Discrimination of different kinds of Luzhou-flavor raw liquors based on their volatile features

Food Research International 56 (2014) 77–84

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Discrimination of different kinds of Luzhou-flavor raw liquors based on their volatile features Jia Zheng a,b,e, Ru Liang a,b, Chongde Wu a,b, Rongqing Zhou a,b,c,d,⁎, Xuepin Liao a,b,c,⁎ a

College of Light Industry, Textile & Food Engineering, Sichuan University, Chengdu 610065, China Key Laboratory of Leather Chemistry and Engineering, Ministry of Education, Sichuan University, Chengdu 610065, China National Engineering Laboratory for Clean Technology of Leather Manufacture, Sichuan University, Chengdu 610065, China d National Engineering Research Center of Solid-State Brewing, Luzhou 646000, China e Department of Food Science and Technology, Oregon State University, Corvallis, OR 97331, USA b c

a r t i c l e

i n f o

Article history: Received 20 October 2013 Accepted 11 December 2013 Keywords: Volatile compound Luzhou-flavor raw liquor GC-MS PCA PLS-DA

a b s t r a c t The volatile constitutions of 36 raw liquors from three distilling stages (head, heart and tail) of two typical Luzhou-flavor liquors (Fenggu-FG and Jiannanchun-JNC) were identified and semi quantified by gas chromatography-mass spectrometry (GC-MS). A total of 63 compounds were identified in all liquors. These two typical liquors had similar volatile constitutions, in which, 3-methylbutanol, hexanoic acid, ethyl hexanoate, ethyl butanoate, ethyl lactate, ethyl pentanoate, 1,1-dimethoxythane and 1,1-diethoxy-3-methylbutane were considered to be main compounds due to their high concentrations. Multivariate analyses including principal component analysis (PCA) and partial least square-discrimination analysis (PLS-DA) were conducted to reveal the detailed distinctions of liquors with different origins and reveal the volatile markers of several kinds of liquors. PCA explanation plane primary revealed the main differentiation between FG and JNC based on their loading plot values on axis PC1. Results of PLS-DA showed the detailed distinctions of liquors, suggesting that 2methylpropanoic acid, butanoic acid, pentanoic acid, nonanoic acid, 2,3-butanediol, 1-hexanol, and ethyl nonanoate strongly correlated with FG liquors, while n-butylformate, isopentyl butanoate, isoamyl caproate, and p-cresol contributed to the specificity of JNC liquors. Furthermore, differences amongst the heart distilling stage liquors from different enterprises were also visualized in the two-dimensional PLS-DA discrimination plane. To our knowledge, this is the first article using the GC-MS paired with multivariate analysis to discriminate different kinds of Luzhou-flavor raw liquors. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Luzhou-flavor liquor is one of the most important alcoholic beverages in China with the ethanol content range of 38–52% vol (Xu, Wang, Fan, Mu, & Chen, 2010). The flavor feature of Luzhou-flavor liquor is influenced by numerous factors including geological location, pit mud, fermentation technology and many other variables (Zhao & Chen, 2008). Sichuan province in China is the primary region for Luzhou-flavor liquor production, and some famous brands are produced in this region such as “Luzhou Laojiao”, “Wuliangye”, “Jiannanchun”, “Shuijinfang” and “Tuopai”. However, it was very difficult to discriminate unique flavor features of several Luzhou-flavor liquors with the accordance of the China National Standard GB/T 10781.1-2006 (strong flavor Chinese liquors). The aroma of liquor comes from volatile compounds at concentrations of mg/L to ng/L levels, and a lot of these trace amounts of compounds play positive roles in contributing the flavor of liquor. Many ⁎ Corresponding authors at: Key Laboratory of Leather Chemistry and Engineering, Ministry of Education, Sichuan University, Chengdu 610065, China. E-mail addresses: [email protected] (R. Zhou), [email protected] (X. Liao). 0963-9969/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodres.2013.12.011

research works have been devoted to identify and quantify the flavor constitution of the commercial Luzhou-flavor liquor. Four organic acids (acetic, butanoic, lactic and hexanoic acid) and their ethyl esters, especially hexanoic acid and ethyl hexanoate, were identified to be main volatile compounds in Luzhou-flavor liquor (Fan & Qian, 2005, 2006a, 2006b; Kim, Kam, & Chung, 2009; Li et al., 2012; Wang & Yin, 2000; Xu et al., 2010). Difference of volatile compounds between young and aged Yanghe Daqu liquor was compared by solid space microextraction (SPME) coupled with gas chromatography-olflactometry (GC-O) dilution analysis, which reported that esters were the main aromacontributor and the aroma profiles of them were similar, but the aroma compounds such as ethyl hexanoate in aged liquor represented higher dilute factor (FD) value than that in young liquor (Fan & Qian, 2005). Kim et al. (2009) compared the volatile composition between Moutai and Wuliangye, which suggested that diethyl succinate was only detected in Wuliangye. In fact, the flavor characteristic of Luzhou-flavor liquor depends on the fermentation technology and geographical environment or microenvironment of fermentation pit. Meanwhile, the blending process is considered to be one of most important procedures for endowing the distinctive flavor feature of Luzhou-flavor liquor (Shen, 2007). In this

