Intensity Prediction of Typical Aroma Characters of Cabernet Sauvignon Wine in Changli County (China)

LWT - Food Science and Technology 43 (2010) 1550e1556

Contents lists available at ScienceDirect

LWT - Food Science and Technology

journal homepage: www.elsevier.com/locate/lwt

Intensity prediction of typical aroma characters of cabernet sauvignon wine in Changli County (China)

Yongsheng Tao*, Li Zhang

College of Enology, Northwest A & F University, Yangling, Shaanxi 712100, China article info abstract

Article history: In this paper, several linear multiple regression models of aroma descriptors were built from potential Received 15 June 2008 active odorants in Cabernet Sauvignon red wines in Changli County. The modified frequency (MF%) of ten Received in revised form aroma description terms in sample wines were evaluated by 30 panelists trained using the aroma 11 March 2010 standards of “Le Nez du Vin”. Aroma compounds of sample wines were detected by Solid Phase Accepted 2 June 2010 Microextraction-Gas Chromatography-Mass (SPME-GC-MS), and 65 aroma compounds were identified and quantified. Those aroma compounds with odor active values (OAV) > 0.5 were chosen to build Keywords: regression models for the eight characteristic aroma terms. Finally, five models were developed for five Cabernet Sauvignon Sensory typical sensory terms: Blackcurrant, Bilberry, Green pepper, Vanilla and Smoked. These models were Red wine related to 13 aroma compounds. These compounds included 3-ethoxy-1-propanol, phenethyl acetate, 4- GCeMS terpinenol, 2-hexen-1-ol, di-tert-butyl-phenol, b-terpinenol, hexanoic acid, octanoic acid, ethyl myr- Changli istate, ethyl 3-hydroxy butyrate, isobutyl alcohol and 4-methyl-5-butyl-2(3H)-furan. ANOVA statistical analysis indicated that all five models regressed at 95% significant levels. t detections of the models showed regression coefficients of 99% or 95% significant levels. Correlation coefficients between the measured and predicted Y ranged from 0.714 to 0.999. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction odor activity (Culleré, Escudero, Cacho, & Ferreira, 2004). Li et al. delineated the aroma compounds of Chardonnay white wine in The flavor of the wine was found to be one of the most impor- Changli County (China), finding that thirteen impact odorants had tant attributes considered when buying wines. Over the past ten OAVs > 1(Li, Tao, Wang, & Zhang, 2008). years, many aroma compounds in mono-variety wines and regional However, aroma compounds in wine are very complex. Many wines have been identified and quantified. Some researches have aroma compounds contribute to the whole aroma of a wine. analyzed the flavor characters of compounds separated by GC Research on the aroma compounds’ contributions to wine should column using Gas ChromatographyeOlfactometry (GCeO) (Culleré, take into account not only the number of odorants and their rela- Escudero, Cacho, & Ferreira, 2004; Lee, & Noble, 2003). However, tive importance but also the possible existence of synergetic not all the aroma compounds detected by GC can produce flavor. interactions between those odors and with the matrix constituents Those with concentrations lower than their olfactometry threshold (Guth, 1997; Li, 2006). We hypothesize that mathematical models cannot be perceived by the human olfactory system. For this reason, could be built to describe the relationship between aroma in some of the literature, odor active value (OAV) was used to descriptors and aroma compounds in wine. From these models, the express the odor activity of one aroma compound in wine. OAV intensities of some aroma characters could be predicted. To realize calculation measures the concentration and threshold of the odor this proposal, three challenges had to be overcome. First, it was in the same matrix. Only those odorants with an OAV > 1 can be necessary to identify and quantify as many aroma compounds in perceived and contribute to the whole aroma of the wine (Allen, wine as possible. Since the number of aroma compounds in wine is Lacey, & Boyd, 1994; Kotseridis, Razungles, Bertrand, & Baumes, significant and the range of their concentrations is great, the 2000). For example, Culleré et al. analyzed the aroma compounds method to detect the aromas must be both consistent and accurate. of six premiums red wines in Spain and found that 40 odorants had This is expensive and complicated. Second, data on the sensory analysis of wine must be obtained. A dependable sensory analysis was carried out by systematically trained panelists. Finally, the prediction result of aroma character from aroma compounds was * Corresponding author. Tel./fax: þ86 29 87091994. E-mail address: [email protected] (Y.S. Tao). a statistical estimation or inference value, so the wine sample

