Correlation between the amino acid content in rice wine and protein content in glutinous rice
2016; Wiley; Volume: 122; Issue: 1 Linguagem: Inglês
10.1002/jib.304
ISSN2050-0416
AutoresGuangfa Xie, Dongdong Yang, Xingquan Liu, Xiu-Xiu Cheng, Hong-Fei Rui, Hui-Jun Zhou,
Tópico(s)Biochemical Analysis and Sensing Techniques
ResumoJournal of the Institute of BrewingVolume 122, Issue 1 p. 162-167 Research articleFree Access Correlation between the amino acid content in rice wine and protein content in glutinous rice Guang-Fa Xie, Guang-Fa Xie National Engineering Research Centre for Chinese Rice Wine, China Shaoxing Rice Wine Group Co. Ltd, Shaoxing, 312000 ChinaSearch for more papers by this authorDong-Dong Yang, Dong-Dong Yang School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Search for more papers by this authorXing-Quan Liu, Corresponding Author Xing-Quan Liu School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Correspondence to: Xing-Quan Liu, School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an 311300, Zhejiang. E-mail: lxqzafu@163.comSearch for more papers by this authorXiu-Xiu Cheng, Xiu-Xiu Cheng School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Search for more papers by this authorHong-Fei Rui, Hong-Fei Rui School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Search for more papers by this authorHui-Jun Zhou, Hui-Jun Zhou National Engineering Research Centre for Chinese Rice Wine, China Shaoxing Rice Wine Group Co. Ltd, Shaoxing, 312000 ChinaSearch for more papers by this author Guang-Fa Xie, Guang-Fa Xie National Engineering Research Centre for Chinese Rice Wine, China Shaoxing Rice Wine Group Co. Ltd, Shaoxing, 312000 ChinaSearch for more papers by this authorDong-Dong Yang, Dong-Dong Yang School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Search for more papers by this authorXing-Quan Liu, Corresponding Author Xing-Quan Liu School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Correspondence to: Xing-Quan Liu, School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an 311300, Zhejiang. E-mail: lxqzafu@163.comSearch for more papers by this authorXiu-Xiu Cheng, Xiu-Xiu Cheng School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Search for more papers by this authorHong-Fei Rui, Hong-Fei Rui School of Agricultural and Food Science, Zhejiang Agricultural and Forest University, Lin'an, Zhejiang, 311300Search for more papers by this authorHui-Jun Zhou, Hui-Jun Zhou National Engineering Research Centre for Chinese Rice Wine, China Shaoxing Rice Wine Group Co. Ltd, Shaoxing, 312000 ChinaSearch for more papers by this author First published: 21 January 2016 https://doi.org/10.1002/jib.304Citations: 13AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Chinese rice wine is a fermentation product of glutinous rice that contains high levels of protein and amino acids. The turnover and catabolism of amino acids by fermentative microorganisms plays an important role in wine quality. The fermentation of Chinese rice wine, using 34 different varieties of glutinous rice, and the analysis of the protein and amino acid content of the resultant rice wine using precolumn derivatization via high-performance liquid chromatography, are reported. A model of correlation and regression analysis of the protein content in the glutinous rice and the amino acids in rice wine was established. Results showed that the correlation coefficient between the total protein in glutinous rice and the total free amino acids in rice wine was 0.557, indicating a significant relevance. The population correlation coefficient between the total protein in the glutinous rice and the amino acids in the rice wine was high, i.e. R= 0.928. The correlation between the soluble protein content in the glutinous rice and the total free amino acids in the rice wine (or individual amino acids) was negligible. The total protein content in the rice variety was positively related to the sensory performance and free amino acid content of the resultant Chinese rice wine. Copyright © 2016 The Institute of Brewing & Distilling Introduction Chinese rice wine is the fermentation product of glutinous rice, which contains high levels of proteins and amino acids. During the fermentation process, proteins, free amino acids and other forms of nitrogen in the glutinous rice are released, broken down and catabolized by microorganisms. The turnover and