with added PBN.

Figure 4. Typical 1H NMR spectrum of peanut oil (after thermal stress at 180 °C) obtained by selective excitation of the low-field region 7-11 ppm. The brackets indicate the actual integration limits adopted to quantify each chemical species.

In the absence of other literature data, we assume the above value as a best estimate of k1 and will use this for subsequent calculations in the kinetics of secondary oxidation. Secondary Oxidation. The kinetics of secondary oxidation was studied by band-selective 1H NMR16 which, in our case, allows one to simultaneously follow the evolution of both peroxides and aldehydes. A sample of vegetable oil prepared as described in the Experimental Section gives the typical NMR spectrum reported in Figure 4. We point out that the hydroperoxide protons are unusually deshielded, since their resonances are typically found in the range 8.5-8.9 ppm in place of the 10.2-10.6 ppm range observed in this work.12a Such a pronounced shift is due to the presence of DMSO-d6 forming strong hydrogen bonds. Indeed, the very same effect could also be observed in a sample of cumene hydroperoxide dissolved in a CDCl3:DMSO-d6 ) 9:1 mixture (see the Supporting Information). When peanut oil is heated at 180 °C, the evolution of first and secondary oxidation products follows the trend reported in Figure 5. It can be seen from the graph that (1) the concentration of aldehydessboth saturated and unsaturatedsbuilds up linearly, while the concentration of peroxides grows to a plateau and (2) the buildup rate of unsaturated aldehydes is larger than that of saturated aldehydes. According to Scheme 1b, this seems to support the involvement of a high-energy intermediate species in the pathway leading to saturated aldehydes, which is consistent with a vinyl radical being generated in the β-scission.

Moreover, according to the same scheme, an induction period is also expected in the formation of aldehydes, since these are generated only from hydroperoxides. Nonetheless, neither induction nor degradation of the aldehydes could be detected, even when additional episodes of thermal stress were carefully examined over shorter and longer periods of time (Figure 6a and b, respectively). It is worth noting that such a linear trend has also been observed in other studies employing UV spectroscopy, where the total amount of aldehydes generated during episodes of thermal stress was estimated from the p-anisidine value (p-AV).9 Last but not least, the linear accumulation of the aldehydes observed through the thermal stress episode may have serious toxicological implications, especially with regard to long-heated or recycled frying oils. On the basis of the mechanism depicted in Scheme 1, the relevant kinetic equations are d[R•] ) k1′ - k2′ [R•] + k3′ [ROO•] dt d[ROO•] ) k2′ [R•] - k3′ [ROO•] - 2 × k4[ROO•]2 dt d[ROOH] ) k3′ [ROO•] - k5[ROOH] dt d[RO•] ) k5[ROOH] - (k6 + k7 + k8)[RO•] dt d[uns] ) k6[RO•] dt d[sat] ) k7[RO•] dt

(1)

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Figure 5. Evolution of the oxidation products during the thermal stress of peanut oil at 180 °C. The curves represent the best fit to the same experimental data set as resulting from (a) the classic kinetic model eq 1 and (b) the alternate kinetic model eq 2. The sum of squared deviations is 0.64 for part a and 0.061 for part b. 9 ) peroxides, b ) unsaturated aldehydes, O ) saturated aldehydes.

Figure 6. Evolution of aldehydes monitored over 80 min (a) and over 320 min (b) during the thermal stress of peanut oil at 180 °C. The linear trend observed in both episodes confirms that neither induction nor degradation occurs within the observed time span. Lines are drawn only with the purpose of guiding the eye. b ) unsaturated aldehydes, O ) saturated aldehydes.