0023-6438/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2010.06.003 Y.S. Tao, L. Zhang / LWT - Food Science and Technology 43 (2010) 1550e1556 1551 number must be large enough for the prediction to be represen- sample and 2 g NaCl. For SPME analyses, the vials were dipped in tative. For the reasons above, the prediction of aroma characters a thermostatic water bath. A magnetic stirring bar was placed in the from aroma compounds is very seldom in the literature. In several vial to provide the sample with agitation. The solid-phase fiber for previous studies, compounds producing some special flavor were micro-extraction is PDMS (100 mm Polydimethylsiloxane). The vial discussed. Past studies have been limited to one compound or one was equilibrated at 40 C for 10 min. Then, the power magnetic group of compounds. Researches have quantified aroma stirrer was added and the Solid-phase micro-extraction was per- compounds or described the flavor characters of them (Arrhenius, formed at 40 C for 30 min. This was immediately followed by the Mccloskey, & Sylvan, 1996; Aznar, Lopez, Cacho, & Ferreira, 2003; desorption of the analytes into the gas chromatograph injector. The Escudero, & Ferreira, 2000). solid-phase fiber remained in the injector for about 3 min. In our work, sensory analysis and aroma compound detection were carried out in order to understand the aroma characters of 2.5. GCeMS analysis Cabernet Sauvignon red wine in Changli County, one of the four districts of Wine Denomination of Origin in China. Our aim was to GCeMS apparatus: TRACE DSQ (Thermo-Finnigan, USA). develop a method of mathematical prediction to describe the Analytical column: DB-Wax capillary column (30 m 0.32 mm i.d., typical aroma characters of the sample wine. Linear multiple 0.25 mm film thickness), J&W (Folsom, USA). Carrier: He at 1 ml/ regression models were used to predict the intensities of aroma min. Temperature program: 40 C for 3 min, then raised to 160 Cat characters based on the contents of aroma compounds. 4 C/min, then raised to 230 Cat7C/min for 8 min. Transfer line temperature was 230 C. Injection temperature was 250 C. Mass 2. Materials and methods spectra were recorded in electron impact (EI) ionization mode. Mass spectrometry: mass range 33e450 amu, scanned at 1 s 2.1. Wine samples intervals. Ion source temperature was 230 C.

Wine samples investigated in this study were the same to that in 2.6. Qualitative and quantitative analysis the article wrote by Tao, Liu, and Li (2009). Eight different mono- e varietal Cabernet Sauvignon wines from 1998 2005 were kindly Identification was achieved by comparing with mass spectra donated by two wineries From Changli County. The winemaking obtained from the sample with those from the pure standards process and some general indexes of wine samples were described injected in the same conditions. Additionally, the sample was by Tao et al. (2009). compared with the Kovats retention index and the mass spectra presents in the NIST2.0 MS library Database or in the literature. 2.2. Reagents The internal standard quantification method was used. Thus, octan-3-ol was chosen as an internal standard. Quantitative data of All reagents used were of analytical grade. Absolute ethanol, the identified compounds was obtained by interpolation of the ’ tartaric acid and sodium chloride were purchased from Xi an relative areas versus the internal standard area in calibration ’ chemical factory (Xi an, China). Water was obtained from a Milli-Q graphs built for pure reference compounds. The concentration of fi puri cation system (Millipore). Solvents did not require additional volatile compounds, for which there was no pure reference, was distillation. The 32 pure reference compounds came from Sigma- obtained using the same calibration graphs as one of the e Aldrich (China sector). These included ethyl acetate, ethyl buty- compounds with the most similar chemical structure according to rate, 1-propanol, 2-methyl thiophene, 2-methyl-1-propanol, the formula and chemical character (Li, Tao, Wang, & Zhang, 2008; isopentyl acetate, 1-butanol, 2,5-dimethyl-tetrahydro-furan, iso- Perestrelo, Fernandes, Albuquerque, Marques, & Camara, 2006). pentyl alcohol, ethyl hexanoate, ethenyl benzene, ethyl lactate, 1- Each sample was extracted in triplicate. hexanol, 3-octanol, ethyl octanoate, furfural, decanal, cis-geraniol, b b -ionone, linalool, -damascenone, ethyl decanoate, phenethyl 2.7. Sensory analysis acetate, 1-decanol, hexanoic acid, benzyl alcohol, 2-phenyl-ethanol, ethyl dodecanoate, ethyl hexadecanoate, octanoic acid, decanoic The sensory analysis was performed as described by Tao et al. acid and p-ethyl-phenol. (2009). A panel of tasters, consisted of 30 students (18males and 12 females, aged from 21 to 24 years) had been trained with “Le Nez 2.3. Standard solutions du Vin” aroma kit to conduct the wine sensory analysis over 70 days. In the analysis of a balanced and completed block design, each Octan-3-ol was employed as an internal standard. Exact panelist was told to describe the wine aroma profile using five or volumes of octan-3-ol were dissolved in absolute ethanol and made six terms of “Le Nez du Vin”. And they needed to score the intensity up to volume (50 mL). Exact volumes of all chemical standard of each term using 5-point scale. The data processed was a mixture compounds were dissolved in synthetic wines to calculate the of intensity and frequency of detection, which was calculated with emendation factor to octan-3-ol. These standard compounds dis- the formula proposed below: solved in synthetic wines at concentrations typically found in wines pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (Li, 2006). The matrix of the synthetic wine was 8.91 g/100 g MF% ¼ Fð%ÞIð%Þ solution of ethanol/water. The synthetic wine had 6 g/L of tartaric acid and its pH was 3.3e3.4 adjusted with 1 mol/L NaOH (synthetic where F(%) is the detection frequency of an aromatic attribute wine matrix). All these solutions were stored at 4 C(Guth, 1997; expressed as percentage and I(%) is the average intensity expressed Ferreira, Lopez, Escudero, & Cacho, 1998a). as percentage of the maximum intensity.