catabolism of amino acids by fermentative microorganisms play a key role in many aspects of wine quality. First, amino acids themselves impart a certain taste 1, for example, serine and glycine are sweet tasting and usually appreciated by rice wine customers, while cysteine, histidine and several other amino acids are perceived as bitter or acerbic. Second, many amino acids are catabolized by microorganisms into higher alcohols responsible for the mellow, soft and plump quality of the rice wine. For example, phenylalanine is transformed into the rose-like flavour molecule 2-phenylethanol via the Erhlich pathway 2, 3. Third, the metabolic products of some amino acids (for example, biogenic) are the cause of controversial safety concerns 4-6. In the present study, the total protein and free amino acid content in rice wine and the 34 starting rice cultivars was measured and their relationship analysed using correlation and regression analyses. The aim of this study was to determine the relationship between rice cultivars and the amino acid pattern in rice wine, in order to provide guidelines for the breeding and selection of rice cultivars. Materials and methods Materials and reagents Thirty-four varieties of glutinous rice were provided by the School of Agricultural and Food Sciences of Zhejiang, A & F University and were designated as samples 1–34. Rice wine yeast (Saccharomyces cerevisiae) was purchased from Angel Yeast Inc. (Yichang, China). The Wheat Qu was obtained from the National Engineering Research Centre for Chinese Rice Wine (China Shaoxing Rice Wine Group Co. Ltd, Shaoxing, China), and used in their brewing facility. Chemicals, including standard amino acids and phenyl isothiocyanate (PITC), were purchased from Sigma-Aldrich Inc. Instruments and equipment The XDS content analyser AD-1100 near-infrared grating spectrometer was provided by Denmark Foss Tecator Inc. The high-performance liquid chromatography equipment (including LC-20AT pump, SPD-M20A diode array detector, CTO-10AS VP automatic temperature-controlled chest, LC Solution workstation, etc.) and UV-1800 UV–vis spectrophotometer were provided by Shimadzu, Japan. Methods Determination of the total protein and soluble protein in glutinous rice Glutinous rice samples were filtered (0.22 µm) and subjected to near-infrared spectrometry; an MPLS model and WinISI software were used for statistics and calculations. The protein content in the glutinous rice was expressed with the mean value (ANL%) obtained from the near-infrared model 7, 8. Soluble protein was determined using Coomassie brilliant blue G-250. The bonding between the protein and Coomassie brilliant blue G-250, within the wavelength of 595 nm, has the maximum light absorption in proportion to the protein content. Evaluation using Coomassie brilliant blue G-250 is a simple and quick method, with a rapid response and high sensitivity; this method is often used for soluble protein assays 9. Brewing methods Brewing of the rice wine followed the conventional practice, namely soaking, steaming, sprinkling, fermentation, filtration and sterilization. Rice was first hydrolysed (soaking stage) using spring water (Tianmu Montain, Lin'an, China) for 2 days, steamed for 15 min and then chilled (sprinkling) with cool water. Freeze-dried yeast (Angel Yeast, Yichang, China) was treated with water (30°C) for 1 h, and mixed with the chilled rice, the Wheat Qu (a pre-fermented mixture of wheat, natural yeast and fungi) and spring water (DWrice–DWyeast–DWWheat qu–Wwater = 1000:1:10: 000). The principle fermentation was carried out in 5 L jars, placed in a growth chamber at 30°C and 75% humidity for 7 days, and then subsequently maintained at 18°C for 60 days. After fermentation, the wine was filtered (by squeezing) using a cotton filter (3 mm thick), then heated at 75°C for 20 min and sealed in 500 mL glass bottles. Determination of the amino acid content in the rice wine The amino acid content was measured after 90 days of storage at room temperature, following the bottle sealing process. After precolumn derivatization with PITC, high-performance liquid chromatography was used to determine the amino acid content of the rice wine. Amino acid derivatives reach a maximum absorption within the UV band of 254 nm 10, 11. The chromatographic conditions were as follows: chromatographic column, Inertsil ODS-4 column (3 µm, 4.6 mm × 250 mm); guard