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Color and alcohol removal for the simultaneous detection of amino acids and sugars in wine by two-dimensional ion chromatography
Journal of Chromatography B 1063 (2017) 36–41
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Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb
Short communication
Color and alcohol removal for the simultaneous detection of amino acids and sugars in wine by two-dimensional ion chromatography ⁎
Yun Faa,b, , Yinghui Liub, Aihua Xuc, Yuexue Yub, Fangfang Lib, Huizhou Liua, a b c
MARK
⁎
University of Chinese Academy of Sciences, Beijing 100049, China Public Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China Shandong Institute of Metrology, Jinan 250014, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Amino acids Sugars Two-dimensional ion chromatography Wine color
An effective pretreatment method for wine color removal by a PS-DVB SPE cartridge and online alcohol elimination by valve switching was presented. The optimum parameters for color removal were investigated: 40-μm and 100 Å poly (styrene)-divinylbenzene (PS-DVB) (0.4 g) was selected as the color removal material and 5 mL of ethanol (10%) as the elution solvent for sample pretreatment under given condition. Moreover, an accurate and automated two-dimensional ion chromatography method for the simultaneous detection of amino acids and sugars was achieved with two valves after injection without alcohol interference. The method had a mean correlation coefficient of > 0.99 and a repeatability of 0.92%–4.30% for eight replicates. The mean recovery of six red wine samples were 97.6%, 96.6%, 96.1%, 95.9%, 97.3% and 96.4% respectively. And this method successfully analyzed the amino acid and sugar contents of six wine samples of different origins.
1. Introduction Sugars and amino acids (AAs) are present in grapes, the raw materials of wine, and are also produced during wine fermentation to contribute many aspects of wine quality and organoleptic properties [1,2]. Sugars, glycerol, and phenolic compounds affect rheological properties (density and viscosity) and mouth-feel sensations (astringency, oiliness, and pungency) [3–5]. The evolution of sugar and AA content is also studied in the aging and classification of wines [6]. AAs and antioxidants are regarded as potentially beneficial compounds for nutrition and people’s health [7]. AAs and antioxidants are the substrates or metabolic intermediates of enzyme catalysis and are markers of the fermentation process during wine-making [2]. Therefore, the determination of sugar and AA content is required in the routine quality control of wine production and is important for wine research. Several analytical techniques have been developed over the years, such as nuclear magnetic resonance (NMR) [8], infrared spectroscopy [9], capillary electrophoresis [10], and mass spectroscopy [11,12]. Wine chemical composition is most commonly analyzed by chromatography methods; chromatography analysis can vary between the routine quantification of wine constituents and in-depth investigation [13–17]. However, these methods usually involve complicated derivatization procedures [13–15]. Recently, anion exchange chromatography coupled
⁎
with integrated pulsed amperometric detection has proved to be a selective and sensitive method for the direct analysis of sugars and AAs without derivatization [18,19]. For example, sugars and AAs in green tea, rice wine, and fermentation broth were determined simultaneously by an AminoPac PA10 column [20–22]. Nevertheless, the separation condition requires a long duration and low NaOH, which possibly causes baseline drift [22]. In the previous work, we improved an easy and timesaving method only by one switching-valve for simultaneous separation of amino acids and carbohydrates with ion chromatography tandem integrated pulsed amperometric detection (IPAD) [23]. And we achieved a precise quantitative analysis for amino acids and carbohydrates in real samples of extracellular broths of Clostridium thermocellum. However, for wine samples, much interference arouses from wine color and an amount of alcohol remains unsolved. This makes it difficult to measure them quantifiably and simultaneously. In addition, the color may be retained in the columns and the excessively high response of alcohol on electrodes results in overlapping peaks of subsequent monosaccharides. These two aspects rapidly decrease column efficiency and result in electrode contamination. Furthermore, the resveratrol in wine can interfere the detection of amino acids and sugars. In this work, three innovative points are as follows: 1) An effective pretreatment method for wine color removal by a poly (styrene)-divinylbenzene (PS-DVB) solid-phase extraction (SPE) cartridge was developed, which protects the separate column from color contamination
Corresponding authors at: University of Chinese Academy of Sciences, Beijing 100049, China. E-mail addresses: [email protected] (Y. Fa), [email protected] (H. Liu).
http://dx.doi.org/10.1016/j.jchromb.2017.08.017 Received 27 February 2017; Received in revised form 5 June 2017; Accepted 14 August 2017 Available online 18 August 2017 1570-0232/ © 2017 Published by Elsevier B.V.
Journal of Chromatography B 1063 (2017) 36–41
Y. Fa et al.
2.4. Valve-switching program
and eliminates the inference of resveratrol. 2) Online alcohol elimination was implemented by a 3-port valve-switching, which avoided the overloading on the electrode. 3) 20 amino acids and 7 sugars were successfully analyzed simultaneously without co-elution in red wine samples from differ origins by two-dimensional ion chromatography (2D-IC). In the first-dimensional analysis, AAs were captured on a trap column, and sugars with alcohol were diverted to the loop. In the second dimensional analysis, AAs were eluted and separated on an analytical column, whereas alcohol was eluted to the waste tube and sugars were isolated on a separate analytical column. This improved method was accurate and effective with a mean correlation coefficient of > 0.99 and repeatability of 0.92%–4.30%. The mean recoveries using three concentrations of standards present directly in six real samples were satisfactory.
All the system procedures were operated with a 10-port valve, 3port valve, and three pumps interconnected by a narrow poly (ether– ether–ketone) tubing system (Fig. 1). T1 and T4 were 39.4 mm long, whereas T2, T3, T5, and T6 were all 5.5 mm long. The inner diameters (I.D.) of T1, T2, T3, T4, T5, and T6 were 0.127 mm. The other parts of the tubing system were 0.254 mm in I.D., and the volumes of loops 1 and 2 were 25 μL and 200 μL, respectively. The five steps in the valve-switching program (Fig. 1) were as follows: (1) The sample was loaded onto loop 1. (2) The sample was injected onto the trap column. AAs were retained and sugars with alcohol were diverted into loop 2. (3) AAs were eluted from the trap column and separated by AminoPac PA10 column and detected by ED1. Sugars and alcohol were transferred to CarboPac PA10 column. The alcohol was eluted and then flowed to the waste tube. (4) AAs and sugars were continually analyzed and sugars were detected by ED2. (5) The last step involved re-equilibration to restart the cycle from the beginning. The time and the status of valves are listed in Table 1.