2.4. SPME sampling conditions 2.8. Data analysis and models establishment

SPME was carried out following the method proposed by Tao All statistical analyses were performed using the SPSS statistical et al. (Tao, Li, & Wang, 2007). Both wine samples and model solu- package version 13.0 for Windows (SPSS Inc, Chicago, IL, USA). tions were analyzed in 15-mL glass vials filled with 10 mL of each Principal component analysis (PCA) was performed on “MF%” data 1552 Y.S. Tao, L. Zhang / LWT - Food Science and Technology 43 (2010) 1550e1556 of aroma description to determine the typical aroma terms of a relevant family of aroma compounds in Cabernet Sauvignon wine sample wines. OAV analysis was applied to concentrations of aroma (Culleré, Escudero, Campo, Cacho, & Ferreira, 2009). Quantification compounds in selected active odorants. Linear multiple regression of this group of compounds involved some difficulties with the models were used to predict the intensities of typical aroma char- method used in this work. The OAV of each compound was acters from the semi-concentrations of selected active odorants in obtained by dividing the compound’s concentration by the odor sample wines. threshold. Twenty-one compounds had OAVs greater than 1. Seven compounds’ OAVs ranged from 0.5 to 1. 3. Results and analysis 3.3. Establishment of prediction models 3.1. Sensory analysis To predict the MF(%) of typical aroma terms, linear multiple The sensory data were great. Some aroma terms with relatively regression was used to build models by semi-concentrations of low MF(%) values (<5.0) were omitted, leaving 32 observed aroma those odorants with OAVs greater than 0.5. Aroma characters, terms. The modified frequency (MF%) of these 32 aroma terms is compounds in models, regression correlations and t detection shown in Table 1. Principle component analysis (PCA) was applied results are shown in Table 3. Though 8 aroma terms were consid- twice to obtain a more simplified view of the total aroma characters ered, only 5 terms could be given appropriate prediction models. of the sample wines. The terms of blackcurrant, green pepper, These were: black currant, bilberry, green pepper, vanilla and smoked, redcurrant, cut hay, vanilla, bilberry and cinnamon most smoked. In the models of the first three terms, there were 3, 3 and 5 commonly described the typical aroma characters of the sample compounds, respectively. The other two models only contained 1 wines. The detailed analysis of the original data was complicated compound per models. Certainly there were other compounds and was published (Tao, Liu & Li, 2009). The objective here was to which contributed to these two descriptors, but their regression make a correlation analysis between sensory data and chemical coefficients were not statistically significant, so their information data. only existed in the constant. t Detections indicated that the regression correlations and constants in the 5 models regressed at 3.2. Aroma compounds analysis by GC-MS significant levels of 99% or 95%. In the model of black currant, there were three compounds Volatile compounds of Cabernet Sauvignon red wines in Changli (3-ethoxy-1-propanol, phenethyl acetate and 4-terpinenol) listed County detected by SPME-GC-MS are shown in Table 2. There are 65 according to regression correlation value. These three compounds aroma compounds and their concentrations vary from 0.5 mg/L to were all positively correlated with the MF(%) of black currant. In the 2.23 g/L. The majority were higher alcohols, esters, and fatty acids. model of bilberry, three compounds (2-hexen-1-ol, 2,4-di-tert- Other minor compounds identified were terpenes, norisoprenoids, butyl-phenol and b- terpinenol) contributed to the MF(%) positively volatile phenols and furans. The methoxypyrazines were not as well. The model of green pepper was composed of 5 compounds quantified, which was unfortunate because it is considered to be and 1 constant. According to regression correlation values, only

Table 1 The modiﬁed frequency (MF%) value of cabernet sauvignon dry red wine from 8 different years from Changli County.

Aroma terms Sample 1998 Sample 1999 Sample 2000 Sample2001 Sample 2002 Sample 2003 Sample 2004 Sample 2005 1 Blackcurrant 53.5 57.6 65.4 43.2 34.8 62.7 46.8 65.4 2 Green pepper 49.5 57.5 24.3 31.0 58.1 61.1 56.6 47.9 3 Smoked 34.3 39.1 32.7 28.2 33.3 41.1 56.1 51.2 4 Prune 35.5 33.3 43.3 30.5 22.0 40.4 46.7 48.2 5 Pepper 28.7 26.5 33.3 20.3 29.9 23.7 38.6 29.9 6 Raspberry 32.1 32.7 18.6 24.0 25.3 27.2 23.1 35.0 7 Black coffee 27.1 32.1 34.1 15.8 19.6 28.7 28.2 31.0 8 Redcurrant 23.7 25.4 28.7 20.3 26.0 25.8 23.7 28.7 9 Cut hay 31.1 32.1 18.4 12.4 26.5 19.2 27.1 28.7 10 Blackberry 24.7 24.8 18.1 15.2 23.0 42.0 14.6 28.7 11 Mint 21.4 14.6 35.0 24.7 22.0 19.2 13.6 30.5 12 Trufﬂe 29.3 36.1 20.9 27.1 17.3 22.0 13.4 14.6 13 Vanilla 20.3 24.4 26.0 22.5 20.8 13.4 18.6 34.3 14 Toast 28.8 31.1 8.5 18.1 21.4 39.4 18.1 11.1 15 Cherry 19.2 28.0 14.1 19.7 27.7 19.8 24.1 20.8 16 Mulberry 31.0 28.8 15.6 14.6 13.0 26.0 12.9 30.8 17 Violet 2.8 5.1 16.8 3.2 4.5 35.6 51.5 52.3 18 Coffee 19.7 16.1 23.7 19.7 13.6 19.7 23.7 24.3 19 Bilberry 15.2 17.3 16.4 20.7 10.7 26.5 19.2 27.7 20 Cinnamon 24.8 18.1 23.7 12.4 15.2 10.7 9.6 18.1 21 Leather 13.6 18.6 10.1 17.3 12.4 23.1 17.4 16.9 22 Licorice 36.4 31.1 8.5 9.6 24.0 8.7 0.0 2.3 23 Eucalypt 0.0 0.0 12.4 17.5 12.4 18.4 24.8 16.3 24 Cedar 15.9 18.1 24.8 8.5 9.0 7.8 5.5 6.2 25 Coco 14.1 7.8 6.2 7.3 11.7 12.4 8.5 13.8 26 Strawberry 3.9 4.8 12.4 6.2 6.2 17.5 19.2 8.7 27 Oak 9.0 4.5 10.1 10.6 9.0 9.6 6.8 11.3 28 Mushroom 8.5 10.1 3.9 15.2 4.8 10.7 7.8 7.3 29 Caramel 10.1 5.1 7.1 7.8 16.9 8.3 4.5 7.8 30 Green grass 0.0 3.6 2.3 0.0 0.0 12.4 15.8 27.1 31 Toast almond 6.0 6.8 0.0 3.2 0.0 16.4 10.1 13.0 32 Soil 0.0 0.0 3.2 6.8 0.0 0.0 24.0 6.8 Y.S. Tao, L. Zhang / LWT - Food Science and Technology 43 (2010) 1550e1556 1553