column, Inertsil ODS-SP (5 µm, 4.0 mm × 10 mm); injection volume, 10 μL; velocity, 1.0 mL/min; column temperature, 38°C; detection wavelength, 254 nm; mobile-phase liquid A, 10 mm PBS, pH 6.9; mobile-phase liquid B, acetonitrile; linear gradient elution (liquid B/%): 0–8 min, 5%; 8–30 min, 5–33%; 30–35 min, 33%; 35.01–40 min, 90%; 40.01–55 min, 5%; 55.01 min. Taste scoring and data analysis The taste scoring of the rice wine was performed as previously reported 12. Five nationally certified wine tasters (National Engineering Research Centre for Chinese Rice Wine) scored the 34 rice wines using 18 criteria categorized by physical appearance, sense of smell and sense of taste. Correlation and regression analysis between, respectively, rice total and soluble protein content, wine amino acid content against scores (from 1 to 10) of the 28 aspects were analysed using a standard correlation and regression analysis with SPSS19.0 analysis software. Results and analysis Total protein and soluble protein in glutinous rice A near-infrared spectrometer was used to determine the total protein content in raw glutinous rice (8.91–12.31%) before fermentation (Table 1). The total protein content of sample 26 was the highest at 12.315%, whereas that of sample 12 was 11.178%. Minimal differences in total protein content were observed amongst samples 1, 21, 25, 11, 32, 34 and 22 (10.0–10.5%); the total protein content of the remaining glutinous rice samples was 60% for the ratio of EAA to non-essential amino acids (NEAA) 12. Table 2 shows that the ratio of EAA in rice wine to TAA (N/T) is 33.7–43.1%, whereas that of EAA to NEAA (N/E) is 50.8–75.6%. The ratio of EAA to TAA in rice wine is similar to the ideal protein concentration in the human body. Correlation analysis of the total protein content of glutinous rice and the amino acid content of rice wine The results of the correlation analysis of the total protein content of the glutinous rice and the free amino acid content of the rice wine are shown in Table 3. The population correlation coefficient (multiple R) of the total protein content of the glutinous rice and the free amino acid content of the rice wine was high (0.928). This finding indicates that the explanatory power of predicting the amino acids in rice wine using the total protein content in glutinous rice was 92.8% and that the R2 after adjustment reached 72.9%. Thus, 16 independent variables explained 92.8% of the variations in the total protein content of the glutinous rice. After model checking, the regression coefficient reached a significant level [F(16, 17) = 6.561, p = 0.001] and the explanatory power was statistically significant. The Pearson correlation analysis of single amino acids showed that Glu, Gly, Pro, Met, Arg, Val, Ile, Leu, Phe, Lys and Tyr were positively correlated with the total protein content of glutinous rice at a significant level (p < 0.05) or at a very significant level (p < 0.01). This result indicated that the amino acid composition in rice wine was highly correlated with the total protein content of the glutinous rice. The results of the coefficient estimation implied that Lys had the maximum explanatory power and that β reached 1.580 with statistical significance (t = 3.816, p = 0.001). Thus, a high total protein content in the glutinous rice resulted in a high Lys content in the rice wine. The β coefficients of Ala and Pro were statistically significant (t = –2.819, p = 0.012; t = 2.438, p = 0.026). Asp, Ser, Ala, Cys and His were also positively correlated with the total protein content. A high total protein content in the glutinous rice resulted in a high content of certain amino acids. Table 3. Correlation analysis of the total protein in glutinous rice and free amino acids in rice wine Amino acids Correlation coefficient Significance level Regression coefficient Standard coefficient Partial correlation Asp 0.151 0.394 –3.976 –0.426 –0.447 Glu 0.542** 0.001 –0.355 –0.044 –0.038 Ser 0.212 0.229 –1.839 –0.091 –0.077 Gly 0.359* 0.037 –14.186 –0.917 –0.397 Ala 0.310 0.075 –3.408 –0.493 –0.564 Pro 0.546** 0.001 6.655 0.647 0.509 Met 0.481** 0.004 –22.114 –0.705 –0.350 Cys 0.297 0.088 6.694 0.228 0.221 His 0.335 0.053 –27.470 –0.651 –0.431 Arg 0.611** 0.000 2.542 0.469 0.409 Val 0.597 ** 0.000 5.168 0.413 0.176 Ile 0.563** 0.001 –0.854 –0.039 –0.010 Leu 0.616 ** 0.000 3.982 0.476 0.148 Phe 0.583** 0.000 –3.812 –0.315 –0.086 