Screening and identification of antioxidant active ingredients from red wine via spectrum-effect relationship analysis

Yuye Gao1#, Xie-an Yu2#, Bing Wang2, Yibao Jin2, Guo Yin2, Jue Wang2, Yi Lu2, Tiejie Wang1,2, Kaishun Bi1*

1School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning, China;

2NMPA Key Laboratory for Quality Research and Evaluation of Traditional Chinese Medicine, Shenzhen Institute for Drug Control, Shenzhen, China


Red wine was a prominent constituent of diets across the globe and modern pharmacological studies showed that red wine had the main function of antioxidant. However, the reported screening methods for active ingredients were inefficient and insensitive. In this research, 35 batches of red wine were used to establish the chromatography fingerprint, which were further utilized to screen and identify the antioxidant active ingredients through spectrum-effect relationship analysis. Indeed, the antioxidant capacity of red wine was evaluated by total antioxidant capacity assay kit (ABTS method). After evaluating the antioxidant capacity of samples, correlations were analyzed by Pearson correlation analysis, Spearman correlation analysis, and Gray Relational Analysis (GRA). Moreover, spectrum-effect relationships combined with ultrafast liquid chromatography/quadrupole-time-of-flight mass spectrometry (UFLC-Q-TOF/MS) revealed that the succinic acid, gallic acid, protocatechuic acid, catechin, syringic acid, salicylic acid, homogentisic acid, ellagic acid, isorhamnetin-3-O-glucoside, and trans- resveratrol show antioxidative efficacy. Collectively, the present study established the spectrum-effect relationship strategy of red wine and identified active antioxidant ingredients, which would give reasonable index components for the health function of red wine and provide reference for quick screening of bioactive compounds for food.

Key words: antioxidant activity, red wine, spectrum-effect relationship, UFLC-Q-TOF/MS

*Corresponding author: Kaishun Bi, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning, China. Email: [email protected]

#These authors contributed equally.

Received: 17 February 2022; Accepted: 5 May 2022; Published: 1 January 2023

DOI: 10.15586/qas.v15i1.1071

© 2023 Codon Publications
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (


Wine is the product of the complete or partial fermentation of fresh grapes, or a mixture of that product and products derived solely from grapes, and a fermented wine with a certain degree of alcohol (Australia New Zealand Food Standards Code, 2004). Wine is classified by color into red wine, white wine, rose wine, and so on. Red wine showed higher antioxidant capacity compared with other type of wines (Fernández-Pachón et al., 2006). Polyphenol was the main bioactive compound in wine, including phenolic acids and flavonoids such as succinic acid, gallic acid, protocatechuic acid, catechin, syringic acid, salicylic acid, homogentisic acid, ellagic acid, transresveratrol, and isorhamnetin-3-O-glucoside, which shows antioxidant, anticoagulant, and lipid-lowering activities (Gao et al., 2021; Haunschild Robin and Marx Werner, 2022). Antioxidant activity was one of the most important biological activities of red wine; therefore, it is important to explore the specific antioxidant activity from red wine.

The spectrum-effect relationship was a subject that established the internal relationship between fingerprint and biochemical detection by multiple data processing methods, which was used to evaluate the internal quality (Rao et al., 2022; Zhang et al., 2018). Chromatography fingerprint had been internationally recognized as a feasible and reasonable quality evaluation method for traditional Chinese medicine, food and their related products with multi-components (Esteki et al., 2019). World Health Organization (WHO) (Guidelines for the assessment of herbal medicines, 1991) and US Food and Drug Administration (FDA) (Botanical Drug Development Guidance for Industry, 2016) have included and promoted chromatographic fingerprint as the reliable method for the quality control (Chen et al., 2019; Yang et al., 2016). Some studies have shown that suitable intake of red wine can reduce oxidative stress damage caused by free radicals (Navajas-Porras et al., 2020; Plumb et al., 2020). Therefore, antioxidant capacity was frequently used to evaluate the intrinsic quality of red wine (Eke et al., 2021). Currently, in vitro antioxidant screening methods are frequently used due to their high-throughput and low-cost characteristics (El-Demerdash Fatma et al., 2021). Among these methods, the stable solid-free radical 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) was employed in the assessment of antioxidant activity. During the relationship analysis, correlation analysis was a statistical method used to calculate correlation coefficient, which can reflect the order of contribution for each chromatographic fingerprint peak component to activity. Some studies have developed the relationship between chromatographic fingerprint and antioxidant activity, which could provide not only a method for screening active ingredients but also a comprehensive strategy for quality evaluation (Du et al., 2021; Xu et al., 2022). Accordingly, the spectrum-effect relationship could be used for quick screening of the potential active ingredients from complex samples.