2. Experimental section 2.1. Instrumentation and chromatography conditions
2.5. Method validation
This study employed an ICS-3000 instrument (Thermo Scientific Dionex, Sunnyvale, CA) equipped with a two DP gradient pumps, two electrochemical detectors (ED), AS40 autosampler, and two valves (a 10-port valve and 3-port valve). The trap solution was pumped with an analytical pump (Servo, Japan). The program was automatically controlled by Chromeleon software. Samples were pretreated with an SPE device (20 port, Agilent). UV/vis spectrometer Lambda 25 (Perkin Elmer, America) was used to check the result of color removal. Carbohydrate Removal Cartridge, separation columns, the gradient programs and electrochemical waveforms for AAs and sugars separation are listed in Section 2.1 of reference [23].
The precision, sensitivity, linearity, and reproducibility of the method were validated. Repeatability (the relative standard deviation) was estimated with eight replicates of the real sample A (0.50 mg/mL standards mixture addition). Reproducibility was obtained with the average value of three concentrations of 0.2 mg/L, 1.00 mg/L, and 2.00 mg/L. Linear regression calibration curves were calculated by plotting the peak area versus standard concentrations. The accuracy of the method was confirmed by the mean recoveries of the 0.2 mg/L, 1.00 mg/L, and 2.00 mg/L standards in six wine samples. 3. Result and discussion
2.2. Preparation of materials and samples
3.1. The optimization of color removal conditions
All the solutions were prepared in 18 MΩ of water (Milli-Q) with a 0.22-um nylon membrane filter. Ethanol, formic acid, sodium hydroxide (50%, ww-1, certified grade) and sodium acetate (purity, > 99%) were purchased from Sinopharm (Shanghai, China), Aladdin (Shanghai, China), Acros Organics (New Jersey, USA) and Sigma-Aldrich (St. Louis, MO, USA). AA and sugar standards were supplied by AccuStandard (St. New Haven, CT, USA). Resveratrol (purity, > 98%) was purchased from Weikeqi Biotech. Co. (Sichuan, China). Polystyrene-divinylbenzene microspheres were purchased from Bona (Jinan, China). Cleanert IC-RP (1 mL) and empty SPE cartridges were purchased from Agela Technologies (Tianjin, China). C18 SPE cartridge was obtained from Welch Materials, Inc. (Shanghai, China). Graphitized carbon SPE cartridge was presented from Longkai, Inc. (Qingdao, China). The A, B, C, D, E, and F (A, Cabernet Sauvignon, Mountain Range, 2014; B, Chardonnay, Mountain Range, 2014; C, Remhoogte, 2011; D, Penedo Da Moura Branco, 2012; E, Latour-Laguens Blanc, 2012; and F, Lathour Bel Argent, 2012) wine samples were purchased from Lida supermarket in Tsingtao.
The color of young red wine is mainly contributed by the anthocyanin composition of grapes, whereas the color of aged red wine is caused by its instability and reactivity [24]. Then, pigment molecules with benzene rings are easily adsorbed on benzene polymer columns as pollutants, consequently decreasing column efficiency. In this section, the selection of an efficient adsorbing material and the optimization of color removal conditions were discussed. The color pictures and UV–vis absorption spectrogram of the filtrate under different conditions were displayed in supporting information. 3.1.1. The selection of the adsorbing material Up to 1 mL of sample A was filtered through C18 SPE, Cleanert ICRP, graphitized carbon SPE cartridge and SPE cartridge filled with monodispersed PS-DVB (40 um). The filtrate of C18 SPE and Cleanert IC-RP still had a color, and that of the latter two materials had no color by visual inspection. UV–vis spectrograms showed that the absorption value of the filtrate through C18 SPE was very high in 400–600 nm. For the filtrate through Cleanert IC-RP, the absorption value reduced but remained higher. While, there were few response in 400–800 nm for the filtrate by graphitized carbon SPE cartridge and PS-DVB SPE cartridge. Graphitized carbon SPE cartridge can adsorb wine color efficiently, although some other amino acids were also absorbed seriously including cysteine, aspartic acid, glutamic acid, histidine, leucine, isoleucine, proline except Phenylalanine, Tyrosine, and Tryptophan. Therefore, PS-DVB SPE cartridge was a better choice to remove wine color in this experiment. So PS-DVB was selected in the following experiment. The results of different PS-DVB with particle sizes of 10 μm, 20 μm, and 40 μm with a pore size of 100 Å showed that the dark red color disappeared with all these kinds of materials. Then, 40-μm PS-DVB
2.3. Preparation of SPE cartridge and sample pretreatment Each empty SPE cartridge was filled with 0.4 g of microspheres. The filler was compacted with a matched sieve tray. The cartridges were activated with 3 mL of methanol and balanced with 9 mL of water before sample treatment. 1 mL wine was loaded onto the SPE cartridge and the adsorbed AAs were flushed with 5 mL 10% (Vethanol: Vwater) ethanol. The eluate spontaneously flowed down and were filtered by a 0.22-um nylon membrane. The eluate was collected in a 50-mL flask and brought to a constant volume with water prior to IC analysis. 37
Journal of Chromatography B 1063 (2017) 36–41
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Fig. 1. Sketch map of the valve-switching program. Continuous black lines represent closed status of the path flow, and arrows indicate the flow direction.
cartridges filled with 0.1, 0.2, 0.3, 0.4, and 0.5 g of filler were employed to adsorb 1 mL of sample A. The picture displays that the color of filtrate lightened as filler weights increased from 0.1 g to 0.3 g and became colorless at 0.4 g and 0.5 g. The spectrogram illustrated that the
particles were selected for its lower cost. Moreover, we compared the effects of 100 Å, 500 Å, and 1000 Å PS-DVB pore sizes with the 40-μm particle size. The spectrogram demonstrated that PS-DVB with a pore size of 100 Å was superior to the other two. Next, PS-DVB SPE 38
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After 1.0 mL of tryptophan was loaded on the PS-DVB SPE cartridges, different concentrations (Vethanol:Vwater) of 10 mL ethanol (5%, 7%, 10%, 15%, 20%, 25%, and 30%) were utilized to desorb tryptophan. Tryptophan recovery increased at ethanol concentrations of 5%–10%. Recovery remained constant at ethanol concentrations of 10%–30%. Different volumes (1, 2, 3, 4, 5, 6, 7, and 8 mL) of 10% ethanol were prepared to elute tryptophan. The results demonstrated that tryptophan recovery increased with 1–5 mL of ethanol. Recovery reached a maximum value with 5 mL of ethanol. Therefore, the optimum washing concentration and volume were 10% and 5 mL ethanol.
