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Characterization of flavor frame in grape wines detected by HS-SPME-GC-MS coupled with HPLC, electronic nose, and electronic tongue

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  • To analyze the flavor components in 17 commercially available wine samples from seven grape varieties (Cabernet Sauvignon, Cabernet Gernischt, Shiraz, Merlot, Pinot Noir, Tempranillo and Chardonnay), comprehensive flavor characterization, volatile and non-volatile compounds of grape wines were evaluated by headspace solid phase micro-extraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS), electronic nose (E-nose), electronic tongue (E-tongue), high performance liquid chromatography (HPLC) and automatic amino acids analyzer. According to GC-MS analysis, a total of 86 volatile compounds were identified, mainly including alcohols, esters, phenols, terpenes and norisoprenoids. Results showed that significant differences of contents of free amino acids and radar fingerprint chart of E-tongue technology were recorded for the 17 grape wines. Moreover, principal component analysis (PCA) of E-nose and E-tongue were used to distinguish the different grape wines effectively, with the cumulative contribution rate accounting for 92.33% and 91.78%, respectively. The results prove that sensors W2S and W1W in the E-nose for wines have a higher influence in the current pattern file. The most abundant phenol in 17 wine samples is catechin. The differences in species and contents of volatile and non-volatile substances give the unique flavor of different grape wines. The results demonstrated that the above mentioned equipment are useful for in-depth grape wine flavor analysis.
  • Starting in the early 2000s, China has experienced rapid growth as an emerging wine market. It has now established itself as the world's second-largest grape-growing country in terms of vineyard surface area. Furthermore, China has also secured its position as the sixth-biggest wine producer globally and the fifth-most significant wine consumer in terms of volume[1]. The Ningxia Hui autonomous region, known for its reputation as the highest quality wine-producing area in China, is considered one of the country's most promising wine regions. The region's arid or semiarid climate, combined with ample sunlight and warmth, thanks to the Yellow River, provides ideal conditions for grape cultivation. Wineries in the Ningxia Hui autonomous region are renowned as the foremost representatives of elite Chinese wineries. All wines produced in this region originate from grapes grown in their vineyards, adhering to strict quality requirements, and have gained a well-deserved international reputation for excellence. Notably, in 2011, Helan Mountain's East Foothill in the Ningxia Hui Autonomous Region received protected geographic indication status in China. Subsequently, in 2012, it became the first provincial wine region in China to be accepted as an official observer by the International Organisation of Vine and Wine (OIV)[2]. The wine produced in the Helan Mountain East Region of Ningxia, China, is one of the first Agricultural and Food Geographical Indications. Starting in 2020, this wine will be protected in the European Union[3].

    Marselan, a hybrid variety of Cabernet Sauvignon and Grenache was introduced to China in 2001 by the French National Institute for Agricultural Research (INRA). Over the last 15 years, Marselan has spread widely across China, in contrast to its lesser cultivation in France. The wines produced from Marselan grapes possess a strong and elegant structure, making them highly suitable for the preferences of Chinese consumers. As a result, many wineries in the Ningxia Hui Autonomous Region have made Marselan wines their main product[4]. Wine is a complex beverage that is influenced by various natural and anthropogenic factors throughout the wine-making process. These factors include soil, climate, agrochemicals, and human intervention. While there is an abundance of research available on wine production, limited research has been conducted specifically on local wines in the Eastern Foot of Helan Mountain. This research gap is of significant importance for the management and quality improvement of Chinese local wines.

    Ion mobility spectrometry (IMS) is a rapid analytical technique used to detect trace gases and characterize chemical ionic substances. It achieves this through the gas-phase separation of ionized molecules under an electric field at ambient pressure. In recent years, IMS has gained increasing popularity in the field of food-omics due to its numerous advantages. These advantages include ultra-high analytical speed, simplicity, easy operation, time efficiency, relatively low cost, and the absence of sample preparation steps. As a result, IMS is now being applied more frequently in various areas of food analysis, such as food composition and nutrition, food authentication, detection of food adulteration, food process control, and chemical food safety[5,6]. The orthogonal hyphenation of gas chromatography (GC) and IMS has greatly improved the resolution of complex food matrices when using GC-IMS, particularly in the analysis of wines[7].

    The objective of this study was to investigate the changes in the physicochemical properties of Marselan wine during the winemaking process, with a focus on the total phenolic and flavonoids content, antioxidant activity, and volatile profile using the GC-IMS method. The findings of this research are anticipated to make a valuable contribution to the theoretical framework for evaluating the authenticity and characterizing Ningxia Marselan wine. Moreover, it is expected that these results will aid in the formulation of regulations and legislation pertaining to Ningxia Marselan wine in China.

    All the grapes used to produce Marselan wines, grow in the Xiban vineyard (106.31463° E and 38.509541° N) situated in Helan Mountain's East Foothill of Ningxia Hui Autonomous Region in China.

    Folin-Ciocalteau reagent, (±)-6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), 2,20-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), 2,4,6-tris (2-pyridyl)-s-triazine (TPTZ), anhydrous methanol, sodium nitrite, and sodium carbonate anhydrous were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Reference standards of (+)-catechin, gallic acid, and the internal standard (IS) 4-methyl-2-pentanol were supplied by Shanghai Yuanye Bio-Technology Co., Ltd (Shanghai, China). The purity of the above references was higher than 98%. Ultrapure water (18.2 MΩ cm) was prepared by a Milli-Q system (Millipore, Bedford, MA, USA).

    Stage 1−Juice processing: Grapes at the fully mature stage are harvested and crushed, and potassium metabisulfite (5 mg/L of SO2) was evenly spread during the crushing process. The obtained must is transferred into stainless steel tanks. Stage 2−Alcoholic fermentation: Propagated Saccharomyces cerevisiae ES488 (Enartis, Italy) are added to the fresh must, and alcoholic fermentation takes place, after the process is finished, it is kept in the tanks for 7 d for traditional maceration to improve color properties and phenolics content. Stage 3−Malolactic fermentation: When the pomace is fully concentrated at the bottom of the tanks, the wine is transferred to another tank for separation from these residues. Oenococcus oeni VP41 (Lallemand Inc., France) is inoculated and malic acid begins to convert into lactic acid. Stage 4−Wine stabilization: After malolactic fermentation, potassium metabisulfite is re-added (35 mg/L of SO2), and then transferred to oak barrels for stabilization, this process usually takes 6-24 months. A total of four batches of samples during the production process of Marselan wine were collected in this study.

    Total polyphenols were determined on 0.5 mL diluted wine sample using the Folin-Ciocalteu method[8], using gallic acid as a reference compound, and expressed as milligrams of gallic acid equivalents per liter of wine. The total flavonoid content was measured on 0.05 mL of wine sample by a colorimetric method previously described[9]. Results are calculated from the calibration curve obtained with catechin, as milligrams of catechin equivalents per liter of wine.

    The antioxidative activity was determined using the ABTS·+ assay[10]. Briefly, the ABTS·+ radical was prepared from a mixture of 88 μL of potassium persulfate (140 mmol/L) with 5 mL of the ABTS·+ solution (7 mmol/L). The reaction was kept at room temperature under the absence of light for 16 h. Sixty μL samples were mixed with 3 mL of ABTS·+ solution with measured absorption of 0.700 ± 0.200 at 734 nm. After 6 min reaction, the absorbance of samples were measured with a spectrophotometer at 734 nm. Each sample was tested in triplicate. The data were expressed as mmol Trolox equivalent of antioxidative capacity per liter of the wine sample (mmol TE/L). Calibration curves, in the range 64.16−1,020.20 μmol TE/L, showed good linearity (R2 ≥ 0.99).

    The FRAP assay was conducted according to a previous study[11]. The FRAP reagent was freshly prepared and mixed with 10 mM/L TPTZ solution prepared in 20 mM/L FeCl3·6H2O solution, 40 mM/L HCl, and 300 mM/L acetate buffer (pH 3.6) (1:1:10; v:v:v). Ten ml of diluted sample was mixed with 1.8 ml of FRAP reagent and incubated at 37 °C for 30 min. The absorbance was determined at 593 nm and the results were reported as mM Fe (II) equivalent per liter of the wine sample. The samples were analyzed and calculated by a calibration curve of ferrous sulphate (0.15−2.00 mM/mL) for quantification.

    The volatile compounds were analyzed on a GC-IMS instrument (FlavourSpec, GAS, Dortmund, Germany) equipped with an autosampler (Hanon Auto SPE 100, Shandong, China) for headspace analysis. One mL of each wine was sampled in 20 mL headspace vials (CNW Technologies, Germany) with 20 μL of 4-methyl-2-pentanol (20 mg/L) ppm as internal standard, incubated at 60 °C and continuously shaken at 500 rpm for 10 min. One hundred μL of headspace sample was automatically loaded into the injector in splitless mode through a syringe heated to 65 °C. The analytes were separated on a MxtWAX capillary column (30 m × 0.53 mm, 1.0 μm) from Restek (Bellefonte, Pennsylvania, USA) at a constant temperature of 60 °C and then ionized in the IMS instrument (FlavourSpec®, Gesellschaft für Analytische Sensorsysteme mbH, Dortmund, Germany) at 45 °C. High purity nitrogen gas (99.999%) was used as the carrier gas at 150 mL/min, and drift gas at 2 ml/min for 0−2.0 min, then increased to 100 mL/min from 2.0 to 20 min, and kept at 100 mL/min for 10 min. Ketones C4−C9 (Sigma Aldrich, St. Louis, MO, USA) were used as an external standard to determine the retention index (RI) of volatile compounds. Analyte identification was performed using a Laboratory Analytical Viewer (LAV) 2.2.1 (GAS, Dortmund, Germany) by comparing RI and the drift time of the standard in the GC-IMS Library.

    All samples were prepared in duplicate and tested at least six times, and the results were expressed as mean ± standard error (n = 4) and the level of statistical significance (p < 0.05) was analyzed by using Tukey's range test using SPSS 18.0 software (SPSS Inc., IL, USA). The principal component analysis (PCA) was performed using the LAV software in-built 'Dynamic PCA' plug-in to model patterns of aroma volatiles. Orthogonal partial least-square discriminant analysis (OPLS-DA) in SIMCA-P 14.1 software (Umetrics, Umeă, Sweden) was used to analyze the different volatile organic compounds in the different fermentation stages.

