Occurrence and evolution of amino acids during grape must cooking

Food Chemistry 121 (2010) 69–77

Contents lists available at ScienceDirect

Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Occurrence and evolution of amino acids during grape must cooking Giuseppe Montevecchi a,*, Francesca Masino a, Fabio Chinnici b, Andrea Antonelli a a b

Dipartimento di Scienze Agrarie e degli Alimenti, Università degli Studi di Modena e Reggio Emilia, Via Amendola 2, 42100 Reggio Emilia, Italy Dipartimento Scienze Alimenti, Università degli Studi di Bologna, Via Fanin 40, 40127 Bologna, Italy

a r t i c l e

i n f o

Article history: Received 10 June 2009 Received in revised form 29 October 2009 Accepted 2 December 2009

Keywords: Grape must Heat concentration Amino acids Non-enzymic browning Thermal degradation

a b s t r a c t A study of the involvement of amino acids and other amino compounds in sugar degradation during must cooking was pursued. Two white musts (Trebbiano toscano and Spergola) and a red one (Lambrusco) were cooked by means of a lab-scale equipment emulating the real process. Must composition and amino compound concentration were studied in order to understand the modifications induced by the heating process. Results showed that amino acids and related compounds tend to decrease at different rates during the 30 h of the cooking process. The behaviour of nitrogen compounds was studied by the ratio between initial and final concentrations, and by plotting amino compound concentrations vs. time. In both cases the effect of concentration was considered to eliminate its influence on discussion. Principal Component Analysis (PCA) clearly showed how time and heating produced similar trends during the cooking process of the different musts. The initial differences in composition characterised the whole process, and samples of each must were clearly apart from the other ones. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Cooked must is a product widespread in many wine-producing countries. In turn, it can be used as it is, for typical local recipes, as for some Spanish sweet wine (Rivero-Pérez, Pérez-Magariño, & González-San José, 2002) or for the production of some typical Italian fermented seasonings and beverages, apart from a direct usage as an ingredient in baking (Piva, Di Mattia, Neri, Dimitri, Chiarini, & Sacchetti, 2008; Repubblica Italiana, 2007). The fermented seasonings, known as Balsamic Vinegars, are typical products of the Emilia Romagna region, in the North of Italy. In a few words, must from white and red grapes of local cultivars (cvs.) are used. Trebbiano toscano, Trebbiano modenese and Spergola are some of the white cvs., while Lambrusco (Salamino, Grasparossa, Marani) and Ancellotta are some of the red ones. Grape must cooking is traditionally performed in open boilers or pans at ambient pressure and over a direct flame to give a cooked must about two times more concentrated than the raw grape juice (Antonelli, Chinnici, & Masino, 2004; Cocchi, Lambertini, Manzini, Marchetti, & Ulrici, 2002). This process is quite long (up to 30 h) and the temperature is set at about (85–90 °C), which allows a gentle simmering of the mass. Once cooled down, the cooked must is worked according to the technical policies for production (Repubblica Italiana, 2000). * Corresponding author. Tel.: +39 0522 522023; fax: +39 0522 522053. E-mail address: [email protected] (G. Montevecchi). 0308-8146/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2009.12.005

As a consequence of the concentration and the prolonged thermal stress, many chemical changes occur during this process. In fact, the thermal instability of sugars is well-known and has been studied in detail for about a century (Ledl & Schleicher, 1990; Maillard, 1912; Nursten, 2005), whereas the analytical study of the modifications occurring during must cooking is still in a pioneering stage (Antonelli et al., 2004; Cocchi, Ferrari, Manzini, Marchetti, & Sighinolfi, 2007; Falcone & Giudici, 2008; Muratore, Licciardello, Restuccia, Puglisi, & Giudici, 2006). At the moment, the routes of sugar degradation in cooked must seem to follow two pathways. A main pathway induced by normally occurring acidity, and a possible parallel secondary pathway induced by amino acids. The characteristics of grape juice (about 20% sugar content and 2.5–3.5 pH), in fact, suggest an acid-induced sugar degradation as the main pathway. However, the contemporary presence of a low but appreciable aminic nitrogen makes the amino acid evolution during cooking worthy of studying. As far as we know, nobody has studied this topic in detail. In fact, only a recent paper on changes of grape must during cooking (Piva et al., 2008) reported an amino acid decrease. This study, however, considered the total amounts of amino acids. The low pH of must makes unlikely a direct involvement of the amino acids in sugar degradation following the Hodge scheme (Hodge, 1953). For these reasons, a study of the fate of amino acids, along with some other main grape parameters, was carried out by means of lab-scale equipment.

70

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77

2. Materials and methods 2.1. Grape musts and their concentration Grape musts of Lambrusco, Trebbiano toscano (from here on Trebbiano), and Spergola were collected from local wineries. They were stored at 20 °C and defrosted immediately before use. Musts were divided into aliquots of 1 l each, and concentrated to half of the initial volume in partially capped 1-l conical flasks. The masses were rapidly heated to boiling point by a hot plate-magnetic stirrer (MR 3003, Heidolph Elektro, Kelheim, Germany) equipped with a thermometer probe. Then the temperature was set at 90 °C for the whole process (30 h). Each concentration process was carried out in duplicate. Every 3 h, 1.5 ml of must were withdrawn and immediately stored at 20 °C for analyses. Analyses were carried out in duplicate. 2.2. Reagents Pure reference compounds (fructose, glucose, 5-hydroxymethyl-2-furaldehyde (HMF), ammonium chloride, ethanolamine, c-aminobutyric acid (GABA), alanine, arginine, asparagine, aspartic acid, cysteine, glutamic acid, glutamine, hydroxyproline, isoleucine, lysine, methionine, proline, serine, threonine, tryptophan, tyrosine, and valine) were purchased from Fluka Sigma–Aldrich (Milan, Italy), while high-purity solvents were supplied by different companies, and they were purified and redistilled before use. Deionised water was obtained by a Milli-Q purification system (Millipore, Milan, Italy). 2.3. Standard solutions Standard solutions in water (1000–250,000 mg/l) of sugars and HMF were prepared. Standard 2000-ppm stock solutions of ammonium ion (NHþ 4 , as ammonium chloride), ethanolamine, GABA, and amino acids, in HCl 0.01 M were prepared, while for cysteine NaOH 0.01 M was used. In the case of amino acids the analytes were detected at 263 and 280 nm UV wavelengths for FMOC and DEEMM methods respectively (see Section 2.5.2). 2.4. Physico-chemical determinations