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process, various aged raw liquors with different volatile features are mixed together with specific proportions to obtain the commercial product representing the unique flavor and taste. Whereas the proportion and flavor features of the raw and commercial liquor are mainly determined by the taste and smell of well trained panelists (Lai, 2007). So, revealing the flavor feature of raw liquors with different origins and distilling stages may contribute to well understand the effect of raw liquor on the quality of final product. However, it was almost impossible that specific characteristics of different batches of raw liquor in the same type or different types were comprehensively qualified and quantified by GC-O and GC-MS technologies. In the last decade, some novel approaches were developed for discriminating subtle difference of same or different type products. For example, three-step IR macro-fingerprint identification method was used to compare different Moutai liquors (Moutai, Kinsly and counterfeit Moutai) (Sun, Li, Wei, Zhou, & Noda, 2006). Colorimetric array sensor with cluster analysis and principal component analysis (PCA) was applied to identify five different Chinese liquors including Langjiu, Fenjiu, Xifeng, Luzhou Laojiao and Sanhua (Huo et al., 2011). Recently, multivariate analysis especially partial least squarediscrimination analysis (PLS-DA) provided a powerful approach to discriminate the samples with different characters and it has been widely applied in the metabolomics analysis (Ramadan, Jacobs, Grigorov, & Kochhar, 2006; Zhang, Liu, Steffen, Ye, & Raftery, 2012). Different commercial brandies either the French brandies or sherry brandies were compared by using PLS-DA based on their volatile profiles (Ledauphin, Le Milbeau, Barillier, & Hennequin, 2010). Son et al. (2008) also applied these methods to evaluate Korean wines from different origins and grapes varieties. Additionally, by employing PLS-DA paired with MS/ NMR technology, metabolite separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was compared and the behaviors of microbes involving in the wine fermentation such as lactic acid bacteria and yeast were also discussed (Lee, Hong, & Lee, 2009; Son et al., 2008, 2009). However, till now, no study was conducted to investigate the volatile constitution and discrimination of raw Luzhouflavor liquors with different origins based on its separation-related volatile markers. Therefore, the main objective of this study was to investigate the volatile constitution of various raw liquors from different liquor manufacturing enterprises, aged pits as well as distilling stages by GCMS. Then, the PCA and PLS-DA methods were conducted to understand their potential characteristics based on the GC-MS data and extract their separate-related volatile markers.

Table 1 Sampling numbers and their related position in fermentation pit. Brand

Pit age

Layer of fermented grains Top

Fenggu (FG)

New

head(a1), heart(a2), tail(a3) 50-year head(b1), heart(b2), tail(b3) Jiannanchun New head(c1), (JNC) heart(c2), tail(c3) 50-year head(d1), heart(d2), tail(d3)

Heart

Bottom

head(a4), heart(a5), tail(a6) head(b4), heart(b5), tail(b6) head(c4), heart(c5), tail(c6) head(d4), heart(d5), tail(d6)

head(a7), heart(a8), tail(a9) head(b7), heart(b8), tail(b9) headc7), heart(c8), tail(c9) head(d7), heart(d8), tail(d9)

company in Sichuan province, China. The head (a1, a4, a7, b1, b4, b7, c1, c4, c7, d1, d4 and d7), heart (a2, a5, a8, b2, b5, b8 c2, c5, c8, d2, d5 and d8) and tail (a3, a6, a9, b3, b6, b9, c3, c6, c9, d3, d6 and d9) stage liquors were collected from the four different fermentation pits (FG-new, FG-50 year, JNC-new and JNC-50 year), respectively. For each sample, approximately 100 mL raw liquor was poured into the glass bottles and sealed storing at 4 °C until analysis. 2.3. Extraction of volatile compounds Volatile compounds in raw liquors were extracted according to the extraction method reported previously with some modification (Qian & Reineccius, 2002). Briefly, 10 mL raw liquor was transferred into the round flask and internal standards (methyl octanoate, octanoic acid) were added. The concentration of ethanol in raw liquor was adjusted to approximately 14%, and the diluted liquor was saturated with NaCl and the pH was adjusted to 10–11 with 1 M NaOH. Then 50 mL anhydrous diethyl ether was added in the pretreated raw liquor mixture to extract volatile compounds. The organic phase was transferred into the clean glass tube and labeled as “neutral fraction”. In terms of the aqueous phase, the pH was adjusted to 2–3 with 2 M H2SO4, and 20 mL anhydrous diethyl ether was added into the aqueous phase. The extracted organic phase was labeled as “acidic fraction”. All fractions were dried with 5 g of anhydrous Na2SO4, and concentrated the filtrate to 0.5 mL under the soft N2. All raw liquors were extracted in triplicate by the same procedure above.