Table 1 The timing and the status of valves. Procedure
Time (min)
Status of injecting valve (6-port)
Status of switching valve (10-port)
Step Step Step Step Step
−4.5 0 1.2 6 Jun-60
Load Inject Inject Inject Load
State State State State State
1 2 3 4 5
1a 1 2b 2 1
Status of switching valve(3-port) State State State State State
Ac A Bd B A
a Status 1: port 0 connected to port 10, port 2 connected to port 3, port 4 connected to port 5, port 6 connected to port 7, port 8 connected to port 9. b Status 2: port 1 connected to port 2, port 3 connected to port 4, port 5 connected to port 6, port 7 connected to port 8, port 9 connected to port 10. c State A: port 1 connected to port 2 which interlinked ED2. d State B: port 1 connected to port3 which interlinked waste tube.
3.2. The switching time of 10-port and 3-port valves To investigate the cut window of the 3-port valve, 5 mg/L of trehalose in 10% alcohol solution was selected as the standard. The areas of trehalose peaks in different switching times (5.2, 5.4, 5.6, 5.8, 6.0, 6.2, 6.4, 6.6, 6.8, 7.0, 7.5 min) were tested. The data illustrated that ethanol was cut completely and trehalose was not lost within 6.0 min. Therefore, 6.0 min was determined as the switching time. The switching time of the 10-port valve was 1.20 min [23].
effect of 0.4 g was slightly better than that of 0.3 g. Therefore, 0.4 g PSDVB (40 μm, 100 Å) was the most suitable. 3.1.2. The optimum solvent concentration and elution volume Although PS-DVB particles can adsorb wine color, these particles also adsorb AAs modified with benzene rings, such as histidine and tryptophan. To precisely quantify AAs, it is essential to identify an appropriate solvent that selectively elute the AAs not the color. We selected 200 mg/L of tryptophan as the standard and ethanol as the solvent to desorb tryptophan. As far as we know, resveratrol may be the interference composition of the wine due to it has the similar structure with tryptophan and it also can be oxidized on electrochemical detector. So, 200 mg/L resveratrol was pretreated through PS-DVB SPE cartridge, 10% alcohol solvent prior to 2D-IC analysis and 1.6 mg/L resveratrol was directly injected to 2D-IC respectively. There was an obvious peak on ED2 (for sugar detection) on latter condition and no peak on former condition. This illustrated that resveratrol was adsorbed on PS-DVB SPE cartridge and was not eluted down. Therefore, it does not influence the retention of amino acids and sugars.
3.3. The optimum chromatography conditions As described above, the optimum chromatography conditions utilized 40-μm and 100 Å PS-DVB SPE (0.4 g) as the color removal cartridge, 5 mL of ethanol (10%) as the elution solvent for sample pretreatment, 3 mM of formic acid as the trap solution at a 0.10 mL/min flow rate, and 1.2 min and 6.0 min as the switching time of the 10-port valve and 3-port valve, respectively. 3.4. Evaluation of the method The linearity of response was measured with 25-uL injections of 0.01, 0.05, 0.10, 0.20, 0.50, 1.00, 2.00, 5.00, and 10.00 mg/L standard mixtures. Table 2 describes the linear range, LOD and LOQ. The mean correlation coefficient of the calibration reached 0.99. Repeatability for the eight replicates of the sample A (0.50 mg/mL standards mixture
Table 2 Repeatability, reproducibility, correlation coefficient, linear range, regression equation, LOD and LOQ. S/N
Analyte
Correlationcoefficient (n = 6)
Regression equation
Linear range (mg/L)
Repeatability (%, n = 8)
Reproducibility (%)
LOD (S/N = 3, mg/L)
LOQ (mg/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Arginine Lysine Glutamine Asparagine Alanine Threonine Glycine Valine Serine Proline Isoleucine Leucine Methionine Histidine Phenylalanine Glutamic acid Aspartic acid Cystine Tyrosine Tryptophan Trehalose Arabinose Galactose Glucose Mannose Fructose Ribose
0.9997 0.9981 0.9992 0.9982 0.9993 0.9991 0.9983 0.9923 0.9995 0.9997 0.9954 0.9933 0.9993 0.9982 0.9983 0.9993 0.9977 0.9985 0.9922 0.9952 99.7459 99.8188 99.8298 99.8516 99.9059 99.6138 99.9075
Y = 18.2955X + 0.4782 Y = 11.1036X + 0.0323 Y = 0.2588X + 0.1759 Y = 1.0955X − 0.0379 Y = 1.2957X + 0.0815 Y = 0.339X − 0.0105 Y = 1.8436X + 0.2976 Y = 2.8364X − 0.1413 Y = 1.3721X + 0.1154 Y = 5.5644X + 0.1922 Y = 5.2363X + 0.8472 Y = 16.4293X + 2.4439 Y = 34.5133X + 4.0863 Y = 0.7342X − 0.4926 Y = 18.7101X + 0.5909 Y = 0.4791X + 0.8563 Y = 3.2065X + 18.4396 Y = 2.9281X + 0.0582 Y = 0.8626X + 0.0026 Y = 11.4436X + 2.8383 Y = 1.9977X + 1.1175 Y = 4.3527X + 0.2436 Y = 3.6019X + 0.2301 Y = 4.0446X + 0.3129 Y = 3.2427X − 0.0204 Y = 1.3114X-0.2724 Y = 3.2300X − 0.1239
0.05–2.00 0.05–2.00 0.05–2.00 0.05–1.00 0.05–2.00 0.05–5.00 0.05–1.00 0.05–5.00 0.05–5.00 0.05–5.00 0.05–5.00 0.05–5.00 0.05–1.00 0.05–1.00 0.05–2.00 0.05–5.00 0.05–5.00 0.05–2.00 0.05–1.00 0.05–2.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00
3.12 1.50 2.07 3.43 2.56 3.10 0.92 1.25 2.30 1.30 2.04 0.93 2.06 1.82 1.17 2.54 1.06 1.20 1.82 1.05 1.48 1.23 1.80 3.75 2.62 4.30 2.00
1.18 1.12 3.38 2.24 1.96 2.24 1.07 1.11 1.11 1.12 1.09 0.73 0.98 0.78 0.79 2.52 0.96 1.61 2.07 0.71 1.11 0.84 0.81 1.95 0.71 2.77 0.98
0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.005 0.02 0.005 0.01 0.005 0.007 0.008 0.008 0.009 0.02 0.01
0.05 0.05 0.02 0.05 0.02 0.05 0.05 0.05 0.01 0.02 0.01 0.05 0.01 0.05 0.05 0.05 0.05 0.05 0.02 0.01 0.02 0.02 0.05 0.05 0.05 0.05 0.05
39
40
Arginine Lysine Glutamine Asparagine Alanine Threonine Glycine Valine Serine Proline Isoleucine Leucine Methionine Histidine Phenylalanine Glutamic acid Aspartic acid Cystine Tyrosine Tryptophan Trehalose Arabinose Galactose Glucose Mannose Fructose Ribose
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
ND means not detected.