2.5.2. Determinations of amino acids and other nitrogen substances The determination of amino acids was performed by a pre-column derivatization method. Two different derivatizing agents were tested. Repeatability of all chromatographic methods was carried out with five determinations of the same sample. Chromatograms were acquired and processed with TotalChrom Workstation version 6.2.1 chromatography system software (Perkin-Elmer, Inc.). Peaks were identified by comparing retention times of pure standards. Quantification was performed through an external standard calibration curve. 2.5.2.1. Derivatization with 9-fluorenylmethyl chloroformate (FMOCCl). A method described in the literature (Fabiani, Versari, Parpinello, Castellari, & Galassi, 2002) was used with some modifications. The sample (300 ll) was mixed with 600 ll of borate buffer 0.2 M and the pH was adjusted to 8.25 with NaOH solution. Then, this mixture was added to 400 ll of FMOC-Cl 15 mM in CH3CN. After 5 min of vortexing, the derivatized sample was filtered through a 0.45 lm nylon filter membrane and immediately injected. This method allows the separation and the quantification of 16 amino acids (alanine, arginine, asparagine, aspartic acid, cystine, glutamic acid, hystidine, isoleucine, lysine, methionine, phenylalanine, proline, serine, threonine, tyrosine, valine) and ammonium ion. 2.5.2.2. Derivatization with diethylethoxymethylenemalonate (DEEMM). The method, described in the literature (Gómez-Alonso, Hermosín-Gutiérrez, & García-Romero, 2007), was used as it is but using half of each sample and all reagents, and it allowed the separation and the quantification of 20 compounds in a single injection: 17 amino acids (alanine, arginine, asparagine, aspartic acid, cysteine, glutamic acid, glutamine, hydroxyproline, isoleucine, lysine, methionine, proline, serine, threonine, tryptophan, tyrosine, valine), ammonium ion, and GABA. With respect to the original method, ethanolamine was further quantified, while biogenic amines were not taken into consideration. 2.6. Statistics Linear regression and Principal Component Analysis (PCA) were carried out by means of the Statistica version 8.0 software (Stat Soft, Inc., Tulsa, USA) on autoscaled values (see Results and discussion section).

EU community methods for the analysis of wines (EEC, 1990) were used for determination of pH and °Brix.

3. Results and discussion

2.5. Chemical determinations

3.1. Cooking model system

2.5.1. Sugar and HMF determinations These substances were determined following a published method (Castellari, Versari, Spinabelli, Galassi, & Amati, 2000), with some minor modifications. A Perkin Elmer HPLC system (Series 200 LCP) equipped with diode-array detector (Series 200) set a 280 nm (kmax), connected in series with a refractive index detector (RI detector, Series 200) was used to quantify simultaneously sugars and HMF. Diluted samples were filtered with 0.45 lm nylon membrane and injected with a 5-ll loop using an injection valve (Rheodyne Inc., Cotati, CA) onto a Bio-Rad Aminex HPX-87H (Hercules, CA) hydrogen-form cation exchange resin-based column (300  7.8-mm id), protected with a pre-column (30  4.6 mm). The column was operated at 50 °C with a flow rate of 0.5 ml/min using an isocratic solvent system (H2SO4 0.045 N; pH 1.35; CH3CN 10%).

To emulate the real process, a 20%-sucrose solution was concentrated in a conical flask that was partially capped with aluminium foil. The other tested laboratory vessels (crystallizer and a wide open conical flask) reached the desired concentration too quickly, and for this reason were rejected. A withdrawal of low sample volumes was an essential requisite to limit perturbation during cooking. At the end of the cooking time, only 16.5 ml of must had been withdrawn from each must. 3.2. Sugars and HMF The behaviour of these parameters was very close to the trend observed in previous papers (Antonelli et al., 2004; Cocchi et al., 2007) and for this reason it was no longer considered.

71

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77 Table 1 Amino acid sample data set (mg/l). Data (±STD) are means of four replications. (A) Lambrusco; (B) Trebbiano toscano; (C) Spergola. HOURS A NHþ 4 Ethanolamine GABA Alanine Arginine Asparagine Aspartic acid Cysteine Glutamic acid Glutamine Hydroxyproline Isoleucine Lysine Methionine Proline Serine Threonine Tryptophan Tyrosine Valine B NHþ 4 Ethanolamine GABA Alanine Arginine Asparagine Aspartic acid Cysteine Glutamic acid Glutamine Hydroxyproline Isoleucine Lysine Methionine Proline Serine

0

3

6

9

12

15

18

21

24

27

30

50.70 (±3.50) 18.78 (±1.44) 109.07 (±9.35) 77.01 (±7.30) 112.24 (±7.72) 2.65 (±0.27) 15.69 (±1.21) 4.81 (±0.41) 48.06 (±4.75) 27.83 (±2.71) 2.10 (±0.17) 4.99 (±0.26) 0.34 (±0.05) 2.86 (±0.25) 953.23 (±87.51) 35.12 (±3.01) 12.64 (±1.39) 5.87 (±0.59) 0.96 (±0.11) 12.75 (±1.26)

55.73 (±3.86) 18.54 (±1.83) 107.74 (±10.32) 74.52 (±7.26) 122.92 (±8.48) 3.15 (±0.24) 16.16 (±1.83) 4.56 (±0.32) 45.41 (±4.53) <0.15

63.80 (±4.39) 20.09 (±1.79) 118.33 (±9.50) 83.96 (±7.43) 146.34 (±7.45) 3.49 (±0.20) 17.55 (±1.66) 3.02 (±0.26) 46.77 (±4.28) <0.15

