Metabolic profiling and analysis of volatile composition of durum wheat semolina and pasta

Journal of Cereal Science 49 (2009) 301–309

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Metabolic profiling and analysis of volatile composition of durum wheat semolina and pasta Romina Beleggia a, *, Cristiano Platani a, Giuseppe Spano b, Massimo Monteleone c, Luigi Cattivelli a a

CRA Cereal Research Centre, S.S.16 Km 675, 71100 Foggia, Italy Department of Food Science, Foggia University, via Napoli 25, 71100 Foggia, Italy c Department of Agro-Environmental Sciences, Chemistry and Plant Defense, Foggia University, via Napoli 25, 71100 Foggia, Italy b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 April 2008 Received in revised form 14 October 2008 Accepted 9 December 2008

Although pasta is generally not considered for its aromatic properties, some evidence proves that cereal flours release volatile compounds and they might have an effect on the aroma of the transformed products. This work reports on the characterization of the volatile components of semolina and pasta obtained from four durum wheat cultivars (Triticum durum Desf., cvs. PR22D89, Creso, Cappelli, Trinakria). Semolina samples were characterized through polar metabolite profiling and fatty acid analysis to identify potential precursors of the volatile components. The results show significant differences among the samples tested with cv. Trinakria characterized by the highest content of sugars and fatty acids. Volatile composition was investigated both in semolina and in cooked pasta using headspace solidphase micro-extraction (HS-SPME) and identified by GC–MS. Thirty-five volatile compounds including aldehydes, ketones, alcohols, terpenes, esters, hydrocarbons and a furan were identified. Significant differences were observed between semolina and pasta samples in terms of composition and amount of the volatile compounds. During cooking an increase in aldehyde content, the appearance of ketones and a decrease in alcohol content were observed. Correlations between metabolites and volatiles demonstrate that the flavour of cooked pasta may differ significantly depending on the durum wheat cultivar employed. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Metabolite profiling Volatile compounds Durum wheat Pasta

1. Introduction Durum wheat (Triticum durum Desf.) is one of the most widely grown food crops in the Mediterranean region and it is the main source of semolina for the production of pasta, couscous, burghul and bread (Quaglia, 1988; Raffio et al., 2003). Although semolina and pasta are generally not considered for their aromatic properties, some data suggest that volatile compounds are present in the cereal flour and they might have an effect on the aroma of the transformed products (Bredie et al., 2002; Parker et al., 2000; Sjo¨vall et al., 1997). Among all factors affecting food quality, aroma has a great influence on consumer acceptability (Zhou et al., 1999) and a wide range of chemical compounds, often present in trace amounts, contribute to the definition of the aroma (Seitz, 1995). In cereals, the aroma is dependent on the kernel chemical composition either directly, because the flour/semolina itself

* Corresponding author. Tel.: þ39 0881 742972; fax: þ39 0881 713150. E-mail address: [email protected] (R. Beleggia). 0733-5210/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcs.2008.12.002

contains some aroma-active volatile compounds, or indirectly because the flour/semolina contains important aroma precursors whose presence leads to the releasing of various flavours during processing and cooking (Hansen, 1995). Volatile composition is also dependent on process parameters such as temperature, moisture and pH (Bemis-Young et al., 1993; Bredie et al., 2002). Considering that pasta can be produced through different pasta making processes characterized by low or high drying temperature, the choice of the transformation process could influence the final aroma formation. The chemical composition of semolina has a primary role in the determination of the volatile composition of pasta. Metabolic profiling offers the unbiased ability to differentiate genotypes based on metabolite levels that may or may not produce visible phenotypes (Fiehn, 2002; Raamsdonk et al., 2001; Roessner et al., 2001; Sumner et al., 2003). Therefore, the evaluation of semolina chemical composition is an essential step to understand the potential volatile compounds of wheat end products. For instance, ornithine and proline act as precursors of 2-acetyl-1-pyrroline, an important volatile compound involved in the sensory quality of the aroma of wheat bread crust (Schieberle, 1990).

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Aromatic characteristics of different cereals such as corn, rye, triticale and rice were investigated in the past using complicated solvent extraction techniques (El-Saharty et al., 1997; Folkes and Gramshaw, 1981). More recently, solid-phase micro-extraction (SPME) was used to detect volatile compounds in combination with GC–MS analysis (Doleschall et al., 2003; Krist et al., 2004; Mazida et al., 2005) and was successfully applied for the identification of volatiles from fermented products (Marsili and Miller, 2000), bread crumbs (Ruiz et al., 2003), distiller grains (Biswas and Staff, 2001) and barley cultivars (Cramer et al., 2005). Here we report on the chemical analysis of semolina and on the volatile composition of semolina and pasta samples produced from four durum wheat cultivars, to assess the effects of the genotypes on the chemical composition of the grains and their consequences on pasta products. 2. Experimental 2.1. Raw materials Four durum wheat (T. durum Desf.) cultivars representing genotypes of different breeding origins and quality levels (Cappelli released in 1915; Trinakria – 1970; Creso – 1974 and PR22D89 – 2005) were grown during the 2005–2006 season in the experimental farm of the CRA Cereal Research Centre located in Foggia (Southern Italy) and used for this study. After harvesting, the seeds were cleaned, bulked and stored at 4  C until analysis. Wheat semolina was obtained by milling durum wheat grains (moisture content of 16%) in an MLU 202 laboratory mill (Bu¨hler brothers, Uzwill, Switzerland) fitted with three breaking and three sizing passages with an attached semolina purifier (Borrelli et al., 2003). 2.2. Pasta making process Semolina was mixed with water at room temperature to reach a total dough moisture content of 33–34%. Dough was processed into spaghetti with diameter of 1.7 mm using a 2 kg capacity laboratory press (Namad, Rome, Italy). The mixing time was 10 min (with constant mixing speed), while the extrusion was performed with a pressure of 9.1–12.1 MPa and vacuum of 700 torr. A lowtemperature drying procedure (50  C for 18h) was applied using a pilot drying plant (Giussani, Fara D’Adda, Bergamo, Italy). After the drying process, 100 g of dried pasta were cooked in 1 L of boiling tap water, and the optimal cooking time was taken when the white core of the strands disappeared after squeezing between two glass plates in accordance with method 66-41 (AACC, 2003). 2.3. Metabolite analysis of semolina Metabolite analysis by GC–MS was carried out following a method previously described (Roessner et al., 2000) with few modifications. All analyses were performed in four replicates. Semolina (w30 mg) was homogenized in 1.4 mL 100% methanol and 60 mL of ribitol (0.2 mg/mL in water) was added to the extraction solution as a quantification standard. The mixture was extracted for 15 min at 70  C while shaking and then centrifuged 10 min at 14 000 rpm. The supernatant was transferred to a glass vial (Schott GL14), then 750 mL of CHCl3 and 1.5 mL of water were added and the samples were vigorously mixed and centrifuged at 4000 rpm for 15 min (pH ¼ 7). Aliquots (50 mL) of the methanol/ water supernatant were dried under vacuum and used for the next steps. The residues were redissolved and derivatized for 2 h at 37  C in 40 mL of 20 mg/mL methoxyamine hydrochloride in pyridine followed by a 30 min treatment with 70 mL MSTFA (N-methyl-N[trimethylsilyl]trifluoroacetamide) at 37  C. Seven mL of a retention