To further elucidate the material basis of the antioxidant effect from red wine, 35 batches of red wine were used to establish the ultra-high-performance liquid chromatography (UHPLC) fingerprint and the antioxidant activity of them was also evaluated through total antioxidant capacity assay kit with ABTS method. Then, the correlation between common peaks and the result of antioxidant activity was calculated by Pearson correlation analysis, Spearman correlation analysis, and Gray Relational Analysis (GRA). Following the results of spectrum-effect relationships and ultrafast liquid chromatography/ quadrupole-time-of-flight mass spectrometry (UFLC-QTOF/MS), succinic acid, gallic acid, protocatechuic acid, catechin, syringic acid, salicylic acid, homogentisic acid, ellagic acid, isorhamnetin-3-O-glucoside, and transresveratrol with the principally antioxidative substances were successfully screened. Hence, the present study established the spectrum-effect relationship strategy of red wine and identified active antioxidant ingredients, which would provide reference for quick screening of bioactive compounds from complex samples.

Materials and Methods

Reagents and chemicals

HPLC-grade acetonitrile (ACN) and methanol (MeOH) were purchased from Merck (Darmstadt, Germany). Trifluoroacetic acid (TFA) was obtained from Guangzhou Chemical Reagent Factory (Guangzhou, China). The water used in this study was purified by a Milli-Q water purification system (MA, USA). A total of 35 batches of red wine samples were collected from four countries, which included three grape varieties (Table 1). Total anti-oxidant capacity assay kit (ABTS) was purchased from Beyotime, Shanghai, China.

Table 1. Origin and varietal of the red wines tested.