Analyte
S/N
84.46 6.02 82.80 46.74 60.90 4.81 ND 2.67 ND 44.99 11.62 4.58 7.83 15.71 12.08 89.99 2.96 7.80 42.31 32.99 102.55 42.08 40.92 243.39 154.07 55.93 193.18
A (mg/L)
Table 3 The result of 6 wines and Recovery of samples.
98.5 89.9 84.7 83.4 95 96.1 102.5 88.4 106.1 91.5 103 94.3 104.8 107.8 97.5 99.3 103 101.1 97.6 102.1 93.6 102.8 103.3 98.6 94.1 91.9 103.2
Recovery (%) 95.56 8.27 18.70 28.04 4.17 6.85 12.42 52.34 ND 127.08 2.91 2.98 8.10 20.38 5.69 12.58 3.13 ND 5.37 24.02 282.68 228.18 58.79 152.70 75.22 127.54 193.13
B (mg/L) 91.7 88.5 88 91.3 92 96.2 96.1 87.9 100.5 90.4 105.1 94.5 104 98.6 101.8 101 103 101.7 92.3 99.1 100.2 102.7 97 99.9 97.8 96.1 100.1
Recovery (%) 94.20 2.78 71.80 4.99 13.65 4.21 2.75 73.68 ND 248.93 2.99 2.63 6.67 ND 2.89 ND 2.65 ND 6.69 15.78 278.80 22.05 56.50 377.79 469.80 185.04 415.27
C (mg/L) 93.9 94.7 97.7 91.1 103.7 92.1 85.7 88.2 96.9 87.7 103.9 92.3 93.3 101 106.4 92.8 96.6 99.4 95.2 99 93.4 100.6 95.8 99.1 99.6 94.1 99.9
Recovery (%) 75.36 2.57 20.13 35.43 14.81 2.60 ND ND ND 39.85 6.25 2.90 5.64 4.96 8.17 ND ND 5.65 ND ND 159.67 43.04 40.44 234.19 175.64 54.27 195.40
D (mg/L) 87.9 88.7 86.4 88.8 91.6 95.9 92.9 95 98.7 86.1 99.8 99.1 93 96.6 96.8 102.2 103.4 102.7 100 102.1 97.9 98.9 97 97.2 100.9 89.5 100.8
Recovery (%) 55.01 4.79 58.27 6.99 7.84 28.16 ND 5.92 ND 19.49 5.15 2.64 4.95 ND 6.42 82.82 ND ND 4.32 ND 49.22 82.57 59.06 343.64 103.19 8.22 121.79
E (mg/L) 84.9 101 84.4 99.2 94 103.6 93.2 95.8 99.8 98.5 103.6 97.5 87.9 97.8 106.2 102.7 95.5 98.7 90.6 99.8 101.5 102.4 95.2 99.8 97.1 99.7 96.9
Recovery (%)
54.70 3.18 ND 17.42 2.60 6.28 3.25 12.16 ND 71.59 3.36 ND 3.23 ND 7.58 72.83 ND ND 3.07 ND 245.17 75.63 49.40 253.58 204.37 69.60 34.73
F (mg/L)
98.4 98 100.2 91.2 98.1 103.2 89,3 87.6 98 93.2 86.2 87.9 87.8 99.4 97.1 100.8 98.4 99.4 87.2 95.7 93.5 103.2 101.6 101.7 96.8 94.4 89.1
Recovery (%)
Y. Fa et al.
Journal of Chromatography B 1063 (2017) 36–41
Journal of Chromatography B 1063 (2017) 36–41
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Fig. 2. Chromatograms of standard mixtures and six samples using the proposed method. Chromatogram a: amino acids chromatogram of A, B, C, D, E, F and standards; Chromatogram b: sugars chromatogram of A, B, C, D, E, F and standards; The serial number 1–27 refer to amino acids and sugars that are listed on Table 2.
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addition) was 0.92%–4.30%. Reproducibility for the three replicates of 0.05, 0.20, and 1.00 mg/L of the standard mixture was 0.70%–3.38%. The mean recoveries of the standard addition of 0.05, 0.20, and 1.00 mg/L of standard mixtures added in each sample are listed in Table 3. 3.5. Sample analysis The 20 AA and seven sugar content of six different wines were determined by the new method. The chromatograms of the six samples showed that the AAs and sugars were completely separated (Fig. 2). Data revealed that all wine samples had a considerably higher concentration of glucose than arabinose, galactose, and fructose (Table 3). Samples A, C, and D also had a larger amount of trehalose, mannose, and ribose. Most AA were lower than sugars. Arginine, glutamine, serine, and glutamic acid contents were higher than that of other AAs. Changes in sugar and AA concentration reflected the diversities in process, quality, or geographical origin of wines [1,2,6]. 4. Conclusions This work developed an effective pretreatment method to remove wine color by a home-made PS-DVB SPE and to eliminate the interference of alcohol online by valve switching. Moreover, sugar and AA were successfully analyzed simultaneously without interference each other in six real wine samples from differ origins. The method can be useful for the wine industry, the applications in wine research and routine quality control during wine production. Acknowledgements This project is supported by the National Special Fund for Major Research Instrumentation Development (No. 2012YQ090229) and the National Natural Science Foundation of China (No. 21405166). The authors are grateful to Engineer Haiyong Yang for the valuable suggestions. References [1] G.F. Xie, D.D. Yang, X.Q. Liu, X.X. Cheng, H.F. Rui, H.J. Zhou, Correlation between the amino acid content in rice wine and protein content in glutinous rice, J. Inst. Brew. 122 (2016) 162–167. [2] B.C. Childs, J.C. Bohlscheid, C.G. Edwards, Impact of available nitrogen and sugar concentration in musts on alcoholic fermentation and subsequent wine spoilage by Brettanomyces bruxellensis, Food Microbiol. 46 (2015) 604–609. [3] F.S. Neto, M.B. de Castilhos, V.R. Telis, J. Telis-Romero, Effect of ethanol, dry extract and reducing sugars on density and viscosity of Brazilian red wines, J. Sci. Food Agric. 95 (2015) 1421–1427. [4] M.P. Saenz-Navajas, V. Ferreira, M. Dizy, P. Fernandez-Zurbano, Characterization of taste-active fractions in red wine combining HPLC fractionation, sensory analysis and ultra- performance liquid chromatography coupled with mass spectrometry detection, Anal. Chim. Acta 673 (2010) 151–159.