70.14 (±4.35) 20.41 (±1.46) 118.60 (±9.33) 82.94 (±7.42) 160.21 (±10.25) 2.92 (±0.24) 20.11 (±1.17) 4.51 (±0.38) 45.02 (±4.21) <0.15

69.07 (±4.58) 18.23 (±1.11) 116.36 (±9.63) 81.26 (±7.47) 163.65 (±14.78) 3.54 (±0.26) 15.10 (±1.89) 4.12 (±0.41) 35.00 (±4.62) <0.15

74.94 (±5.15) 18.80 (±1.84) 119.37 (±12.56) 76.79 (±6.46) 183.98 (±21.13) 4.60 (±0.31) 27.28 (±1.22) 2.61 (±0.23) 34.22 (±4.92) <0.15

81.40 (±6.24) 22.36 (±1.41) 119.39 (±9.20) 90.26 (±7.87) 182.99 (±16.69) 3.43 (±0.17) 39.94 (±2.05) 1.55 (±0.14) 25.74 (±3.25) <0.15

97.43 (±9.35) 18.60 (±0.95) 120.44 (±7.84) 100.02 (±8.14) 178.38 (±18.24) 2.50 (±0.23) 44.57 (±3.27) 1.78 (±0.16) 21.95 (±2.65) <0.15

98.92 (±9.56) 17.24 (±1.54) 121.32 (±7.23) 107.48 (±10.72) 161.40 (±13.86) 3.35 (±0.21) 46.12 (±3.66) <0.19

109.95 (±10.64) 16.85 (±1.43) 117.82 (±8.59) 111.76 (±10.55) 174.57 (±14.37) 3.19 (±0.24) 47.62 (±3.47) 1.58 (±0.10) 17.91 (±2.12) <0.15

112.07 (±12.45) 17.18 (±1.71) 114.32 (±8.71) 121.78 (±10.64) 170.15 (±13.38) 3.42 (±0.12) 47.03 (±4.78) 1.25 (±0.13) 13.42 (±1.11) <0.15

3.15 (±0.19) 4.84 (±0.42) 0.45 (±0.07) 1.56 (±0.12) 966.43 (±95.31) 38.87 (±3.83) 14.90 (±1.78) 4.92 (±0.58) 2.33 (±0.11) 14.32 (±1.46)

4.69 (±0.43) 5.48 (±0.67) 1.56 (±0.14) 1.42 (±0.19) 847.28 (±78.33) 42.05 (±4.63) 14.29 (±1.60) 3.45 (±0.42) 2.07 (±0.13) 15.80 (±1.02)

5.46 (±0.54) 7.26 (±0.68) 0.78 (±0.23) 2.33 (±0.10) 884.98 (±80.88) 44.67 (±4.35) 16.61 (±1.82) 3.30 (±0.14) 6.07 (±0.25) 16.94 (±1.64)

4.71 (±0.43) 6.77 (±0.51) 0.76 (±0.27) 2.66 (±0.12) 762.49 (±78.93) 43.03 (±3.22) 13.22 (±1.40) <0.20

4.55 (±0.32) 6.33 (±0.66) 0.34 (±0.11) 1.86 (±0.18) 965.28 (±101.57) 43.47 (±3.85) 11.93 (±1.59) <0.20

3.70 (±0.26) 6.41 (±0.61) 1.06 (±0.18) 1.48 (±0.23) 843.86 (±87.80) 45.86 (±3.96) 11.91 (±1.35) <0.20

2.81 (±0.24) 6.17 (±0.75) 1.08 (±0.13) 0.23 (±0.04) 815.48 (±84.70) 47.57 (±3.55) 9.26 (±0.74) <0.20

3.53 (±0.34) 6.71 (±0.95) 1.38 (±0.12) <0.11 868.84 (±94.88) 45.30 (±5.67) 9.02 (±0.93) <0.20

2.66 (±0.38) 8.16 (±0.96) 1.90 (±0.30) 1.82 (±0.18) 629.88 (±58.48) 47.24 (±4.61) 6.53 (±0.77) <0.20

3.57 (±0.45) 7.79 (±0.88) 4.82 (±0.87) 3.17 (±0.56) 623.72 (±64.63) 47.46 (±4.78) 9.75 (±0.71) <0.20

4.70 (±0.35) 17.20 (±1.87)

2.73 (±0.32) 20.19 (±1.95)

3.43 (±0.22) 18.32 (±1.08)

6.59 (±0.41) 21.80 (±0.88)

4.66 (±0.38) 22.54 (±2.10)

10.54 (±0.94) 24.98 (±3.37)

12.44 (±1.67) 29.22 (±2.93)

69.19 (±7.43) 22.95 (±1.85) 94.70 (±8.54) 59.32 (±6.47) 217.80 (±18.22) 4.43 (±0.38) 20.52 (±1.01) 5.82 (±0.63) 27.29 (±4.25) <0.15

75.01 (±7.53) 23.87 (±1.78) 97.93 (±9.12) 62.58 (±6.44) 225.18 (±21.32) 4.23 (±0.36) 22.32 (±1.48) 5.26 (±0.67) 23.86 (±2.83) <0.15

86.46 (±8.91) 24.56 (±1.24) 100.04 (±9.68) 68.09 (±6.30) 218.47 (±22.83) 4.56 (±0.26) 24.88 (±1.52) 5.87 (±0.58) 21.98 (±2.27) <0.15

90.49 (±8.17) 24.47 (±1.31) 103.54 (±9.90) 70.46 (±7.78) 234.61 (±21.73) 4.11 (±0.41) 26.63 (±1.54) 4.29 (±0.42) 18.56 (±2.20) <0.15

95.85 (±10.18) 22.18 (±1.59) 103.45 (±10.38) 75.47 (±6.60) 219.91 (±23.46) 5.02 (±0.55) 29.11 (±1.48) 3.11 (±0.25) 15.93 (±2.22) <0.15