2. Materials and methods

2.4. GC-MS analysis

2.1. Chemicals and standards

One microliter extracts were analyzed on a Trace GC Ultra gas chromatograph-DSQ ΙΙ mass spectrometer (Thermo Electron Corporation, Waltham, USA) equipped with a HP-5MS capillary column with 5% diphenyl 95% dimethylpolysiloxane (30.0 m × 0.25 mm × 0.25 μm, J&W, Santa Clara, CA, USA). GC analyses were performed under the following conditions: an inlet temperature of 250 °C, split ratio of 10:1, and Helium (purity: 99.999%) carrier gas flow of 1 mL/min. The oven temperature was kept at 40 °C for 5 min, followed by an increase of 5 °C/min to 200 °C, and then programmed to 220 °C at 10 °C/min, and held for 5 min. For mass spectrometer, the temperatures of the transfer line, quadruple and ionization source were of 250 °C, 150 °C, and 230 °C, respectively. The mass spectrum was generated in the electron impact (EI) mode at 70 eV and the detector was in the full scan mode with the range of 35–400 amu.

All standards used in this study including ethyl acetate (99.9%), ethyl lactate (99.0%), ethyl butyrate (99.5%), ethyl hexanoate (99.0%), ethyl palmitate (99.0%), ethyl linoeate (99.0%), ethyl phenylacetate (98.0%), ethyl oleate (98.0%), methyl octanoate (99.0%), phenethyl alcohol (99.0%), furfural (98.0%), benzenacetaldehyde (99.0%), butanoic acid (99.0%), hexanoic acid (98.0%), octanoic acid (98.0%) and C8–C20 nalkanes were purchased from Sigma–Aldrich (Shanghai, China) and all standards used were of GC quality. All chemicals including NaCl, Na2SO4, NaOH and H2SO4 were of analytical quality. 2.2. Sampling method Three layers (top, heart and bottom) and the position of fermented grains in Luzhou-flavor liquor fermentation pit were displayed as that in previous literature (Zheng et al., 2013a,b; Zheng, Wu, Zhou, & Liao, 2013b). As shown in Table 1, a total of 36 fresh raw liquors (12 head, 12 heart and 12 tail) were collected from three distilling stages of all layers of fermented grains from Fenggu (FG) and Jiannanchun (JNC)

2.5. Identification and quantification of volatile compounds The identification of volatile compound was according to the search result of comparison of their mass spectrum with the NIST05 spectrum database. Kováts retention indices (RI) of each compound were

J. Zheng et al. / Food Research International 56 (2014) 77–84

calculated by using C8–C20 n-alkanes (Cates & Meloan, 1963). The volatile compound was additionally confirmed by comparison of their RI with the RI reported in literatures (Zheng, Wu, Zhou, & Liao, 2014). The relative concentration of volatile compound was estimated as: relative volatile compounds (mg/L) = (relative peak area of analyte/ peak area of internal standard) × concentration of internal standard [methyl octanoate (8.77 mg/L) and octanoic acid (9.11 mg/L) for neutral fraction and acidic fraction, respectively]. 2.6. Statistical methods Significant differences among raw liquors were determined by employing one-way ANOVA, and F test and p value were generated for all volatile compounds using a SPSS 17.0 (SPSS Inc., USA). PCA was also performed using the SPSS software. PLS-DA was conducted by SIMCA-P software with the version of 12.0.1 (UMETRICS, Sweden). PCA is an unsupervised technique, which has been proved to be a powerful tool in summarizing and further explaining large data sets statistically and visually (Zheng et al., 2013a,b). In this study, PCA was initially used to reduce the dimensionality of the original data matrix, allowing the visualization of liquors with different origins in a two or three-dimensional space. PLS-DA was further conducted to develop models to discriminate liquors, and the calculation process of PLS-DA was according to the previous method (Ledauphin et al., 2010). PLS-DA was applied to find a twodimensional plane (discriminating plane) in which the liquor samples (projected observations) on the PLS components were well separated according to their volatile compounds. The X and Y matrix in this plane consisted of the volatile composition data of the observation and dummy variables, respectively. As for the PLS weight plot, composition variables of which can reveal the variables (specific volatile compounds) contributing to the separation. Volatile compounds which close to the dummy variables of class membership contribute strongly to the separation. 3. Results and discussion 3.1. Volatile profiles of raw liquors with different origins Main volatile compounds such as ethyl lactate, ethyl butanoate, ethyl hexanoate, butanoic acid and hexanoic acid, determined in previous literatures (Fan & Qian, 2006a,b; Li et al., 2012) were chosen to