time standard mixture (C12–C36) were added before trimethylsilylation. Sample volumes of 1.0 mL were then injected onto the GC column in splitless mode connected to a GC 8000 series gas chromatograph (Finnigan) equipped with the AS 2000 autosampler and the MD800 quadrupole mass spectrometer (Finnigan). The mass spectrometer was tuned according to the manufacturer’s recommendations using tris-(perfluorobutyl)-amine (CF4)3. Gas chromatography was performed on an Rtx 5 Sil MS w/ Integra Guard RESTEK (30m  0.25 mm i.d., 0.25 mm film thickness) column. Injection temperature was 230  C, the transfer line was set to 250  C and the ion source adjusted to 200  C. Helium was used as carrier gas at a constant flow rate of 1 mL/min. The oven was heated at 70  C for 1 min, then the temperature was increased to 76  C (6  C/min) and subsequently to 350  C (5  C/min), after 1 min at 350  C the final heating was set at 310  C for 10 min. The system was equilibrated for 6 min at 70  C before sample injection. The spectrometer was operated in electron-impact (EI), the ionization voltage was 70 eV and mass spectra were recorded at 2 scans/s with an m/z 50–600 scanning range. The chromatograms and mass spectra were evaluated using the MassLab program (ThermoQuest, Manchester, UK). The absolute concentration of most metabolites was determined by comparison with standard calibration curve response ratios of various concentrations of standard, including the internal standard ribitol derivatized together with the samples. Unless stated otherwise, all chemicals were purchased either from Sigma–Aldrich Chemical Company (Deisenhofen, Germany) or from Merck KGaG (Darmstadt, Germany), N-methyl-N-[trimethylsilyl] trifluoroacetamide was purchased from Fluka. 2.4. Fatty acids analysis of semolina The lipids were extracted from semolina using acid hydrolysis (AACC, 2003, method 30-10) and all analyses were performed in triplicate. Semolina (1 g) was added to 5 mL 25% HCl for 30 min at 80  C, successively cooled at room temperature, extracted with 3 mL of diethyl ether and centrifuged for 7 min at 4000 rpm. The extraction procedure was repeated twice and the organic layers were collected in a 10 mL flask. 2 mL of extract were supplemented with 150 mL of methyl tricosanoate (1 mg/mL) as internal standard and dried under nitrogen at 40  C in a water bath. The residues were redissolved in 500 mL CHCl3 and 500 mL 3% Acetylchloride in methanol (Instant Methanolic HCl kit-Alltech) were added. The samples were heated at 60  C in a water bath for 30 min. The methylated samples were dried under nitrogen at 40  C and the residues redissolved in 1 mL of hexane and transferred onto GC vials. Sample volumes of 1 mL were injected onto the GC column in splitless mode. The FAMEs (fatty acid methyl esters) were analyzed through the Agilent 6890N gas chromatograph coupled with an Agilent 5973 quadrupole mass spectrometer. The detector was tuned according to the manufacturer’s recommendations using tris-(perfluorobutyl)-amine (CF4)3. Gas chromatography was performed on a DB-WAX J&W Scientific, USA (30 m  0.25 mm i.d., 0.25 mm film thickness) column. Injection temperature was 250  C, the transfer line was set at 250  C and the ion source adjusted to 230  C. Helium was used as carrier gas at a constant flow of 1 mL/min. The oven was heated at 100  C for 3 min, followed by a 6  C/min increasing to 235  C, then the temperature was held for 30 min. The system was equilibrated for 3 min at 100  C before sample injection. The spectrometer was operated in electron-impact (EI), the ionization voltage was 70 eV and mass spectra were recorded at 2.83 scans/s with an m/z 30–550 scanning range. FAMEs were identified through the comparison of the retention times with those of the standard and through the comparison of