114.24 (±11.51) 23.38 (±1.45) 115.49 (±10.71) 94.08 (±8.26) 234.22 (±22.92) 5.78 (±0.63) 34.47 (±1.38) 3.52 (±0.44) 15.86 (±2.03) <0.15

110.67 (±11.03) 24.48 (±1.89) 108.60 (±10.40) 94.59 (±10.25) 220.06 (±23.71) 5.48 (±0.82) 33.69 (±1.75) 2.07 (±0.46) 13.10 (±1.48) <0.15

112.25 (±11.43) 18.66 (±1.28) 101.24 (±10.81) 94.24 (±10.07) 202.53 (±22.26) 3.14 (±0.24) 33.69 (±2.56) <0.19 10.67 (±0.86) <0.15

117.51 (±10.36) 17.45 (±1.53) 96.61 (±8.35) 94.49 (±8.69) 195.29 (±21.81) 4.85 (±0.37) 35.19 (±1.01) 0.35 (±0.05) 8.07 (±0.76) <0.15

127.42 (±11.43) 18.77 (±1.62) 95.45 (±10.20) 101.56 (±11.35) 184.90 (±20.44) 1.42 (±0.15) 38.96 (±3.88) 1.34 (±0.16) 8.36 (±0.72) <0.15

2.25 (±0.26) 5.82 (±0.35) 2.38 (±0.14) 0.49 (±0.07) 140.54 (±12.39) 31.23 (±2.93)

2.14 (±0.11) 5.26 (±0.33) 2.50 (±0.57) <0.11

2.46 (±0.14) 6.31 (±0.24) 2.43 (±0.10) 0.51 (±0.08) 134.38 (±16.88) 30.24 (±2.56)

1.51 (±0.24) 5.69 (±0.45) 2.54 (±0.38) <0.11

0.70 (±0.10) 5.75 (±0.32) 2.05 (±0.35) <0.11

1.78 (±0.08) 7.64 (±0.32) 1.82 (±0.40) <0.11

1.09 (±0.03) 6.63 (±0.28) 2.17 (±0.36) <0.11

<0.16

<0.16

92.78 (±10.45) 31.21 (±3.83)

76.77 (±9.07) 30.01 (±2.88)

93.61 (±9.14) 34.74 (±3.66)

81.13 (±7.72) 33.20 (±2.81)

5.21 (±0.57) 1.69 (±0.28) 1.08 (±0.08) 64.13 (±7.37) 31.34 (±2.48)

6.45 (±0.37) 1.98 (±0.20) 0.86 (±0.12) 61.82 (±6.32) 28.82 (±2.54)

1.26 (±0.03) 7.70 (±0.48) 2.09 (±012) <0.11

61.28 (±6.39) 21.89 (±1.56) 93.61 (±8.42) 53.49 (±5.37) 199.62 (±15.73) 3.80 (±0.18) 17.79 (±1.67) 4.79 (±0.21) 28.23 (±3.42) 66.90 (±6.33) 1.79 (±0.16) 5.73 (±0.32) 3.07 (±0.17) 0.51 (±0.03) 111.36 (±13.78) 26.16 (±2.44)

123.17 (±15.50) 31.15 (±3.10)

21.85 (±1.85) <0.15

55.96 (±5.27) 35.61 (±2.57)

(continued on next page)

72

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77

Table 1 (continued) HOURS

0

3

6

9

12

15

18

21

24

27

30

Threonine

9.90 (±1.32) 6.53 (±0.62) 3.34 (±0.28) 12.67 (±1.25)

13.90 (±1.02) 6.33 (±0.54) 9.58 (±0.56) 11.35 (±1.43)

16.39 (±1.05) 3.56 (±0.32) 9.04 (±1.21) 12.06 (±1.13)

21.13 (±1.13) 1.35 (±0.12) 9.84 (±0.82) 13.80 (±1.34)

11.19 (±1.56) 1.54 (±0.21) 9.22 (±0.47) 13.59 (±1.45)

16.56 (±1.52) 0.95 (±0.15) 9.54 (±1.36) 14.11 (±1.44)

11.63 (±1.35) 1.77 (±0.14) 12.40 (±1.40) 19.81 (±1.69)

14.38 (±1.96) 2.01 (±0.23) 11.26 (±1.08) 17.08 (±1.76)

11.06 (±1.22) <0.20 10.35 (±1.72) 17.32 (±1.56)

5.01 (±0.44) 0.25 (±0.03) 16.01 (±0.89) 20.88 (±2.34)

5.41 (±0.35) 0.99 (±0.15) 15.06 (±1.43) 18.19 (±1.47)

55.69 (±5.38) 27.28 (±2.45) 61.80 (±6.65) 30.10 (±2.94) 220.16 (±24.40) 1.27 (±0.13) 12.29 (±0.72) 9.84 (±1.05) 12.73 (±1.24) 121.47 (±13.78) 3.60 (±0.14) 4.20 (±0.36) <0.04

65.75 (±4.25) 28.02 (±2.09) 60.72 (±7.47) 30.63 (±3.58) 225.02 (±27.83) 2.13 (±0.12) 13.11 (±0.90) 6.36 (±0.55) 12.69 (±1.25) <0.15

77.17 (±6.66) 29.91 (±2.23) 63.41 (±6.85) 32.27 (±4.05) 231.05 (±19.88) 2.07 (±0.14) 14.90 (±0.39) 3.95 (±0.32) 11.38 (±1.54) <0.15

96.59 (±7.71) 30.83 (±2.57) 69.75 (±5.42) 43.99 (±6.43) 249.71 (±17.34) 2.54 (±0.23) 16.95 (±0.98) 5.70 (±0.68) 10.78 (±1.75) <0.15

83.75 (±8.63) 30.27 (±2.42) 74.00 (±8.18) 44.89 (±4.58) 241.49 (±17.22) 3.27 (±0.30) 18.76 (±0.43) 6.58 (±1.34) 10.05 (±1.07) <0.15