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Contents lists available at ScienceDirect
Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb
Short communication
Color and alcohol removal for the simultaneous detection of amino acids and sugars in wine by two-dimensional ion chromatography ⁎
Yun Faa,b, , Yinghui Liub, Aihua Xuc, Yuexue Yub, Fangfang Lib, Huizhou Liua, a b c
MARK
⁎
University of Chinese Academy of Sciences, Beijing 100049, China Public Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China Shandong Institute of Metrology, Jinan 250014, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Amino acids Sugars Two-dimensional ion chromatography Wine color
An effective pretreatment method for wine color removal by a PS-DVB SPE cartridge and online alcohol elimination by valve switching was presented. The optimum parameters for color removal were investigated: 40-μm and 100 Å poly (styrene)-divinylbenzene (PS-DVB) (0.4 g) was selected as the color removal material and 5 mL of ethanol (10%) as the elution solvent for sample pretreatment under given condition. Moreover, an accurate and automated two-dimensional ion chromatography method for the simultaneous detection of amino acids and sugars was achieved with two valves after injection without alcohol interference. The method had a mean correlation coefficient of > 0.99 and a repeatability of 0.92%–4.30% for eight replicates. The mean recovery of six red wine samples were 97.6%, 96.6%, 96.1%, 95.9%, 97.3% and 96.4% respectively. And this method successfully analyzed the amino acid and sugar contents of six wine samples of different origins.
1. Introduction Sugars and amino acids (AAs) are present in grapes, the raw materials of wine, and are also produced during wine fermentation to contribute many aspects of wine quality and organoleptic properties [1,2]. Sugars, glycerol, and phenolic compounds affect rheological properties (density and viscosity) and mouth-feel sensations (astringency, oiliness, and pungency) [3–5]. The evolution of sugar and AA content is also studied in the aging and classification of wines [6]. AAs and antioxidants are regarded as potentially beneficial compounds for nutrition and people’s health [7]. AAs and antioxidants are the substrates or metabolic intermediates of enzyme catalysis and are markers of the fermentation process during wine-making [2]. Therefore, the determination of sugar and AA content is required in the routine quality control of wine production and is important for wine research. Several analytical techniques have been developed over the years, such as nuclear magnetic resonance (NMR) [8], infrared spectroscopy [9], capillary electrophoresis [10], and mass spectroscopy [11,12]. Wine chemical composition is most commonly analyzed by chromatography methods; chromatography analysis can vary between the routine quantification of wine constituents and in-depth investigation [13–17]. However, these methods usually involve complicated derivatization procedures [13–15]. Recently, anion exchange chromatography coupled
⁎
with integrated pulsed amperometric detection has proved to be a selective and sensitive method for the direct analysis of sugars and AAs without derivatization [18,19]. For example, sugars and AAs in green tea, rice wine, and fermentation broth were determined simultaneously by an AminoPac PA10 column [20–22]. Nevertheless, the separation condition requires a long duration and low NaOH, which possibly causes baseline drift [22]. In the previous work, we improved an easy and timesaving method only by one switching-valve for simultaneous separation of amino acids and carbohydrates with ion chromatography tandem integrated pulsed amperometric detection (IPAD) [23]. And we achieved a precise quantitative analysis for amino acids and carbohydrates in real samples of extracellular broths of Clostridium thermocellum. However, for wine samples, much interference arouses from wine color and an amount of alcohol remains unsolved. This makes it difficult to measure them quantifiably and simultaneously. In addition, the color may be retained in the columns and the excessively high response of alcohol on electrodes results in overlapping peaks of subsequent monosaccharides. These two aspects rapidly decrease column efficiency and result in electrode contamination. Furthermore, the resveratrol in wine can interfere the detection of amino acids and sugars. In this work, three innovative points are as follows: 1) An effective pretreatment method for wine color removal by a poly (styrene)-divinylbenzene (PS-DVB) solid-phase extraction (SPE) cartridge was developed, which protects the separate column from color contamination
Corresponding authors at: University of Chinese Academy of Sciences, Beijing 100049, China. E-mail addresses: [email protected] (Y. Fa), [email protected] (H. Liu).
http://dx.doi.org/10.1016/j.jchromb.2017.08.017 Received 27 February 2017; Received in revised form 5 June 2017; Accepted 14 August 2017 Available online 18 August 2017 1570-0232/ © 2017 Published by Elsevier B.V.
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2.4. Valve-switching program
and eliminates the inference of resveratrol. 2) Online alcohol elimination was implemented by a 3-port valve-switching, which avoided the overloading on the electrode. 3) 20 amino acids and 7 sugars were successfully analyzed simultaneously without co-elution in red wine samples from differ origins by two-dimensional ion chromatography (2D-IC). In the first-dimensional analysis, AAs were captured on a trap column, and sugars with alcohol were diverted to the loop. In the second dimensional analysis, AAs were eluted and separated on an analytical column, whereas alcohol was eluted to the waste tube and sugars were isolated on a separate analytical column. This improved method was accurate and effective with a mean correlation coefficient of > 0.99 and repeatability of 0.92%–4.30%. The mean recoveries using three concentrations of standards present directly in six real samples were satisfactory.
All the system procedures were operated with a 10-port valve, 3port valve, and three pumps interconnected by a narrow poly (ether– ether–ketone) tubing system (Fig. 1). T1 and T4 were 39.4 mm long, whereas T2, T3, T5, and T6 were all 5.5 mm long. The inner diameters (I.D.) of T1, T2, T3, T4, T5, and T6 were 0.127 mm. The other parts of the tubing system were 0.254 mm in I.D., and the volumes of loops 1 and 2 were 25 μL and 200 μL, respectively. The five steps in the valve-switching program (Fig. 1) were as follows: (1) The sample was loaded onto loop 1. (2) The sample was injected onto the trap column. AAs were retained and sugars with alcohol were diverted into loop 2. (3) AAs were eluted from the trap column and separated by AminoPac PA10 column and detected by ED1. Sugars and alcohol were transferred to CarboPac PA10 column. The alcohol was eluted and then flowed to the waste tube. (4) AAs and sugars were continually analyzed and sugars were detected by ED2. (5) The last step involved re-equilibration to restart the cycle from the beginning. The time and the status of valves are listed in Table 1.