Table 2 Concentrations and OAVs of volatile compounds from cabernet sauvignon wines from Changli County.

No. KI Compounds Concentration (mg/L) Odor thresholda (mg/L) OAVb

Max. Min. Mean 1 885 Ethyl acetate 90.005 11.684 42.551 7.5 [1] >1 2 999 Isobutyl acetate 0.180 0.070 0.076 1.6[2] 0.1 3 1026 Ethyl butyrate 1.941 0.530 0.779 0.02 [3] >1 4 1036 1-Propanol 20.395 5.824 10.257 50.0 [4] 0.1e0.5 5 1062 Ethyl isovalerate 0.075 0.020 0.024 0.003[5] >1 6 1108 Isobutyl alcohol 105.212 31.005 52.856 40.0 [3] >1 7 1132 Isopentyl acetate 2.784 0.205 0.591 0.03 [3] >1 8 1165 1-Butanol 4.712 1.556 2.779 150.0 [3] <0.1 9 1230 Isopentyl alcohol 567.524 164.391 328.137 30.0 [3] >1 10 1242 Ethyl hexanoate 1.315 0.373 0.722 0.014 [3] >1 11 1269 3-Methyl-3-buten-1-ol 0.268 0.111 0.138 0.6 [*] 0.1e0.5 12 1273 1-Pentyl alcohol 0.374 0.231 0.252 80.0[4] <0.1 13 1287 Hexyl acetate 0.019 0.007 0.009 1.5 [3] <0.1 14 1300 2-O-2-Phenyl-ethyl formate 2.594 0.064 0.605 n.d. 15 1330 Isohexyl alcohol 0.597 0.218 0.365 5.0[*] 0.5 16 1335 2-Heptanol 0.039 0.015 0.020 0.2e0.3 [*] 0.1e0.5 17 1343 3-Methyl-1-pentanol 0.886 0.183 0.466 0.5 [*] 1 18 1363 Ethyl lactate 237.415 43.284 100.139 14.0 [4] >1 19 1392 1-Hexanol 28.420 11.382 17.315 8.0 [3] >1 20 1401 (E)-3-Hexen-1-ol 2.074 0.594 0.983 0.4 [6] >1 21 1409 3-Ethoxy-1-propanol 0.627 0.097 0.065 0.1 [7] 0.5e1 22 1415 (Z)-3-Hexen-1-ol 1.522 0.706 0.933 0.4 [6] >1 23 1418 (E)-2-Hexen-1-ol 0.753 0.151 0.339 0.4 [6] 0.5-1 24 1420 (Z)-2-Hexen-1-ol 0.370 0.108 0.107 0.4 [6] 0.1e0.5 25 1431 Ethyl 2-hydroxy -3-methyl bytyrate 0.053 0.012 0.027 1.0 [8] <0.1 26 1446 Ethyl octanoate 0.741 0.125 0.399 0.005 [3] >1 27 1450 1-Heptanol 0.259 0.044 0.093 0.2e0.3 [*] 0.1e0.5 28 1464 Linalool oxide 0.047 0.008 0.007 0.5 [8] <0.1 29 1492 2-Ethyl hexanol 0.077 0.029 0.040 8.0 [*] <0.1 30 1516 Isooctanol 0.372 0.061 0.147 0.9 [4] 0.1e0.5 31 1524 b-Ionone 0.009 0.001 0.004 0.00009 [6] >1 32 1528 a-Ionone 0.006 0.002 0.003 0.00009 [6] >1 33 1555 Ethyl 2-hydroxy-4-methyl valerate 0.075 0.007 0.039 n.d. 34 1561 Linalool 0.132 0.014 0.040 0.025 [2] >1 35 1595 1-Octanol 0.232 0.071 0.138 0.9 [4] 0.5e0.1 36 1615 Isopentyl lactate 0.740 0.166 0.317 0.2 [*] >1 37 1618 Isobutyric acid 0.199 0.043 0.059 8.1 [4] <0.1 38 1620 2,3-Butanediol 8.612 0.792 3.187 120.0 [4,10] <0.1 39 1633 4-Terpineol 0.115 0.014 0.023 0.11e0.4 [9] 0.1e0.5 40 1643 2(3H)-dihydro-furanone 0.903 0.110 0.333 50.0[2] <0.1 41 1657 Ethyl decanoate 0.109 0.004 0.030 0.2 [8] 0.1e0.5 42 1671 Isopentyl octanoate 0.236 0.037 0.085 0.125 [4] 0.5-1 43 1674 1-Nonanol 0.113 0.026 0.044 0.6 [*] 0.1e0.5 44 1681 Diethyl succinate 52.765 4.812 23.138 200.0 [3] 0.1e0.5 45 1691 Ethyl 9-decenoate 0.005 0.001 0.001 0.1 [*] <0.1 46 1696 b-Terpineol 0.191 0.017 0.083 0.11e0.4 [9] 0.1e0.5 47 1707 3-Methoil-1-propanol 0.119 0.055 0.072 1.0 [2] 0.1 48 1781 1-Decanol 0.152 0.017 0.056 0.4 [4] 0.1e0.5 49 1829 Phenethyl acetate 0.490 0.083 0.174 0.25 [3] 0.5-1 50 1832 b-Damascenone 0.021 0.003 0.007 0.00005 [3] >1 51 1849 Ethyl laurate 0.043 0.000 0.005 1.5 [*] <0.1 52 1863 Hexanoic acid 1.670 0.111 0.868 0.42 [3] >1 53 1869 Benzyl alcohol 1.979 0.489 0.887 200.0 [2] <0.1 54 1931 2-Phenyl-ethanol 140.086 30.783 71.699 14.0 [3] >1 55 1964 5-Butyl-dihydro-4-methyl-2(3H)-furanone 1.350 0.078 0.169 0.067 [4] >1 56 1981 Dodecan-1-ol 0.036 0.000 0.011 1.0 [4] <0.1 57 2034 p-Ethyl-2-methoxy phenol 0.011 0.000 0.001 0.033 [6] 0.1 58 2055 [E]-Nerolidol 0.183 0.006 0.027 0.7 [*] 0.1e0.5 59 2065 Ethyl myristate 0.010 0.000 0.001 2.0 [*] <0.1 60 2083 Octanoic acid 10.026 0.652 3.406 0.5 [3] >1 61 2176 Eugenol 0.006 0.001 0.001 0.006 [4,6] 0.1e0.5 62 2185 p-Ethyl-phenol 0.024 0.008 0.002 0.44 [2] <0.1 63 2274 Ethyl hexadecanoate 0.024 0.000 0.002 1.5 [4,10] <0.1 64 2296 n-Decanoic acid 0.735 0.010 0.138 1.0 [3] 0.1e0.5 65 2330 2,4-Di-tert-butyl-phenol 0.371 0.056 0.151 0.2 [4,6] 0.5e1