Samples Cultivar(s) Location
S1 Cabernet Sauvignon Maipo Valley, Chile (33°27’S; 70°40’W)
S2 Cabernet Sauvignon The Central Valley of Chile (33°27’S; 70°40’W)
S3 Cabernet Sauvignon Maipo Valley, Chile (33°27’S; 70°40’W)
S4 Cabernet Sauvignon Maipo Valley and Aconcagua Valley, Chile (33°27’S; 70°40’W and 32°29’S; 70°01’W)
S5 Cabernet Sauvignon Aconcagua Valley, Chile (32°29’S; 70°01’W)
S6 Cabernet Sauvignon Mendoza, Argentina (32°53’S; 68°51’W)
S7 Cabernet Sauvignon Mendoza, Argentina (32°53’S; 68°51’W)
S8 Cabernet Sauvignon Mendoza, Argentina (32°53’S; 68°51’W)
S9 Cabernet Sauvignon Mendoza, Argentina (32°53’S; 68°51’W)
S10 Cabernet Sauvignon Mendoza, Argentina (32°53’S; 68°51’W)
S11 Cabernet Sauvignon Clare Valley, Australia (33°50’S; 138°36’E)
S12 Cabernet Sauvignon Limestone Coast, Australia (30°20’S; 135°93’E)
S13 Cabernet Sauvignon South Australia (33°40’S; 135°80’E)
S14 Cabernet Sauvignon South Australia (33°40’S; 135°80’E)
S15 Cabernet Sauvignon Clare Valley, Australia (33°50’S; 138°36’E)
S16 Cabernet Sauvignon Changyu Vineyard, China (37°45’N; 121°43’E)
S17 Cabernet Sauvignon Ganchengzi District, Qingtongxia, Ningxia, China (38°01’N; 106°04’E)
S18 Cabernet Sauvignon Ningxia Helan Mountain East Foot Production Area, China (38°33’N; 106°20’E)
S19 Cabernet Sauvignon Changyu Vineyard, China (37°45’N; 121°43’E)
S20 Cabernet Sauvignon Changyu Vineyard, China (37°45’N; 121°43’E)
S21 Shiraz Limestone Coast of Australia (30°20’S; 135°93’E)
S22 Shiraz Great West, Australia (34°22’S; 117°60’E)
S23 Shiraz Hunter Valley, Australia (33°30’S; 151°40’E)
S24 Shiraz South Australia (33°40’S; 135°80’E)
S25 Shiraz Barossa, Australia (34°30’S; 137°80’E)
S26 Shiraz Pertway, Australia (36°50’S; 140°70’E)
S27 Shiraz Kunawala, Australia (36°40’S; 140°50’E)
S28 Shiraz Moray Valley, Chile (35°14’S; 71°27’W)
S29 Shiraz Espinal Vineyard, Chile (33°27’S; 70°40’W)
S30 Shiraz Pascual Toso, Argentina (32°53’S; 68°51’W)
S31 Merlot Cachabel Valley, Chile (33°89’S; 71°45’W)
S32 Merlot Curisco Valley, Chile (35°12’S; 71°31’W)
S33 Merlot Konggagua Valley, Chile (34°90’S; 71°50’W)
S34 Merlot Southeast Australia (33°40’S; 135°80’E)
S35 Merlot Southeast Australia (33°40’S; 135°80’E)

Sample preparation

The red wine was analyzed immediately after it was opened. All red wine samples (2 mL) were filtrated through a 0.22 μm polyvinylidene fluoride membranes. All the samples were stored at 4°C in the dark prior to analysis.

UHPLC chromatographic analysis

The UHPLC analysis was performed by an UltiMate 3000 RSLCnano UltiMate 3000 RSLCnano System liquid chromatograph (Thermo Technologies, MA, USA) equipped with a Dionex UltiMate 3000 pump, a Dionex UltiMate 3000 autosampler, and a Dionex UltiMate 3000 diode array detector. The chromatographic separation of red wine was achieved on an ACQUITY UPLC column (HSS T3, 1.8 μm, 2.1 × 100 mm, Waters, Ireland) at 30°C. The mobile phase was composed of water containing 0.1% TFA (A) and methanol (B) at a flow rate of 0.30 mL/min. The fingerprint elution program was conducted as follows: 0–15 min at 0–6% B, 15–20 min at 6–11% B, 20–30 min at 1–11% B, 30–45 min at 11–20% B, 45–55 min at 20–30% B, and 55–60 min at 30–90% B. The sample injection volume was 2 μL and the total run time for analysis was 60 min. The DAD wavelength was 220 nm.

Similarity analysis

All chromatographic data of 35 batches of samples were exported as *.AIA file from UHPLC. Similarity analysis (SA) was performed on those files by using “Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine” software (Version 2004A, Chinese Pharmacopoeia Committee). S1 was set as the reference fingerprint, and a new control fingerprint (R) was generated after multi-point correction and automatic matching, then the similarity values of these samples were calculated. In addition, the common peaks in UHPLC fingerprints were observed.

Antioxidant activity assay

The antioxidant activity of red wine was exhibited to restrict the absorbance of free radical cation ABTS, which showed a characteristic long-wavelength absorption spectrum with a maximum at 734 nm The ABTS radical was formed through a reaction with an oxidizing agent. Initially, 0.4 mL ABTS stock solution was mixed with 0.4 mL of the oxidizing agent solution for the preparation of the radical. This solution was maintained at room temperature for 12–16 h in an amber bottle. Subsequently, the mixture was diluted in phosphate-buffered saline (PBS) to obtain an absorbance of approximately 0.7 ± 0.05. Finally, different batches of red wine (10 μL) were added and the absorbance of all test tubes was determined at 734 nm (Spagnol et al., 2019).