    The results of the changes in the antioxidant activity of Marselan wines during the entire brewing process are listed in Table 1. It can be seen that the contents of flavonoids and polyphenols showed an increasing trend during the brewing process of Marselan wine, which range from 315.71−1,498 mg CE/L and 1,083.93−3,370.92 mg GAE/L, respectively. It was observed that the content increased rapidly in the alcoholic fermentation stage, but slowly in the subsequent fermentation stage. This indicated that the formation of flavonoid and phenolic substances in wine mainly concentrated in the alcoholic fermentation stage, which is consistent with previous reports. This is mainly because during the alcoholic fermentation of grapes, impregnation occurred to extract these compounds[12]. The antioxidant activities of Marselan wine samples at different fermentation stages were detected by FRAP and ABTS methods[11]. The results showed that the ferric reduction capacity and ABST·+ free radical scavenging capacity of the fermented Marselan wines were 2.4 and 1.5 times higher than the sample from the juice processing stage, respectively, indicating that the fermented Marselan wine had higher antioxidant activity. A large number of previous studies have suggested that there is a close correlation between antioxidant activity and the content of polyphenols and flavonoids[1315]. Previous studies have reported that Marselan wine has the highest total phenol and anthocyanin content compared to the wine of Tannat, Cabernet Sauvignon, Merlot, Cabernet Franc, and Syrah[13]. Polyphenols and flavonoids play an important role in improving human immunity. Therefore, Marselan wines are popular because of their high phenolic and flavonoid content and high antioxidant capacity.

    Table 1.  GC-IMS integration parameters of volatile compounds in Marselan wine at different fermentation stages.
    No. Compounds Formula RI* Rt
    [sec]**
    Dt
    [RIPrel]***
    Identification
    approach
    Concentration (μg/mL) (n = 4)
    Stage 1 Stage 2 Stage 3 Stage 4
    Aldehydes
    5 Furfural C5H4O2 1513.1 941.943 1.08702 RI, DT, IS 89.10 ± 4.05c 69.98 ± 3.22c 352.16 ± 39.06b 706.30 ± 58.22a
    6 Furfural dimer C5H4O2 1516.6 948.77 1.33299 RI, DT, IS 22.08 ± 0.69b 18.68 ± 2.59c 23.73 ± 2.69b 53.39 ± 9.42a
    12 (E)-2-hexenal C6H10O 1223.1 426.758 1.18076 RI, DT, IS 158.17 ± 7.26a 47.57 ± 2.51b 39.00 ± 2.06c 43.52 ± 4.63bc
    17 (E)-2-pentenal C5H8O 1129.2 333.392 1.1074 RI, DT, IS 23.00 ± 4.56a 16.42 ± 1.69c 18.82 ± 0.27b 18.81 ± 0.55b
    19 Heptanal C7H14O 1194.2 390.299 1.33002 RI, DT, IS 17.28 ± 2.25a 10.22 ± 0.59c 14.50 ± 8.84b 9.11 ± 1.06c
    22 Hexanal C6H12O 1094.6 304.324 1.25538 RI, DT, IS 803.11 ± 7.47c 1631.34 ± 19.63a 1511.11 ± 26.91b 1526.53 ± 8.12b
    23 Hexanal dimer C6H12O 1093.9 303.915 1.56442 RI, DT, IS 588.85 ± 7.96a 93.75 ± 4.67b 92.93 ± 3.13b 95.49 ± 2.50b
    29 3-Methylbutanal C5H10O 914.1 226.776 1.40351 RI, DT, IS 227.86 ± 6.39a 33.32 ± 2.59b 22.36 ± 1.18c 21.94 ± 1.73c
    33 Dimethyl sulfide C2H6S 797.1 193.431 0.95905 RI, DT, IS 120.07 ± 4.40c 87.a02 ± 3.82d 246.81 ± 5.62b 257.18 ± 3.04a
    49 2-Methylpropanal C4H8O 828.3 202.324 1.28294 RI, DT, IS 150.49 ± 7.13a 27.08 ± 1.48b 19.36 ± 1.10c 19.69 ± 0.92c
    Ketones
    45 3-Hydroxy-2-butanone C4H8O2 1293.5 515.501 1.20934 RI, DT, IS 33.20 ± 3.83c 97.93 ± 8.72b 163.20 ± 21.62a 143.51 ± 21.48a
    46 Acetone C3H6O 836.4 204.638 1.11191 RI, DT, IS 185.75 ± 8.16c 320.43 ± 12.32b 430.74 ± 3.98a 446.58 ± 10.41a
    Organic acid
    3 Acetic acid C2H4O2 1527.2 969.252 1.05013 RI, DT, IS 674.66 ± 46.30d 3602.39 ± 30.87c 4536.02 ± 138.86a 4092.30 ± 40.33b
    4 Acetic acid dimer C2H4O2 1527.2 969.252 1.15554 RI, DT, IS 45.25 ± 3.89c 312.16 ± 19.39b 625.79 ± 78.12a 538.35 ± 56.38a
    Alcohols
    8 1-Hexanol C6H14O 1365.1 653.825 1.32772 RI, DT, IS 1647.65 ± 28.94a 886.33 ± 32.96b 740.73 ± 44.25c 730.80 ± 21.58c
    9 1-Hexanol dimer C6H14O 1365.8 655.191 1.64044 RI, DT, IS 378.42 ± 20.44a 332.65 ± 25.76a 215.78 ± 21.04b 200.14 ± 28.34b
    13 3-Methyl-1-butanol C5H12O 1213.3 414.364 1.24294 RI, DT, IS 691.86 ± 9.95c 870.41 ± 22.63b 912.80 ± 23.94a 939.49 ± 12.44a
    14 3-Methyl-1-butanol dimer C5H12O 1213.3 414.364 1.49166 RI, DT, IS 439.90 ± 29.40c 8572.27 ± 60.56b 9083.14 ± 193.19a 9152.25 ± 137.80a
    15 1-Butanol C4H10O 1147.2 348.949 1.18073 RI, DT, IS 157.33 ± 9.44b 198.92 ± 3.92a 152.78 ± 10.85b 156.02 ± 9.80b
    16 1-Butanol dimer C4H10O 1146.8 348.54 1.38109 RI, DT, IS 24.14 ± 2.15c 274.75 ± 12.60a 183.02 ± 17.72b 176.80 ± 19.80b
    24 1-Propanol C3H8O 1040.9 274.803 1.11042 RI, DT, IS 173.73 ± 4.75a 55.84 ± 2.16c 80.80 ± 4.99b 83.57 ± 2.34b
    25 1-Propanol dimer C3H8O 1040.4 274.554 1.24784 RI, DT, IS 58.20 ± 1.30b 541.37 ± 11.94a 541.33 ± 15.57a 538.84 ± 9.74a
    28 Ethanol C2H6O 930.6 231.504 1.11901 RI, DT, IS 5337.84 ± 84.16c 11324.05 ± 66.18a 9910.20 ± 100.76b 9936.10 ± 101.24b
    34 Methanol CH4O 903.6 223.79 0.98374 RI, DT, IS 662.08 ± 13.87a 76.94 ± 2.15b 61.92 ± 1.96c 62.89 ± 0.81c
    37 2-Methyl-1-propanol C4H10O 1098.5 306.889 1.35839 RI, DT, IS 306.91 ± 4.09c 3478.35 ± 25.95a 3308.79 ± 61.75b 3313.85 ± 60.88b
    48 1-Pentanol C5H12O 1257.6 470.317 1.25222 RI, DT, IS 26.13 ± 2.52c 116.50 ± 3.71ab 112.37 ± 6.26b 124.17 ± 7.04a
    Esters
    1 Methyl salicylate C8H8O3 1859.6 1616.201 1.20489 RI, DT, IS 615.00 ± 66.68a 485.08 ± 31.30b 470.14 ± 23.02b 429.12 ± 33.74b
    7 Butyl hexanoate C10H20O2 1403.0 727.561 1.47354 RI, DT, IS 95.83 ± 17.04a 62.87 ± 3.62a 92.59 ± 11.88b 82.13 ± 3.61c
    10 Hexyl acetate C8H16O2 1298.6 524.366 1.40405 RI, DT, IS 44.72 ± 8.21a 33.18 ± 2.17d 41.50 ± 4.38c 40.89 ± 4.33b
    11 Propyl hexanoate C9H18O2 1280.9 499.577 1.39274 RI, DT, IS 34.65 ± 3.90d 70.43 ± 5.95a 43.97 ± 4.39b 40.12 ± 4.05c
    18 Ethyl hexanoate C8H16O2 1237.4 444.749 1.80014 RI, DT, IS 55.55 ± 5.62c 1606.16 ± 25.63a 787.24 ± 16.95b 788.91 ± 28.50b
    20 Isoamyl acetate C7H14O2 1127.8 332.164 1.30514 RI, DT, IS 164.22 ± 1.00d 243.69 ± 8.37c 343.51 ± 13.98b 365.46 ± 1.60a
    21 Isoamyl acetate dimer C7H14O2 1126.8 331.345 1.75038 RI, DT, IS 53.61 ± 4.79d 4072.20 ± 11.94a 2416.70 ± 49.84b 2360.46 ± 43.29c
    26 Isobutyl acetate C6H12O2 1020.5 263.605 1.23281 RI, DT, IS 101.65 ± 1.81a 15.52 ± 0.67c 44.87 ± 3.21b 45.96 ± 1.41b
    27 Isobutyl acetate dimer C6H12O2 1019.6 263.107 1.61607 RI, DT, IS 34.60 ± 1.05d 540.84 ± 5.64a 265.54 ± 8.31c 287.06 ± 3.66b
    30 Ethyl acetate dimer C4H8O2 885.2 218.564 1.33587 RI, DT, IS 1020.75 ± 6.86d 5432.71 ± 6.55a 5052.99 ± 9.65b 5084.47 ± 7.30c
    31 Ethyl acetate C4H8O2 878.3 216.574 1.09754 RI, DT, IS 215.65 ± 3.58a 38.29 ± 2.37c 71.59 ± 2.99b 69.32 ± 2.85b
    32 Ethyl formate C3H6O2 838.1 205.127 1.19738 RI, DT, IS 175.48 ± 3.79d 1603.20 ± 13.72a 1472.10 ± 5.95c 1509.08 ± 13.26b
    35 Ethyl octanoate C10H20O2 1467.0 852.127 1.47312 RI, DT, IS 198.86 ± 36.71b 1853.06 ± 17.60a 1555.51 ± 24.21a 1478.05 ± 33.63a
    36 Ethyl octanoate dimer C10H20O2 1467.0 852.127 2.03169 RI, DT, IS 135.50 ± 13.02d 503.63 ± 15.86a 342.89 ± 11.62b 297.28 ± 14.40c
    38 Ethyl butanoate C6H12O2 1042.1 275.479 1.5664 RI, DT, IS 21.29 ± 2.68c 1384.67 ± 8.97a 1236.52 ± 20.21b 1228.09 ± 5.09b
    39 Ethyl 3-methylbutanoate C7H14O2 1066.3 288.754 1.26081 RI, DT, IS 9.70 ± 1.85d 200.29 ± 4.21a 146.87 ± 8.70b 127.13 ± 12.54c
    40 Propyl acetate C5H10O2 984.7 246.908 1.48651 RI, DT, IS 4.57 ± 1.07c 128.63 ± 4.28a 87.75 ± 3.26b 88.49 ± 1.99b
    41 Ethyl propanoate C5H10O2 962.1 240.47 1.46051 RI, DT, IS 10.11 ± 0.34d 107.08 ± 3.50a 149.60 ± 5.39c 167.15 ± 12.90b
    42 Ethyl isobutyrate C6H12O2 971.7 243.229 1.56687 RI, DT, IS 18.29 ± 2.61d 55.22 ± 1.07c 98.81 ± 4.67b 104.71 ± 4.73a
    43 Ethyl lactate C5H10O3 1352.2 628.782 1.14736 RI, DT, IS 31.81 ± 2.91c 158.03 ± 2.80b 548.14 ± 74.21a 527.01 ± 39.06a
    44 Ethyl lactate dimer C5H10O3 1351.9 628.056 1.53618 RI, DT, IS 44.55 ± 2.03c 47.56 ± 4.02c 412.23 ± 50.96a 185.87 ± 31.25b
    47 Ethyl heptanoate C9H18O2 1339.7 604.482 1.40822 RI, DT, IS 39.55 ± 6.37a 38.52 ± 2.47a 28.44 ± 1.52c 30.77 ± 2.79b
    Unknown
    1 RI, DT, IS 15.53 ± 0.18 35.69 ± 0.80 12.70 ± 0.80 10.57 ± 0.86
    2 RI, DT, IS 36.71 ± 1.51 120.41 ± 3.44 198.12 ± 6.01 201.19 ± 3.70
    3 RI, DT, IS 44.35 ± 0.88 514.12 ± 4.28 224.78 ± 6.56 228.32 ± 4.62
    4 RI, DT, IS 857.64 ± 8.63 33.22 ± 1.99 35.05 ± 5.99 35.17 ± 3.97
    * Represents the retention index calculated using n-ketones C4−C9 as external standard on MAX-WAX column. ** Represents the retention time in the capillary GC column. *** Represents the migration time in the drift tube.
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    This study adopted the GC-IMS method to test the volatile organic compounds (VOCs) in the samples from the different fermentation stages of Marselan wine. Figure 1 shows the gas phase ion migration spectrum obtained, in which the ordinate represents the retention time of the gas chromatographic peaks and the abscissa represents the ion migration time (normalized)[16]. The entire spectrum represents the aroma fingerprints of Marselan wine at different fermentation stages, with each signal point on the right of the relative reactant ion peak (RIP) representing a volatile organic compound detected from the sample[17]. Here, the sample in stage 1 (juice processing) was used as a reference and the characteristic peaks in the spectrum of samples in other fermentation stages were compared and analyzed after deducting the reference. The colors of the same component with the same concentration cancel each other to form a white background. In the topographic map of other fermentation stages, darker indicates higher concentration compared to the white background. In the 2D spectra of different fermentation stages, the position and number of peaks indicated that peak intensities are basically the same, and there is no obvious difference. However, it is known that fermentation is an extremely complex chemical process, and the content and types of volatile organic compounds change with the extension of fermentation time, so other detection and characterization methods are needed to make the distinction.