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check the reliability of the detection method prior to GC-MS detection. The peak area ratios (neutral fraction/methyl caprylate and acidic fraction/caprylic acid) were used for the quantification of each compound. As shown in Supplement Table 1, linearity values of calibration curve with the R2 ranging from 0.852 to 0.990 was obtained, and extraction recoveries were all higher than 90% for all testing compounds. It demonstrated that methyl octanoate and octanoic acid used as the internal standards was suitable in semi quantitatively evaluating the relative concentration of volatile compounds in Luzhou-flavor liquors although part of acidic compounds represent low R2 values (b 0.99). The total concentration of volatile compounds in raw liquors from different distilling stages and brands was semi quantified (Fig. 1). The total level of volatile compounds always significantly decreased from the head liquor to the tail liquor (P b 0.05). This result was in accordance with the study of Li et al. (2012). Fig. 1 also illustrates that the concentration of raw liquors from FG were slightly higher than that from JNC liquor. Sample b4 has the highest total level of volatile compounds with the concentration of 9196 mg/L, followed by the samples b7 (8600 mg/L), c7 (8235 mg/L), c4 (6610 mg/L), d4 (6401 mg/L), etc. It suggested that the volatile feature of Luzhou-flavor liquors had significant spatial feature. After comparing the total content of each kind of liquor, Table 2 summarizes the mean value and standard deviations (mean ± SD, n = 3) calculated for each volatile compound in different raw liquors. The ANOVA, by using the Turkey's test with the level of 0.05 (P b 0.05), of volatile compounds from different distilling stages and brands was also shown in this table. Similar volatile constitution between FG and JNC liquor was observed by comparing the quality and concentration of volatiles. All volatile compounds identified in FG liquor also emerged in JNC liquor, and FG liquor exhibited higher concentrations of volatile compounds especially ethyl hexanoate compared to JNC liquor. Previous study showed that ethyl hexanoate, the most important aroma contributor with the fruity, anise, apple, sweetish and pear odors (Jiang & Zhang, 2010), had a level of 2–3 g/L in commercial Luzhou-flavor liquor (Wang & Yin, 2000), which was in agreement with the result of this study. It once again confirmed that the flavor characteristic of Luzhou-flavor liquor is mainly determined by the content of ethyl hexanoate due to its relatively high content and low threshold (0.005 mg/L) (Guth, 1997). Simultaneously, result of this study also showed that major volatile compounds were butanoic acid [2], 3-methylbutanoic acid [3], hexanoic acid [5], isobutyl alcohol [9], 3-methylbutanol [12], 1-hexanol [16], ethyl

Fig. 1. Variation of total concentration of Fenggu and Jiannanchun liquors from different distilling processes. The concentration of volatile compounds was represented as mean with standard derivation (mean ± SD, n = 3). Label “*” represented the significant variation among raw liquors from three distilling stages (one-way ANOVA with Turkey's test, P b 0.05). Letters a, b, c and d represented the FG-new, FG-50 year, JNC-new and JNC-50 year, respectively. Each number (1 to 9) in this figure represented the liquor number listing in Table 1.

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Number

RIhp-5MS

Volatile compound

Fenggu (FG) Heada)

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 28 29 30

b800 871 839 916 1037 1115 1296 b800 b800 b800 b800 b800 b800 b800 855 875 1116 b800 811 b800 806 823 904 925 998 1005 1059 1061 1072 1096

2-methylpropanoic aicd butanoic acid 3-methylbutanoic acid pentanoic acid hexanoic acid heptanoic acid nonanoic acid 1-propanol isobutyl alcohol 2-pentanol isopentyl alcohol 3-methyl butanol 1-pentanol 2,3-butanediol furfuryl alcohol 1-hexanol phenylethyl alcohol ethyl acetate n-butylfomate ethyl 2-methylpropanoate ethyl butanoate ethyl lactate ethyl pentanoate methyl caproate butyl lactate ethyl hexanoate isopentyl butanoate ethyl 2-hydroxy-4- methylpentanoate 3-methylbutyl methoxyaetate propyl hexanoate

6.46 29.1 11.2 29.4 179 4.23 0.45 9.8 35.1 27.2 0.0762 173 1.69 1.49 0.64 38.1 1.23 68.3 7.47 1.52 191 80.2 123 2.56 0.29 4038 1.63 4.44 0.45 10.9

Jiannanchun (JNC) Heart

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

10.1b) 14.5 16.7 40.8 204 4.52 0.41b 11.6b 32.6 63.6 0.118 64.8 2.20 1.42 0.62 16.28 0.54a 35.8 12.3 1.26 119 36.7 52.1bc 0.80a 0.42 1749c 0.51ab 2.94a 0.18a 6.18ab

2.70 ± 32.0 ± 5.69 ± 12.9 ± 108 ± 2.82 ± 0.214 ± 0.652 ± 13.3 ± T.R.c) 27.8 ± 94.7 ± 1.82 ± 0.63 ± 0.95 ± 49.7 ± 2.04 ± 39.5 ± 0.005 ± 0.46 ± 97.1 ± 185 ± 62.6 ± 1.98 ± 0.50 ± 2326 ± 1.03 ± 15.5 ± 1.11 ± 6.56 ±

Tail 2.12 21.4 3.81 6.75 42.7 1.44 0.096b 1.32ab 13.5 68.2 84.4 1.61 0.39 0.91 35.3 0.75a 13.3 0.026 0.50 80.8 231 18.5abc 0.75a 0.51 828abc 0.56a 14.5ab 1.19ab 3.10ab