a The reference from which the odor threshold has been taken is given in parentheses. [1] Guth (1997). The matrix was a 8.10 g/100 g water/ethanol solution; [2], [3], [5], [6] and [10] Aznar et al. (2003), Culleré et al. (2004), Ferreira et al. (2000), Goméz et al.(2007) and Lopez, Ezpeleta, Sanchez., Cacho, and Ferreira (2004). The matrix was an 8.91 g/ 100 g water/ethanol solution containing 7 g/L glycerol and 5 g/L tartaric acid, with the pH adjusted to 3.4 with 1 mol/L NaOH; [4] and [8] Li (2006) and Sun and Liu (2004). The matrix was 9.72 g/100 g ethanol/water mixture containing 5 g/L tartaric acid at pH 3.2. [7] Peinado, et al. (2004). The matrix was an 8.91 g/100 g water/ethanol solution containing 5 g/L tartaric acid, with the pH adjusted to 3.4 with 1 mol/L NaOH; [9] José et al. (2004). The matrix was an 8.10 g/100 g water/ethanol solution containing 5 g/L tartaric acid. [*] Calculated in the Laboratory of Wine Olfactometry, College of Enolgy, Northwest A & F University, China. Orthonasal thresholds were calculated in 9.72 g/100 g ethanol/water mixture containing 5 g/L tartaric acid at pH 3.2. n.d., not detected. b Odor activity value was calculated by dividing concentration by odor threshold value of the compound. 1554 Y.S. Tao, L. Zhang / LWT - Food Science and Technology 43 (2010) 1550e1556

Table 3 Regression coefﬁcients detection and 95% conﬁdence interval of predicting model.a

Descriptors Compounds/constant Regression coefﬁcients tt0.05/t0.01 95% Conﬁdence interval

Lower bound Upper bound Blackcurrant Constant 0.177 4.693** 2.776/4.604 0.072 0.281 3-Ethoxy-1-propanol 42.621 9.236** 29.808 55.434 4-Terpinenol 0.287 4.376* 0.105 0.469 phenethyl acetate 5.418 2.815* 0.0748 10.762