    Figure 1.  2D-topographic plots of volatile organic compounds in Marselan wine at different fermentation stages.

    To visually display the dynamic changes of various substances in the fermentation process of Marselan wine, peaks with obvious differences were extracted to form the characteristic fingerprints for comparison (Fig. 2). Each row represents all signal peaks selected from samples at the same stage, and each column means the signal peaks of the same volatile compound in samples from different fermentation stages. Figure 2 shows the volatile organic compounds (VOCs) information for each sample and the differences between samples, where the numbers represent the undetermined substances in the migration spectrum library. The changes of volatile substances in the process of Marselan winemaking is observed by the fingerprint. As shown in Fig. 2 and Table 2, a total of 40 volatile chemical components were detected by qualitative analysis according to their retention time and ion migration time in the HS-GC-IMS spectrum, including 17 esters, eight alcohols, eight aldehydes, two ketones, one organic acid, and four unanalyzed flavor substances. The 12 volatile organic compounds presented dimer due to ionization of the protonated neutral components before entering the drift tube[18]. As can be seen from Table 2, the VOCs in the winemaking process of Marselan wine are mainly composed of esters, alcohols, and aldehydes, which play an important role in the construction of aroma characteristics.

    Figure 2.  Fingerprints of volatile organic compounds in Marselan wine at different fermentation stages.
    Table 2.  Antioxidant activity, total polyphenols, and flavonoids content of Marselan wine at different fermentation stages.
    Winemaking stage TFC (mg CE/L) TPC (mg GAE/L) FRAP (mM FeSO4/mL) ABTs (mM Trolox/L)
    Stage 1 315.71 ± 0.00d 1,083.93 ± 7.79d 34.82c 38.92 ± 2.12c
    Stage 2 1,490.00 ± 7.51c 3,225.51 ± 53.27c 77.32b 52.17 ± 0.95b
    Stage 3 1,510.00 ± 8.88a 3,307.143 ± 41.76b 77.56b 53.04 ± 0.76b
    Stage 4 1,498.57 ± 6.34b 3,370.92 ± 38.29a 85.07a 57.46 ± 2.55a
    Means in the same column with different letters are significantly different (p < 0.05).
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    Esters are produced by the reaction of acids and alcohols in wine, mainly due to the activity of yeast during fermentation[19], and are the main components of fruit juices and wines that produce fruit flavors[20,21]. In this study, it was found that they were the largest detected volatile compound group in Marselan wine samples, which is consistent with previous reports[22]. It can be observed from Table 2 that the contents of most esters increased gradually with the extension of fermentation time, and they mainly began to accumulate in large quantities during the stage of alcohol fermentation. The contents of ethyl hexanoate (fruity), isoamyl acetate (banana, pear), ethyl octanoate (fruity, pineapple, apple, brandy), ethyl acetate (fruity), ethyl formate (spicy, pineapple), and ethyl butanoate (sweet, pineapple, banana, apple) significantly increased at the stage of alcoholic fermentation and maintained a high level in the subsequent fermentation stage (accounting for 86% of the total detected esters). These esters can endow a typical fruity aroma of Marselan wine, and played a positive role in the aroma profiles of Marselan wine. Among them, the content of ethyl acetate is the highest, which is 5,153.79 μg/mL in the final fermentation stage, accounting for 33.6% of the total ester. However, the content of ethyl acetate was relatively high before fermentation, which may be from the metabolic activity of autochthonous microorganisms present in the raw materials. Isobutyl acetate, ethyl 3-methyl butanoate, propyl acetate, ethyl propanoate, ethyl isobutyrate, and ethyl lactate were identified and quantified in all fermentation samples. The total contents of these esters in stage 1 and 4 were 255.28 and 1,533.38 μg/mL, respectively, indicating that they may also have a potential effect on the aroma quality of Marselan wine. The results indicate that esters are an important factor in the formation of flavor during the brewing process of Marselan wine.

    Alcohols were the second important aromatic compound in Marselan wine, which were mainly synthesized by glucose and amino acid decomposition during alcoholic fermentation[23,24]. According to Table 2, eight alcohols including methanol, ethanol, propanol, butanol, hexanol, amyl alcohol, 3-methyl-1-butanol, and 2-methyl-1-propanol were detected in the four brewing stages of Marselan wine. The contents of ethanol (slightly sweet), 3-methyl-1-butanol (apple, brandy, spicy), and 2-methyl-1-propanol (whiskey) increased gradually during the fermentation process. The sum of these alcohols account for 91%−92% of the total alcohol content, which is the highest content of three alcohols in Marselan wine, and may be contributing to the aromatic and clean-tasting wines. On the contrary, the contents of 1-hexanol and methanol decreased gradually in the process of fermentation. Notably, the content of these rapidly decreased at the stage of alcoholic fermentation, from 2,026.07 to 1,218.98 μg/mL and 662.08 to 76.94 μg/mL, respectively, which may be ascribed to volatiles changed from alcohols to esters throughout fermentation. The reduction of the concentration of some alcohols also alleviates the strong odor during wine fermentation, which plays an important role in the improvement of aroma characteristics.

    Acids are mainly produced by yeast and lactic acid bacteria metabolism at the fermentation stage and are considered to be an important part of the aroma of wine[22]. Only one type of acid (acetic acid) was detected in this experiment, which was less than previously reported, which may be related to different brewing processes. Acetic acid content is an important factor in the balance of aroma and taste of wine. Low contents of volatile acids can provide a mild acidic smell in wine, which is widely considered to be ideal for producing high-quality wines. However, levels above 700 μg/mL can produce a pungent odor and weaken the wine's distinctive flavor[25]. The content of acetic acid increased first and then decreased during the whole fermentation process. The content of acetic acid increased rapidly in the second stage, from 719.91 to 3,914.55 μg/mL reached a peak in the third stage (5,161.81 μg/mL), and decreased to 4,630.65 μg/mL in the last stage of fermentation. Excessive acetic acid in Marselan wine may have a negative impact on its aroma quality.

    It was also found that the composition and content of aldehydes produced mainly through the catabolism of amino acids or decarboxylation of ketoacid were constantly changing during the fermentation of Marselan wines. Eight aldehydes, including furfural, hexanal, heptanal, 2-methylpropanal, 3-methylbutanal, dimethyl sulfide, (E)-2-hexenal, and (E)-2-pentenal were identified in all stage samples. Among them, furfural (caramel bread flavor) and hexanal (grass flavor) are the main aldehydes in Marselan wine, and the content increases slightly with the winemaking process. While other aldehydes such as (E)-2-hexenal (green and fruity), 3-methylbutanol (fresh and malt), and 2-methylpropanal (fresh and malt) were decomposed during brewing, reducing the total content from 536.52 to 85.15 μg/mL, which might potently affect the final flavor of the wine. Only two ketones, acetone, and 3-hydroxy-2-butanone, were detected in the wine samples, and their contents had no significant difference in the fermentation process, which might not affect the flavor of the wine.

    To more intuitively analyze the differences of volatile organic compounds in different brewing stages of Marselan wine samples, principal component analysis was performed[2628]. As presented in Fig. 3, the points corresponding to one sample group were clustered closely on the score plot, while samples at different fermentation stages were well separated in the plot. PC1 (79%) and PC2 (18%) together explain 97% of the total variance between Marselan wine samples, indicating significant changes in volatile compounds during the brewing process. As can be seen from the results in Fig. 3, samples of stages 1, 2, and 3 can be distinguished directly by PCA, suggesting that there are significant differences in aroma components in these three fermentation stages. Nevertheless, the separation of stage 3 and stage 4 samples is not very obvious and both presented in the same quadrant, which means that their volatile characteristics were highly similar, indicating that the volatile components of Marselan wine are formed in stage 3 during fermentation (Fig. S1). The above results prove that the unique aroma fingerprints of the samples from the distinct brewing stages of Marselan wine were successfully constructed using the HS-GC-IMS method.