5.97 27.1 22.0 12.5 132 3.89 0.178 14.3 12.0 4.41 0.056 116 1.91 0.77 1.18 53.3 3.43 38.6 6.64 0.31 67.5 449 38.7 1.25 1.18 1558 0.62 38.9 2.88 4.56

Head ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

9.15 14.6 36.5 5.61 70.5 1.63 0.116ab 20.9ab 15.2 10.4 0.102 66.6 1.54 0.31 0.86 23.0 0.88a 33.2 12.9 0.30 49.3 417 13.3ab 0.46a 0.80 632ab 0.58a 26.2b 1.81b 2.40a

0.82 22.0 13.7 10.1 110 3.42 0.081 21.5 26.5 1.86 41.9 190 4.80 0.53 0.60 25.6 3.43 78.5 5.00 98.5 197 14.9 137 5.05 0.38 2825 2.78 1.88 0.25 18.6

ANOVA Heart

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.63 18.6 26.2 5.99 66.5 1.01 0.054a 24.4a 45.0 3.99 67.2 253 7.84 0.47 0.37 16.1 0.84a 123 7.62 238 198 17.6 88.9c 2.09b 0.62 1439bc 1.71b 1.85a 0.19a 13.2b

1.25 18.1 9.91 7.99 84.2 3.21 0.054 23.9 75.3 26.7 13.7 276 2.58 0.49 1.00 29.7 3.64 30.3 17.0 0.44 85.8 24.8 71.0 2.53 1.19 1730 1.30 9.62 0.81 10.4

Tail ± 1.02 ± 20.4 ± 14.2 ± 6.60 ± 31.5 ± 1.18 ± 0.044a ± 28.7a ± 66.8 ± 53.7 ± 31.9 ± 286 ± 2.71 ± 0.45 ± 0.75 ± 26.1 ± 3.47a ± 29.4 ± 33.0 ± 0.40 ± 90.1 ± 29.4 ± 55.4abc ± 2.01a ± 1.01 ± 1070ab ± 0.74ab ± 9.07a ± 0.60ab ± 9.24ab

0.69 12.6 25.8 3.25 97.9 5.32 0.091 5.38 24.2 17.3 29.7 89.8 1.72 0.40 1.03 15.8 18.1 25.4 28.7 0.40 45.0 387 27.4 0.914 1.45 721 0.82 21.9 3.14 3.19

F ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.83 13.9 31.0 3.89 43.6 3.26 0.062a 9.78a 17.7 23.0 32.3 105 1.84 0.33 1.19 15.6 15.9b 17.4 42.1 0.09 47.9 404 26.0a 0.751a 0.89 556a 0.80a 18.2a 2.35b 4.46a

p 1.28 1.04 0.61 1.55 0.734 0.742 3.82 1.42 2.37 0.737 0.940 1.23 0.645 2.08 0.469 2.34 5.45 0.879 1.20 1.01 2.05 3.22 4.79 7.49 2.75 6.20 4.37 5.08 5.37 3.42

0.299 0.410 0.694 0.204 0.604 0.598 0.00847 0.246 0.0631 0.602 0.470 0.319 0.667 0.0957 0.796 0.0661 0.00110 0.507 0.334 0.429 0.0993 0.192 0.00246 0.000116 0.0369 0.000466 0.00419 0.00172 0.00121 0.0145

J. Zheng et al. / Food Research International 56 (2014) 77–84

Table 2 Mean values (mg/L) with standard deviations (SD, n = 3) of volatile compounds in Luzhou-flavor raw liquor from different layers and distillation stages and ANOVA.

a) b) c)

1100 1153 1134 1199 1148 1248 1278 1252 1297 1352 1367 1385 1397 1597 1796 1899 1996 N2000 N2000 N2000 b800 b800 b800 1044 b800 b800 867 b800 b800 958 1317 1700 1746

ethyl heptanoate isobutyl hexanoate butyl hexanoate ethyl octanoate isoamyl caproate ethyl benzeneacetate n-amyl caproate isopentyl caproate ethyl nonanoate ethyl benzenepropanoate furfuryl hexanoate n-hexyl hexanoate ethyl decanoate ethyl dodecanoate ethyl tetradecanoate ethyl pentadecanoate ethyl palmitate ethyl linoleate ethyl oleate ethyl 15-methylheptadecanoate 2-methylbutanal 3-furaldehyde 1,1-diethoxy-2-methylpropane benzenacetaldehyde 2-pentanone 3-hydroxy-2-butanone p-cresol 1,1-diethoxyethane 2,4,5-trimethyl-1,3-dioxolane 1,1-diethoxy-3-methylbutane 2,4-decadienal heptadecane 2,6,10-trimethylhexadecane

72.0 6.81 8.98 81.3 2.31 1.00 15.3 1.06 1.77 1.09 0.76 10.5 0.09 1.73 1.52 0.33 24.0 18.1 11.2 0.80 2.14 5.15 15.1 0.44 135 71.7 0.13 419 0.49 101 0.05 0.35 0.72