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their spectra with those present in the NIST98 and Wiley Mass Spectral Databases. Quantification of FAMEs was obtained with the calibration curve response ratios of various concentrations of standards normalized with the internal standard methyl tricosanoate. Unless stated otherwise all chemicals were purchased from Sigma–Aldrich Inc. 2.5. Volatile compounds To identify and quantify volatile compounds in wheat samples (semolina and cooked pasta), the static headspace solid-phase micro-extraction (HS)-SPME technique was adopted. All analyses were performed in triplicate. The fiber’s choice was based on the affinity for cereal volatile compounds and on the symmetry of chromatographic peaks over a range of temperatures and extraction times (data not shown). The final extraction protocol employed 5 g samples, without the addition of water, subjected to solid-phase micro-extraction using a 50/30 mm DVB/Carboxen/PDMS StableFlex fiber directly inserted in the headspace in a 40 mL amber vial with cap and PTFE/Silicon septa (Supelco, Co., Bellefonte, PA) for 24 h. The vials were maintained at 30  0.1  C in a water bath. After sampling, the SPME device was placed immediately into a splitless mode injection port of the GC–MS instrument. The spectrometer was operated in electron-impact (EI) mode and the ionization voltage was 70 eV and the mass tuning was performed according to manufacturer’s recommendations using tris-(perfluorobutyl)amine (CF4)3. The scan range was from 15 to 300 amu and recorded at 4.86 scan/s. The volatile compounds were separated using a 30 m  0.25 mm (i.d.), film thickness 0.25 mm HP-5ms column connected with the Agilent 6890 gas chromatograph equipped with an Agilent 5973 quadrupole mass spectrometer. The volatile compounds adsorbed to the SPME fiber were thermally desorbed at 230  C for 15 min. Helium was used as the carrier gas at a constant flow rate of 1 mL/min. The oven temperature, 40  C, was held for 1 min and then increased 3  C/min to 180  C and held for 1 min. Subsequently, the temperature was increased to 240  C with a rate of 10  C/min and held for 5 min. The transfer line and ion source temperature were 250  C and 230  C respectively. Volatile compounds were identified by comparing their spectra with those contained in the NIST98 and Wiley Mass Spectral Databases. In order to calculate the retention indices, 1 mL of a normal alkane solution (C6–C14) was added to the samples prior SPME extraction. The identity of some volatiles that are marked with an asterisk (*) in the tables should be considered tentative. The relative concentrations of individual compounds were determined by normalizing the peak area of the compounds in chromatogram with that of internal standard (decane). Unless stated otherwise, all chemicals were purchased from Sigma–Aldrich Inc. 2.6. Statistical analysis The analysis of variance (ANOVA) was carried out with respect to each analytical compound detected in semolina or cooked pasta; the effect of cultivars was assessed according to a completely randomized design (with 4 repetitions). Mean discrimination was performed applying the LSD (least significant difference) test; statistically significant differences were determined at the probability level P < 0.05. The MSTAT-C program (Michigan State University, East Lansing, MI, USA) was employed to perform this task. To obtain a general and comprehensive characterization of the semolina and cooked pasta properties, the detected compounds were considered together performing a Principal Component Analysis (PCA) followed by a Factor Analysis (FA). Since some

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metabolic compounds of semolina could be interpreted as volatile precursors, the metabolites were kept separated from the volatile compounds, both in semolina and in cooked pasta. Three different data sets were therefore generated (semolina metabolites, semolina volatile compounds and cooked pasta volatile compounds). The PCA and FA procedures allowed us to synthesize the data sets, avoiding redundancy and identifying a limited number of new, uncorrelated variables named ‘‘factors’’ (linear combinations of the original ones) able to account for a significant portion of the total variance. Having defined the proper number of factors useful to summarize each data set, a procedure called ‘‘varimax rotation’’ allowed the optimization of the factor-loadings on each factor. Pairwise correlations between factors related to semolina composition and the corresponding factors related to semolina and pasta volatile compounds were finally performed to detect potential precursors of possible aromatic traits. Statistically significant correlation was detected according to the Pearson coefficient. PCA, FA and correlations were performed using the JMP statistical package (SAS Institute Inc.). 3. Results 3.1. Principal Component Analysis (PCA) of metabolites and cultivar discrimination The chemical characterization of semolina obtained from different durum wheat cultivars was performed on the basis of the metabolites identified after methanol extraction and fatty acid analysis. Metabolic profiling of semolina samples led to the identification of 34 compounds including amino acids, sugars, organic acids and sugar-alcohols, while the analysis of fatty acid composition yielded 10 fatty acids either saturated (from C14:0 to C24:0) or unsaturated (from C16:1 to C18:3) (Table 1). The analytical results for each compound, the analysis of variance and the least significant difference test among the means of all detected compounds are reported in Table 1. The most abundant polar metabolites were sugars and, among these, raffinose showed the greatest amount in all cultivars followed by sucrose. Semolina from Trinakria was characterized by the highest total sugar content. Trinakria had also the highest organic acids content and within this class, shikimic acid was the most abundant. Samples from Trinakria and Creso showed the highest and the lowest total amino acid content, respectively, although the greater amount of Leucine was detected in PR22D89. Semolina from Trinakria and Cappelli were characterized by the highest and the lowest content of total fatty acids, respectively. Palmitic and linoleic acids were the main saturated and unsaturated fatty acids, according to Solina et al. (2007). Trinakria and PR22D89 contained more oleic acid than Creso and Cappelli, while Trinakria showed the greatest content for all detected fatty acids with the exception of behenic acid, more abundant in PR22D89. To obtain a representation of the chemical composition of the four pure semolina samples, the two groups of compounds (polar metabolites and fatty acids) were subjected to Principal Component Analysis (PCA). The number of factors that can properly describe the data was determined on the basis of the Eigenvalues and consequently on the percentage of the total variance explained. Three main factors accounting for 96.1% of the total variances were selected: the metabolic profiling factor 1 (MP1) explains 52.8% of variance, MP2 26% and MP3 17.6% (Table 1). As shown in Table 1, most of the sugars, sugar-alcohols, amino acids and the palmitoleic fatty acid had a significant positive loading on the first factor, while the sorbitol/galactitol and behenic acid showed a negative loading on the same factor. Quinic acid, threonic acid, GABA, leucine, glycine, stearic, oleic and tetracosanoic

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Table 1 Mean values (mg/g DW) of each metabolites identified in semolina of different durum wheat cultivarsa and correlation coefficients between each original variable and each of the first three factors derived from PCA with respect to metabolites of semolina. Chemical group

Compound-derivativeb

PR22D89

Cappelli

Creso

Trinakria

LSD0.05

MP2 (26%)

MP3 (17.6%)

Amino acids

Arginine/ornithine N,N,N,O-TMS Aspartic acid N,O,O-TMS GABA N,O,O-TMSe Homoserine N,O,O-TMS Beta-alanine N,O,O-TMS Threonine N,O,O-TMS Serine N,O,O-TMS Glycine N,N,O-TMS Proline N,O-TMS Isoleucine N,O-TMS Leucine N,O-TMS Valine N,O-TMS Alanine N,O-TMS