Statistical analysis

The correlation analysis was conducted between the common peak areas and activity data of red wine by statistical analysis of the GRA, Pearson correlation coefficient, and Spearman correlation coefficient. SPSS 24.0 (International Business Machines Corporation, New York, USA) was used to process the data and find common peaks that were significantly related to pharmacological effects.

Mass spectrometry

The identification was performed on a hybrid quadrupole time-of-flight tandem mass spectrometry, UFLCQ-TOF/MS (X500R QTOF, AB SCIEX, Foster City, CA) equipped with an electrospray ionization (ESI) interface. The mass spectrometer was operated in the negative ion mode. The following parameter settings were used: the ion spray voltage of 7 eV, turbo spray temperature (TEM) of 550°C, declustering potential (DP) of –100 V, collision energy (CE) of –45 V, nebulizer gas (gas 1) of 55 psi, heater gas (gas 2) of 55 psi, and curtain gas of 35 psi. Nitrogen was kept as the nebulizer and auxiliary gas. TOF-MS and TOF-MS/MS were scanned with the mass range of m/z 100–1500 Da and 50–1500 Da, respectively. The experiments were run with 150 ms accumulation time for TOF MS and 50 ms accumulation time for TOF-MS/MS. Continuous recalibration was carried out at each five samples. In addition, dynamic background subtraction (DBS) trigger information-dependent acquisition (IDA) was used to trigger acquisition of MS/MS of low-level constituents. The accurate mass and composition for the precursor ions and fragment ions were analyzed using the SCIEX OS software integrated with the instrument.

Results and Discussion

Optimization of the chromatographic conditions

In order to obtain useful chemical information and better separation, several parameters including mobile phase composition, detection wavelength, and column temperature were carefully inspected as these parameters could positively influence the separation. The types and proportions of acid in the mobile phase were investigated. The conditions of adding 0.05% formic acid, 0.05% trifluoroacetic acid, 0.05% acetic acid, and pure water were also tested. As a result, 0.05% trifluoroacetic acid was better (Figure 1A). The conditions of adding 0.03% trifluoroacetic acid, 0.05% trifluoroacetic acid, and 0.1% trifluoroacetic acid in water were compared. The result showed that 0.1% trifluoroacetic acid was better (Figure 1B). The detection wavelength was tested at 210, 220, 254, and 280 nm. Under the wavelength of 220 nm, it was found that more detectable common peaks, larger response values, and smoother baseline could be observed (Figure 1C). The column temperatures of 25°C, 30°C, and 35°C were evaluated. The result showed that the peak separation at 30°C was better (Figure 1D). The optimization of the chromatographic conditions is shown in Figure 1 and the fingerprint spectrum is shown in Figure 2.

Figure 1. The optimization of the chromatographic conditions.

Figure 2. UHPLC fingerprint chromatogram of red wine.

Methodology validation

The analytical method was validated for precision, repeatability, and stability. In precision testing, six consecutive injections of one sample solution were performed on the same day. Precision of peak area (PA) and retention time (RT) of 41 common peaks did not exceed 5.61% and 1.82% in relative standard deviation (RSD), respectively. Six independent samples were extracted and determined in parallel for the evaluation of repeatability. The RSD of peak area and RT of 41 common peaks was less than 5.99% and 1.77%. The stability was assessed by repeatedly analyzing one sample solution after being placed at room temperature for 0, 2, 4, 8, 12, and 24 h. The RSD of PA and RT of 41 common peaks was less than 5.70% and 1.72%, respectively. These results indicated that the established UHPLC fingerprint method for red wine was stable and reliable.