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

39.2b 3.52ab 5.41 54.2 1.85 0.35a 8.80ab 0.39 1.18 0.51a 0.58 4.55 0.05 0.79a 0.86a 0.15a 15.9a 14.5a 8.31a 0.35a 3.32 2.57a 6.77ab 0.10a 172 55.8 0.21 252.71b 0.65 38.5bc 0.03a 0.13 0.22

46.3 3.96 7.30 60.8 1.64 2.52 11.8 0.90 1.64 2.49 1.77 10.9 0.08 2.69 1.79 0.29 20.4 14.8 9.07 0.61 0.04 15.1 7.62 1.52 42.2 35.3 0.38 175 29.5 60.9 0.03 0.38 0.58

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

22.9ab 2.63ab 6.61 38.9 1.30 1.37ab 6.97a 0.44 1.17 1.52a 1.79 5.96 0.03 1.20ab 0.67ab 0.11a 6.95a 6.63a 3.86a 0.41a 0.10 18.0ab 2.75a 0.83ab 34.1 33.7 0.90 39.3ab 72.2 22.3abc 0.03a 0.15 0.27

33.4 ± 3.03 ± 5.34 ± 47.6 ± 1.25 ± 5.48 ± 9.11 ± 0.724 ± 1.50 ± 5.43 ± 2.88 ± 9.41 ± 0.08 ± 3.74 ± 3.05 ± 0.34 ± 27.9 ± 18.7 ± 11.1 ± 0.80 ± 7.55 ± 38.5 ± 5.77 ± 4.11 ± 30.1 ± 28.1 ± T.R. 131 ± 0.09 ± 39.8 ± 0.06 ± 0.48 ± 0.71 ±

18.6ab 2.01a 4.75 32.8 1.06 1.90b 5.78a 0.37 0.98 1.92a 1.86 5.20 0.01 1.40abc 0.49ab 0.12a 6.72a 3.05a 1.58a 0.20a 17.7 27.3ab 1.59a 1.40bc 22.5 26.9 19.0ab 0.07 12.4ab 0.04a 0.15 0.16

56.9 8.83 16.1 79.9 2.00 0.80 31.0 2.03 1.54 2.87 0.64 14.4 0.47 8.43 7.79 1.78 163 201 203 7.85 1.50 31.9 21.3 0.45 91.5 25.2 0.44 463 0.31 121 0.41 0.43 0.81

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

30.1ab 4.93b 23.4 38.7 0.95 0.39a 16.7b 2.43 0.29 1.11a 0.46 11.0 0.68 2.50bc 3.33ab 1.08b 169b 178b 193b 5.85b 1.67 50.0ab 11.3b 0.26a 61.4 36.0 0.76 431b 0.37 72.2c 0.19b 0.21 0.30

32.9 4.83 10.9 49.4 1.09 2.75 18.2 1.39 1.01 6.12 0.79 10.5 0.15 9.13 5.28 0.60 30.9 20.4 11.6 1.02 1.42 35.6 9.06 2.22 53.6 56.2 0.08 305 5.04 63.1 0.17 0.34 0.68

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

25.1ab 3.48ab 19.4 35.7 0.84 1.66ab 12.6ab 2.28 0.29 3.34a 0.53 12.9 0.19 5.58c 2.75ab 0.41a 17.5a 11.7a 5.54a 0.83a 1.83 15.0ab 4.34a 1.34ab 23.5 68.0 0.20 195ab 11.93 26.1abc 0.21a 0.16 0.24

14.4 1.86 5.80 23.0 0.50 5.09 6.92 0.735 0.74 12.5 1.55 5.40 0.18 6.53 8.27 0.79 48.0 25.6 13.6 0.85 1.47 71.6 7.68 6.50 10.1 1.56 0.73 30.8 0.02 8.95 0.17 0.35 0.65

± 14.4a ± 1.95a ± 10.6 ± 22.7 ± 0.49 ± 3.39b ± 6.76a ± 1.37 ± 0.31 ± 7.46b ± 1.90 ± 8.22 ± 0.25 ± 5.91abc ± 7.93b ± 0.82ab ± 53.0ab ± 32.1a ± 15.4a ± 0.59a ± 0.53 ± 61.2b ± 8.81a ± 3.50c ± 10.5 ± 0.78 ± 1.05 ± 23.8a ± 0.02 ± 8.53a ± 0.15a ± 0.15 ± 0.33

3.58 3.71 0.510 1.99 1.91 7.11 4.20 0.664 1.45 8.07 2.41 0.690 1.40 4.51 3.81 5.69 3.37 5.99 5.87 8.36 0.757 2.50 4.52 12.0 2.09 2.02 1.04 3.58 0.925 7.28 7.25 0.753 0.494

Head, heart and tail liquors indicated each kind of liquors from Fenggu and Jiannanchun, e.g. head in Fenggu represented samples a1, a4, a7, b1, b4 and b7. The concentrations of volatile compounds were represented as mean value of triplicate samples ± standard deviation (mean ± SD). Different letters indicate significant differences (P b 0.05, ANOVA, Turkey's test). ‘T.R.’ represented this compound of trace amount.