    Figure 3.  PCA based on the signal intensity obtained with different fermentation stages of Marselan wine.

    Based on the results of the PCA, OPLS-DA was used to eliminate the influence of uncontrollable variables on the data through permutation test, and to quantify the differences between samples caused by characteristic flavors[28]. Figure 4 revealed that the point of flavor substances were colored according to their density and the samples obtained at different fermentation stages of wine have obvious regional characteristics and good spatial distribution. In addition, the reliability of the OPLS-DA model was verified by the permutation method of 'Y-scrambling'' validation. In this method, the values of the Y variable were randomly arranged 200 times to re-establish and analyze the OPLS-DA model. In general, the values of R2 (y) and Q2 were analyzed to assess the predictability and applicability of the model. The results of the reconstructed model illustrate that the slopes of R2 and Q2 regression lines were both greater than 0, and the intercept of the Q2 regression line was −0.535 which is less than 0 (Fig. 5). These results indicate that the OPLS-DA model is reliable and there is no fitting phenomenon, and this model can be used to distinguish the four brewing stages of Marselan wine.

    Figure 4.  Scores plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.
    Figure 5.  Permutation test of OPLS-DA model of volatile components in Marselan wine at different fermentation stages (n = 200).

    VIP is the weight value of OPLS-DA model variables, which was used to measure the influence intensity and explanatory ability of accumulation difference of each component on classification and discrimination of each group of samples. In previous studies, VIP > 1 is usually used as a screening criterion for differential volatile substances[2830]. In this study, a total of 22 volatile substances had VIP values above 1, indicating that these volatiles could function as indicators of Marselan wine maturity during fermentation (see Fig. 6). These volatile compounds included furfural, ethyl lactate, heptanal, dimethyl sulfide, 1-propanol, ethyl isobutyrate, propyl acetate, isobutyl acetate, ethanol, ethyl hexanoate, acetic acid, methanol, ethyl formate, ethyl 3-methylbutanoate, ethyl acetate, hexanal, isoamyl acetate, 2-methylpropanal, 2-methyl-1-propanol, and three unknown compounds.

    Figure 6.  VIP plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.

    This study focuses on the change of volatile flavor compounds and antioxidant activity in Marselan wine during different brewing stages. A total of 40 volatile aroma compounds were identified and collected at different stages of Marselan winemaking. The contents of volatile aroma substances varied greatly at different stages, among which alcohols and esters were the main odors in the fermentation stage. The proportion of furfural was small, but it has a big influence on the wine flavor, which can be used as one of the standards to measure wine flavor. Flavonoids and phenols were not only factors of flavor formation, but also important factors to improve the antioxidant capacity of Marselan wine. In this study, the aroma of Marselan wines in different fermentation stages was analyzed, and its unique aroma fingerprint was established, which can provide accurate and scientific judgment for the control of the fermentation process endpoint, and has certain guiding significance for improving the quality of Marselan wines (Table S1). In addition, this work will provide a new approach for the production management of Ningxia's special wine as well as the development of the native Chinese wine industry.

  • The authors confirm contribution to the paper as follows: study conception and design: Gong X, Fang L; data collection: Fang L, Li Y; analysis and interpretation of results: Qi N, Chen T; draft manuscript preparation: Fang L. All authors reviewed the results and approved the final version of the manuscript.

  • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

  • This work were supported by the project of Hainan Province Science and Technology Special Fund (ZDYF2023XDNY031) and the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences in China (Grant No. 1630122022003).

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Relative contents of volatile compounds of grape wines from different varieties using HS-SPME-GC-MS.
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  • Cite this article

    Fan X, Pan L, Chen R. 2023. Characterization of flavor frame in grape wines detected by HS-SPME-GC-MS coupled with HPLC, electronic nose, and electronic tongue. Food Materials Research 3:9 doi: 10.48130/FMR-2023-0009
    Fan X, Pan L, Chen R. 2023. Characterization of flavor frame in grape wines detected by HS-SPME-GC-MS coupled with HPLC, electronic nose, and electronic tongue. Food Materials Research 3:9 doi: 10.48130/FMR-2023-0009

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Characterization of flavor frame in grape wines detected by HS-SPME-GC-MS coupled with HPLC, electronic nose, and electronic tongue

Food Materials Research  3 Article number: 9  (2023)  |  Cite this article

Abstract: To analyze the flavor components in 17 commercially available wine samples from seven grape varieties (Cabernet Sauvignon, Cabernet Gernischt, Shiraz, Merlot, Pinot Noir, Tempranillo and Chardonnay), comprehensive flavor characterization, volatile and non-volatile compounds of grape wines were evaluated by headspace solid phase micro-extraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS), electronic nose (E-nose), electronic tongue (E-tongue), high performance liquid chromatography (HPLC) and automatic amino acids analyzer. According to GC-MS analysis, a total of 86 volatile compounds were identified, mainly including alcohols, esters, phenols, terpenes and norisoprenoids. Results showed that significant differences of contents of free amino acids and radar fingerprint chart of E-tongue technology were recorded for the 17 grape wines. Moreover, principal component analysis (PCA) of E-nose and E-tongue were used to distinguish the different grape wines effectively, with the cumulative contribution rate accounting for 92.33% and 91.78%, respectively. The results prove that sensors W2S and W1W in the E-nose for wines have a higher influence in the current pattern file. The most abundant phenol in 17 wine samples is catechin. The differences in species and contents of volatile and non-volatile substances give the unique flavor of different grape wines. The results demonstrated that the above mentioned equipment are useful for in-depth grape wine flavor analysis.

    • Grape wine is one of the most global widely known and appreciated alcoholic beverages. Moderate consumption may have some beneficial effects on human health due to the high antioxidant activity of wine[1]. Aroma, taste, and appearance are three important indicators to evaluate food quality[2]. Among them, the aroma profile of wine is one of the key factors influencing its quality[3]. Understanding consumer preferences and predicting their behavior is a difficult task for the wine industry. Previous studies[410] have documented the organoleptic characteristic such as aroma appreciated by wine consumers. Grape wine is a complex matrix consisting of a wide range of volatile and non-volatile compounds[11]. Although the overall composition of most grape cultivars is very similar, there are distinct aroma and flavor differences between most varieties. These differences can mostly be attributed to relatively minor variations in the proportion of the compounds that constitute the aroma profile of the grape[12]. Especially, the varietal component derived from grape aroma and aromatic precursors, impart specific aroma depending on the cultivars characteristics[13,14] Further, wine flavor is also dependent on fermentation process, storage and aging. The most important aroma substances of wine have been identified as alcohols, esters, aldehydes, ketones, acids, terpenes[15], ethers, lactones, pyrazines, phenolic compounds[16] and sulfur containing compounds. These sulfur-containing compounds can have either a positive or negative impact on the aroma and flavor of wine, compounds such as 3-mercaptohexanol can impart fruity flavors to a wine[17]. Although some of these compounds are present at low concentrations in the grape wine, they normally have a huge impact on the overall aroma profile[18].

      Grape wine is well-known for its health benefits, and most of them are, at least partially, attributed to the presence of phenolic compounds. It has been reported that moderate consumption of alcoholic beverages, especially wine, could protect from cardiovascular disease. This phenomenon defined as the French paradox was proposed for the first time by Serge Renaud[19]. The phenolic compounds originate from original grape and/or formed during alcohol fermentation. Additionally, volatile substances present in concentrations at below their perception threshold may contribute to the final wine aroma and flavor palette by interactive effects with each other in various ways other compounds in wine[20]. Studies also showed that when the ethanol concentration in wine was lowered to 7%, a significant increase in the intensities of the fruity, flowery, and acid flavors and aromas was seen[21].

      Flavor is responsible for the overall distinctive sensory properties of grape wine, and is vital in the evaluation of quality. The subtle differences that distinguish one varietal wine from another may depend on the concentration and types of the volatile and non-volatile substances. The quality of wine can be evaluated through both chemical and sensory analysis. The most widely accepted chemical analytical method to detect, identify and quantify flavor compounds is GC-MS combined with HS-SPME for its high selectivity, sensitivity and precision[2224]. Equipments such as electronic nose and electronic tongue consisting of an array of sensors are widely applied to detect flavor of food by simulating the olfaction and taste of humans with the advantages of excellent selectivity, high sensitivity, less time-consuming and relatively lower price[25]. Among them, gas sensor arrays are referred to as electronic nose, with partial specificity and an appropriate pattern-recognition system, while chemical sensor arrays are defined as electronic tongue, identifying the five basic tastes (sweet, salty, sour, bitter, and umami)[26]. Depending on the sensing materials, gas sensors of E-nose can be classified into several types including, metal-oxide semiconductor (MOS), conducting polymers (CP), quartz crystal microbalance (QCM), and surface acoustic wave (SAW) sensors[27]. Among them, MOS gas sensor is most widely used for E-nose, it was reported that MOS sensors are sensitive to hydrogen and unsaturated hydrocarbons or solvent vapors containing hydrogen atoms[25]. The common E-tongue has the following types: potentiometry, voltammetry, and impedance spectroscopy[28]. E-tongue can detect the overall taste of food, they cannot identify specific compounds. Taste-active compounds, such as free amino acids (FAAs), were responsible for the characteristic taste of grape wines and also act as precursors to the formation of aromas. Thus, the individual taste compounds can be determined by amino acid detection. Currently, E-nose and E-tongue have been widely researched on quality evaluation of red wine. The E-Nose was revealed like a powerful tool for the objective differentiation of the wines obtained from the authorized grape variety in a Protected Denomination of Origin[29]. A multi-sensor fusion technology based on a novel low-cost E-nose and a voltammetric E-tongue was developed to classify red wines that differ in geographical origins, brands, and grape varieties[30]. Compared to GC-MS, E-noses do not provide information on the quantity of the individual volatile compounds but rather a global analysis of the volatile chemical profile so-called 'fingerprints', which is more similar to the human olfactory perception[31,32].

      There is a growing interest in developing rapid methods for the analysis of organoleptic properties of grape wine such as aroma and taste which play a crucial role in consumer preferences and choices[33]. Therefore, accurately and efficiently identifying different wines are of particular importance. In addition, it is important for quality control, storage, and brand recognition as well. In the literature, different methods for wine age prediction[34,35], the influence of grape maturity on wine volatiles and the optimum drying time of the grape to produce sweet wines of higher aromatic quality[36] were investigated. However, there are no systematic studies describing the combined application of HS-SPME-GC-MS, E-nose, E-tongue, HPLC and amino acids analyzer in grape wines flavor studies. Hence, we set up a comprehensive method to analyze the flavor of commercially available grape wines (Cabernet Sauvignon, Cabernet Gernischt, Shiraz, Merlot, Pinot Noir, Tempranillo and Chardonnay). Principal component analysis (PCA) of E-nose and E-tongue was applied to analyze the difference in volatile and non-volatile organic compounds of grape wines. The combination of flavor chemistry with sensory analysis techniques could provide a comprehensive odor and taste characterization of wines, which could provide an effective method for consumers to choose their preferred grape wines. The information obtained in this study would have important referential value for the flavor research of grape wines.