2. Experimental section 2.1. Instrumentation and chromatography conditions
2.5. Method validation
This study employed an ICS-3000 instrument (Thermo Scientific Dionex, Sunnyvale, CA) equipped with a two DP gradient pumps, two electrochemical detectors (ED), AS40 autosampler, and two valves (a 10-port valve and 3-port valve). The trap solution was pumped with an analytical pump (Servo, Japan). The program was automatically controlled by Chromeleon software. Samples were pretreated with an SPE device (20 port, Agilent). UV/vis spectrometer Lambda 25 (Perkin Elmer, America) was used to check the result of color removal. Carbohydrate Removal Cartridge, separation columns, the gradient programs and electrochemical waveforms for AAs and sugars separation are listed in Section 2.1 of reference [23].
The precision, sensitivity, linearity, and reproducibility of the method were validated. Repeatability (the relative standard deviation) was estimated with eight replicates of the real sample A (0.50 mg/mL standards mixture addition). Reproducibility was obtained with the average value of three concentrations of 0.2 mg/L, 1.00 mg/L, and 2.00 mg/L. Linear regression calibration curves were calculated by plotting the peak area versus standard concentrations. The accuracy of the method was confirmed by the mean recoveries of the 0.2 mg/L, 1.00 mg/L, and 2.00 mg/L standards in six wine samples. 3. Result and discussion
2.2. Preparation of materials and samples
3.1. The optimization of color removal conditions
All the solutions were prepared in 18 MΩ of water (Milli-Q) with a 0.22-um nylon membrane filter. Ethanol, formic acid, sodium hydroxide (50%, ww-1, certified grade) and sodium acetate (purity, > 99%) were purchased from Sinopharm (Shanghai, China), Aladdin (Shanghai, China), Acros Organics (New Jersey, USA) and Sigma-Aldrich (St. Louis, MO, USA). AA and sugar standards were supplied by AccuStandard (St. New Haven, CT, USA). Resveratrol (purity, > 98%) was purchased from Weikeqi Biotech. Co. (Sichuan, China). Polystyrene-divinylbenzene microspheres were purchased from Bona (Jinan, China). Cleanert IC-RP (1 mL) and empty SPE cartridges were purchased from Agela Technologies (Tianjin, China). C18 SPE cartridge was obtained from Welch Materials, Inc. (Shanghai, China). Graphitized carbon SPE cartridge was presented from Longkai, Inc. (Qingdao, China). The A, B, C, D, E, and F (A, Cabernet Sauvignon, Mountain Range, 2014; B, Chardonnay, Mountain Range, 2014; C, Remhoogte, 2011; D, Penedo Da Moura Branco, 2012; E, Latour-Laguens Blanc, 2012; and F, Lathour Bel Argent, 2012) wine samples were purchased from Lida supermarket in Tsingtao.
The color of young red wine is mainly contributed by the anthocyanin composition of grapes, whereas the color of aged red wine is caused by its instability and reactivity [24]. Then, pigment molecules with benzene rings are easily adsorbed on benzene polymer columns as pollutants, consequently decreasing column efficiency. In this section, the selection of an efficient adsorbing material and the optimization of color removal conditions were discussed. The color pictures and UV–vis absorption spectrogram of the filtrate under different conditions were displayed in supporting information. 3.1.1. The selection of the adsorbing material Up to 1 mL of sample A was filtered through C18 SPE, Cleanert ICRP, graphitized carbon SPE cartridge and SPE cartridge filled with monodispersed PS-DVB (40 um). The filtrate of C18 SPE and Cleanert IC-RP still had a color, and that of the latter two materials had no color by visual inspection. UV–vis spectrograms showed that the absorption value of the filtrate through C18 SPE was very high in 400–600 nm. For the filtrate through Cleanert IC-RP, the absorption value reduced but remained higher. While, there were few response in 400–800 nm for the filtrate by graphitized carbon SPE cartridge and PS-DVB SPE cartridge. Graphitized carbon SPE cartridge can adsorb wine color efficiently, although some other amino acids were also absorbed seriously including cysteine, aspartic acid, glutamic acid, histidine, leucine, isoleucine, proline except Phenylalanine, Tyrosine, and Tryptophan. Therefore, PS-DVB SPE cartridge was a better choice to remove wine color in this experiment. So PS-DVB was selected in the following experiment. The results of different PS-DVB with particle sizes of 10 μm, 20 μm, and 40 μm with a pore size of 100 Å showed that the dark red color disappeared with all these kinds of materials. Then, 40-μm PS-DVB
2.3. Preparation of SPE cartridge and sample pretreatment Each empty SPE cartridge was filled with 0.4 g of microspheres. The filler was compacted with a matched sieve tray. The cartridges were activated with 3 mL of methanol and balanced with 9 mL of water before sample treatment. 1 mL wine was loaded onto the SPE cartridge and the adsorbed AAs were flushed with 5 mL 10% (Vethanol: Vwater) ethanol. The eluate spontaneously flowed down and were filtered by a 0.22-um nylon membrane. The eluate was collected in a 50-mL flask and brought to a constant volume with water prior to IC analysis. 37
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Fig. 1. Sketch map of the valve-switching program. Continuous black lines represent closed status of the path flow, and arrows indicate the flow direction.
cartridges filled with 0.1, 0.2, 0.3, 0.4, and 0.5 g of filler were employed to adsorb 1 mL of sample A. The picture displays that the color of filtrate lightened as filler weights increased from 0.1 g to 0.3 g and became colorless at 0.4 g and 0.5 g. The spectrogram illustrated that the
particles were selected for its lower cost. Moreover, we compared the effects of 100 Å, 500 Å, and 1000 Å PS-DVB pore sizes with the 40-μm particle size. The spectrogram demonstrated that PS-DVB with a pore size of 100 Å was superior to the other two. Next, PS-DVB SPE 38
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After 1.0 mL of tryptophan was loaded on the PS-DVB SPE cartridges, different concentrations (Vethanol:Vwater) of 10 mL ethanol (5%, 7%, 10%, 15%, 20%, 25%, and 30%) were utilized to desorb tryptophan. Tryptophan recovery increased at ethanol concentrations of 5%–10%. Recovery remained constant at ethanol concentrations of 10%–30%. Different volumes (1, 2, 3, 4, 5, 6, 7, and 8 mL) of 10% ethanol were prepared to elute tryptophan. The results demonstrated that tryptophan recovery increased with 1–5 mL of ethanol. Recovery reached a maximum value with 5 mL of ethanol. Therefore, the optimum washing concentration and volume were 10% and 5 mL ethanol.