where “uns” and “sat” are the unsaturated and saturated aldehydes and the pseudo-first-order constants for the first, second, and third steps are defined as k1′ ) k1[RH], k2′ ) k2[O2], and k′3 ) k3[RH]. Integration of the above differential equations followed by fitting of the experimental data provides the curves shown in Figure 5a. While the evolution of peroxide concentration is adequately described by this model, the calculated curves for the aldehydes do not fit the linear trend of the experimental data, otherwise showing an induction period. To address this inconsistency, we propose that the aldehydes, rather than being generated from hydroperoxide species only, can also be formed via the parallel process highlighted in Scheme 3 (ROO• + ROO• f 2 RO• + O2). In fact, peroxyl radicals can undergo a bimolecular termination reaction to form intermediate tetraoxides which are unstable at high temperatures and decompose through different pathways, including the formation of two alkoxyl radicals and molecular oxygen.25 This particular reaction was chosen among others possibly leading to aldehyde formation, since its insertion in the classic model requires no rearrangement of the model itself. However, the linear buildup of aldehydes could be explained by any second (or higher) order kinetics with respect to the peroxyl radicals.

On the basis of the mechanism depicted in Scheme 3, the relevant kinetic equations become

d[R•] ) k1′ - k2′[R•] + k3′[ROO•] dt d[ROO•] ) k2′[R•] - k3′[ROO•] - 2 × dt k4[ROO•]2 - 2 × k3b[ROO•]2 d[ROOH] ) k3′[ROO•] - k5[ROOH] dt d[RO•] ) k5[ROOH] + 2 × dt

(2)

k3b[ROO•]2 - (k6 + k7 + k8)[RO•] d[uns] ) k6[RO•] dt d[sat] ) k7[RO•] dt where the pseudo-first-order rate constants are again defined as above. Integration of the model expressed by eq 2 provides estimated rate constants leading to curves that adequately fit the experimental data (Figure 5b). In order to test the reliability of such estimates, the proposed model was employed to fit

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SCHEME 3: Alternate Kinetic Model for the Peroxidation of Unsaturated Fatty Acidsa

Silvagni et al. TABLE 2: Best Estimates of Kinetic Constants as Resulting from the Fit of the Model Equations (2) to the Data Reported in Figure 7 (Details on the Fitting Procedure Are Given in the Supporting Information) constants

pretreated sample

raw sample

k1′ k2′ k′3 k3b k4 k5 k6 k7 k8

1.08 × 10-4 a 106 0.50 2.52 × 105 8.8 × 106 4.4 × 10-4 7.1 × 102 2.5 × 102 4.26 × 103

1.08 × 10-4 a 106 0.31 1.83 × 105 9.1 × 106 7.3 × 10-4 5.9 × 102 2.1 × 102 3.07 × 103

literature values 106-1010 b 10-2-105 c 103-107

units M s-1 s-1 s-1 M-1 s-1 M-1 s-1 s-1 s-1 s-1 s-1

Estimated from EPR spectra (see text). Note that k1′ ) k1 × [RH], where RH refers to the allylic and bis-allylic hydrogens. [RH] ≈ 10.2 M in our case. b Value referred to k2. Note that k2′ ) k2 × [O2], where [O2] ≈ 10-3 M in oil under normal conditions.26 c Value referred to k3. Note that k3′ ) k3 × [RH] depends on the unsaturation degree. a

a The parallel pathway leading to the aldehyde formation is marked with dashed arrows.

experimental data obtained from episodes of thermal stress on nontreated, raw peanut oil (Figure 7b). Since the raw and pretreated samples only differ in the initial concentration of peroxides, the same set of kinetic constants is expected to adequately describe the concentration profiles of the oxidation products when the thermal stress is carried out under identical conditions. The best estimates of the kinetic constants for the model eq 2 are listed in Table 2. We point out that the literature values available for k3′ (the rate-determining step constant) span a very wide range, since this constant depends on the unsaturation degree of the substrate and on the steric hindrance of the allylic and bis-allylic hydrogens.6 In fact, lipid peroxidation of unsaturated fatty acids easily takes place because allylic and, even more so, bis-allylic