101.93 (±9.56) 27.30 (±2.78) 68.41 (±8.56) 39.80 (±3.39) 212.31 (±19.28) 1.72 (±0.13) 18.61 (±1.75) 1.76 (±0.24) 7.86 (±0.75) <0.15

98.45 (±9.60) 29.35 (±2.92) 72.91 (±8.83) 49.64 (±3.88) 251.41 (±23.67) 2.40 (±0.32) 21.72 (±1.88) 2.66 (±0.24) 7.13 (±0.58) <0.15

107.02 (±9.42) 29.91 (±2.85) 73.98 (±6.47) 52.04 (±3.79) 256.91 (±26.74) 3.12 (±0.24) 21.83 (±1.38) 3.41 (±0.27) 7.15 (±0.71) <0.15

121.09 (±10.80) 27.99 (±3.18) 75.29 (±7.84) 57.53 (±4.18) 236.04 (±21.37) 2.18 (±0.19) 25.02 (±1.63) 1.83 (±0.28) 6.35 (±0.76) <0.15

119.15 (±10.75) 24.97 (±3.11) 73.39 (±8.49) 61.15 (±7.43) 219.95 (±23.34) 1.93 (±0.14) 25.03 (±2.48) 1.79 (±0.33) 4.85 (±0.45) <0.15

128.66 (±11.49) 22.18 (±3.02) 69.36 (±5.78) 67.69 (±8.05) 218.42 (±23.82) 1.98 (±0.12) 30.15 (±2.72) 0.86 (±0.30) 5.17 (±0.33) <0.15

3.05 (±0.27) 3.17 (±0.33) <0.04

3.98 (±0.32) 3.38 (±0.51) 0.21 (±0.03) <0.11 183.84 (±13.17) 17.65 (±1.35) 1.81 (±0.18) <0.20

1.70 (±0.13) 1.76 (±0.25) 0.30 (±0.02) <0.11 117.54 (±17.96) 18.10 (±1.49) 1.69 (±0.14) <0.20

2.26 (±0.27) 2.72 (±0.36) <0.04

1.64 (±0.24) 4.62 (±0.52) <0.04

<0.11 138.17 (±15.37) 21.53 (±1.90) 2.94 (±0.38) <0.20

<0.11 119.94 (±11.75) 19.02 (±1.76) 3.55 (±0.30) <0.20

2.96 (±0.34) 2.67 (±0.28) 0.13 (±0.04) <0.11 92.51 (±8.93) 19.48 (±1.43) 3.82 (±0.39) <0.20

1.90 (±0.22) 2.08 (±0.27) <0.04

<0.11 187.69 (±23.07) 15.12 (±1.22) 3.19 (±0.29) 6.61 (±0.75) 9.48 (±0.61) 6.86 (±0.84)

3.00 (±0.26) 2.78 (±0.34) 0.23 (±0.02) <0.11 188.38 (±12.53) 17.23 (±1.54) 3.70 (±0.25) 1.58 (±0.15) 13.40 (±1.00) 7.47 (±0.88)

2.81 (±0.25) 2.75 (±0.49) <0.04

<0.11 230.94 (±28.55) 13.06 (±1.46) 3.08 (±0.27) 7.72 (±0.89) 10.46 (±0.96) 7.12 (±0.72)

2.62 (±0.22) 2.35 (±0.32) 0.63 (±0.03) <0.11 140.88 (±15.21) 16.51 (±1.80) 2.38 (±0.25) 1.05 (±0.08) 10.48 (±0.47) 7.47 (±0.78)

<0.11 76.33 (±7.57) 20.79 (±1.48) 2.30 (±0.14) <0.20

<0.11 65.85 (±6.44) 21.16 (±2.43) 2.87 (±0.21) <0.20

13.35 (±2.35) 8.72 (±0.54)

8.62 (±1.38) 7.06 (±0.65)

15.33 (±1.24) 9.25 (±0.70)

16.17 (±1.48) 10.14 (±0.43)

21.37 (±1.49) 11.70 (±0.48)

15.17 (±1.72) 9.89 (±0.89)

19.28 (±1.94) 12.31 (±1.17)

Tryptophan Tyrosine Valine C NHþ 4 Ethanolamine GABA Alanine Arginine Asparagine Aspartic acid Cysteine Glutamic acid Glutamine Hydroxyproline Isoleucine Lysine Methionine Proline Serine Threonine Tryptophan Tyrosine Valine

3.3. Amino acids The two derivatization methods were able to react with primary and secondary amino acids and for this reason both were tested. The presence of a lower number of reaction-interfering substances, with a very stable baseline, and a better repeatability, made DEEMM more friendly and reliable as a derivatizing agent, if compared with FMOC. In addition, DEEMM gave derivatives that were stable for several days, except for secondary amino acids. In any case, the injections of the samples were carried out within 24 h from the derivatization to avoid an underestimation of proline and hydroxyproline (Gómez-Alonso et al., 2007). The peaks of glycine and hystidine were partially covered by an interference due to reaction by-products. Moreover, the peaks of leucine and phenylalanine are often overlapped. Because of this, the data about these four amino acids were unreliable and for this reason they are not included in this paper. The concentrations of amino acids and other nitrogen substances in original musts are reported in Table 1. Proline was the most copious amino acid in Lambrusco, while in the other two

musts arginine was considerable, as well. Alanine, glutamic acid, and glutamine were present in fairly high concentration, as well as GABA. A second group of substances, ranging from 10 to 20 mg/l, included aspartic acid, serine, threonine, tyrosine, and valine. Finally, the other amino acids, such as asparagine, cysteine, hydroxyproline, isoleucine, lysine, methionine, and tryptophan were present in very low amounts. The NHþ 4 concentration was similar (in all the musts 50–60 mg/l), whereas the ethanolamine was a little lower (20–30 mg/l). Because of the high amount of proline, Lambrusco samples had the highest content of nitrogen compounds (1500 mg/l), which was almost double if compared with the other two musts. Glutamine was the most sensitive amino acid to temperature. In fact, it completely disappeared within the first 3 h in all the musts. Under the experimental conditions, glutamine was probably converted into pyroglutamic acid. When heated, in fact, glutamine releases ammonia on a molar basis mainly due to the deamidation of the amide group (Airaudo, Gayte-Sorbier, & Armand, 1987). Unfortunately, pyroglutamic acid does not react with DEEMM, and its presence could not be confirmed.