54.55 c 4.04 b 51.89 b 5.57 c 3.87 b 17.73 d 14.08 d 2.79 b 13.27 b 14.71 c 424.10 a 0.92 c 4.57 b

78.47 b 3.22 c 32.39 c 8.84 b 3.30 c 36.15 b 26.43 b 1.92 c 9.37 c 27.49 b 357.40 c 1.19 b 4.25 b

46.83 d – 30.80 d 3.99 d 2.62 d 19.75 c 17.33 c 2.08 c 6.13 d 7.78 d 263.70 d 0.66 d 3.57 c

99.57 a 9.16 a 58.91 a 20.66 a 5.76 a 54.62 a 30.79 a 4.24 a 26.44 a 39.25 a 377.30 b 2.41 a 8.03 a

2.81 0.60 0.56 0.83 0.50 0.81 0.69 0.56 1.84 0.66 16.10 0.09 0.54

0.97d 0.73 0.31 0.88 0.65 0.97 0.99 0.47 0.67 0.95 0.19 0.86 0.76

0.24 0.67 0.92 0.37 0.65 0.13 0.09 0.71 0.66 0.29 0.83 0.41 0.50

0.01 0.11 0.23 0.28 0.25 0.18 0.06 0.44 0.29 0.04 0.47 0.28 0.38

N-compound

Putrescine N,N,N,O-TMS

0.70  103 c

0.98  103 b

0.65  103 c

1.84  103 a

0.06

0.88

0.34

0.33

Organic acids

Saccharic acid-TMS Gluconic acid-TMS Galacturonic acid MEOX1 TMS Quinic acid-TMS Citric acid-TMS Shikimic acid-TMS Threonic acid-TMS Malic acid-TMS Glyceric acid-TMS

6.80 c 96.08 b 92.68 b 13.39 a 76.44 d 0.75  103 c 13.39 a 3.00 b 13.03 c

6.34 c 65.34 c 81.89 c 6.99 b 84.29 c 1.11  103 b 6.99 b 3.10 b 24.22 b

7.72 b 44.73 d 46.28 d 5.99 b 93.33 b 0.61  103 d 5.99 c 2.16 c 13.58 c

12.40 a 335.60 a 354.70 a 13.82 a 119.10 a 1.59  103 a 13.82 a 5.39 a 29.72 a

0.63 3.73 5.94 1.07 2.27 0.04 0.96 0.69 1.40

0.65 0.72 0.76 0.19 0.69 0.95 0.20 0.77 1.00

0.31 0.54 0.50 0.95 0.06 0.29 0.95 0.52 0.07

0.67 0.43 0.41 0.14 0.71 0.09 0.13 0.19 0.00

Sugars (103)

Raffinose-TMS Maltose-MEOX1, TMS Maltose-MEOX2, TMS Trehalose-TMS Sucrose-TMS Glucose-MEOX1, TMS Glucose-MEOX2, TMS Fructose-MEOX1, TMS Fructose-MEOX2, TMS Ribose-MEOX, TMS Arabinose-MEOX, TMS

36.88 b 7.98 b 2.83 b 0.53 c 24.21 c 0.75 c 0.05 c 0.25 d 0.70 c 0.02 c 0.25 d

38.46 b 12.29 a 4.42 a 0.70 b 26.09 b 1.11 b 0.08 b 0.66 b 0.98 b 0.04 b 0.64 b

30.41 c 7.54 b 2.76 c 0.65 bc 22.39 d 0.61 d 0.05 c 0.41 c 0.65 c 0.02 c 0.41 c

64.72 a 8.14 b 3.48 b 1.56 a 43.87 a 1.59 a 0.10 a 1.21 a 1.87 a 0.05 a 1.21 a

1.60 0.80 0.27 0.15 0.87 0.05 0.02 0.05 0.10 0.02 0.05

0.81 0.42 0.81 0.78 0.82 0.95 0.95 0.94 0.88 0.90 0.92

0.49 0.40 0.49 0.30 0.41 0.30 0.30 0.10 0.34 0.14 0.12

0.30 0.79 0.30 0.53 0.38 0.08 0.08 0.33 0.32 0.21 0.35

Sugar-alcohols

Myo-inositol-TMS Sorbitol/Galattitol-TMS Mannitol-TMS

109.50 b 11.83 a 27.57 b

109.40 b 5.78 b 21.91 c

76.84 c 12.05 a 13.14 d

176.30 a 5.40 b 71.24 a

0.95 0.80 0.93

0.80 0.96 0.74

0.57 0.04 0.57

0.18 0.21 0.36

Saturated fatty acids

Myristic acid-ME Palmitic acid-ME Stearic acid-ME Behenic acid-ME Tetracosanoic acid-ME

16.45 b 2527.35 c 289.65 a 68.21 a 39.24 a

12.70 d 2589.35 bc 215.68 c 49.13 c 35.80 b

13.57 c 2691.91 b 240.63 b 57.75 b 34.55 b

17.87 a 3022.52 a 289.17 a 37.55 d 36.57 ab

0.19 141.80 21.98 6.66 2.81

0.24 0.68 0.04 0.90 0.18

0.88 0.15 0.89 0.14 0.79

0.40 0.69 0.41 0.24 0.38

Unsaturated fatty acids

Palmitoleic acid-ME Elaidic acid-ME Oleic acid-ME Linoleic acid-ME Linolenic acid-ME

16.94 c 847.06 b 1029.31 a 4281.50 bc 378.77 b

25.65 b 797.36 c 677.96 b 4146.56 c 335.45 c

24.94 b 1089.25 a 750.54 b 4437.91 ab 397.14 b

29.79 a 1120.44 a 1046.09 a 4722.30 a 450.96 a

3.61 73.23 92.13 285.30 28.89

0.79 0.18 0.06 0.36 0.29

0.37 0.05 0.94 0.27 0.41

0.42 0.96 0.29 0.81 0.85

a b c d e

MP1c (52.8%)