Bilberry Constant 0.067 6.777** 2.776/4.604 0.094 0.039 2-Hexen-1-ol 16.715 32.932** 15.306 18.124 Di-tert-butyl-phenol 1.362 22.649** 1.195 1.529 b-Terpinenol 0.384 13.544** 0.305 0.463

Green pepper Constant 0.363 97.691** 4.303/9.925 0.347 0.379 Hexanoic acid 1.975 122.721** 2.044 1.905 Octanoic acid 2.463 85.751** 2.339 2.586 Ethyl myristate 0.215 66.254** 0.229 0.201 Ethyl 3-hydroxy bytyrate 1.581 35.009** 1.775 1.387 3-Ethoxy-1-propanol 4.598 13.125** 6.105 3.090

Vanilla Constant 0.096 2.700* 2.447/3.707 0.009 0.183 Isobutyl alcohol 0.109 3.874** 0.040 0.178

Smoked Constant 0.702 13.189** 2.447/3.707 0.572 0.833 5-Butyl-dihydro-4-methyl-2(3H)-furanone 2.553 5.982** 3.598 1.509

a The sensory data of MF were used here. ** Indicates a signiﬁcance level of 99%, and * level of 95%.

octanoic acid contributed to the MF(%) positively. The other one compound or group of compounds. For example, volatile compounds (hexanoic acid, ethyl myristate, ethyl 3-hydroxy byty- phenols were shown to be synthesized mainly by Brettanomyces rate and 3-ethoxy-1-propanol) were negatively correlated with MF (Chatonnet, Dubourdieu, & Boidron, 1995); terpenoid compounds (%). For the models of vanilla and smoked, isobutyl alcohol and 5- produced the typical aroma of Muscat wines (Etievant, & Bayonove, butyl-dihydro-4-methyl-2(3H)-furanone entered the respective 1983); norisoprenoids gave a kerosene odor to ageing Riesling wines models. Isobutyl alcohol was positively correlated with MF(%), (Strauss, Wilson, Anderson, & Williams, 1987); some amino- while 5-butyl-dihydro-4-methyl-2(3H)-furanone contributed acetophenone compounds produced the “foxy” smell of Labruscana negatively. grapes and wine (Acree, & Lavin,1990); some lactones contributed to The variance significances of the 5 regression models are shown a sherry flavor (Martín, Etevant, Quere, & Schlich, 1992) and the in Table 4. The results show that the 5 models regressed at the peppery flavor of sauvignon varieties was attributed to methox- significant level at 99%. This indicates that the 5 models could be ypyrazines (Allen, Lacey, Harris, & Brown, 1991); a-Ylangene was used to predict the MF(%) of respective terms. The correlation a satisfactory marker for the ‘pepper’ aroma and flavor in Shiraz coefficients between the measured and predicted values (R2)of Grape Berries (Parker, Pollnitz, Cozzolino, Francis, & Herderich, black currant, bilberry and green pepper were greater than 0.9. R2 2007). Further studies showed ethyl esters of fatty acids and of smoked was below 0.9 and R2 of vanilla below 0.8, indicating that acetates esters gave a full-bodied fruity taste to fresh white wines, the first three models accurately predict the effects while the last while the same compounds in rose or red wines produced weak two models’ predictive abilities are limited. aroma or no aroma at all (Ferreira, Lopez, Escudero, & Cacho,1998b). In past decades, many studies reported aroma compounds that Many flavor characters of aroma compounds in wine have been produced characteristic flavors in wine. These aromas consisted of examined by gas chromatography and olfactometry (GC-O). With

Table 4 ANOVA signiﬁcance detection and predicting effect of regression model.

Descriptors Error source Sum of squares df Mean square FF0.05/F0.01 Correlation coefﬁcients between the measured and predicted value Blackcurrant Regression 0.0870 3 0.0290 72.38** 6.65/16.69 0.982 Residual 0.0016 4 0.0004 Total 0.0885 7

Bilberry Regression 0.0227 3 0.0076 472.35** 6.65/16.69 0.997 Residual 6.40E05 4 1.60E05 Total 0.0227 7

Green pepper Regression 0.1290 5 0.0258 11812.17** 19.30/99.30 0.999 Residual 4.3692E06 2 2.185E06 Total 0.1290 7

Vanilla Regression 0.0186 1 0.0186 15.01** 5.99/13.75 0.714 Residual 0.0074 6 0.0012 Total 0.0261 7

Smoked Regression 0.0561 1 0.0561 35.78** 5.99/13.75 0.856 Residual 0.0094 6 0.0016 Total 0.0655 7