Lys 0.531** 0.001 18.489 1.580 0.679 Tyr 0.578** 0.000 2.839 0.219 0.084 Constant 8993.662 Correlation coefficient R = 0.928; determination coefficient R2 = 0.861 and adjusted correlation coefficient R2 = 0.729. * Significant correlation at 0.05 (bilateral); ** significant correlation at 0.01 (bilateral). The results of the correlation analysis of the total protein content of the glutinous rice and the total free amino acid content of the rice wine are shown in Table 4. The correlation coefficient (multiple R) of the total protein in the glutinous rice and the total free amino acids in the rice wine, as well as the standard coefficient (β), was 0.557. The test values of the coefficients were similar and reached a very significant level [F(1, 32) = 14.365, p = 0.001], whereas the explanatory power was also statistically significant. The result of the coefficient estimation indicated that it was possible to effectively predict the amino acid content of the rice wine using the total protein content of the glutinous rice. Moreover, the β coefficient was determined as 0.557 (t = 3.790, p = 0.001); this finding implied that a high total protein content of the glutinous rice resulted in a high amino acid content in the rice wine. Sample 26 of the glutinous rice variety had the highest total protein content; thus, the resulting amino acid content in the rice wine was also the highest. Comparatively, the content of the specific free amino acids was also high. Table 4. Correlation analysis of the total protein in glutinous rice and total free amino acids in rice wine Item Correlation coefficient Significance level Regression coefficient Standard coefficient Partial correlation Σ AA 0.557** 0.001 0.479 0.557 0.557 Constant 8,391.733 Correlation coefficient R = 0.557; determination coefficient R2 = 0.310 and adjusted correlation coefficient R2 = 0.288 Σ AA=total amino acids. Correlation analysis on the soluble protein content of the glutinous rice and the amino acid content of the rice wine The results of the correlation analysis of the soluble protein in the glutinous rice and the free amino acids in rice wine are shown in Table 5. The population correlation coefficient of the soluble protein in the glutinous rice and the free amino acids in rice wine was 0.552, which was lower than the correlation coefficient between the protein content of glutinous rice and the amino acids in rice wine. Moreover, the correlation coefficient after adjustment was only 0.304. The results of model checking provided the test value of F(16, 17) = 0.465 (p = 0.934), hence the explanatory power of the correlation coefficient was not statistically significant. The correlation analysis of a single amino acid and the soluble protein content indicated that the correlation coefficient between various amino acids and the soluble protein content was <0.162, which was lower than the significant level; thus, a correlation between the soluble protein content of glutinous rice and the amino acids of rice wine was not evident. This result is understandable as most of the rice amino acid nutrition are in the form of storage protein, which are not released, hydrolysed or catabolized until the corresponding soaking and fermentation steps. Table 5. Correlation analysis of soluble protein in glutinous rice and free amino acid in rice wine Amino acid Correlation coefficient Significance level Regression coefficient Standard coefficient Partial correlation Asp 0.004 0.981 0.954 0.320 0.166 Glu –0.075 0.675 –0.069 –0.027 –0.010 Ser 0.162 0.361 3.436 0.532 0.197 Gly 0.038 0.831 –2.135 –0.432 –0.091 Ala –0.012 0.947 0.618 0.280 0.171 Pro 0.020 0.909 –0.657 –0.200 –0.081 Met 0.100 0.573 17.355 1.731 0.380 Cys –0.009 0.958 –5.405 –0.575 –0.248 His 0.107 0.548 4.440 0.329 0.107 Arg 0.006 0.973 0.637 0.368 0.155 Val –0.033 0.853 –1.170 –0.293 –0.057 Ile –0.002 0.991 –6.664 –0.941 –0.110 Leu –0.011 0.949 1.447 0.541 0.076 Phe –0.003 0.987 –2.315 –0.598 –0.074 Lys –0.002 0.990 –5.087 –1.360 –0.336 Tyr 0.025 0.887 2.100 0.507 0.087 Constant 1,499.852 Correlation coefficient R = 0.552; determination coefficient R2 = 0.304 and adjusted correlation coefficient R2 = –0.350. The results of the correlation analysis of the soluble protein content in glutinous rice and the total free amino acids in rice wine are shown in Table 6. The correlation coefficient of the soluble protein content of the glutinous rice and