Values followed by the same letter in the same row are not significantly different (P ¼ 0.05). MEOX ¼ methoxyaminated derivative, TMS ¼ trimethylsilylated derivative, ME ¼ methyl ester. MP1, MP2, MP3 ¼ first, second and third factor of metabolites. Higher factor-loadings show higher contributions of the corresponding metabolites in variability of the factor. GABA ¼ gamma-amino butyric acid.

acids had a significant negative loading on the second factor (MP2), maltose-MEOX1 had a positive loading for the third factor (MP3) while the citric acid, linoleic, linolenic and elaidic acids had a significant negative loading on MP3. The scatter plot of Fig. 1 gives a three-dimensional representation of the variability detected among the four cultivars based on PCA output. Cappelli and Trinakria had a positive factor score while Creso and PR22D89 had a negative one for MP1. These results confirm those obtained by ANOVA analysis: Trinakria showed the highest amount of all compounds positively associated with MP1, while PR22D89 and Creso showed the highest content of behenic acid and sorbitol/galactitol that were negatively associated with MP1. PR22D89 and Creso had a negative and a positive factor score

for MP2 respectively, confirming the high and the low content for the compounds associated with this factor, i.e. leucine, oleic and tetracosanoic acid. Finally, Cappelli had a positive factor score for MP3 due to the high content of maltose-MEOX1 and the low amounts of elaidic, linoleic and linolenic acids. 3.2. Determination of volatile composition The ANOVA results of volatile composition in semolina and cooked pasta samples are shown in Tables 2 and 3. The volatile composition of semolina from Trinakria was characterized by the highest content of alcohols (i.e. 1-hexanol), esters and terpenes. 2,3-butandiol was identified only in Trinakria. Nonanal and 2-

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Cappelli 1,27

MP3

PR22D89 0,53

Trinakria Creso

-0,85

1,25

-0,95

0,61 -0,84 -1,01

1,03 0,84 -0,68

MP2

P1 M

-1,19

Fig. 1. Scatter plot based on projections of three factor scores of metabolites in semolina samples. MP1 (52.8%), MP2 (27%) and MP3 (17.6%) of total variance.

pentylfuran were the only aldehyde and furan, respectively, identified in semolina; they were present at high level in Creso, but absent in PR22D89. Volatile composition of PR22D89- and Cresoderived samples showed the lowest and the highest content of most hydrocarbons, respectively. PR22D89 was the only semolina containing 5-methylundecane, while Cappelli was the only one with toluene. Pasta of Cappelli and Creso had a high and low aldehyde content, respectively. Hexanal was the most abundant aldehyde in the volatiles of pasta samples. Seven different alcohols were detected and also for this class of compounds, Cappelli and Creso were the cultivars with the highest and the lowest content, respectively.

305

PR22D89 showed the highest total content of ketones mainly due to the presence of 2-octanone. Notably, alpha-pinene and limonene were found only in cooked pasta of PR22D89. 2-Pentylfuran was found in all cooked pasta samples, more abundant in Trinakria and less abundant in PR22D89. An opposite behaviour was detected for hydrocarbons. As for the metabolites, the volatile composition of semolina and cooked pasta was separately subjected to PCA. Three main factors were identified for both data sets. They accounted for 99% of total variance in semolina, with 46.2%, 39.2% and 13.6% for SV1 (semolina volatile composition factor 1), SV2 and SV3, respectively. PCA of cooked pasta revealed three main factors accounting for 97.5% of total variance, with 38.9%, 33.4% and 25.2% for pasta volatile composition factor 1 (PV1), PV2 and PV3, respectively. As reported in Table 2, SV1 had a positive loading on all alcohols, alpha-pinene and on ester ethyl hexanoate. Most hydrocarbons, nonanal and 2pentylfuran gave a positive loading on SV2, while only toluene negatively loaded on SV3. Table 3 reports also the correlation between volatile compounds and the three main factors detected for cooked pasta. Ten, nine and seven volatile compounds had a significant loading on the first, the second and the third factor respectively, while nonanal and 2-pentylfuran had no significant loading-factor. Fig. 2(A) shows the three-dimensional scatter plot for volatile compounds in semolina obtained from different durum wheat cultivars. The first factor discriminates Trinakria, the second factor allows to differentiate the opposite behaviour of Creso and PR22D89, while the third factor separates the semolina of Cappelli from all the other samples. Fig. 2(B) shows the scatter plot based on projections of the three factor scores of volatile compounds identified in cooked pasta samples. Cappelli and PR22D89 had positive factor score for PV1 and PV2, respectively. Both cultivars were indeed characterized by a high content of the volatile compounds correlated on these factors. Pasta from Trinakria and Creso was differentiated by the third factor, with positive and negative factor

Table 2 Mean values (ng/g DW) of volatile compounds identified in semolina of different durum wheat cultivarsa and correlation coefficients between each original variable and each of the three factors obtained from PCA with respect to volatile compounds of semolina. RIb

SV1c (46.2%)

Chemical group

Compounds

PR22D89

Cappelli

Creso

Trinakria

LSD0.05

Alcohols

3-Methyl-1-butanol 2-Methyl-1-butanol 1-Pentanol 2,3-Butandiol 1-Hexanol 1-Heptanol 1-Octen-3-ol

736 739 781 840 886 981 989

0.228 c – 1.139 b – 7.974 d 0.552 c 0.830 d

0.195 d 0.295 c 1.223 b – 15.570 c 0.356 d 1.960 b

0.333 b 0.430 b 1.325 b – 16.840 b 0.693 b 1.680 c

0.429 a 0.580 a 2.258 a 1.722 a 23.530 a 0.913 a 3.274 a

0.020 0.020 0.268 0.020 0.807 0.020 0.020

0.76d 0.71 0.95 0.99 0.82 0.71 0.92

Terpene

Alpha-pinene

940

0.299 bc

0.280 b

0.346 c

0.661 a

0.063

Ester

Ethyl hexanoate

1007

Aldehyde

Nonanal

1118

Furan

2-Pentylfuran

Hydrocarbons

Toluene Ethylbenzene p/o-Xilene* p/o-Xilene* 2,2,4,6,6-Pentamethylheptane* Undecane 5-Methylundecane Tridecane 2,2,6-Trimethyldecane*