Table 1 The timing and the status of valves. Procedure
Time (min)
Status of injecting valve (6-port)
Status of switching valve (10-port)
Step Step Step Step Step
−4.5 0 1.2 6 Jun-60
Load Inject Inject Inject Load
State State State State State
1 2 3 4 5
1a 1 2b 2 1
Status of switching valve(3-port) State State State State State
Ac A Bd B A
a Status 1: port 0 connected to port 10, port 2 connected to port 3, port 4 connected to port 5, port 6 connected to port 7, port 8 connected to port 9. b Status 2: port 1 connected to port 2, port 3 connected to port 4, port 5 connected to port 6, port 7 connected to port 8, port 9 connected to port 10. c State A: port 1 connected to port 2 which interlinked ED2. d State B: port 1 connected to port3 which interlinked waste tube.
3.2. The switching time of 10-port and 3-port valves To investigate the cut window of the 3-port valve, 5 mg/L of trehalose in 10% alcohol solution was selected as the standard. The areas of trehalose peaks in different switching times (5.2, 5.4, 5.6, 5.8, 6.0, 6.2, 6.4, 6.6, 6.8, 7.0, 7.5 min) were tested. The data illustrated that ethanol was cut completely and trehalose was not lost within 6.0 min. Therefore, 6.0 min was determined as the switching time. The switching time of the 10-port valve was 1.20 min [23].
effect of 0.4 g was slightly better than that of 0.3 g. Therefore, 0.4 g PSDVB (40 μm, 100 Å) was the most suitable. 3.1.2. The optimum solvent concentration and elution volume Although PS-DVB particles can adsorb wine color, these particles also adsorb AAs modified with benzene rings, such as histidine and tryptophan. To precisely quantify AAs, it is essential to identify an appropriate solvent that selectively elute the AAs not the color. We selected 200 mg/L of tryptophan as the standard and ethanol as the solvent to desorb tryptophan. As far as we know, resveratrol may be the interference composition of the wine due to it has the similar structure with tryptophan and it also can be oxidized on electrochemical detector. So, 200 mg/L resveratrol was pretreated through PS-DVB SPE cartridge, 10% alcohol solvent prior to 2D-IC analysis and 1.6 mg/L resveratrol was directly injected to 2D-IC respectively. There was an obvious peak on ED2 (for sugar detection) on latter condition and no peak on former condition. This illustrated that resveratrol was adsorbed on PS-DVB SPE cartridge and was not eluted down. Therefore, it does not influence the retention of amino acids and sugars.
3.3. The optimum chromatography conditions As described above, the optimum chromatography conditions utilized 40-μm and 100 Å PS-DVB SPE (0.4 g) as the color removal cartridge, 5 mL of ethanol (10%) as the elution solvent for sample pretreatment, 3 mM of formic acid as the trap solution at a 0.10 mL/min flow rate, and 1.2 min and 6.0 min as the switching time of the 10-port valve and 3-port valve, respectively. 3.4. Evaluation of the method The linearity of response was measured with 25-uL injections of 0.01, 0.05, 0.10, 0.20, 0.50, 1.00, 2.00, 5.00, and 10.00 mg/L standard mixtures. Table 2 describes the linear range, LOD and LOQ. The mean correlation coefficient of the calibration reached 0.99. Repeatability for the eight replicates of the sample A (0.50 mg/mL standards mixture
Table 2 Repeatability, reproducibility, correlation coefficient, linear range, regression equation, LOD and LOQ. S/N
Analyte
Correlationcoefficient (n = 6)
Regression equation
Linear range (mg/L)
Repeatability (%, n = 8)
Reproducibility (%)
LOD (S/N = 3, mg/L)
LOQ (mg/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Arginine Lysine Glutamine Asparagine Alanine Threonine Glycine Valine Serine Proline Isoleucine Leucine Methionine Histidine Phenylalanine Glutamic acid Aspartic acid Cystine Tyrosine Tryptophan Trehalose Arabinose Galactose Glucose Mannose Fructose Ribose
0.9997 0.9981 0.9992 0.9982 0.9993 0.9991 0.9983 0.9923 0.9995 0.9997 0.9954 0.9933 0.9993 0.9982 0.9983 0.9993 0.9977 0.9985 0.9922 0.9952 99.7459 99.8188 99.8298 99.8516 99.9059 99.6138 99.9075
Y = 18.2955X + 0.4782 Y = 11.1036X + 0.0323 Y = 0.2588X + 0.1759 Y = 1.0955X − 0.0379 Y = 1.2957X + 0.0815 Y = 0.339X − 0.0105 Y = 1.8436X + 0.2976 Y = 2.8364X − 0.1413 Y = 1.3721X + 0.1154 Y = 5.5644X + 0.1922 Y = 5.2363X + 0.8472 Y = 16.4293X + 2.4439 Y = 34.5133X + 4.0863 Y = 0.7342X − 0.4926 Y = 18.7101X + 0.5909 Y = 0.4791X + 0.8563 Y = 3.2065X + 18.4396 Y = 2.9281X + 0.0582 Y = 0.8626X + 0.0026 Y = 11.4436X + 2.8383 Y = 1.9977X + 1.1175 Y = 4.3527X + 0.2436 Y = 3.6019X + 0.2301 Y = 4.0446X + 0.3129 Y = 3.2427X − 0.0204 Y = 1.3114X-0.2724 Y = 3.2300X − 0.1239
0.05–2.00 0.05–2.00 0.05–2.00 0.05–1.00 0.05–2.00 0.05–5.00 0.05–1.00 0.05–5.00 0.05–5.00 0.05–5.00 0.05–5.00 0.05–5.00 0.05–1.00 0.05–1.00 0.05–2.00 0.05–5.00 0.05–5.00 0.05–2.00 0.05–1.00 0.05–2.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00 0.05–10.00
3.12 1.50 2.07 3.43 2.56 3.10 0.92 1.25 2.30 1.30 2.04 0.93 2.06 1.82 1.17 2.54 1.06 1.20 1.82 1.05 1.48 1.23 1.80 3.75 2.62 4.30 2.00
1.18 1.12 3.38 2.24 1.96 2.24 1.07 1.11 1.11 1.12 1.09 0.73 0.98 0.78 0.79 2.52 0.96 1.61 2.07 0.71 1.11 0.84 0.81 1.95 0.71 2.77 0.98
0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.005 0.02 0.005 0.01 0.005 0.007 0.008 0.008 0.009 0.02 0.01
0.05 0.05 0.02 0.05 0.02 0.05 0.05 0.05 0.01 0.02 0.01 0.05 0.01 0.05 0.05 0.05 0.05 0.05 0.02 0.01 0.02 0.02 0.05 0.05 0.05 0.05 0.05
39
40
Arginine Lysine Glutamine Asparagine Alanine Threonine Glycine Valine Serine Proline Isoleucine Leucine Methionine Histidine Phenylalanine Glutamic acid Aspartic acid Cystine Tyrosine Tryptophan Trehalose Arabinose Galactose Glucose Mannose Fructose Ribose
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
ND means not detected.