hydrogen atoms are prone to abstraction by peroxyl radicals. Most of the literature constants available for this step have been estimated using experimental data from methyl esters of unsaturated fatty acids (e.g., oleic, linoleic, linolenic)27 and range from 0.2 M-1 s-1 for methyl oleate to 60 M-1 s-1 for methyl linolenate. Conversely, the experimental values reported in this work are lower than those expected for an unsaturated vegetable oil containing a significant percentage of linoleic acid (21% as calculated from 1H NMR15b). It can thus be speculated that the forced proximity of the alkyl chains in triacylglycerols exerts some hindrance effect which lowers the rate-determining-step constants. The possibility to quantify this particular effect by studying thermal stress of vegetable oils with different unsaturation degrees is currently under investigation. Conclusions In this work, we investigated the kinetics of thermally induced lipid peroxidation of peanut oil using nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR). The use of EPR, combined with the spin trapping technique, allowed us to detect the buildup kinetics of the primary alkyl radicals, and provided a lower-limit estimate of the radical generation rate. Overall, it is concluded that at 180 °C alkyl radicals reach a stationary concentration in a short time, within the first 10 min of thermal stress. Band-selective 1H NMR is capable of detecting small amounts of chemical species in complex matrices, and as such, it is a

Figure 7. Best fitting curves resulting from the alternate kinetic model eq 2 to the experimental data sets obtained from (a) a pretreated sample and (b) a non-pretreated sample (raw oil).

Thermoinduced Lipid Oxidation of a Culinary Oil powerful tool to investigate the thermal degradation of edible oils. In this particular case, the advantage of NMR with respect to other analytical methods lies in its ability to simultaneously detect the products of primary and secondary oxidation, thus allowing a more detailed kinetic investigation. Preliminary NMR experiments of thermal stress at 180 °C on peanut oil highlighted that the growth of both saturated and unsaturated aldehydes is linear, with no induction periods. This peculiar trend is in contrast with the accepted model where aldehydes are generated only via peroxide consumption: in fact, if this hypothesis were true, an induction period would be observed in the aldehyde buildup curves. Moreover, it was also observed that, over longer time periods (up to 320 min), aldehydes do not degrade significantly. We therefore propose an alternate model where aldehydes and peroxides are generated independently starting from a common peroxyl radical (ROO•) precursor. Although other assumptions and mechanistic refinements can be invoked to explain the experimental evidence, we found that this simple modification to the kinetic model provides a satisfactory fitting of the experimental data. In order to test the reliability of the alternate model, two similar systems were investigated, notably raw peanut oil and pretreated peanut oil (i.e., oil with peroxide species removed). As expected, the set of curves that best fit the experimental data results from rate constants of comparable magnitude in both cases. In this context, it is worth noting that discrepancies of several orders of magnitude are commonplace in the literature for primary oxidation constants (e.g., k4 ) 105-107 and k3c ) 10-2-105).6,7 The method developed in this work is generally applicable to other edible oils and to different experimental conditions. In fact, based on the same approach, the effect of pro-oxidants and antioxidants on the oxidation products is currently under investigation, alongside with the effect of different unsaturation degrees of other edible oils. Acknowledgment. This work was financially supported by the University of Padova (Progetto di Ricerca di Ateneo CPDA078133/07). We thank Dr. L. J. Prins for helpful discussions and suggestions on the manuscript. Supporting Information Available: Additional UV, EPR, and NMR spectra discussed in the text. Details on the data fitting procedure. This material is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) See, for example: Grootveld, M.; Silwood, C. J. L.; Claxson, A. D. W. Food Chem. 1999, 67, 211–213, and references cited therein.