    • By researching the types of grape wines sold in the local supermarket in Nanjing, China, 17 commercially available grape wines from seven different grape varieties (Cabernet Sauvignon, Cabernet Gernischt, Shiraz, Merlot, Pinot Noir, Tempranillo and Chardonnay) were studied as experimental samples (Table 1). HPLC grade methanol, acetic acid, ethyl acetate and phenolic acid standards (gallic acid, protocatechuic acid, vanillic acid, catechin, caffeic acid, syringic acid, p-coumaric acid and ferulic acid) were purchased from Sigma-Aldrich Chemical Company (St. Louis, MO, USA). Water was purified on Simplicity system (Millipore) to prepare the aqueous solutions.

      Table 1.  The details of the grape wines utilized in the experiment.

      Sample numberGrape wine varietiesCountry of originAlcohol content
      (V/V %)
      1Cabernet Sauvignon-AChina12.0
      2Cabernet Sauvignon-BChina13.0
      3Cabernet Sauvignon-CChina12.5
      4Cabernet Sauvignon-DChina12.0
      5Cabernet Sauvignon-EFrance12.0
      6Cabernet Gernischt-AChina12.5
      7Cabernet Gernischt-BChina12.5
      8Shiraz-AChina13.0
      9Shiraz-BAustralia14.5
      10Pinot Noir-AChina13.0
      11Pinot Noir-BChina12.0
      12Merlot-AAustralia13.5
      13Merlot-BAustralia14.0
      14Merlot-CAustralia13.8
      15Merlot-DChina12.5
      16TempranilloSpain13.0
      17ChardonnayAustralia13.0
    • The volatile compounds of grape wine were determined using HS-SPME-GC-MS according to the reported methods[37] with slight modification. The methods have been proved to develop a derivatization protocol for untargeted GC-MS analysis.

      Grape wine (10 mL) was mixed with 2.0 g sodium chloride. The mixture was placed in a 20 mL headspace vial, and stirred at 40 °C for 30 min. To extract volatile compounds from grape wine, a 50/30 µm (DVB/CAR/PDMS) fibre (Supelco, Bellefonte, USA) was used which was preconditioned at 250 °C for 10 min. The fibre was exposed to the sample headspace and extracted at 40 °C for 40 min. After extraction, the fibre was inserted into the splitless injector of the GC-MS (7890A-5975C, Agilent, USA) to identify the volatile compounds. The gas chromatograph was equipped with a 5% phenylmethyl silicone capillary column (HP-5, 30 m × 0.25 mm × 0.25 μm, Agilent, USA). The injector temperature was 250 °C. The carrier gas was helium at a constant flow rate of 1.0 mL/min. Analysis was carried out in the electronic impact mode at 70 eV. The temperature of ionization source and quadrupole was 250 °C and 150 °C, respectively. Detection was performed in full scan mode, from 29 aum to 550 aum. The identification was determined using the NIST.08 libraries and the minimum matching requirement was 80%. The relative content was calculated on the basis of peak area percentage. Each sample was measured in triplicate.

    • The extraction method of phenolic compounds referred to Caceres-Mella et al.[16]. Phenol analysis was carried out with HPLC (LC-20AD, Shimadzu, Japan).The HPLC system consists of a diode array detector (SPD-M20A), autosampler (SIL-20A) and a column oven (CTO-20A). HPLC assay was conducted as described by Beta et al.[38] with some modifications. Their analysis results verify the validity and universality of the method. 250 mm × 4.6 mm, 5 µm ZORBAX SB-C18 (Agilent, USA) was used for separation. The mobile phase consisted of A (0.1% acetic acid in water) and B (0.1% acetic acid in methanol), and the flow rate was 0.9 mL/min. The contents of phenolic compounds were quantified using external calibration curves. The gradient elution program was as follow: 91%–86% A for 0–11 min, 86%–85% A for 11–17 min, 85%–81% A for 17–28 min, 81%–72% A for 28–38 min, 72%–60% A for 38–46 min, 60%–30% A for 46–65 min, and 30%–91% A for 65–75 min. The column oven temperature was held at 30 °C. The injection volume was set to 20 μL and detection wavelength was 280 nm. Analyses were performed in triplicate.

    • The procedures were conducted according to the published literature by Xia et al.[39]. Ten mL grape wine sample was mixed with 10 mL sulfosalicylic acid (10%) to precipitate protein and then centrifuged at 4 °C for 20 min (10,000 rpm/min). Subsequently, the supernatants were filtered with a 0.45 µm micro-pore filter membrane. The content of free amino acids in grape wines was detected by automatic amino acid analyzer (L-8900, Hitachi Ltd., Tokyo, Japan) with a column packed with Hitachi custom ion-exchange resin 2622 (4.6 mm × 60 mm, particle size 5 μm) and then calculated by calibrating with standard amino acids (0.1 μmol/mL). Twenty µL sample solution was injected into the automatic analyzer to obtain the peak area of each amino acids in grape wine. Each sample was measured in triplicate. Quantitation was analyzed by an external standard method and the content of amino acids in the sample was calculated by the formula as follows:

      Mi=Xi×(VW+VS)V0×Vw

      Where Mi (mg/L) is the content of amino acid 'i' in samples, Vs (mL) is the volume of sulfosalicylic acid, Xi (ng) is the concentration of amino acid 'i' detected by the instrument, V0 (μL) is the injection volume, and Vw (mL) is the volume of the wine sample.

    • The analysis of grape wine was performed with a portable electronic nose PEN 3, (Airsense Analytics GmbH, Germany) which was composed of an array of 10 metal oxide semiconductors (MOS). The response characteristics of each sensor were shown as follows: W1C (aromatic compounds); W5S (nitrogen oxide); W3C (ammonia and aromatic compounds); W6S (hydrogen); W5C (olefin and aromatic compounds); W1S (hydrocarbons); W1W (hydrogen sulphide); W2S (alcohols and partially aromatic compounds); W2W (aromatic compounds and organic sulphides); W3S (alkanes (methane, etc.). E-nose was applied to identify different volatile species. The pattern recognition software (Win Muster v.1.6.2) was used for data recording and elaboration.

      The E-nose analysis was conducted according to a method of Liu et al.[40], 10 mL grape wine was injected into a headspace vial of 40 mL volume and equilibrated at 25 ± 2 °C for 30 min to reach a steady state. The headspace gas was pumped through the sensor array for 80 s (injection time) with a flow rate of 300 mL/min. After sample analysis, the system was purged for 100 s with filtered air to enable the signals to return to the baseline. Each sample was measured in triplicate.

    • This experiment was conducted with the Taste-Sensing System SA402B (Intelligent Sensor Technology Co. Ltd. Japan) according to the method from Liu et al.[40]. This E-tongue system was comprised of reference electrodes (Ag/AgCl), auto-sampler, and sensor array. Taste sensors used in this experiment include sourness, bitterness, astringency, umami and saltiness. In this experiment, all the wine bottles were opened on the same day, and samples were stored at a constant temperature of 25 °C before measurement. After centrifugation at 12,000 rpm for 15 min, 80 mL grape wine was filtrated, and the supernatant was gained for electronic tongue determination. Each sample was repeated four times, and the last three stable sets of data were retained.

    • All the assays were performed in triplicate for each of grape wine and the experimental data was expressed as mean values. The PCA data were organized by Origin 95. Radar fingerprint chart was organized by Excel. Electronic nose measurement of grape wine sample was performed using Win Muster software (Winmuster1.6.2) for loading analysis. Least significant difference (LSD, defined when P < 0.05) were used to analyze the significant differences among 17 wine samples via SAS (V8.0, the SAS Institute, USA).

    • A total of 86 volatile flavor compounds were identified in 17 samples from seven kinds of grape wines using HS-SPME-GC-MS, including 10 alcohols, 44 esters, 14 terpenes and norisoprenoids, eight hydrocarbons, five acids, one aldehyde, two phenols, and two other compounds (Supplemental Table S1). About 46, 41, 45, 45, 59, 16 and 13 kinds of volatile compounds were identified on Cabernet Sauvignon, Cabernet Gernischt, Shiraz, Merlot, Pinot Noir, Tempranillo and Chardonnay, respectively. As shown in Fig. 1, the sum content of esters and alcohols made up the most of total volatile content. Alcohols were the predominant flavor substances in Cabernet Sauvignon-A, Cabernet Sauvignon-D, Cabernet Gernischt-A, Merlot-A, Merlot-B and Tempranillo with relative contents of 58.16%, 55.96%, 51.13%, 75.44%, 74.61% and 66.57%, respectively. However, in Cabernet Sauvignon-B, Cabernet Sauvignon-C, Cabernet Sauvignon-E, Cabernet Gernischt-B, Shiraz-A, Shiraz-B, Merlot-C, Merlot-D, Pinot Noir-A, Pinot Noir-B and Chardonnay, esters were found to be the main volatile compounds. The most abundant volatile compounds of 17 samples were 3-methyl-1-butanol, phenylethyl alcohol, butanedioic acid diethyl ester, hexanoic acid ethyl ester and octanoic acid ethyl ester, decanoic acid, ethyl ester. 3-methyl-1-butanol is major contributor to the alcoholic fraction and it is formed by the deamination and decarboxylation of leucine. 2-Phenylethanol, an alcohol that gives a pleasant rose aroma can be considered as a component of the primary aroma. The esters are the largest class of volatile compounds present in wine. They are responsible for the secondary and the tertiary aroma of wines. The main volatile compounds in Cabernet Sauvignon-A and Cabernet Sauvignon-D were 3-methyl-1-butanol, hexanoic acid ethyl ester (apple, fruity, sweetish notes), butanedioic acid diethyl ester. 3-methyl-1-butanol and octanoic acid ethyl ester (ripe fruits, pear, sweety notes) were found to be the major volatile compounds contributing to the flavor of Cabernet Gernischt-A. Hexanoic acid, 2-methylpropyl ester and 1-Isopropyl-2-methoxy-4-methylbenzene were the two unique flavor compounds of Shiraz-A. 3-methyl-1-butanol accounts for a relatively high proportion in Merlot-A, Merlot-B and Tempranillo and high levels of 3-methyl-1-butanol (smokey and unpleasant aroma) might contribute negatively to the grape wine aroma profile. Terpenoids and norisoprenoids have great benefits for the human body and they contribute to some highly desirable descriptors such as floral and citrus notes[41]. In the present study, 14 different terpenoids and norisoprenoids were identified for the seventeen samples. 6, 5, 5, 6 and 6 kinds of terpenoids and norisoprenoids were identified on Cabernet Sauvignon-A, Shiraz-A, Merlot-C, Pinot Noir-A and Pinot Noir-B, respectively. 1,2-dihydronaphthalene-1,1,6-trimethyl (TDN) which is described as petroleum, kerosene and diesel was generally detected in all samples except Cabernet Sauvignon-B. Among 17 grape wines, the variety of volatile compounds in Pinot Noir was the most abundant. Hydrocarbons in wine result from the waxy components of the grape surface, appear in very small quantities and participate in the varietal aroma but without any special organoleptic significance[42]. Phenolic compounds play a key role in defining the quality of a red wine, because they participate directly in color, the antioxidant properties, astringency and bitterness of the wine[16]. 3-ethylphenol and 2,4-bis(1,1-dimethylethyl)phenol were detected among the 17 wines. The proportion between the different volatile compounds is fundamental in order to impress a harmonious equilibrium to the grape wine profile. For example, the presence of alcohols in too high concentrations could be a negative feature since they may hide the positive contribution of esters or aldehydes (floral and fruity).