993 763 867 876 900 991 1113 1115 1311 1330

–

SV2 (39.2%)

SV3 (13.6%)

0.40 0.70 0.13 0.01 0.56 0.24 0.33

0.51 0.05 0.16 0.16 0.11 0.65 0.19

0.95

0.33

0.19

–

–

1.640 a

0.020

0.99

0.01

0.16

1.217 c

1.534 a

1.292 b

0.020

0.33

0.91

0.27

–

5.047 b

6.517 a

4.784 b

0.638

0.22

0.93

0.28

– 1.634 d 2.199 d 2.648 d 21.900 c 1.717 d 1.164 a 1.503 a 10.220 a

0.152 a 10.060 c 10.840 c 3.859 c 22.780 c 1.749 c – 1.094 c 9.171 b

– 15.930 b 23.330 a 7.829 a 35.350 a 2.324 a – 1.184 b 10.130 a

– 16.500 a 14.720 b 5.302 b 25.960 b 1.871 b – 0.818 d 5.221 c

0.020 0.485 0.460 0.616 1.498 0.020 0.020 0.089 0.771

0.18 0.54 0.14 0.08 0.12 0.17 0.41 0.83 0.99

0.00 0.84 0.98 0.94 0.89 0.89 0.81 0.44 0.01

0.98 0.01 0.12 0.30 0.42 0.43 0.42 0.29 0.05

Asterisk (*) indicates tentative identification. a Values followed by the same letter in the same row are not significantly different (P ¼ 0.05). b RI ¼ retention indices. c SV1, SV2, SV3 ¼ first, second and third factor of volatile compounds in semolina. d Higher factor-loadings show higher contributions of the corresponding metabolites in variability of the factor.

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R. Beleggia et al. / Journal of Cereal Science 49 (2009) 301–309

Table 3 Mean values (ng/g DW) of volatile compounds identified in cooked pasta of different durum wheat cultivarsa and correlation coefficients between each original variable and each of the first three factors obtained from PCA with respect to volatile compounds of cooked pasta. Chemical group

Compounds

RIb

PR22D89

Cappelli

Creso

Trinakria

LSD0.05

PV1c (38.9%)

PV2 (33.4%)

PV3 (25.2%)

Alcohols

1-Pentanol 1-Hexanol 1-Heptanol 1-Octen-3-ol 2-Ethyl-1-hexanol 1-Octanol 2-Buthyl-2,7-octadien-1-ol*

781 886 981 989 1043 1097 1383

1.855 b 3.819 c 0.941 a 5.454 b 2.143 a – 0.254 b

3.027 a 7.934 a – 6.279 a – – –

– 1.140 d – 5.148 b 1.223 b 0.991 b 0.261 b

– 4.399 b – 6.321 a – 1.589 a 0.486 a

0.189 0.167 0.020 0.532 0.020 0.020 0.020

0.98d 0.81 0.10 0.31 0.16 0.85 0.91

0.19 0.16 0.99 0.34 0.81 0.45 0.13

0.06 0.56 0.07 0.83 0.56 0.28 0.38

Aldehydes

Hexanal Heptanal Octanal Nonanal Decanal Benzaldehyde 2,5bis(trimethylsilyloxy)Benzaldehyde*

805 914 1006 1118 1210 967 1169

18.880 b 2.164 c – 3.803 c 1.808 a 0.454 c 2.093 b

30.470 a 3.829 a – 4.648 bc 1.263 b – 2.382 a

6.302 d 0.812 d – 6.492 a 1.912 a 1.002 a –

13.780 c 3.282 b 2.828 a 5.726 ab 0.980 b 0.881 b 1.047 c

1.310 0.020 0.209 1.621 0.316 0.020 0.020

0.93 0.55 0.56 0.55 0.07 0.98 0.81

0.04 0.17 0.22 0.65 0.40 0.11 0.39

0.37 0.82 0.79 0.22 0.85 0.18 0.43

Ketones

2-Heptanone 6-Methyl-2-heptanone 2-Octanone 2-Nonanone

901 965 1003 1111

2.592 b 1.556 a 6.102 a –

2.896 a 1.130 b – –

1.253 c 0.532 d – 0.822 a

2.644 b 0.566 c – 0.780 b

0.155 0.020 0.236 0.020

0.56 0.63 0.10 0.86

0.22 0.77 0.99 0.50

0.79 0.02 0.07 0.07

Furan

2-Pentylfuran

993

8.164 d

12.900 c

17.880 b

25.990 a

2.065

0.65

0.61

0.43

Terpenes

Alpha-pinene Limonene

940 1031

0.485 a 2.263 a

– –

– –

– –

0.020 0.020

0.10 0.10

0.99 0.99

0.07 0.07

Hydrocarbons

2-Octene* Ethylbenzene p/o-Xilene* p/o-Xilene* 2,2,4,6,6-Pentamethylheptane* Undecane Tridecane 2,2,6-Trimethyldecane*

916 867 876 900 991 1113 1311 1330

3.846 a 1.819 b 2.343 c 2.760 a 5.798 b 0.570 a 0.415 b 0.403 a

– 1.625 c 2.539 b 1.864 b 7.075 a – – 0.477 a

– 2.465 a 3.771 a 1.517 c 3.657 d 0.245 c 0.348 c 0.235 b

– 0.866 d 1.025 d 1.257 d 4.521 c 0.386 b 0.588 a 0.380 a

0.189 0.167 0.189 0.063 0.400 0.020 0.020 0.126

0.10 0.06 0.13 0.44 0.93 0.56 0.90 0.69

0.99 0.05 0.13 0.87 0.17 0.82 0.32 0.16

0.07 0.99 0.98 0.20 0.29 0.13 0.30 0.59

Asterisk (*) indicates tentative identification. a Values followed by the same letter in the same row are not significantly different (P ¼ 0.05). b RI ¼ retention index. c PV1, PV2, PV3 ¼ first, second and third factor of volatile compounds in cooked pasta. d Higher factor-loadings show higher contributions of the corresponding metabolites in variability of the factor.

scores respectively in accordance with the content of the volatile compounds significantly correlated with PV3.