** Indicates a significance level of 99%, and * level of 95%. Y.S. Tao, L. Zhang / LWT - Food Science and Technology 43 (2010) 1550e1556 1555 regard to the compounds used in the predictive models in our However, due to the limited number of sample wines, not all the work, 3-ethoxy-1-propanol has been found to give a chemical odor typical descriptors were accurately modeled. of organic solvents. Phenyl-ethyl acetate produced a pleasant floral aroma. 2-Hexenol had a green grass flavor at low concentrations Acknowledgements and produced a light fruity flavor at high concentrations. 2,4-Di- tert-butyl-phenol had an herbal flavor. Terpinenol gave the wine This project was supported by Elementary Science Research wood and soil odors. Hexanoic acid and octanoic acid yielded Fund of NWSUAF (QN2009061) and China National Science Fund a cheese flavor at low concentrations while producing rancid harsh (30571281). The authors are grateful to Huaxia Winemaking odors at high concentrations. Ethyl myristate produced sweet Company and Yueqiannian Winemaking Company (Changli fruity, butter and fatty odors. Ethyl 3-hydroxy butyrate had a fruity County) for the supply of the samples used in this study. flavor. Isobutyl alcohol gave the light and fruity flavor of fusel alcohol. 5-Butyl-dihydro-4-methyl-2(3H)-furanone produced peach and coco flavors (Culleré, Escudero, Cacho, & Ferreira, 2004; References Ferreira, Lopez, & Cacho, 2000; Goméz, Cacho, Ferreira, Vicario, & Heredia, 2007; Guth, 1997; Peinado, Moreno, Medina, & Mauricio, Acree, T. E., & Lavin, E. H. (1990). o-Aminoacetophenone, the “foxy” smelling fl component of Labruscana grapes. In Y. Bessiere, & A. F. Thomas (Eds.), Flavor 2004; Sun, & Liu, 2004). The avor of the individual compound in science and technology (pp. 49e52). Chichester, UK: Wiley. each model was somewhat different from the aroma character of Allen, M. S., Lacey, M. J., Harris, R. L. N., & Brown, W. V. (1991). Contribution of the term predicted, which implies that one aroma character was methoxypyrazines to Sauvignon blanc wine aroma. American Journal of Enology & Viticulture, 42,109e112. created by the combination of several aroma compounds; the Allen, M. S., Lacey, M. J., & Boyd, S. (1994). Determination of methoxypyrazine in red whole aroma of the wine was composed of several main flavor wine by stable isotope dilution gas chromatographyemass spectrometry. characters. In the models, the regression correlations of compounds Journal of Agricultural and Food Chemistry, 42,1734e1738. fi Arrhenius, S. P., Mccloskey, L. P., & Sylvan, M. (1996). Chemical markers for aroma of were signi cantly different and those compounds contributed to Vitis vinifera Var. Chardonnay regional wines. Journal of Agricultural and Food the whole intensity positively or negatively. This indicates that Chemistry, 44, 1085e1090. various aroma compounds cooperated complexly. The content Aznar, M., Lopez, R., Cacho, J., & Ferreira, V. (2003). Prediction of aged red wine change of any one aroma compound may bring about intensity aroma properties from aroma chemical composition. Partial least squares regression models. Journal of Agricultural and Food Chemistry, 51, 2700e2707. changes in several other aroma characters. Chatonnet, P., Dubourdieu, D., & Boidron, J. (1995). The influence of Brettanomyces Wine sensory reconstitution studies showed that compounds dekkera sp. yeast and lactic acid bacteria on the ethylphenol content of red e with OAV greater than 0.5 in a synthetic wine were observed with wines. American Journal of Enology & Viticulture, 46, 463 468. Culleré, L., Escudero, A., Cacho, J., & Ferreira, V. (2004). Gas chromatography- a high qualitative similarity with the aroma of the wine, while the eolfactory and chemical qualitative study of the aroma of six premium quality addition of compounds with OAV less than 0.5 did not improve the Spanish aged red wines. Journal of Agricultural and Food Chemistry, 52, sensory model (Ferreira, Ortin, Escudero, Lopez, & Cacho, 2002). 1653e1660. Culleré, L., Escudero, A., Campo, E., Cacho, J., & Ferreira, V. (2009). Multidimensional Thus, aroma compounds with OAV greater than 0.5 were used to gas chromatographyemass spectrometry determination of 3-alkyl-2-methox- build sensory prediction models in our work. Aznar et al. studied ypyrazines in wine and must. A comparison of solid-phase extraction and the correlation between sensory data and chemical compositions in headspace solid-phase extraction methods. Journal of Chromatography A, 1216, 4040e4045. wine using regression models. However, panelists were not trained Etievant, P. X., & Bayonove, C. L. (1983). Aroma components of pomaces and wine by aroma standards, and they gave aromatic descriptors of their from the variety Muscat de Frontignan. Journal of the Science of Food and own words. For the data process, non-descriptive or abstract terms Agriculture, 34, 393e403. Escudero, A., & Ferreira, V. (2000). Clues about the role of methional as character were directly eliminated, and the remaining terms were grouped impact odorant of some oxidized wines. Journal of Agricultural and Food using statistical methods. The frequencies of these aroma groups Chemistry, 48, 4268e4272. were modeled by the contents of active odorants. Correlation Ferreira, V., Lopez, R., Escudero, A., & Cacho, J. F. (1998a). Quantitative-determina- fl coefficients between the measured and predicted aroma frequen- tion of trace and ultra-trace avor active compounds in red wines through gas- chromatographic ion-trap mass-spectrometric analysis of micro-extracts. Jour- cies ranged from 0.62 to 0.81 (Aznar, Lopez, Cacho, & Ferreira, nal of Chromatography A, 806, 349e354. 2003). In our work, one mono-variety wine in the Cabernet Sau- Ferreira, V., Lopez, R., Escudero, A., & Cacho, J. F. (1998b). The aroma of Grenache red vignon wine in Changli County was studied by sensory analysis and wine: hierarchy and nature of its main odorants. Journal of the Science of Food and Agriculture, 77, 259e267. chemical analysis. Aroma description terms used in sensory anal- Ferreira, V., Lopez, R., & Cacho, J. F. (2000). Quantitative determination of the ysis derived from “Le Nez du Vin” method, which was used to train odorants of young red wines from different grape varieties. Journal of the panelists. The modified frequency of several typical aroma char- Science of Food and Agriculture, 80, 1659e1667. fi Ferreira, V., Ortin, N., Escudero, A., Lopez, R., & Cacho, J. (2002). Chemical charac- acters of sample wines could be predicted well by semi-quanti ed terization of the aroma of Grenache Rose wines: aroma extract dilution anal- data of potential active odorants. The exact contributions of these ysis, quantitative determination, and sensory reconstitution studies. Journal of compounds to one aroma descriptors or the whole wine aroma Agricultural and Food Chemistry 4048e4054. Goméz, M. J., Cacho, J. F., Ferreira, V., Vicario, I. M., & Heredia, F. J. (2007). Volatile require further experimentation with aroma reconstitution and components of Zalema white wines. Food Chemistry, 100, 1464e1473. omission. Guth, H. (1997). Quantitation and sensory studies of character impact odorants of different white wine varieties. Journal of Agricultural and Food Chemistry, 45, 3027e3032. 4. Conclusion José, M. O., Isabel, M. A., Óscar, M. P., José, S. M., & António, J. A. (2004). Charac- terization and differentiation of five “Vinhos Verdes” grape varieties on the Cabernet Sauvignon red wines in 8 years from Changli County basis of monoterpenic compounds. Analytica Chimica Acta, 513, 269e275. were studied by sensory and chemical analyses. Five regression Kotseridis, Y., Razungles, A., Bertrand, A., & Baumes, R. (2000). Differentiation of the aromas of Merlot and Cabernet Sauvignon wines using sensory and instru- models of typical aroma terms of sample wines were built from mental analysis. Journal of Agricultural and Food Chemistry, 48, 5383e5388. potential active odorants. t Detections indicated that regression Lee, S. J., & Noble, A. C. (2003). Characterization of odor-active compounds in Californian Chardonnay wines using GCeolfactometry and GCemass spec- correlations and constants in the 5 models regressed at the e fi trometry. Journal of Agricultural and Food Chemistry, 51, 8036 8044. signi cant level of 99% or 95%. Variance analysis showed that the 5 Li, H. (2006). Wine tasting. Beijing, China: Science Press. pp. 29e106. models regressed at the signi ficant level at 99%. The correlation Li, H., Tao, Y. S., Wang, H., & Zhang, L. (2008). Impact odorants of Chardonnay dry coefficients between the measured and predicted value ranged white wine from Changli County (China). European Food Research and Tech- e fi nology, 227(1), 287 292. from 0.714 to 0.999. Our work con rmed the feasibility of pre- Lopez, R., Ezpeleta, E., Sanchez, I., Cacho, J., & Ferreira, V. (2004). Analysis of the dicting sensory characters’ intensities from aroma compounds. aroma intensities of volatile compounds released from mild acid hydrolysates 1556 Y.S. Tao, L. Zhang / LWT - Food Science and Technology 43 (2010) 1550e1556