73

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77

Fig. 1. Concentration ratio [C ratio] for the amino acids and other nitrogen compounds. This graph shows only substances present in the highest concentrations.

Tryptophan completely disappeared in Lambrusco and Spergola by 12 h, showing a similar but less drastic behaviour in Trebbiano, as well. Although it was not one of the main amino acids, it showed great reactivity.

Two opposite phenomena influenced final amino acid amount: concentration, as a consequence of water evaporation, and a decrease, as a consequence of their thermal degradation.

Table 2 Equations of the straight lines obtained from the autoscaled values of amino acid concentrations/°Brix during cooking process for p < 0.01 (bold for p < 0.001). The same procedure was also applied for the sums of total nitrogen compounds, primary, and secondary amino acid are also reported.

NHþ 4 Ethanolamine GABA Alanine Arginine

Lambrusco

Trebbiano toscano

Spergola

ns y = 0.0970x + 1.4548 R2 = 0.9313 y = 0.0988x + 1.4827 R2 = 0.9673 y = 0.0926x + 1.3885 R2 = 0.8483 ns

ns y = 0.0979x + 1.4692 R2 = 0.9497 y = 0.0987x + 1.4807 R2 = 0.9646 ns

Ns y = 0.0998x + 1.4968 R2 = 0.9858 y = 0.0994x + 1.4911 R2 = 0.9783 ns y = 0.0994x + 1.4916 R2 = 0.9790 ns

Glutamine Hydroxyproline

y = 0.0779x + 1.1683 R2 = 0.6006 ns y = 0.0935x + 1.403 R2 = 0.866 y = 0.0993x + 1.4889 R2 = 0.9754 ns ns

Isoleucine

ns

Lysine

ns

Methionine Proline

ns y = 0.0977x + 1.4657 R2 = 0.9452 y = 0.094x + 1.4103 R2 = 0.8752 y = 0.0954x + 1.4303 R2 = 0.9002 y = 0.0862x + 1.2926 R2 = 0.7351 ns ns

y = 0.0984x + 1.4764 R2 = 0.9590 y = 0.084x + 1.2599 R2 = 0.6984 ns y = 0.0954x + 1.4315 R2 = 0.901 y = 0.0985x + 1.4768 R2 = 0.959 ns y = 0.0879x + 1.3187 R2 = 0.7652 y = 0.0888x + 1.3322 R2 = 0.7809 y = 0.095x + 1.425 R2 = 0.8934 ns y = 0.0952x + 1.4276 R2 = 0.8968 y = 0.0949x + 1.4231 R2 = 0.891 y = 0.0801x + 1.2013 R2 = 0.6350 y = 0.0838x + 1.2574 R2 = 0.6957 ns ns

y = 1.1x + 16.6 R2 = 0.882 y = 0.8x + 12.7 R2 = 0.889 y = 0.2x + 2.4 R2 = 0.806

y = 1.4x + 21.1 R2 = 0.905 y = 1.3x + 41.4 R2 = 0.938 y = 0.2x + 2.7 R2 = 0.848

Asparagine Aspartic acid Cysteine Glutamic acid

Serine Threonine Tryptophan Tyrosine Valine Total nitrogen compounds Primary amino acids Secondary amino acids

ns = not significant.

ns y = 0.0882x + 1.3225 R2 = 0.7695 y = 0.0985x + 1.4773 R2 = 0.9603 ns y = 0.0919x + 1.3781 R2 = 0.835 y = 0.0790x + 1.1854 R2 = 0.6183 ns ns y = 0.0959x + 1.4382 R2 = 0.9101 y = 0.0921x + 1.3812 R2 = 0.8394 y = 0.0684x + 1.2561 R2 = 0.7042 y = 0.075x + 1.1257 R2 = 0.5576 ns y = 0.086x + 1.301 R2 = 0.745 y = 1.4x + 21.7 R2 = 0.923 y = 1.1x + 16.6 R2 = 0.902 y = 0.2x + 2.8 R2 = 0.889

74

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77

A

50 45 40

conc/°Brix

35 30 25

PRO L y = -0.4684x + 46.474 PRO T y = -0.2279x + 10.016 PRO S y = -0.1247x + 6.3015

20 15 10 5 0 0

3

6

9

12

15

18

21

24

27

30

33

time (h)

B

2.5 2

PRO L y = -0.0977x + 1.4657 PRO T y = -0.0952x + 1.4276 PRO S y = -0.0959x + 1.4382

autoscaled values

1.5 1 0.5 0 -0.5 -1 -1.5 -2 0

3

6

9

12

15

18

21

24

27

30

33

time (h) Fig. 2. Regression straight lines for proline concentration vs. time of cooking in the three different musts: Lambrusco (PRO L), Trebbiano toscano (PRO T), and Spergola (PRO S). In A, raw data were plotted vs. time, while in B, autoscaled data were used.

Table 3 Loading values for the first three principal components. PC1 pH °Brix HMF Glucose Fructose NHþ 4 Ethanolamine GABA Alanine Arginine Asparagine Aspartic acid Cysteine Glutamic acid Glutamine Hydroxyproline Isoleucine Lysine Methionine Proline Serine Threonine Triptophan Tyrosine Valine

0.85 0.69

PC2

PC3

0.68 0.79

0.87 0.67

3.3.1. Concentration ratio Considering that the degradation of amino acids strongly affected their concentration during the process, whereas sugar degradation was negligible, the rate of concentration of each amino acid during the process compared to a reference concentration parameter has been expressed as concentration ratio [C ratio]:

0.95 0.99 0.87 0.94 0.68 0.81

½C ratio ¼ ðaat0 = Brixt0 Þ=ðaat30 = Brixt30 Þ

0.94

that is

0.66 0.89 0.78 0.73 0.83 0.96 0.85 0.69 0.85

The reactivity of amino acids was investigated by using two different approaches. In the former, the ratio between the concentration of each amino acid and the corresponding °Brix at the beginning of the process (t0) and at the end (t30) was compared. In the latter the autoscaled values of the amino acid concentrations divided by the corresponding °Brix were plotted against time, and the slopes of the straight lines were compared.