      Figure 1. 

      The relative contents of volatile compounds classes of seventeen wine samples.

    • Phenolic acids also contribute to the taste of grape wines. In this study, eight phenolic acids including gallic acid, protocatechuic acid, vanillic acid, catechin, caffeic acid, syringic acid, p-coumaric acid and ferulic acid were analyzed and the quantitative results were shown in Table 2. In total, the highest concentration of phenolic compounds was observed in Pinot Noir-A (167.743 ± 2.395 mg/L), while the lowest was in Chardonnay (48.321 ± 1.628 mg/L). The most abundant phenols in sixteen red wine samples were gallic acid and catechin. Wine made from Pinot Noir grape variety had the highest concentration of catechin compared to the other sixteen wine samples, which is in accordance with the results published by Krstonosic et al.[43]. Concerning other abundant phenols, protocatechuic acid was detected in a relatively high concentration (8.658−27.230 mg/L) in seventeen wines. The observed differences in the phenolic content could be attributed to many factors, including terroirs, grape maturity, and varietal characteristics, as well as the applied winemaking technology.

      Table 2.  Phenolic acids in seventeen grape wines using HPLC.

      Phenolic acidsContents of phenolic acids (mg/L)
      Cabernet Sauvignon-ACabernet Sauvignon-BCabernet Sauvignon-CCabernet Sauvignon-DCabernet Sauvignon-ECabernet Gernischt-ACabernet Gernischt-BShiraz-AShiraz-BMerlot-AMerlot-BMerlot-CMerlot-DPinot
      Noir-A
      Pinot
      Noir-B
      TempranilloChardonnay
      gallic acid22.322 ± 2.408fg19.224 ± 2.945h19.187 ± 0.882h30.553 ± 4.171cd34.722 ± 0.512b22.912 ± 0.691f18.660 ± 1.355h39.721 ± 1.848a32.877 ± 0.162bc23.673 ± 0.179f27.293 ± 0.031e28.391 ± 0.055de31.806 ± 0.626c41.739 ± 1.206a15.424 ± 0.126i19.900 ± 0.039gh3.108 ±
      0.068j
      protocatechuic acid21.171 ± 1.289bc9.788 ± 1.216kl12.985 ± 0.209fgh19.860 ± 2.938cd10.796 ± 0.168jk11.607 ± 0.127hijk14.315 ± 1.597f10.949 ± 1.008ijk27.230 ± 1.376a8.658 ± 0.114l17.858 ± 0.134e18.319 ± 0.021de26.639 ± 0.207a22.171 ± 0.631b12.739 ± 0.985fghi13.551 ± 0.056fg12.295 ± 0.163ghij
      vanillic acid6.851 ±
      0.202h
      8.362 ±
      0.201g
      9.792 ±
      0.756f
      2.150 ±
      0.151j
      4.939 ±
      0.042i
      2.117 ± 0.368j9.495 ± 1.301f2.832 ± 0.225j14.841 ± 0.188d2.980 ± 0.150j22.551 ± 0.327a16.620 ± 0.201c17.987 ± 1.514b17.272 ± 0.975bc9.006 ± 0.515fg11.034 ± 0.133e5.999 ± 0.705h
      catechin34.540 ± 2.871h20.991 ± 1.712l29.309 ±
      1.03i
      33.886 ± 1.693h55.238 ± 0.983c45.555 ± 0.632d24.244 ± 1.062k46.814 ± 0.439d40.728 ± 0.448ef34.660 ± 0.965h42.315 ± 0.987e37.839 ± 0.482g39.583 ± 1.280fg58.853 ± 0.347b63.725 ± 1.595a26.547 ± 0.947j13.135 ± 0.109m
      caffeic acid1.565 ±
      0.293i
      1.498 ±
      0.106ij
      3.313 ±
      0.029h
      9.050 ±
      0.149b
      11.735 ± 0.008a6.262 ± 0.557d5.050 ± 0.430f7.807 ± 0.480c3.991 ± 0.281g7.644 ± 0.026c5.588 ± 0.469e7.342 ± 0.243c4.098 ± 0.249g6.198 ± 0.201d1.026 ± 0.149j6.008 ± 0.428de2.010 ±
      0.045i
      syringic acid10.954 ± 1.501d9.369 ± 0.537ef10.348 ± 1.127de15.011 ± 0.497b8.751 ±
      0.007f
      17.211 ± 0.249a12.294 ± 0.786c13.370 ± 0.877c9.908 ± 0.359edf5.360 ± 0.019g5.755 ± 0.133g9.205 ± 0.283ef8.966 ± 0.623f14.902 ± 0.713b3.925 ± 0.372h5.416 ± 0.070g6.217 ± 0.683g
      p-coumaric acid6.128 ± 0.305cde4.327 ± 0.495fg5.560 ±
      0.233e
      7.353 ±
      0.480b
      6.309 ± 0.024cd6.351 ± 0.657cd4.311 ± 0.306fg7.897 ± 0.469b1.420 ± 0.301h5.828 ± 0.143de8.792 ± 0.055a4.733 ± 0.079f4.879 ± 0.013f4.229 ± 0.622fg6.697 ± 0.630c3.848 ± 0.087g0.741 ±
      0.042i
      ferulic acid1.444 ±
      0.250f
      0.643 ±
      0.101h
      0.956 ±
      0.078g
      0.954 ±
      0.075g
      1.023 ± 0.022g1.616 ± 0.079ef1.612 ± 0.164ef1.876 ± 0.131de1.596 ± 0.035f3.080 ± 0.177b1.903 ± 0.172d2.063 ± 0.054d2.011 ± 0.030d2.377 ± 0.171c0.999 ± 0.070g2.517 ± 0.191c4.817 ± 0.347a
      Total104.975 ± 3.850g74.204 ± 5.231j91.450 ± 2.602i118.816 ± 0.709e133.513 ± 0.701bc113.631 ± 1.378f89.982 ± 3.098i131.267 ± 3.470c132.591 ± 1.132bc99.539 ± 1.254h132.055 ± 1.331bc124.512 ± 0.298d135.969 ± 1.285b167.743 ± 2.395a113.542 ± 2.107f88.821 ± 1.140i48.321 ± 1.628k
      Each value is expressed as mean ± SD (n=3) and data in the same row with different letters are significantly different (P < 0.05).
    • The amino acids can not only provide nitrogen for the growth of microorganisms, but also they can bring nice color for the wine[44]. As one of the essential components of grape wine, amino acids supply diverse tastes which were umami (monosodium glutamate, MSG)-like (including Asp and Glu), bitter (including Val, Met, Ile, Leu, Phe, His and Arg) and sweet (including Thr, Ser, Gly and Ala)[45]. In this study, 17 kinds of free amino acids (FAAs) in seventeen grape wines were detected. The total content of amino acids varied from 144.702 ± 8.589 to 510.153 ± 6.708 mg/L as shown in Table 3. The top five grape wines with the highest total amino acids were Pinot Noir-A, Tempranillo, Cabernet Sauvignon-A, Pinot Noir-B and Merlot-B. There was a significant difference (P < 0.05) in MSG-like amino acids content among Pinot Noir-A, Chardonnay, Cabernet Sauvignon-D, Cabernet Sauvignon-B, Cabernet Gernischt-B and Shiraz-B. However, no notable difference in bitter amino acids was observed among Cabernet Sauvignon-A, Cabernet Sauvignon-D and Cabernet Sauvignon-E. The content of essential amino acids among Merlot-D, Pinot Noir-A and Chardonnay were significantly different from each other. Cabernet Sauvignon-E, Shiraz-A, Merlot-B and Pinot Noir-B had little difference in the content of sweet amino acids. Further, our results revealed that among these amino acids, glutamic acid, proline, lysine, arginine and alanine predominated. Glutamic acid which has the umami taste can improve the taste of grape infusions. Pinot Noir-A had the highest content of total amino acids, taste-active amino acids (MSG-like, bitter and sweet components) and essential amino acids among seventeen grape wines. Since free amino acids are precursors of flavor compounds, the different contents of free amino acids were highly correlated to the complex synthesis of flavor compounds in grape wines. Free amino acids are closely related to the taste of the grape wines, which determines the quality of the grape wines.

      Table 3.  Comparison of free amino acids (FAAs) in different kinds of grape wines.