J. Phys. Chem. A, Vol. 114, No. 37, 2010 10065 (2) Choe, E.; Min, D. B. Compr. ReV. Food Sci. Food Saf. 2006, 5, 169–186. (3) Frankel, E. N. Prog. Lipid Res. 1985, 23, 197–221. (4) Belitz, H.-D.; Grosch, W.; Schieberle, P. Food Chemistry, 4th ed.; Springer-Verlag: Berlin, Heidelberg, 2009; p 205. (5) See, for example: (a) Yanishlieva, N. V.; Kamal-Eldin, A.; Marinova, E. M.; Toneva, A. G. Eur. J. Lipid Sci. Technol. 2002, 104, 262–270. (b) Brimberg, U. I.; Kamal-Eldin, A. Eur. J. Lipid Sci. Technol. 2003, 105, 83–91. (c) Mosca, M.; Ceglie, A.; Ambrosone, L. J. Phys. Chem. B 2010, 114, 3550–3558. (6) Antunes, F.; Salvador, A.; Marinho, H. S.; Alves, R.; Pinto, R. E. Free Radical Biol. Med. 1996, 21, 917–943. (7) Doktorov, A. B.; Lukzen, N. N.; Pedersen, J. B. J. Phys. Chem. B 2008, 112, 11854–11861, and references therein. (8) Van Dyck, S. M. O.; Verleyen, T.; Dooghe, W.; Teunckens, A.; Adams, C. A. J. Agric. Food Chem. 2005, 53, 887–892. (9) Houhoula, D. P.; Oreopoulou, V.; Tzia, C. J. Am. Oil Chem. Soc. 2002, 79, 133–137. (10) Guille´n, M. D.; Cabo, N. Food Chem. 2002, 77, 503–510. (11) Andersson, K.; Lingnert, H. J. Am. Oil Chem. Soc. 1998, 75, 1041– 1046. (12) See, for example: (a) Claxson, A. W. D.; Hawkesb, G. E.; Richardson, D. P.; Naughton, D. P.; Haywoodayb, R. M.; Chander, C. L.; Atherton, M.; Lynch, E. J.; Grootveld, M. C. FEBS Lett. 1994, 355, 81– 90. (b) Silwood, C. J. L.; Grootveld, M. Lipids 1999, 34, 741–756. (13) Moya Moreno, M. C. M.; Mendoza Olivares, D.; Ame´zquita Lo´pez, F. J.; Peris Martı´nez, V.; Bosch Reig, F. J. Mol. Struct. 1999, 483, 557– 561. (14) Schiller, J.; Su¨β, R.; Petkovic´, M.; Hanke, G.; Vogel, A.; Arnold, K. Eur. J. Lipid Sci. Technol. 2002, 104, 496–505. (15) (a) Guillen, M. D.; Ruiz, A. Eur. J. Lipid Sci. Technol. 2004, 106, 680. (b) Guillen, M. D.; Uriarte, P. S. J. Agric. Food Chem. 2009, 57, 7790–7799, and references therein. (16) Rastrelli, F.; Schievano, E.; Bagno, A.; Mammi, S. Magn. Reson. Chem. 2009, 47, 868–872. (17) Elias, R. J.; Andersen, M. L.; Skibsted, L. H.; Waterhouse, A. L. J. Agric. Food Chem. 2009, 57, 4359–4365. (18) (a) Vicente, M. L.; Empis, J. A.; Deighton, N.; Glidewell, S. M.; Goodman, B. A.; Rowlands, C. C. J. Chem. Soc., Perkin Trans. 1998, 2, 449–454. (b) Quilesa, J. L.; Ramı´rez-Tortosa, M. C.; Gomez, J. A.; Huertasa, J. R.; Mataixa, J. Food Chem. 2002, 76, 461–468. (c) Velasco, J.; Andersen, M. L.; Skibsted, L. H. Food Chem. 2004, 85, 623–632. (19) Janzen, E. G. Acc. Chem. Res. 1971, 4, 31–40. Buettner, G. R. Free Radical Biol. Med. 1987, 3, 259–303. (20) Jensen, R. G.; Marks, T. A.; Sampugna, J.; Quinn, J. G.; Carpenter, D. L. Lipids 1966, 1 (6), 451–452. (21) Stoll, S.; Schweiger, A. J. Magn. Reson. 2006, 178, 42–55. (22) Matlab, release 2008; The MathWorks, Inc.: Natick, MA, 2008. (23) Mathematica, version 6.0; Wolfram Research, Inc.: Champaign, IL, 2007. (24) Scientist for Windows; MicroMath Research: St. Louis, MO, 1995. (25) von Sonntag, C.; Schuchmann, H.-P. Angew. Chem., Int. Ed. Engl. 1991, 30, 1229–1253, and references therein. (26) Parenti, A.; Spugnoli, P.; Masella, P.; Calamai, L. Eur. J. Lipid Sci. Technol. 2007, 1180–1185. (27) See, e.g.: Howard, J. A.; Ingold, K. U. Can. J. Chem. 1967, 45, 793–802.

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