0.0121 0.00984 0.767 0.109 0.122 0.000173 0.00517 0.654 0.237 0.000642 0.0593 0.635 0.254 0.00345 0.00948 0.000838 0.0155 0.000588 0.000677 0.0000484 0.588 0.0526 0.00345 0.00000194 0.0948 0.105 0.415 0.0116 0.479 0.000144 0.000148 0.590 0.779

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31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

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Fig. 2. Two-dimensional plane of principal component analysis (PCA) of 36 raw Luzhouflavor liquors via their volatile compounds. Scores along the first 2 principal components (PC1:48.9% and PC2:42.0%) explained 90.9% of the total variability.

acetate [18], ethyl butanoate [21], ethyl lactate [22], ethyl hexanoate [26], ethyl palmitate [47], ethyl linoleate [48], ethyl oleate [49], 3furaldehyde [52], 2-pentanone [55], 3-hydroxy-2-butanone [56], 1,1diethoxyethane [58] and 1,1-diethyoxy-3-methylbutane [60] (Table 2).

Contents of acids such as butanoic acid [2], 3-methylbutanoic acid [3] and hexanoic acid [5] were not changed significantly in each sample (P b 0.05). Contents of most ethyl esters, e.g. ethyl acetate [18], hexanoate [26] and octanoate [34], 2-pentanone [55] and 3-hydroxy2-butanone [56] significantly decreased from the head liquor to the tail liquor, while ethyl lactate [22], ethyl 2-hydroxy-4-methylpentanoate [28], ethyl benzeneacetate [36], ethyl benzenepropanoate [40], furfuryl hexanoate [41], 3-furaldehyde [52], 1,1-diethyoxy-3-methylbutane [53], benzenacetaldehyde [54], and 1,1-diethoxyethane [58] represented the contrary trend (P b 0.05). It was documented that some important volatile compounds, such as hexanoic acid, butanoic acid, 1-hexanol, 3-methylbutanol and 1,1diethoxyethane were identified to be the most important compounds in Luzhou-flavor liquor based on either their FD value (Fan & Qian, 2005). Result of this study also confirmed that these compounds were also considered to be dominant compounds based on their concentrations (Table 2). Meanwhile, F test and p value were calculated to find the significant difference in liquors from different distilling stages and brands. It was worth noting that ethyl hexanoate [26], the most abundant volatile compound in Luzhou-flavor liquor, significantly varied in liquor samples from different origins based on its p value. Other compounds including nonanoic aicd [7], 1-propanol [8], phenylethyl alcohol [17], ethyl pentanoate [23], methyl hexanoate [24], ethyl 2-hydroxy-4-methyl-

Fig. 3. (a) PLS-DA of various raw liquors by their volatile components given as a two-dimensional representation of the scores (t[1] and t[2]) on the first and second PLS components. The first PLS component R2X [1] and the second PLS component (R2X [2]) explained 11.4% and 9.4% of the variation of the X data, respectively. (b) PLS-DA weight plot of variables (volatile compounds), w*c[1] and w*c[2], on the first and second components. Each number in the atlas represented one compounds listed in Table 2. $M1.DA1, 2, 3 and 4 represented FG-new, FG50 year, JNC-new and JNC-50 year, respectively.

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pentanoate [28], 3-methylbutyl methoxyacetate [29], propyl hexanoate [30], ethyl heptanoate [31], isobutyl hexanoate [32], ethyl benzeneacetate [36], ethyl benzene- propanoate [40], ethyl dodecanoate [44], ethyl tetradecanoate [45], ethyl pentadecanoate [46], ethyl palmitate [47], ethyl linoleate [48], ethyl oleate [49], 3-furaldehyde [52], benzenacetaldehyde [54], 1,1-diethoxyethane [58], 1,1-diethyoxy3-methylbutane [60] and 2,4-decadienal [61] were monitored to be significantly different in liquors with different origins (P b 0.05). It was interesting to find that several volatile compounds which exhibited high concentrations and were confirmed to be the dominant compounds previously (Wang & Yin, 2000), showed no significant variations between FG and JNC liquors based on their p values, e.g. butanoic acid [2], hexanoic acid [5], 1-hexanol [16], ethyl butanoate [21] and ethyl lactate [22]. These results suggested that volatile constitutions of these two Luzhou-flavor liquors were similar, but the significant deviation between them may attribute to the non-dominance compounds. 3.2. Discrimination of FG and JNC liquors In order to clearly elucidate the difference amongst these liquors and find their separation-related volatile markers, PCA was firstly conducted to discuss the main differences between these raw liquors based on