UHPLC fingerprints of red wine and similarity evaluation

The fingerprints of 35 batches red wine were analyzed by similarity evaluation system. The values of similarity of generated reference fingerprint and sample fingerprints were calculated by similarity evaluation system. As a result, 41 common peaks were observed in UHPLC fingerprints (Figure 3). In Table 2, the similarity values between generated reference fingerprint and sample fingerprint were in the range of 0.445–0.972. According to the similarity evaluation system of chromatographic fingerprint of traditional Chinese medicine, the similarity between 0.9 and 1.0 was considered to meet the similarity requirement. These results demonstrated that there were differences between these samples. It was possible that the relative retention time (RT) or relative peak area (PA) of some peaks of red wine were not stably expressed, which might affect the antioxidant activity of red wine.

Figure 3. UHPLC fingerprints of different red wine samples.

Table 2. Similarity evaluation of 35 batches of red wine samples.

Sample No. Similarity Sample No. Similarity
S1 0.886 S19 0.754
S2 0.916 S20 0.848
S3 0.824 S21 0.912
S4 0.870 S22 0.940
S5 0.914 S23 0.775
S6 0.959 S24 0.913
S7 0.812 S25 0.905
S8 0.936 S26 0.935
S9 0.936 S27 0.871
S10 0.947 S28 0.821
S11 0.946 S29 0.836
S12 0.934 S30 0.901
S13 0.920 S31 0.852
S14 0.946 S32 0.928
S15 0.972 S33 0.890
S16 0.445 S34 0.504
S17 0.863 S35 0.767
S18 0.918

Results of antioxidant activity

Antioxidant activity was one of the reasons why red wine was interested in food field. In this research, the antioxidant capacity of red wine was analyzed by ABTS scavenging assay and expressed as IC50. IC50 was inversely proportional to the antioxidant capacity of red wine. Thirty-five batches of red wine were diluted to 5, 10, 20, 50, 100, 200, and 400 times to evaluate the antioxidant activity. The result is shown in Figure 4. The IC50 values of antioxidant activity for ABTS were in the range of 52.66–114.4 times of dilution times, hence a higher IC50 of dilution times showed a stronger antioxidant capacity. The results given in Table 3 show that red wine has good antioxidant activity.

Figure 4. Determination of antioxidant activity of 35 batches of red wine.

Table 3. The IC50 value of red wine samples.

Sample IC50 Sample IC50
S1 68.23 S19 93.1
S2 70.88 S20 77.03
S3 79.89 S21 63.38
S4 52.66 S22 66.25
S5 67.99 S23 74.33
S6 102.1 S24 110.7
S7 106.0 S25 109.3
S8 74.18 S26 78.14
S9 75.98 S27 73.71
S10 92.8 S28 65.74
S11 68.37 S29 80.9
S12 81.9 S30 114.4
S13 102.4 S31 59.05
S14 79.80 S32 73.71
S15 72.87 S33 74.22
S16 54.67 S34 38.82
S17 84.4 S35 50.29
S18 71.72

Results of correlation analysis

Correlation analysis can reflect the degree of correlation between variables. GRA, Pearson correlation, and Spearman correlation were used to examine the correlation of each peak and antioxidant activity directly; the closer the relationship between variables, the larger the absolute value of the correlation coefficient. The results of GRA show that the correlation coefficient from high to low as follows: P21 > P25 > P4 > P27 > P1 > P24 > P10 > P3 > P39 > P29 > P9 > P23 > P6 > P19 > P11 > P22 > P33 > P36 > P3 > P28 > P16 > P18 > P15 > P13 > P31 > P7 > P12 > P38 > P8 > P17 > P20 > P26 > P32 > P14 > P30. The larger correlation coefficients of P21, P25, P4, P27, P1, P24, and P10 of GRA were more than 0.95 suggesting that these seven peaks were closely related to antioxidant activity. The results of Pearson coefficient values were P24 > P25 > P27 > P38 > P39 > P29 > P21 > P33. Similarly, the results of Spearman coefficient values were P10 > P38 > P39 > P33 (Table 4). In the Pearson and Spearman correlation analysis, the correlations of P24, P25, P27, P38, P39, P29, P21, P33, and P10 were significant at the 0.05 level, P39 and P33 were significant at the 0.01 level, which show that these nine components play key roles in the antioxidant activity of red wine. Taken together, integrated the three correlation analysis methods via the correlation coefficient (>0.95) of GRA and P value (<0.05) of Pearson correlation coefficient and Spearman correlation coefficient. Thus, P4, P10, P21, P24, P38, P1, P25, P27, P29, P39, and P33 were selected as potential antioxidant active ingredients, which when increased in concentration may enhance the antioxidant effect of red wine. Furthermore, the red wine was analyzed by UFLCQ-TOF/MS and the chemical structures for 10 of the 11 screened compounds were identified.