the total free amino acids in rice wine was 0.005. The test value of the regression coefficient was similar (F(1, 32) = 0.001, p = 0.978) and the explanatory power was not statistically significant. The results of the coefficient estimation were statistically significant and the β coefficient was 0.005 (t = 0.028, p = 0.978); this finding indicated that a correlation between the soluble protein content of the glutinous rice and the TAA in rice wine was not evident. Table 6. Correlation analysis of soluble protein in glutinous rice and total free amino acids in rice wine Item Correlation coefficient Significance level Regression coefficient Standard coefficient Partial correlation ΣAA 0.005 0.978 0.014 0.005 0.005 Constant 13,122.388 Correlation coefficient R = 0.005; determination coefficient R2 = 0.000 and adjusted correlation coefficient R2 = –0.331. Correlation analysis of the protein content in the glutinous rice and the taste scoring of the rice wine Li et al. 12 reported that the correlation coefficient for the multiple regression equation of the amino acid content in rice wine and the taste scoring of rice wine was 0.9094, which established the multiple regression equation of the amino acid content in rice wine and the sensory value. In the present study, the sensory scoring of the 34 rice wine samples was established using the equation of Li et al. Amongst these rice wines, samples 32, 11, 26 and 27 obtained the highest sensory scores (Table 7). Table 7. Taste scoring by amino acid content Item no. Sensory scoring Item no. Sensory scoring 1 20.67 18 15.95 2 10.53 19 14.19 3 16.40 20 19.12 4 14.77 21 17.61 5 16.85 22 15.66 6 15.89 23 16.54 7 14.81 24 16.09 8 15.42 25 23.27 9 16.60 26 26.18 10 16.79 27 25.09 11 28.07 28 18.21 12 17.02 29 13.95 13 14.52 30 13.04 14 23.02 31 17.15 15 13.45 32 28.35 16 16.92 33 8.82 17 17.23 34 16.71 The results of the correlation analysis of the total protein content of the glutinous rice and the taste scoring of rice wine are shown in Table 8. The correlation coefficient between the total protein in glutinous rice and the taste scoring of rice wine was 0.469, reaching a very significant level. This finding indicated that the explanatory power of estimating the taste of rice wine, using the total protein content of glutinous rice, was 46.9%. The results of model checking showed that the regression coefficient reached a significant level [F(1, 32) = 9.047, p = 0.005] and that the explanatory power was statistically significant. The results of the coefficient estimation indicated the total protein content of the glutinous rice; thus effectively predicting the taste of the rice wine was statistically significant (t = 3.008, p = 0.005). A higher total protein content in the glutinous rice resulted in better-tasting rice wine. Table 8. Correlation analysis of the total protein in glutinous rice and the taste scoring of rice wine Item Correlation coefficient Significance level Regression coefficient Standard coefficient Partial correlation Total protein 0.469** 0.005 3.103 0.469 0.469 Constant –12.927 Correlation coefficient R = 0.469; determination coefficient R2 = 0.220; and adjusted correlation coefficient R2 = 0.196. The results of the correlation analysis of the soluble protein content of the glutinous rice and the taste scoring of rice wine are shown in Table 9. The correlation coefficient between the soluble protein content in glutinous rice and the taste scoring of rice wine was 0.063, which was lower than the correlation coefficient between the total protein in glutinous rice and the taste scoring of rice wine (0.469). The results of model checking demonstrated that the regression coefficient was not significant [F(1, 32) = 0.129, p = 0.722]; hence, the explanatory power was not statistically significant. The results of the coefficient estimation (t = 0.359, p = 0.722) showed that the correlation between the soluble protein in the glutinous rice and the taste of the rice wine was negligible. Table 9. Correlation analysis of the soluble protein in glutinous rice and the taste scoring of rice wine Item Correlation coefficient Significance level Regression coefficient Standard coefficient Partial correlation Soluble protein 0.063 0.722 0.131 0.063 0.469 Constant 15.771 Correlation coefficient R = 0.063; determination coefficient R2 = 0.004; and adjustment coefficient R2 = –0.027. Conclusions The population