3.3. Volatile compounds in semolina and cooked pasta: correlations with metabolites The volatile composition of semolina and cooked pasta is obviously dependent on the chemical composition of semolina itself. To find a relationship between the metabolic profile (polar metabolites and fatty acids) of semolina and the aroma of semolina and pasta, the correlation among the PCA main factors (MP vs SV and MP vs PV) was investigated. The results are shown in Table 4 (A and B). A significant positive correlation was found between the volatile compounds loading to SV1 and metabolites loading to MP1, similar results were obtained with SV2 vs MP2, while a negative correlation was found between SV3 and MP3. On the basis of the correlation coefficients, the volatile compounds associated with SV1 and SV2, have a positive relation with the metabolites associated with MP1 and MP2 factors. For example, SV1 included 2-methylbutanol, a volatile compound obtained from isoleucine catabolism (Ardo¨, 2006) and, accordingly, isoleucine was a component of MP1 while the correlation between SV2 and MP2 can be, partially, ascribed to the contribution of nonanal and 2-pentylfuran to SV2, these compounds are indeed obtained from the oxidation of oleic acid loaded to MP2 (Rodrı´guezBernaldo De Quiro´s et al., 2000).

Instead, in cooked pasta a different trend of significant correlations was observed: PV1 and PV3 were positively correlated with MP3 and MP1 respectively, while PV2 was negatively correlated with MP2. Notably, as for semolina, also in cooked pasta the volatile compounds resulting from fatty acid degradation were closely connected to the lipid composition of the starting material. Alcohols (i.e. 1-pentanol, 1-hexanol, 1-octanol) and aldehydes (i.e. hexanal and benzaldehyde), are volatile compounds represented by PV1, these volatiles were derived and, consequently, significantly linked to the presence of unsaturated fatty acids such as linoleic and linolenic represented by MP3 (Belitz and Grosch, 1988; Neff et al., 1983). Since the metabolites represented by MP2 were all negatively related to this factor, the negative correlation between PV2 and MP2 reflects a direct dependence of PV2 volatiles on MP2 metabolites.

4. Discussion In this study, the volatile composition of four pure pasta varieties was assessed after cooking to describe some of the chemical features of pasta flavour. The findings were, then, correlated with the chemical composition of the corresponding semolina. Significant differences were observed between semolina and pasta samples in terms of composition and amount of the volatile compounds. During pasta making and cooking, an increase in aldehyde content and the appearance of ketones, absent in

R. Beleggia et al. / Journal of Cereal Science 49 (2009) 301–309

A

Table 4 Correlation between metabolic profiling factors and volatile composition factors of semolina and pasta samples.

Creso

Trinakria

0,67

A (semolina)

PR22D89

0,26

MP1a MP2 MP3

SV3

MP1 MP2 MP3

1,64

Cappelli -0,30 -0,67

1,36

-0,01

SV 1

-1,63

-1,34

SV2

B Trinakria 1,31

Cappelli PR22D89

PV3

307

0,17 -0,11

1,50 -1,37

Creso

1

PV2

PV

1,64

0,16

-0,72 -0,94 -0,37 -0,69

Fig. 2. (A) Scatter plot based on projections of three factor scores of volatile compounds in semolina samples: SV1 (46.2%), SV2 (39.2%), SV3 (13.6%) of total variance. (B) Scatter plot based on projections of three factor scores of volatile compounds in cooked pasta samples: PV1 (38.9%), PV2 (33.4%) and PV3 (25.2%) of total variance.

semolina, were observed. Furthermore, the volatile composition of pasta may differ depending on the durum wheat cultivar. Hexanal was the most abundant aldehyde in the volatiles of pasta samples. It is well known that its presence in cereal grains has been associated with the oxidation of unsaturated fatty acids due to autoxidation, thermal oxidation, photooxidation or lypoxygenaseassisted oxidation (Gardner, 1996; Vernooy-Gerritsen et al., 1984). The differences in the amount of volatile compounds deriving from the lipid degradation might reflect corresponding differences in the content of the substrates and in the activity of the lipoxygenase enzymes (Borrelli et al., 1999). Hexanal, octanal, nonanal and decanal generally derive from oxidative degradation of unsaturated fatty acids oleic, linoleic and linolenic (Rodrı´guezBernaldo De Quiro´s et al., 2000). Therefore, the high content of these compounds might be the consequence of enzymatic (e.g. lipoxygenase and hydroperoxydase lyase) or thermal oxidation of lipids (Sayaslan et al., 2000). During the cooking process, the starch-lipid, probably included as complexes with amylose may be

B (cooked pasta)

SV1b

SV2

SV3

0.85 0.33 0.41

0.05 0.89 0.55

0.53 0.46 0.71

PV1c

PV2

PV3

0.05 0.26 0.95

0.56 0.79 0.24

0.83 0.55 0.10

The values in bold were significantly at P  0.01. a MP1, MP2, MP3 ¼ first, second and third factor of metabolites. b SV1, SV2, SV3 ¼ first, second and third factor of volatile compounds in semolina. c PV1, PV2, PV3 ¼ first, second and third factor of volatile compounds in cooked pasta.