      FAAsContents of FAAs (mg/L)
      Cabernet Sauvignon-ACabernet Sauvignon-BCabernet Sauvignon-CCabernet Sauvignon-DCabernet Sauvignon-ECabernet Gernischt-ACabernet Gernischt-BShiraz-AShiraz-BMerlot-AMerlot-BMerlot-CMerlot-DPinot
      Noir-A
      Pinot
      Noir-B
      TempranilloChardonnay
      Aspartic acid (Asp)21.525 ±
      1.519c
      13.433 ±
      1.031f
      17.666 ±
      0.575d
      14.921 ±
      0.762e
      25.437 ±
      0.502b
      15.846 ±
      1.601e
      11.094 ±
      0.09g
      15.391 ±
      0.05d
      13.668 ±
      0.06f
      8.076 ±
      0.168h
      22.347 ±
      0.339c
      7.850 ±
      0.210h
      6.347 ±
      0.451i
      33.736 ±
      0.464a
      24.744 ±
      0.267b
      7.291 ±
      0.152hi
      24.332 ±
      1.113b
      Threonine (Thr*)16.269 ±
      1.056c
      12.063 ±
      0.853ef
      10.578 ±
      0.714g
      11.488 ±
      0.611gf
      13.503 ±
      0.262d
      12.406 ±
      0.507ef
      8.693 ±
      1.461h
      13.749 ±
      0.110d
      9.002 ±
      0.039h
      8.181 ±
      0.056h
      13.022 ±
      0.231de
      5.339 ±
      0.144i
      3.458 ±
      0.251j
      27.163 ±
      0.401b
      13.234 ±
      0.121de
      6.562 ±
      0.139i
      31.053 ±
      2.011a
      Serine (Ser)16.291 ±
      0.906c
      10.945 ±
      0.827g
      12.117 ±
      1.050f
      10.472 ±
      0.498g
      14.156 ±
      0.282d
      12.004 ±
      0.791f
      8.572 ±
      0.105h
      13.099 ±
      0.113e
      7.701 ±
      0.038hi
      6.924 ±
      0.119i
      13.434 ±
      0.189de
      6.768 ±
      0.180i
      4.393 ±
      0.314j
      22.375 ±
      0.321b
      13.233 ±
      0.142de
      5.119 ±
      0.095j
      37.086 ±
      1.283a
      Glutamic acid (Glu)49.592 ±
      1.962c
      29.761 ±
      1.331i
      37.523 ±
      0.552f
      33.494 ±
      1.637h
      42.911 ±
      0.879d
      36.752 ±
      1.665e
      29.229 ±
      0.262i
      40.975 ±
      0.0918de
      22.722 ±
      0.097j
      24.139 ±
      0.344j
      35.406 ±
      0.557g
      22.510 ±
      0.547j
      19.312 ±
      1.210k
      82.522 ±
      0.901a
      35.544 ±
      0.415g
      17.984 ±
      0.270k
      78.470 ±
      1.532b
      Proline (Pro)47.364 ±
      1.523c
      44.537 ±
      1.459d
      41.971 ±
      1.562e
      36.592 ±
      0.454f
      12.211 ±
      0.253j
      40.114 ±
      1.500e
      36.604 ±
      0.997f
      40.966 ±
      0.212e
      35.376 ±
      1.341fg
      33.374 ±
      0.677g
      45.784 ±
      2.334cd
      37.380 ±
      1.495f
      22.142 ±
      0.251i
      65.491 ±
      0.840a
      33.837 ±
      1.868g
      30.493 ±
      0.151h
      59.197 ±
      1.503b
      Glycine (Gly)23.421 ±
      2.099b
      14.103 ±
      0.939f
      16.887 ±
      0.242de
      16.110 ±
      0.835e
      18.429 ±
      0.352c
      15.579 ±
      1.300e
      13.557 ±
      0.137f
      16.509 ±
      0.188e
      12.292 ±
      0.054g
      10.829 ±
      0.127h
      18.142 ±
      0.337cd
      10.299 ±
      0.289h
      10.842 ±
      0.771h
      32.777 ±
      0.752a
      18.313 ±
      0.188c
      7.6452 ±
      0.142i
      18.943 ±
      0.631c
      Alanine (Ala)39.205 ±
      1.851bc
      26.972 ±
      1.254g
      30.702 ±
      1.026e
      35.004 ±
      1.763d
      38.996 ±
      1.659bc
      34.357 ±
      1.659d
      28.894 ±
      0.451f
      38.139 ±
      0.197c
      27.647 ±
      0.134fg
      22.450 ±
      0.235h
      40.208 ±
      1.078b
      20.594 ±
      0.587i
      18.195 ±
      1.326j
      75.277 ±
      0.950a
      37.903 ±
      0.413c
      16.628 ±
      0.304j
      16.820 ±
      0.008j
      Cysteine (Cys)5.540 ±
      0.104cd
      1.783 ±
      0.082g
      1.354 ±
      0.095h
      2.178 ±
      0.084f
      1.465 ±
      0.021hg
      5.280 ±
      0.511ed
      1.401 ±
      0.084hg
      5.873 ±
      0.100c
      1.281 ±
      0.025h
      5.126 ±
      0.084e
      1.352 ±
      0.148h
      1.131 ±
      0.061h
      1.132 ±
      0.62h
      7.094 ±
      0.151b
      1.467 ±
      0.039hg
      5.095 ±
      0.041e
      9.678 ±
      0.677a
      Valine (Val*)17.734 ±
      0.779b
      11.333 ±
      0.920g
      10.746 ±
      0.591g
      11.000 ±
      0.547g
      12.718 ±
      0.219ef
      12.414 ±
      0.624f
      8.266 ±
      0.139h
      14.721 ±
      0.071c
      7.854 ±
      0.042h
      8.522 ±
      0.194h
      13.401 ±
      0.334de
      6.770 ±
      0.183i
      4.781 ±
      0.329j
      27.156 ±
      0.393a
      13.798 ±
      0.141d
      8.534 ±
      0.065h
      17.712 ±
      0.611b
      Methionine (Met*)8.801 ±
      0.302b
      4.274 ±
      0.253f
      3.833 ±
      0.311g
      4.509 ±
      0.208ef
      5.550 ±
      0.272d
      2.976 ±
      0.332hi
      3.393 ±
      0.090h
      7.962 ±
      0.039c
      2.733 ±
      0.011ij
      2.167 ±
      0.603k
      4.914 ±
      0.144e
      2.118 ±
      0.055k
      1.543 ±
      0.11l
      11.846 ±
      0.131a
      4.765 ±
      0.056e
      2.429 ±
      0.026jk
      5.465 ±
      0.466d
      Isoleucine (Ile*)7.964 ±
      0.353b
      5.198 ±
      0.235hg
      5.155 ±
      0.258hg
      4.997 ±
      0.249hg
      6.017 ±
      0.111de
      5.337 ±
      0.677fg
      3.611 ±
      0.044i
      5.623 ±
      0.045ef
      3.388 ±
      0.026i
      1.805 ±
      0.066k
      6.385 ±
      0.113cd
      2.878 ±
      0.081j
      1.708 ±
      0.118k
      9.591 ±
      0.158a
      6.544 ±
      0.083c
      1.776 ±
      0.003k
      4.836 ±
      0.329h
      Leucine (Leu*)17.252 ±
      0.553d
      16.345 ±
      0.735e
      14.193 ±
      0.407g
      15.124 ±
      0.783f
      21.805 ±
      0.425b
      11.825 ±
      0.750h
      10.729 ±
      0.104i
      14.612 ±
      0.025fg
      11.344 ±
      0.057hi
      5.575 ±
      0.073k
      18.994 ±
      0.339c
      8.527 ±
      0.262j
      5.383 ±
      0.400k
      25.117 ±
      0.451a
      17.209 ±
      0.215d
      5.225 ±
      0.162k
      17.531 ±
      0.642d
      Tyrosine (Tyr)ND13.138 ±
      0.663d
      12.427 ±
      0.839e
      10.907 ±
      0.445g
      9.98 ±
      0.156h
      ND16.776 ±
      0.129c
      12.025 ±
      0.019e
      6.011 ±
      0.012k
      7.871 ±
      0.062i
      23.243 ±
      0.342b
      6.382 ±
      0.130k
      7.041 ±
      0.463j
      ND10.059 ±
      0.06g
      4.621 ±
      0.338l
      28.734 ±
      0.508a
      Phenylalanine (Phe*)17.956 ±
      0.721d
      15.889 ±
      0.609f
      14.612 ±
      0.535g
      16.983 ±
      0.871e
      23.974 ±
      0.416c
      14.005 ±
      0.601g
      11.978 ±
      0.104h
      17.690 ±
      0.060de
      12.123 ±
      0.083h
      10.386 ±
      0.083i
      18.076 ±
      0.293d
      10.289 ±
      0.263i
      5.070 ±
      0.367j
      29.293 ±
      0.341b
      18.266 ±
      0.184
      10.089 ±
      0.107i
      33.473 ±
      1.008a
      Lysine (Lys*)25.432 ±
      0.979b
      17.212 ±
      0680f
      18.807 ±
      0.749
      19.639 ±
      0.963e
      26.293 ±
      0.475b
      21.102 ±
      0.684d
      14.359 ±
      0.082h
      23.706 ±
      0.062c
      15.486 ±
      0.053g
      14.371 ±
      0.189h
      23.719 ±
      0.378c
      11.691 ±
      0.339i
      9.576 ±
      0.613j
      35.700 ±
      0.679a
      21.965 ±
      0.276d
      12.052 ±
      0.061i
      25.346 ±
      0.916b
      Histidine (His)3.439 ±
      0.038j
      3.164 ±
      0.157j
      3.604 ±
      0.145j
      6.410 ±
      0.276g
      11.158 ±
      0.181b
      1.513 ±
      0.130l
      2.186 ±
      0.10k
      10.079 ±
      0.033c
      5.502 ±
      0.022h
      6.711 ±
      0.060fg
      4.287 ±
      0.052i
      7.488 ±
      0.195ed
      7.033 ±
      0.423ef
      4.784 ±
      0.137i
      7.797 ±
      0.091d
      3.409 ±
      0.076j
      27.230 ±
      1.189a
      Arginine (Arg)21.602 ±
      1.500f
      23.530 ±
      0.928e
      14.266 ±
      0.564h
      34.958 ±
      1.719b
      12.218 ±
      0.282j
      23.194 ±
      1.510e
      31.221 ±
      0.515c
      17.041 ±
      0.141h
      25.002 ±
      0.318d
      23.730 ±
      0.317de
      17.294 ±
      0.242h
      10.844 ±
      0.304j
      16.746 ±
      1.144h
      20.205 ±
      0.315g
      46.065 ±
      0.512a
      13.638 ±
      0.313i
      16.389 ±
      0.768h
      Essential amino acids111.389 ±
      3.63c
      82.317 ±
      3.414ef
      77.924 ±
      3.549f
      83.739 ±
      1.636e
      109.860 ±
      1.636c
      80.0643 ±
      3.377ef
      61.030 ±
      2.024g
      98.063 ±
      0.178d
      61.931 ±
      0.305g
      51.007 ±
      0.474h
      98.511 ±
      1.831d
      47.612 ±
      1.328h
      31.519 ±
      2.177i
      165.887 ±
      2.473a
      95.782 ±
      1.076d
      46.668 ±
      0.543h
      135.415 ±
      5.299b
      MSG-like71.118 ±
      3.171c
      43.195 ±
      2.346h
      55.189 ±
      1.059ef
      48.415 ±
      2.400g
      68.348 ±
      1.382c
      52.597 ±
      3.263f
      40.323 ±
      0.356i
      56.365 ±
      0.142e
      36.390 ±
      0.159j
      32.216 ±
      0.492k
      57.753 ±
      0.896de
      30.363 ±
      0.757k
      25.659 ±
      1.661l
      116.256 ±
      1.241a
      60.288 ±
      0.682d
      25.275 ±
      0.420l
      102.806 ±
      2.626b
      Bitter94.728 ±
      3.420d
      80.185 ±
      3.159f
      66.410 ±
      3.045h
      93.981 ±
      4.683d
      93.439 ±
      1.361d
      71.264 ±
      4.026g
      71.264 ±
      4.026g
      87.728 ±
      0.060e
      67.946 ±
      0.342gh
      58.895 ±
      0.430i
      83.351 ±
      1.516f
      48.914 ±
      1.343j
      42.264 ±
      2.879k
      128.010 ±
      1.877a
      114.444 ±
      1.282c
      45.101 ±
      0.744jk
      122.636 ±
      3.935b
      Sweet95.186 ±
      5.489c
      64.083 ±
      3.794f
      70.283 ±
      1.930e
      73.073 ±
      3.707e
      85.084 ±
      1.674d
      74.346 ±
      4.020e
      59.716 ±
      2.155fg
      81.496 ±
      0.587d
      56.643 ±
      0.264g
      48.384 ±
      0.521h
      84.384 ±
      1.834d
      43.001 ±
      1.200i
      36.888 ±
      2.661j
      157.592 ±
      2.405a
      82.684 ±
      0.863d
      35.961 ±
      0.672j
      103.903 ±
      3.889b
      Total339.369 ±
      13.086c
      264.133 ±
      8.793h
      266.442 ±
      6.253h
      284.785 ±
      11.827g
      296.221 ±
      5.321fg
      264.703 ±
      12.407h
      240.567 ±
      4.807i
      308.159 ±
      0.947ef
      219.133 ±
      0.527j
      200.237 ±
      1.429k
      320.009 ±
      7.448de
      178.862 ±
      5.324l
      144.702 ±
      8.589n
      510.153 ±
      6.708a
      324.744 ±
      1.333d
      158.597 ±
      2.238m
      452.296 ±
      13.074b
      Each value is expressed as mean ± SD (n = 3) and data in the same row with different letters are significantly different (P < 0.05). a ND: not detected. * Means essential amino acids.
    • The E-nose was a good method to analyze aroma, as it could offer a fast and non-destructive method to sense volatile substances[46]. PCA was a statistical tool that explained the differentiation between samples as well as the relationship between the objects[4749]. A clear separation of the samples into 17 groups was found according to the PCA plot of the different grape varieties as shown in Fig. 2a. The principal components PC1 and PC2 represented 73.58% and 18.75% of the total variance, respectively, with the cumulative contribution rate accounting for 92.33%. In general, when the accumulated contribution of certain principal compounds (PCs) is over 85%, the PCs can represent the original data. The clusters of the data were divided into three groups labeled A, B and C. Group A was composed of ten subclusters without being overlapped. However, they were closer to each other, indicating that similar volatile ingredients existing in these ten samples (Cabernet Sauvignon-B, Cabernet Sauvignon-C, Cabernet Sauvignon-D, Cabernet Sauvignon-E, Merlot-B, Merlot-C, Merlot-D, Cabernet Gernischt-B, Shiraz-B and Pinot Noir-B). Related to HS-SPME-GC-MS analysis, the difference in the aroma profile among Chardonnay and other wines, Chardonnay located in the left-bottom area labeled B and clearly isolated from the other 16 red wines in PCA plot of E-nose. Group C was made up of six wine samples including Cabernet Sauvignon-A, Shiraz-A, Pinot Noir-A, Cabernet Gernischt-A, Merlot-A, and Tempranillo, clearly isolated from each other. All of them generate special aroma because of their unique fermentation process and raw materials. E-nose is sensitive for obtaining smell information, and slight changes of flavor could result in different sensors response. The results illustrate that this non-destructive method by E-nose is an effective assay for grape wine discrimination.