Table 4. The results of GRA, Pearson, and Spearman correlation coefficient.

GRA Pearson correlation coefficient Spearman correlation coefficient
Peak no. Correlation Coefficient Peak no. TEAC Peak no. TEAC
21 0.961 10 Correlation Coefficient 0.406* 24 Correlation Coefficient 0.359*
25 0.96 P value 0.015 P value 0.034
4 0.959 38 Correlation Coefficient 0.364* 25 Correlation Coefficient 0.391*
27 0.957 P value 0.031 P value 0.02
1 0.954 39 Correlation Coefficient 0.467** 27 Correlation Coefficient 0.391*
24 0.953 P value 0.005 P value 0.02
10 0.951 33 Correlation Coefficient 0.450** 38 Correlation Coefficient 0.356*
- - P value 0.007 P value 0.036
- - - - - 39 Correlation Coefficient 0.454**
- - - - - P value 0.006
- - - - - 29 Correlation Coefficient 0.411*
- - - - - P value 0.014
- - - - - 21 Correlation Coefficient 0.357*
- - - - - P value 0.035
- - - - - 33 Correlation Coefficient 0.403*
- - - - - P value 0.017

*P<0.05; ** P<0.01.

UFLC-Q-TOF/MS analyses of red wine

According to the result of three correlation analysis methods (GRA, Pearson, and Spearman correlation coefficient) as explained in the previous section, the peaks P1, P4, P10, P21, P24, P25, P27, P29, P33, P38, and P39 were selected as potential antioxidant active ingredients. The potential active compounds were identified by UFLCQ-TOF/MS. The peak number, retention time, MS/MS fragmentation ions information, and identification of 10 components are listed in Table 5. The total ion chromatography is shown in Figure 5. Peak 1 has the major first-order mass spectrum at m/z 117.0194 (C4H6O4) [M–H]. The MS/MS fragments are at 99.0094 (C3H6O3) [M–H–H2O] and 73.0292 (C4H4O3) [M–H–CO2]. Therefore, it is identified as succinic acid (Huang et al., 2012). Peak 4 shows the mass spectrum ion at m/z 169.0141 (C7H6O5) [M–H] and the product ion at 125.0247 (C6H6O3) [M–H–CO2] is also observed. As a result, it is identified as gallic acid (Gao et al., 2021). Peak 10 has a molecular weight of 153.1092 (C7H6O4) with a fragment of 109.0296 (C6H6O2) [M–H–CO2], and it is identified as protocatechuic acid (Ma et al., 2015). Peak 21 has an [M–H] ion at m/z 289.0715 (C15H14O6), the MS/MS fragmentation ions are at 245.0849 (C13H14O4) [M–H–CO2] and 203.0706 (C10H12O3) [M–H–C3H2O3], which is identified as catechin (Svoboda et al., 2015). Peak 24 shows the mass spectrum ion at m/z 197.0456 (C9H10O5) [M–H], the product ion at 123.0077 [M–H–2CH3–CO2] (C6H4O3) is supported to surmise it as syringic acid (Gao et al., 2021). Peak 27 and Peak 29 have the major ions at m/z 137.0244 [M–H] (C7H6O3) and 167.0351 [M–H] (C8H8O4), both have missed a CO2 fragment, the MS/MS fragments are at 93.0345 [M–H–CO2] (C6H6O) and 123.0450 [M–H–CO2] (C7H8O2), Peak 29 has the other MS/ MS fragment at 81.0352 [M–H–CO2–C2H2O] (C5H5O), which is identified as salicylic acid and homogentisic acid (Gao et al., 2021). Peak 33 shows the mass spectrum ion at 300.9987 [M–H] (C14H6O8), the product fragment ions at 257.0110 [M–H–CO2] (C13H6O6) and 229.0126 [M–H–CO2–CO] (C12H6O5), constituting the structure of ellagic acid (Yan et al., 2014). Peak 38 has an [M–H] ion at m/z 477.1044 (C22H22O12), the MS/MS fragmentation ions are at 315.0519 (C16H12O7) [M–H–C6H10O5], 271.0258 (C14H8O6) [M–H–C6H11O5–CH2–CHO], and 243.0308 (C13H8O5) [M–H–C6H11O5–CH2–CHO– CO], which is identified as isorhamnetin-3-O-glucoside (Tkacz et al., 2020). Peak 39 has the major ions at m/z 227.0717 [M–H] (C14H12O3) and the MS/MS fragment at 143.0496 [M–H–2C2H2O] (C10H6O). Therefore, it is identified as trans-resveratrol (Bai et al., 2021). Peak 25 is not identified by current qualitative analysis methods.