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their volatiles data sets. PCA was carried out on the data matrix of 36 liquors × 63 volatile compounds. In Fig. 2, a total of two significant principal components (PCs) accounting for 90.9% of the total variance in data matrix was extracted. The first PC (PC1) which explained 48.9% of the total variance and a general separation between FG liquor (negative position) and JNC liquor (positive position) was obviously separated according to the loading plot values on PC1 axis. However, the samples in different distilling stages from the same origin especially JNC liquors closely located with each other, and could not be satisfactorily discriminated by this approach. A PLS-DA model was then conducted to clearly discriminate liquors with different origins (totally 36 raw liquors) according to the data matrix of their volatile compounds. As shown in Fig. 3a, 39.3% of the total variance with R2Y = 98.5% and Q2 = 61.1% was explained, and two groups of samples can be clearly defined: a group for the sample dots of JNC liquor (negative position) and another one for the sample dots of FG liquor (positive position). PLS-DA was further performed to display specific volatile markers that better explain one type of liquor. The results of PLS-DA highly coincided with the comparison result of the concentration. For example, some volatile compounds such as acids [1–5], 2,3-butanediol [14], 1hexanol [16], ethyl acetate [18], ethyl hexanoate [26], 2-pentanone

Fig. 4. (a) PLS-DA of various heart raw liquors by their volatile components given as a two-dimensional representation of the scores (t[1] and t[2]) on the first and second PLS components. The first PLS component R2X [1] and the second PLS component (R2X [2]) explained 17.7% and 18.2% of the variation of the X data, respectively. (b) PLS-DA weight plot of variables (volatile compounds), w*c[1] and w*c[2], on the first and second components. Each number in the atlas represented one compound listed in Table 2. $M1.DA1, 2, 3 and 4 represented FG-new, FG-50 year, JNC-new and JNC-50 year, respectively.

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[55] and 1,1-diethoxy-3-methylbutane, presenting higher concentrations in FG liquor than in JNC liquor (Table 2) were strongly correlated with FG liquor and located in the positive position of w*c axis (Fig. 3b). In terms of FG liquors, a lot of volatile compounds contributed to the specificity of them, such as 2-methylpropanoic acid [1], butanoic acid [2], pentanoic acid [4], nonanoic acid [7], 2,3-butanediol [14], 1hexanol [16], and ethyl nonanoate [39] strongly correlated with FG liquor. This trend was confirmed by the comparison between FG and JNC liquor (Table 2) which showed that concentrations of these compounds in FG liquors were higher than in JNC ones. Similarly, several specific compounds located in the negative position of w*c axis such as n-butylformate [19], isopentyl butanoate [27], isoamyl caproate [37], and p-cresol [57] may contribute to the specificity of JNC liquor and they also displayed higher contents in JNC liquor when compared with that in FG liquor. 3.3. Specific volatile markers of heart stage liquors The quality of raw liquor is generally controlled by dividing the distillates into several appropriate fractions (e.g. discarding the head fraction, collecting the heart fraction and redistilling/discarding the tail fraction) (Scanavini et al., 2010). Traditionally, the heart stage liquor is usually used as the storage product for further blending of commercial liquor. So it is necessary to distinguish heart stage Luzhou-flavor liquors with different origins. The results concerning the heart stages of different origins were obviously monitored in Fig. 4 by PLS-DA. The subdivided figure (Fig. 4a) showed a good discrimination of different liquors, in which, 35.9% of the total variance with R2Y = 96.7% and Q2 = 64.1% was explained. A total of 4 groups of samples can be clearly defined: groups 1 (■) and 2 (●) belong to FG liquor and groups 3 (◆) and 4 (▲) belong to JNC liquor. It was worth noting that samples from FG liquor clustered together with each other, which suggested that the flavor feature of heart liquors from new and 50-year old pit was similar. Whereas the volatile feature of heart liquors from the new pit was obviously different from that from the 50-year old pit of JNC, because they located in two contrary positions in t[1] axis. Fig. 4b reveals the volatile markers contributing to the discrimination. In terms of FG liquors, compounds including 2-methylpropanoic acid [1], butanoic acid [2], pentanoic acid [4], 1-hexanol [16], ethyl acetate [18], and ethyl lactate [22] strongly correlated with FG-new heart liquors and ethyl nonanoate [39] was with FG-50 year liquors. As for the JNC liquor, 1-pentanol [13] and butyl lactate [25] were strongly correlated with JNC-new liquors and isobutyl alcohol [9], 2-methybutanal [51] and 1,1-diethoxyethane [58] were with JNC-50 year liquors. In conclusion, the volatile compounds of raw liquors with different origins were extracted and semi quantified. Results showed that the FG liquor had a similar volatile constitution with JNC liquor and the deviation of concentrations of volatile compounds may contribute to the discrimination of them. PCA could only represent the main differentiation between FG and JNC which may be attributed to the similar volatile constitution. PLS-DA not only provided powerful ability to discriminate different raw liquors in relation with the deviation of concentrations of volatile compounds, but also obviously depicted the volatile markers of each kind of raw liquors especially the heart distilling stage raw liquors. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.foodres.2013.12.011. Acknowledgments This work was financially supported by the National Science Foundation of China (31171742) and the living and learning expenses of Jia

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