½C ratio ¼ ðaat0 =aat30 Þ  ð Brixt30 = Brixt0 Þ

ð1Þ

where aat0, aat30, °Brixt0, and °Brixt30 are the amino acid and °Brix concentration mean values at the beginning (t0) and at the end (t30) of the cooking process, respectively. In this way, the effect of the mere must concentration is eliminated. When the result of Eq. (1) is 1, the amino acid degradation is negligible. On the contrary, values >1 indicate amino compound degra-

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77

75

Fig. 3. Principal Component Analysis of sample data set. (A) Plot of the first two principal components (PC1 and PC2) with the explained variance are reported. L = Lambrusco, T = Trebbiano, S = Spergola. (B) Principal Component Analysis of samples. Plot of the first and third principal components (PC1 and PC3) with the explained variance are reported. L = Lambrusco, T = Trebbiano, S = Spergola.

dation. The higher the number, the more remarkable the phenomenon (Fig. 1). Finally, values <1 show that amino compound accumulation overtakes the normal course of must concentration. As a matter of fact, figures between 0 and 1 occurred only in a few cases (aspartic acid in Lambrusco; tyrosine in Lambrusco and Trebbiano). It is very risky to discuss this last behaviour. In fact, it is neither always verified for all low concentration amino acids, nor for all musts. Apart from analytical errors, this behaviour might be due to an unexpected contribution of amino acids from protein hydrolysis. Proline and glutamic acid showed figures quite high (>3), but not constant among the musts. Proline, in particular, showed the greatest differences among the three musts. In Spergola, in fact, its ratio was very high (about 9), more than two times if compared with Lambrusco and Trebbiano. At the moment there is no apparent justification for this behaviour.

Arginine had a concentration ratio in white musts higher than in Lambrusco. Ethanolamine, GABA, and serine have concentration ratios quite similar in all the three musts. In all musts, NHþ 4 shows an almost perfect equilibrium among volatilization of NH3, concentration of NHþ 4 , and neo-formation, as a consequence of amino acid degradation (Sohn & Ho, 1995). Unfortunately this model does not consider the changes occurring during the cooking process, but it focuses on the final stage of process only. The following model bypasses this limit. 3.3.2. Comparison of slopes The slopes of the linear regression straight lines give information about the degradation rate of each amino acid during the cooking process. A positive sign of the slope implies the predominance of amino acid concentration effect, while the degradation

76

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77

prevails with a negative slope. A value of the slope near to zero reveals a balance between concentration and degradation, i.e. a constant concentration. However, the different concentrations of the various amino acids have great influence on slopes, thus creating false differences. To eliminate this effect, data were autoscaled [Aaut] using the following expression:

½Aaut  ¼ ðaa  aamean Þ=aasd where aa is the ratio between the concentration of each amino acid and the corresponding °Brix, aamean is the mean of the same aa during the whole process for each must, and aasd is its standard deviation. This data manipulation reduces the real values into a comparable set, thus eliminating the effect of the higher initial content, but keeping unchanged data trend (Table 2). The case of proline is very explicative when plotted using raw data (amino acid concentration/°Brix vs. time; Fig. 2A) if compared with autoscaled values (Fig. 2B). Aspartic acid, methionine, tyrosine, and NHþ 4 were not correlated with time in a linear way. Glutamine was not correlated with time, as well. However, it disappeared after 3 h only, for its extreme heat susceptibility and for this reason it was not significant. Autoscaled data showed a common trend for many amino acids, which had very different concentrations in the three musts. Besides proline, it was the same case for ethanolamine, GABA, cysteine, glutamic acid, serine, threonine, and tryptophan. Their slopes were very similar independently from the must and from the compound. These substances disappeared at the highest rate in all the tested samples and they were the most sensitive to high temperature. Finally, arginine, hydroxyproline, and isoleucine were very highly correlated with time, but in the case of Trebbiano and Spergola only, while asparagine was correlated with time for Lambrsuco and Trebbiano. Alanine, lysine, and valine were correlated with time in Lambrusco, in Trebbiano, and in Spergola only, respectively. When total nitrogen is considered, the behaviour of the three musts was slightly different (Table 2). Trebbiano and Spergola showed a comparable degradation rate, while Lambrusco nitrogen compounds seemed more stable. Primary amino acids degraded with the same pattern of total nitrogen, while secondary amino acids, presented the same trend for the three musts with a considerable lower slope. 3.4. Principal Component Analysis (PCA) To gain an overall vision of the whole sample set, thus evidencing their distribution, a PCA analysis was carried out on the autoscaled data matrix (see Section 3.3.2), which was composed of the values of the 25 chemical parameters measured for three musts. The total variability of the sample data set (79.62%) was explained by the first three components PC1, PC2 and PC3. Considering only loadings with an absolute value >0.65 (Table 3), pH, GABA, alanine, asparagine, glutamic acid, hydroxyproline, isoleucine, methionine, proline, serine, threonine, and valine were loaded on PC1 (47.13% of the total variance) with a negative sign. Only °Brix, fructose, glucose, and tyrosine showed a positive weight. PC2 explains a further 24.06% of the total variability of the data set, with °Brix and HMF, with positive weights, while ethanolamine, arginine, and cysteine with negative ones. These first two components (PC1 and PC2) arrange samples regularly on the plane in 3 different clusters (Fig. 3A). The samples of each cv. showed an almost linear trend, consistent with the cooking process. Initial musts lie in the lower part of each cluster and they go up during cooking in a regular manner.