Table 5. UFLC-Q-TOF/MS data and identification of 11 compounds with antioxidant activity from red wine.

Peak no. RT (min) [M-H]- MS/MS fragmentation ions Formula Identification ppm
1 1.38 117.0194 99.0094, 73.0292 C4H6O4 Succinic acid -1.2
4 5.92 169.0141 125.0247 C7H6O5 Gallic acid -1.0
10 11.72 153.0192 109.0296 C7H6O4 Protocatechuic acid 0.3
21 20.34 289.0715 245.0849, 203.0706, 123.0457, 109.0303 C15H14O6 Catechin -1.5
24 22.06 197.0456 123.0077, 95.0134 C9H10O5 Syringic acid -0.3
27 29.06 137.0244 93.0345 C7H6O3 Salicylic acid 1.0
29 30.67 167.0351 123.0450, 83.0137, 81.0352 C8H8O4 Homogentisic acid 0.8
33 38.70 300.9987 257.0110, 229.0126 C14H6O8 Ellagic acid -0.9
38 45.61 477.1044 315.0519, 271.0258, 243.0308 C22H22O12 Isorhamnetin-3- O-glucoside 0.6
39 47.17 227.0717 143.0496 C14H12O3 Trans-resveratrol 0.6

Figure 5. UFLC-Q-TOF/MS chromatography in a negative mode.

The identified compounds are polyphenols, which were reported many times for their antioxidant activity, anticoagulant activity, and so on (Gao et al., 2021; Wang et al., 2022). Gallic acid increases the antioxidant enzyme activity and inhibits oxidative stress through a nuclear factor erythroid-2/nuclear factor kappa B-dependent (Nrf2/NF-κB-dependent) mechanism. In addition, gallic acid inhibits oxidative stress by increasing the expression of heme oxygenase-1 (HO-1) and Nrf2 genes, activating the signaling pathway of Nrf2/ HO-1, decreasing the expression of NF-κB, inhibiting NF-κB pathway, and exerting anti-inflammatory effects (Sohrabi et al., 2021). Protocatechuic acid plays an antioxidant role by increasing catalase and superoxide dismutase levels, reduces the level of glutathione and malondialdehyde (Albarakati, 2022). Syringic acid, salicylic acid, and ellagic acid exert the antioxidant capacity by activating Nrf2 (Lavigne et al., 2022; Somade et al., 2022; Zhang et al., 2022). Isorhamnetin-3-Oglucoside significantly increases the level of glutathione (GSH) and the expression level of antioxidant enzymes (including superoxide dismutase (SOD), catalase, glutathione reductase, and HO-1), preventing reactive oxygen species (ROS) induced cell damage (Kong et al., 2009). Trans-resveratrol inhibits the levels of m-RNA of inflammatory markers NF-κB and tumor necrosis factor α (TNF-α), reduces the concentration of creatine kinase (CK-MB), lactate dehydrogenase (LDH), and alkaline phosphatase, increases the concentration of superoxide dismutase and catalase, which plays an antioxidant role (Manjunatha et al., 2020). Catechins can repair oxidative damage at a certain degree to some extent by regulating the activity of key enzymes related to GSH pathway (Zhao et al., 2022). Some experiments showed that succinic acid and homogentisic acid have antioxidant activity (Al Khatib Maher et al., 2021; Zarubina et al., 2012), but the anti-oxidation mechanism has not been found. Therefore, comparison of our results to other researches shows that the screened compounds certainly have the antioxidant activity.