of odorless precursors extracted from Tempranillo and Grenache grapes using Perestrelo, R., Fernandes, A., Albuquerque, F. F., Marques, J. C., & Camara, J. S. (2006). gas chromatography-olfactometry. Food Chemistry, 88,95e103. Analytical characterization of the aroma of Tinta Negra Mole red wine: iden- Martín, B., Etevant, P. X., Quere, J., & Schlich, P. (1992). More clues about sensory tification of the main odorants compounds. Analytica Chimica Acta, 563, impact of sotolon in some flor sherry wines. Journal of Agricultural and Food 154e164. Chemistry, 40,475e478. Strauss, R., Wilson, B., Anderson, R., & Williams, P. J. (1987). Development of Parker,M.,Pollnitz, A.P., Cozzolino, D.,Francis, I. L., & Herderich, M. J. (2007).Identification precursors of C13 nor-isoprenoid flavorants in Riesling grapes. American Journal and quantification of a marker compound for ‘pepper’ aroma and flavor in Shiraz of Enology & Viticulture, 38,23e27. grape berries by combination of chemometrics and gas chromatographyemass Sun, B. G., & Liu, Y. P. (2004). Food spice and flavor handbook. China Petroleum Press. spectrometry. Journal of Agricultural and Food Chemistry, 55, 5948e5955. Tao, Y. S., Li, H., & Wang, H. (2007). Optimization of wine aroma analysis by solid- Peinado, R. A., Moreno, J., Medina, M., & Mauricio, J. C. (2004). Changes in volatile phase microextraction. Journal of Northwest A & F University: Natural Science compounds and aromatic series in sherry wine with high gluconic acid levels Edition, 35(12), 181e185. subjected to aging by submerged flour yeast cultures. Biotechnology Letters, 26, Tao, Ys. S., Liu, Y. Q., & Li, H. (2009). Sensory characters of Cabernet Sauvignon dry 757e762. red wine from Changli County (China). Food Chemistry, 114, 565e569.