released, becoming susceptible to thermal oxidation (Morgan et al., 1993; Sayaslan et al., 2000), and although the enzymes (e.g. lipoxygenase and hydroperoxidase lyase) involved in the production of aldehydes are heat denaturated, the high temperatures accelerate autoxidation, a process that does not require an enzymatic catalysis (Rodrı´guez-Bernaldo De Quiro´s et al., 2000). Fatty acid oxidation is also the source of 1-octen-3-ol and 2-pentylfuran (Neff et al., 1983). The conjugated diene radical, generated from 9hydroxyradical scission of linoleic acid, reacts with oxygen with the formation of vinyl hydroperoxide that can lead to 2-pentylfuran through cyclization via an alkoxy radical. It is remarkable that the content of 2-pentylfuran increases four times during processing and cooking. In accordance with Kermasha et al. (1988), the general increases of aldehydes and ketones during processing and cooking is associated with a decrease in the content of alcohol and ester compounds. The loss of alcohol compounds can be due to the high solubility of alcohols in water (Josephson et al., 1987), an effect greatly enhanced by cooking process because of the large volume of water employed (1 L for 100 g of pasta). The major formation pathway of the branched-chain aldehydes seems to be the oxidative deamination–decarboxylation, via Strecker degradation (Garcı´a et al., 1991). Benzaldehyde was a product of phenylalanine degradation (Adamiec et al., 2001). In our study, the increase of benzaldehyde after a thermal treatment may be attributed to thermal Strecker degradation (Hayashi et al., 1990). Indeed benzaldehyde derives from shikimic acid metabolism through a phenylalanine intermediate step (Dourtoglou et al., 1994). Nevertheless, in this study, no significant direct correlation was found between benzaldehyde and shikimic acid detected in semolina samples of different durum wheat cultivars. Terpenes are the constituents of the unsaponifiable fraction of vegetable fats, these compounds are known to have distinctive aromatic properties and to contribute to the flavour of many foods (Horvat et al., 1991) and notably, alpha-pinene and limonene were found only in pasta made with PR22D89. To avoid generation of flavour compound artefacts, this study was performed at 30  0.1  C, the amounts of volatile compounds detected under this condition were significantly lower than those reported in the study of Cramer et al. (2005) dedicated to the analysis of the volatile compounds of barley flours. Differences are mainly due to the different extraction conditions; in our work the volatiles were extracted without addition of water while the volatile compounds detected by Cramer et al. were obtained by mixing the flour samples with water and sodium chloride, a condition that improves the release of volatiles. Notably, some of the volatile compounds (hexanal, heptanal, octanal, nonanal, 1-octen-3-ol and 2-pentylfuran) detected in the

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pasta samples were found in concentration above their odour detection thresholds in water (Leffingwell and Associates): this finding suggests that these volatiles may have a role in pasta aroma but further studies are necessary to confirm this hypothesis. 5. Conclusions In this study we present the combined analysis of metabolites and volatile compounds of durum wheat semolina and pasta obtained from four different cultivars. Semolina samples were characterized through fatty acid analysis and polar metabolite profiling to identify potential precursors of the volatile compounds that might be released during pasta processing and cooking. The results presented demonstrate that the cultivars studied are significantly different in the content of sugars, amino acids, organic acids and fatty acids. The analysis of volatile compounds showed significant differences between the volatile composition of semolina and cooked pasta with the appearance of new compounds such as ketones, an increasing of most aldehydes and the decreasing of alcohols content. The correlations between cooked pasta volatiles and semolina metabolites demonstrate that the flavour of the end product may significantly differ depending on the durum wheat cultivar employed. Whether these differences can be perceived by the consumer remains to be evaluated in future studies. Nevertheless, our results open the way to a conscious activity for the selection and modification of the flavour of monovarietal pasta. Acknowledgements The authors are thankful to Dr. Alisdair R. Fernie for the support on metabolic profiling in his laboratory at Max Planck Institute for Molecular Plant Physiology of Golm (Germany) and Dr. Clara Fares (CRA Cereal Research Centre, Foggia, Italy) for technical support in pasta processing. The research was partially supported by a grant from the EU project DEVELONUTRI, contract number FP6-036296 and by a grant from the Ministero dell’Universita` e della Ricerca (MIUR) project PARALLELOMICS (RBIP06CTBR). References AACC International, 2003. Approved Methods of the American Association of Cereal Chemists, tenth ed.. In: Methods 30-10 and 66-41 The Association, St. Paul, MN. Adamiec, J., Ro¨ssner, J., Velı´sˇek, J., Cejpek, K., S avel, J., 2001. Minor Strecker degradation products of phenylalanine and phenylglycine. European Food Research and Technology 212, 135–140. Ardo¨, Y., 2006. Flavour formation by amino acid catabolism. Biotechnology Advances 24, 238–242. Belitz, H.D., Grosch, W., 1988. Quı´mica de los alimentos. Acribia, Zaragoza. Bemis-Young, G.L., Huang, J., Bernhard, R.A., 1993. Effect of pH on pyrazine formation in glucose–glycine model system. Food Chemistry 46, 383–387. Biswas, S., Staff, C., 2001. Analysis of headspace compounds of distillers grains using SPME in conjunction with GC/MS and TGA. Journal of Cereal Science 33, 223–229. Borrelli, G.M., Troccoli, A., Di Fonzo, N., Fares, C., 1999. Durum wheat lypoxigenase activity and other quality parameters that affect pasta color. Cereal Chemistry 76, 335–340. Borrelli, G.M., De Leonardis, A.M., Fares, C., Platani, C., Di Fonzo, N., 2003. Effects of modified processing conditions on oxidative properties of semolina dough and pasta. Cereal Chemistry 80, 225–231. Bredie, W.L.P., Mottram, D.S., Guy, R.C.E., 2002. Effect of temperature and pH on the generation of flavor volatiles in extrusion cooking of wheat flour. Journal of Agricultural and Food Chemistry 50, 1118–1125. Cramer, A.-C.J., Scott Mattinson, D., Fellmann, J.K., Baik, B.K., 2005. Analysis of volatile compounds from various types of barley cultivars. Journal of Agricultural and Food Chemistry 53, 7526–7531. Doleschall, F., Rocseg, K., Ke´meny, K., Kovari, K., 2003. Comparison of different coated SPME fibers applied for monitoring volatile substances in vegetable oils. European Journal of Lipid Science and Technology 105, 333–338. Dourtoglou, V.G., Yannovits, N.G., Tychopoulos, V.G., Vamvakias, M.M., 1994. Effect of storage under carbon dioxide atmosphere on the volatile, amino acid, and pigment constituents in red grape (Vitis vinifera L-Var. Agiorgitiko). Journal of Agricultural and Food Chemistry 42, 338–344.

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