Confirmation of the antioxidant activity of the standards

In order to demonstrate the antioxidant capacity of the screened compounds, the antioxidant activity of the nine standards was determined. The antioxidant capacity of standards was analyzed by ABTS scavenging assay and expressed as IC50. The standards were formulated as reserve fluids. Gallic acid (990 μg/mL), protocatechuic acid (1005 μg/mL), catechin (502.5 μg/mL), syringic acid (1025 μg/mL), salicylic acid (1004 μg/mL), homogentisic acid (1025 μg/mL), ellagic acid (980 μg/mL), isorhamnetin-3-O-glucoside (495 μg/mL), and isorhamnetin-3-Oglucoside (992 μg/mL) were diluted to 1, 2, 4, 8, 16, 32, 64, 128, 256, and 512 times to evaluate the antioxidant activity. The results showed that the screened compounds and selected compounds have antioxidant activity. The IC50 values are displayed in Figure 6 and the results are given in Table 6. An external experiment verified the result of correlation analysis.

Figure 6. Determination of antioxidant activity of standards.

Table 6. The IC50 value of standards.

Standards IC50 (μg/mL)
Gallic acid 188.71
Protocatechuic acid 129.84
Catechin 141.55
Syringic acid 222.97
Salicylic acid 383.79
Homogentisic acid 158.94
Ellagic acid 76.26
Isorhamnetin-3-O-glucoside 248.99
Trans-resveratrol 75.38


In this study, the antioxidant components of red wine were analyzed and partially identified using spectrum-effect relationship strategy and UFLC-Q-TOF/MS. The fingerprint of 35 batches were obtained using UHPLC and 41 common peaks were chosen through similarity analysis. Combined with the results of the ABTS assays, the spectrum-effect relationship was carried out to screen the active compounds. The results showed that the peaks with antioxidant components were P21, P24, P10, P38, P4, P27, P1, P39, P29, and P33, respectively. Finally, these peaks were identified as succinic acid, gallic acid, protocatechuic acid, catechin, syringic acid, salicylic acid, homogentisic acid, ellagic acid, isorhamnetin-3- O-glucoside, and trans-resveratrol through UFLC-QTOF/MS. Accordingly, the spectrum-effect relationship strategy was established and screened the antioxidant components from red wine, which could be used for quick screening of bioactive compounds from complex samples.

Author contributions

Conceptualization, Kaishun Bi; data collection and methodology, Yuye Gao, Xie-an Yu, Bing Wang, Yibao Jin, Guo Yin, Jue Wang, and Yi Lu; data analysis, Yuye Gao and Xie-an Yu; writing—original draft preparation, Yuye Gao and Xie-an Yu; writing—review and editing, Tiejie Wang and Kaishun Bi. All authors have read and agreed to the published version of the manuscript.


The authors would like to thank all the companions who selflessly supported this work.


This research was funded by National Natural Science Foundation of China (No. 82104357) and Shenzhen Science and Technology Program (No. JCYJ20210324130408021).

Conflicts of interest

The authors declare no conflicts of interest.


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