      Figure 2. 

      (a) PCA plot, (b) LDA plot, (c) loading analysis of E-nose for 17 grape wines and (d) graph of sensory scores of the 17 grape wines.

      Linear discriminant analysis (LDA) is frequently used for classification in food field and its main aim is to find the linear combinations of noted attributes that can well separate two or more than two classes of objects[50]. The classification results of the 17 grape wines on the coordinates based on first linear discriminant (LD1) and second linear discriminant (LD2) represented 76.84% and 18.72% of the total variance, respectively, with the discriminant accuracy accounting for 95.56% are shown in Fig. 2b. The results indicate that the PEN 3 E-nose with LDA is an effective instrument to distinguish the 17 grape wines via their odors.

      Loading analysis is useful to check for the influence of a sensor on the distribution of data sets. The loading factor associated to PC1 and PC2 for each sensor is represented in Fig. 2c. The points in the plot represent the sensors used in the experiment. The sensor with loading parameters close to zero for a particular principal component has a low contribution to the total response of the array, whereas high values indicates a discriminating sensor[51]. It is shown that sensors W2S and W1W have a higher influence in the current pattern file while sensors W1S and W2W have relatively low influence. The detectable compounds by sensors W2S and W1W were alcohols and sulfur-containing organics. Sensors W1C, W3S, W5C, W3C, W5S and W6S have closer influence so that they might be represented by one of the group member and this group has a minor influence in the current pattern file.

      Fifty mL grape wine was put into a beaker of 250 mL volume, and they were randomly offered to panelists, the aroma descriptors of samples were recorded by panelists. Panelists agreed that the aroma of grape wine samples could be described using five attributes: fruity aroma, floral aroma, alcoholic aroma, color, and overall acceptability. The intensities of the aroma attributes were scored using a scale from 0 to 10, the higher scores, the stronger intensities. Each sample was evaluated three times by each panelist. Data were expressed as mean. A trained panel quantitated the intensity of the aroma attributes of tea samples were evaluated by ten panelists (six females and four males), with aged between 20 and 30 years old. Panelists were trained by a series of important grape wine aroma compounds.

      The scores of human aroma sensory evaluation analysis are plotted on the radar chart and shown in Fig. 2d. The result analysis demonstrated that fruity aroma, alcoholic aroma and overall acceptability showed significant differences among Chardonnay and other 16 kinds of red wines in the sensory evaluation scores. This is consistent with the results analyzed by E-nose. Shiraz-B exhibited higher level of floral aroma and fruity aroma which may be related to high relative content of higher alcohols and esters detected by HS-SPME-GC-MS. In addition, Pinot Noir-A and Merlot-D have beautiful color, which perhaps due to their relatively high phenolic contents detected by HPLC.

    • The radar fingerprint chart of E-tongue with different grape varieties was presented in Fig. 3a. The mainly typical taste of grape wine includes astringency and sourness. Significant difference (P < 0.05) was observed in aftertaste-B and sour taste. However, there was a minor difference on the flavor from bitterness, astringency, aftertaste-A richness and umami among 17 grape wines. Further, there was a great correlation between the E-tongue and the human sensory evaluation score[52]. The values of saltiness and richness of Tempranillo and Chardonnay were lower than 15 other grape wines. Astringency taste intensity based on the E-tongue measurement ranged from 1.147 ± 0.045 to 4.400 ± 0.046. The bitterness and astringency of grape wines are mainly correlated to the phenol profile[16, 53]. Pinot Noir-A exhibited the highest sourness intensity with a score of −9.733 ± 0.207. Cabernet Sauvignon-E with the highest umami taste levels also appeared to have the highest saltiness scores. It can therefore be concluded that E-tongue could be a rapid method for taste evaluation in wineries. Consumers can easily choose their preferred grape wines according to the satisfactory taste results offered by E- tongue.

      Figure 3. 

      (a) Radar fingerprint chart of the sensory score, (b) PCA plot of E-tongue data for 17 grape wines.

      For variable reduction and separation into classed, PCA was used applied[54]. PCA of non-volatile compounds of 17 samples from seven kinds of grape varieties were presented in Fig. 3b. It was observed that variance contribution rates of PC1 and PC2 were 64.65% and 27.13%, respectively. The accumulative variance contribution rate of the first two PCs was 91.78% (> 85%), which were considered most information to represent the entire samples. In the PCA plot, a better separation effect of 17 grape wines was shown. Tempranillo, Chardonnay, Shiraz-B, Cabernet Sauvignon-E and Pinot Noir-A were clearly separated from other wines. Shiraz-A, Merlot-A, Cabernet Gernischt-A and Cabernet Sauvignon-A were slightly clustered in the centre of the PCA plot. A group comprised of Cabernet Sauvignon-B, Cabernet Sauvignon-C, Cabernet Sauvignon-D, Pinot Noir-B, Merlot-B, Merlot-C, Merlot-D and Cabernet Gernischt-B was located close together, all eight samples had positive score values at PC1. For E-tongue results, the PCA was able to distinguish the 17 wines from seven grape types completely.

    • In this study, the volatile and non-volatile flavor components of grape wines were analyzed by HS-SPME-GC-MS, E-nose, E-tongue, HPLC, and automatic amino acids analyzer techniques. The phenolic substances detected by HPLC are related to the color of the wine and the content of amino acids and phenols affect the taste of the wine, such as bitterness and astringency detected by E-tongue. Meanwhile, the combined use of HS-SPME-GC-MS and electronic nose technology analyzes the volatile flavor of 17 wines. The floral aroma and fruity aroma of the wine are closely related to alcohols and esters. Pinot Noir-A had the highest content of bitter amino acids, phenols and it was clearly separated from other wines in the PCA plot of E-tongue. The flavor and taste of Chardonnay showed great significance compared to 16 other kinds of red wines. Shiraz-B exhibited higher scores of floral aroma and fruity aroma in sensory evaluation, which may be related to its relative amount of volatile aroma substance. A total of 86 volatile compounds were identified among the 17 samples from seven kinds of wine samples. Alcohols and esters were the main flavor substances. The results clearly show that it is possible to classify grape wines from seven varieties by using E-nose and E-tongue. Sensors W2S and W1W in the E-nose for wines have a higher influence in the current pattern file. In addition, the PCA results of E-nose and E-tongue were obtained with the cumulative contribution rate accounting for 92.33% and 91.78%, respectively. Additionally, the content of free amino acids especially the taste-active amino acids, exhibited significant difference (P < 0.05). Gallic acid and catechin made up a large percentage of the grape wines. This study highlighted the usefulness of combining aroma and taste analysis techniques of grape wines, which could effectively instruct consumers to choose their preferred wines. Meanwhile, this research also provided some efficient methods to monitor grape wine quality in the actual process of industrialization. The beverage industry can certainly follow the protocols and parameters presented in this work in order to make use and apply the techniques immediately.

      • The work was financially supported by the Fundamental Research Funds for the Central Universities (KJQN201944).

      • The authors declare that they have no conflict of interest.

      • Supplemental Table S1 Relative contents of volatile compounds of grape wines from different varieties using HS-SPME-GC-MS.
      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (3)  Table (3) References (54)
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    Fan X, Pan L, Chen R. 2023. Characterization of flavor frame in grape wines detected by HS-SPME-GC-MS coupled with HPLC, electronic nose, and electronic tongue. Food Materials Research 3:9 doi: 10.48130/FMR-2023-0009
    Fan X, Pan L, Chen R. 2023. Characterization of flavor frame in grape wines detected by HS-SPME-GC-MS coupled with HPLC, electronic nose, and electronic tongue. Food Materials Research 3:9 doi: 10.48130/FMR-2023-0009

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