The contribution of the third component (PC3, 9.43% of the total variance) is entirely characterised by lysine (0.78). Its concentration, however, was very low in all considered musts, thus making its contribution almost negligible. As a matter of fact for Trebbiano samples, PC3 allows a partial distinction up to 21 h cooking (T0– T21). This must be characterised by a greater concentration of lysine if compared to the other two musts. Only the last samples (T24–T30) are mixed with those of the other two cvs. In order to give a clearer interpretation of amino acid influence on the trends of the tested musts, PCA was carried out on the amino acids only. The results stress the importance of these compounds on the trends of the three musts during the cooking process. In fact, PCA results are very similar to those of the whole data set (Fig. 3A and B). Loadings are similar to their observed for the PCA on the whole data set, as well. The most important analytes generally give a higher contribute to total variance explanation. 4. Conclusions In spite of the different concentrations of amino acids in the three musts at the beginning of the process, the trends of degradation during cooking were basically similar in many cases. Generally speaking, sugar and amino acid degradation can follow acid-induced and amino acid-induced pathways. Both yield HMF as the main compound of sugar degradation. In our samples, however, it is very likely that sugars and amino acids followed two independent pathways of degradation. Sugars were degraded by the acid-induced pathway, while, amino acids gave still unidentified substances in this medium and under our condition at least, except for ammonium ion that was almost at the same concentration during the whole process. Therefore, a lot of work should be done to explore the transformation of the amino acid fraction, and to find out the role on sensory properties and consumer health of these neo-formation substances. Amino acid, in fact, might give a large number of N-containing substances of significant sensory relevance. However, low pHs of musts probably prevent the classical Maillard reaction. At the moment, however, neither Maillard intermediates (e.g. Amadori compounds), nor amino acid moieties from amino acid fragmentation have been detected. The identification of these compounds is currently under investigation. References Airaudo, C. B., Gayte-Sorbier, A., & Armand, P. (1987). Stability of glutamine and pyroglutamic acid under model system conditions: Influence of physical and technological factors. Journal of Food Science, 52(6), 1750–1752. Antonelli, A., Chinnici, F., & Masino, F. (2004). Heat-induced chemical modification of grape must as related to its concentration during the production of traditional balsamic vinegar: A preliminary approach. Food Chemistry, 88(1), 63–68. Castellari, M., Versari, A., Spinabelli, U., Galassi, S., & Amati, A. (2000). An improved HPLC method for the analysis of organic acids, carbohydrates, and alcohols in grape musts and wines. Journal of Liquid Chromatography and Related Technologies, 23(13), 2047–2056. Cocchi, M., Ferrari, G., Manzini, D., Marchetti, A., & Sighinolfi, S. (2007). Study of the monosaccharides and furfurals evolution during the preparation of cooked grape musts for Aceto Balsamico Tradizionale production. Journal of Food Engineering, 79(4), 1438–1444. Cocchi, M., Lambertini, P., Manzini, D., Marchetti, A., & Ulrici, A. (2002). Determination of carboxylic acids in vinegars and in Aceto Balsamico Tradizionale di Modena by HPLC and GC methods. Journal of Agricultural and Food Chemistry, 50(19), 5255–5261. EEC (1990). Commission Regulation 2676/1990. Community methods for the analysis of wines. Official Journal of the European Community, L272, 1–192. Fabiani, A., Versari, A., Parpinello, G. P., Castellari, M., & Galassi, S. (2002). Highperformance liquid chromatographic analysis of free amino acids in fruit juices using derivatization with 9-fluorenylmethyl-chloroformate. Journal of Chromatographic Science, 40(1), 14–18. Falcone, P. M., & Giudici, P. (2008). Molecular size and molecular size distribution affecting traditional balsamic vinegar aging. Journal of Agricultural and Food Chemistry, 56(16), 7057–7066.

G. Montevecchi et al. / Food Chemistry 121 (2010) 69–77 Gómez-Alonso, S., Hermosín-Gutiérrez, I., & García-Romero, E. (2007). Simultaneous HPLC analysis of biogenic amines, amino acids, and ammonium ion as aminoenone derivatives in wine and beer samples. Journal of Agricultural and Food Chemistry, 55(3), 608–613. Hodge, J. E. (1953). Dehydrated foods, chemistry of browning reactions in model systems. Journal of Agricultural and Food Chemistry, 1(15), 928–943. Ledl, F., & Schleicher, E. (1990). New aspects of the Maillard reaction in foods and in the human body. Angewandte Chemie International Edition in English, 29(6), 565–594. Maillard, L. C. (1912). Action des acides amines sur les sucres: Formation des melanoidines par voie methodique. Comptes Rendus de l’Académie des Sciences, 154, 66–68. Muratore, G., Licciardello, F., Restuccia, C., Puglisi, M. L., & Giudici, P. (2006). Role of different factors affecting the formation of 5-hydroxymethyl-2furancarboxaldehyde in heated grape must. Journal of Agricultural and Food Chemistry, 54(3), 860–863.

77

Nursten, H. E. (2005). The Maillard reaction: Chemistry, biochemistry and implications. Cambridge: Royal Society of Chemistry. Piva, A., Di Mattia, C., Neri, L., Dimitri, G., Chiarini, M., & Sacchetti, G. (2008). Heatinduced chemical, physical and functional changes during grape must cooking. Food Chemistry, 106(3), 1057–1065. Repubblica Italiana (2000). Gazzetta Ufficiale della Repubblica Italiana, 124(30 May) (and following modifications). Repubblica Italiana (2007). Decreto Direttoriale 19/06/2007. Settima revisione dell’elenco nazionale dei prodotti agroalimentari tradizionali. Gazzetta Ufficiale della Repubblica Italiana, 147(27 June) (S.O. n. 146). Rivero-Pérez, M. D., Pérez-Magariño, S., & González-San José, M. L. (2002). Role of melanoidins in sweet wines. Analytica Chimica Acta, 458(1), 169–175. Sohn, M., & Ho, C. T. (1995). Ammonia generation during thermal degradation of amino acids. Journal of Agricultural and Food Chemistry, 43(12), 3001–3003.