correlation coefficient of the total protein in the glutinous rice and the free amino acids in the rice wine was 0.928, which was statistically significant. The population regression coefficient of the total protein in the glutinous rice and the total free amino acids in rice wine was 0.557, which was very significant. In the correlation between the protein in the glutinous rice and the individual amino acid composition in the rice wine, the correlation between Glu/Gly/Pro/Met/Arg/Val/Ile/Leu/Phe/Lys/Tyr and the protein in glutinous rice reached a significant or very significant level. The correlation coefficient between the total protein in the glutinous rice and the taste scoring by amino acid content was 0.469, which is very significant. A correlation between the soluble protein content of the glutinous rice and the amino acid content (or taste) of the rice wine was not evident. These findings have shown that the total protein content of the glutinous rice affected the free amino acids in the rice wine. A high total protein content of glutinous rice resulted in a high free amino acid content in the rice wine. Moreover, the total protein content of the glutinous rice could affect the sensory scoring of the rice wine. A higher total protein content in the glutinous rice resulted in a better-tasting rice wine. Among the 34 glutinous rice varieties, the total protein content in glutinous rice, the amino acid content in rice wine and the sensory scoring of rice wine were evaluated. When the total content of protein in the glutinous rice varieties (samples 26 and 11) was high, the TAA in the corresponding rice wine samples was also high, with good sensory scoring. Thus, these samples were considered to be a high-quality feedstock for producing rice wine. Acknowledgements This work was funded by the Shaoxing Sci-Tech Program (2014A22002) and Zhejiang Sci-Tech Program (2013C32011) to X.Q.L., and NSFC(31301547), ZAFU(2012FR066) and SRF for ROCS to D.D.Y. References 1Anson, L. (2002) Neurobiology: The bitter-sweet taste of amino acids, Nature 416(6877), 136. 2Hazelwood, L. A., Daran, J. M., van Maris, A. J., Pronk, J. T., and Dickinson, J. R. (2008) The Ehrlich pathway for fusel alcohol production: A century of research on Saccharomyces cerevisiae metabolism, Appl. Environ. Microbiol. 74, 2259– 2266. 3Liu, X. Q., Chen, X. S., and Zhuge, Q. (2011) Determination of β-phenylethanol in Chinese rice wine by reverse phase high-performance liquid chromatography, J. Inst. Brew. 117, 578– 581. 4Lu, Y., Lu, X., Chen, X., Jiang, M., Li, C., and Dong, M. (2007) A survey of biogenic amines in Chinese rice wines, Food Chem. 100, 1424– 1428. 5Zhong, J., Ye, X., Fang, Z., Xie, G., Liao, N., Shu, J., and Liu, D. (2012) Determination of biogenic amines in semi-dry and semi-sweet Chinese rice wines from the Shaoxing region, Food Control 28, 151– 156. 6Wu, D., Li, X., Shen, C., Lu, J., Chen, J., and Xie, G. (2014) Decreased ethyl carbamate generation during Chinese rice wine fermentation by disruption of CAR1 in an industrial yeast strain, Int. J. Food Microbiol. 180, 19– 23. 7Zhang, B., Rong, Z. Q., Shi, Y., Wu, J. G., and Shi, C. H. (2011) Prediction of the amino acid composition in brown rice using different sample status by near-infrared reflectance spectroscopy, Food Chem. 127, 275– 281. 8Muccillo, L., Gambuti, A., Frusciante, L., Iorizzo, M., Moio, L., Raieta, K., Rinaldi, A., Colantuoni, V., and Aversano, R. (2014) Biochemical features of native red wines and genetic diversity of the corresponding grape varieties from Campania region, Food Chem. 143, 506– 513. 9Bradford, M. M. (1976) Rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding, Anal. Biochem. 72, 248– 254. 10Vilanova, M., Genisheva, Z., Masa, A., and Oliveira, J. M. (2010) Correlation between volatile composition and sensory properties in Spanish Albariño wines, Microchem. J. 95, 240– 246. 11Shen, F., Ying, Y., Li, B., Zheng, Y., and Zhuge, Q. (2011) Multivariate classification of rice wines according to ageing time and brand based on amino acid profiles, Food Chem. 129, 565– 569. 12Li, B. B., Zeng, J. H., Liu, X. Q., Zhuge, Q., and Yu, Y. F. (2010) Study on quantitative relationships between amino acids and sensory taste of yellow rice wine, Brewing Tech. 10, 23– 25. Citing Literature Volume122, Issue12016Pages 162-167 ReferencesRelatedInformation
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