Analyte
S/N
84.46 6.02 82.80 46.74 60.90 4.81 ND 2.67 ND 44.99 11.62 4.58 7.83 15.71 12.08 89.99 2.96 7.80 42.31 32.99 102.55 42.08 40.92 243.39 154.07 55.93 193.18
A (mg/L)
Table 3 The result of 6 wines and Recovery of samples.
98.5 89.9 84.7 83.4 95 96.1 102.5 88.4 106.1 91.5 103 94.3 104.8 107.8 97.5 99.3 103 101.1 97.6 102.1 93.6 102.8 103.3 98.6 94.1 91.9 103.2
Recovery (%) 95.56 8.27 18.70 28.04 4.17 6.85 12.42 52.34 ND 127.08 2.91 2.98 8.10 20.38 5.69 12.58 3.13 ND 5.37 24.02 282.68 228.18 58.79 152.70 75.22 127.54 193.13
B (mg/L) 91.7 88.5 88 91.3 92 96.2 96.1 87.9 100.5 90.4 105.1 94.5 104 98.6 101.8 101 103 101.7 92.3 99.1 100.2 102.7 97 99.9 97.8 96.1 100.1
Recovery (%) 94.20 2.78 71.80 4.99 13.65 4.21 2.75 73.68 ND 248.93 2.99 2.63 6.67 ND 2.89 ND 2.65 ND 6.69 15.78 278.80 22.05 56.50 377.79 469.80 185.04 415.27
C (mg/L) 93.9 94.7 97.7 91.1 103.7 92.1 85.7 88.2 96.9 87.7 103.9 92.3 93.3 101 106.4 92.8 96.6 99.4 95.2 99 93.4 100.6 95.8 99.1 99.6 94.1 99.9
Recovery (%) 75.36 2.57 20.13 35.43 14.81 2.60 ND ND ND 39.85 6.25 2.90 5.64 4.96 8.17 ND ND 5.65 ND ND 159.67 43.04 40.44 234.19 175.64 54.27 195.40
D (mg/L) 87.9 88.7 86.4 88.8 91.6 95.9 92.9 95 98.7 86.1 99.8 99.1 93 96.6 96.8 102.2 103.4 102.7 100 102.1 97.9 98.9 97 97.2 100.9 89.5 100.8
Recovery (%) 55.01 4.79 58.27 6.99 7.84 28.16 ND 5.92 ND 19.49 5.15 2.64 4.95 ND 6.42 82.82 ND ND 4.32 ND 49.22 82.57 59.06 343.64 103.19 8.22 121.79
E (mg/L) 84.9 101 84.4 99.2 94 103.6 93.2 95.8 99.8 98.5 103.6 97.5 87.9 97.8 106.2 102.7 95.5 98.7 90.6 99.8 101.5 102.4 95.2 99.8 97.1 99.7 96.9
Recovery (%)
54.70 3.18 ND 17.42 2.60 6.28 3.25 12.16 ND 71.59 3.36 ND 3.23 ND 7.58 72.83 ND ND 3.07 ND 245.17 75.63 49.40 253.58 204.37 69.60 34.73
F (mg/L)
98.4 98 100.2 91.2 98.1 103.2 89,3 87.6 98 93.2 86.2 87.9 87.8 99.4 97.1 100.8 98.4 99.4 87.2 95.7 93.5 103.2 101.6 101.7 96.8 94.4 89.1
Recovery (%)
Y. Fa et al.
Journal of Chromatography B 1063 (2017) 36–41
Journal of Chromatography B 1063 (2017) 36–41
Y. Fa et al.
Fig. 2. Chromatograms of standard mixtures and six samples using the proposed method. Chromatogram a: amino acids chromatogram of A, B, C, D, E, F and standards; Chromatogram b: sugars chromatogram of A, B, C, D, E, F and standards; The serial number 1–27 refer to amino acids and sugars that are listed on Table 2.
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addition) was 0.92%–4.30%. Reproducibility for the three replicates of 0.05, 0.20, and 1.00 mg/L of the standard mixture was 0.70%–3.38%. The mean recoveries of the standard addition of 0.05, 0.20, and 1.00 mg/L of standard mixtures added in each sample are listed in Table 3. 3.5. Sample analysis The 20 AA and seven sugar content of six different wines were determined by the new method. The chromatograms of the six samples showed that the AAs and sugars were completely separated (Fig. 2). Data revealed that all wine samples had a considerably higher concentration of glucose than arabinose, galactose, and fructose (Table 3). Samples A, C, and D also had a larger amount of trehalose, mannose, and ribose. Most AA were lower than sugars. Arginine, glutamine, serine, and glutamic acid contents were higher than that of other AAs. Changes in sugar and AA concentration reflected the diversities in process, quality, or geographical origin of wines [1,2,6]. 4. Conclusions This work developed an effective pretreatment method to remove wine color by a home-made PS-DVB SPE and to eliminate the interference of alcohol online by valve switching. Moreover, sugar and AA were successfully analyzed simultaneously without interference each other in six real wine samples from differ origins. The method can be useful for the wine industry, the applications in wine research and routine quality control during wine production. Acknowledgements This project is supported by the National Special Fund for Major Research Instrumentation Development (No. 2012YQ090229) and the National Natural Science Foundation of China (No. 21405166). The authors are grateful to Engineer Haiyong Yang for the valuable suggestions. References [1] G.F. Xie, D.D. Yang, X.Q. Liu, X.X. Cheng, H.F. Rui, H.J. Zhou, Correlation between the amino acid content in rice wine and protein content in glutinous rice, J. Inst. Brew. 122 (2016) 162–167. [2] B.C. Childs, J.C. Bohlscheid, C.G. Edwards, Impact of available nitrogen and sugar concentration in musts on alcoholic fermentation and subsequent wine spoilage by Brettanomyces bruxellensis, Food Microbiol. 46 (2015) 604–609. [3] F.S. Neto, M.B. de Castilhos, V.R. Telis, J. Telis-Romero, Effect of ethanol, dry extract and reducing sugars on density and viscosity of Brazilian red wines, J. Sci. Food Agric. 95 (2015) 1421–1427. [4] M.P. Saenz-Navajas, V. Ferreira, M. Dizy, P. Fernandez-Zurbano, Characterization of taste-active fractions in red wine combining HPLC fractionation, sensory analysis and ultra- performance liquid chromatography coupled with mass spectrometry detection, Anal. Chim. Acta 673 (2010) 151–159.
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