Development of drinking product from the quinoa seed (chenopodium quinoa)

VIETNAM NATIONAL UNIVERSITY OF AGRICULTURE LE MY HANH DEVELOPMENT OF DRINKING PRODUCT FROM THE QUINOA SEED (CHENOPODIUM QUINOA) Major : Food science and Technology Code : 24180560 Supervisor : Dr Tran Thi Lan Huon AGRICULTURAL UNIVERSITY PRESS - 2017 DECLARATION I hereby declare that this thesis is my own work and effort and that it has not been submitted anywhere for any degree Where other sources of information have been used, they have been acknowledged Hanoi, May 10th, 2017 Master candidate Le My Hanh i ACKNOWLEDGEMENTS I would like to express the deepest thanks to my supervisor Dr Tran Thi Lan Huong for her guidance during the time I conduct the research She provided me the material to perform all experiments and taught me how stuff works She opened my mind for any kind of research conversation, and always being a good listener I am also grateful to Msc Nguyen Huy Bao who taught me how to process data on design expert software and some skills in Microsoft He helped me better understand the theories related to optimization and sensory evaluation Moreover, he used to encourage me whenever I was depressed during this study Sincerest thanks to my colleagues in the Department of Quality Management and Food Safety who helped me to some works in the office Therefore, I could spend more time to study and complete my research thesis The students in the Faculty of Food science and Technology are Chuc and Linh who helped me a lot in the experiments of this study I would like to express my thanks to them Last but most definitely not least, I would like to express my deepest gratitude to my parents and the rest of the family who always emphasized the importance of education It is not the tiniest bit exaggerated to say this thesis wouldn’t exist without some “parental guidance” throughout my (numerous) years in education Hanoi, May 10th, 2017 Master candidate Le My Hanh ii TABLE OF CONTENTS Declaration i Acknowledgements ii Table of contents iii List of abbreviations iv List of tables v List of figures vi Thesis abstract vii Chapter Introduction 1.1 Introduction 1.2 Objective Chapter Literature Review 2.1 Overview of material 2.2 Cereal and grain for non-alcohol beverage 14 Chapter Materials and methods 18 3.1 Materials 18 3.2 Research content 18 3.3 Methods 18 Chapter Results and discussion 26 4.1 Analysing the main chemical content 26 4.2 Enzymatic hydrolysis optimization of quinoa starch 27 4.3 Effect of sterilization regime (temperature and time) on product quality 33 Chapter Conclusion and recommendations 40 5.1 Conclusion 40 5.2 Recommendations 40 Reference 41 Appendix 50 iii LIST OF ABBREVIATIONS Acronym ANOVA CFU db FAO ULS QS QP RSM SICA WHO Abbreviations Analyze of variance Colony forming unit Dry basis Food and Agriculture Organization Unstructured line scale Quinoa seed Quinoa protein Response surface methodology Agricultural Census and Information System World Health Organization iv LIST OF TABLES Table 2.1 Amino acids composition of quinoa seed, barley, soybean, and wheata Table 2.2 Comparison of essential amino acids content of barley, corn and wheat to FAO/WHO suggested requirement Table 2.3 Mineral composition whole quinoa seed, dehulled quinoa seed, quinoa flour, oat, barley (mg/100 g) Table 3.1 Factors and their levels for the Box-Behnken design 21 Table 3.2 Experimental design and responses of the dependent variables to the hydrolysis parameters 22 Table 4.1 The main chemical content of quinoa seed (% dry weight) 26 Table 4.2 Amino acid profiles (g/100g protein) 27 Table 4.3 Responses of the dependent variables to the hydrolysis parameters 28 Table 4.4 Regression coefficients of the predicted second-order polynomial models for the total sugar content, score of color and score of appearance 29 Table 4.5 Experimental data of verification of predicted technical parameters of enzyme 33 Table 4.6 The number of microbe of sample sterilized at 1100C (CFU/ml) 35 Table 4.7 The numerous of microbe of sample sterilized at 1100C in 10min (CFU/ml) 36 v LIST OF FIGURES Fig 2.1 Structure α-amylase (Payan, 2004) 13 Fig 2.2 Commercial rice milk 14 Fig 2.3 Commercial soy milk 16 Fig 2.4 Commercial oat milk 16 Fig 3.1 Flow chart of producing quinoa milk 19 Fig 3.2 Unstructured line scale for training and sensorial evaluation 25 Fig 4.1 Surface plot of the total sugar content (Y1) as a function of concentration of enzyme and temperature at time of 80 and a function of temperature and time at concentration of 0,15% enzyme 30 Fig 4.2 Surface plot of the score of color (Y2) as a function of concentration of enzyme and temperature at time of 80 and as a function of temperature and time at concentration of 0.15% enzyme 31 Fig 4.3 Surface plot of the score of appearance (Y3) as a function of time and temperature at concentration of 0.15% enzyme and as a function of temperature and enzyme concentration at time of 63.1 32 Fig 4.4 Contour plot showing optimal values of responses 32 Fig 4.5 Effect of sterilization on color of product 33 Fig 4.6 Distribution of response level on hedonic scale 37 Fig 4.7 Average scores of quinoa milk and rice milk 38 Fig 4.8 Flow chart of producing process of quinoa milk with the attached technological parameters 39 vi THESIS ABSTRACT The quinoa milk was processed from quinoa seeed (Chenopodium quinoa) which has high nutrition value aiming to diversification of beverage products in the market Our study focused on the following content The optimization of enzymatic hydrolysis using response surface methodology to study the influence of the variables (enzyme concentration, hydrolysis temperature and hydrolysis time) on the variability of total sugar content and sensory properties (color and appearance) of quinoa milk Validation of models showed a good agreement between experimental results and the predicted responses The results indicated that all three variables had a significant impact on responses The hydrolysis temperature had the most significant effect on the total sugar content However, the variable which had the most significant effect on the score of color and appearance of quinoa milk as well was hydrolysis time The optimal hydrolysis parameters were the enzyme concentration of 0.12%, hydrolysis temperature of 91.9°C and time of 69.6 according to the response surface analysis Under this condition, the total sugar content, score of color and score of appearance was 83.95mg/ml, 8.47 and 8.5 respectively The quality and shelf-life of product is greatly determined by sterilization regime The impact of twelve different regimes of heat sterilization, defined by their combinations of temperature and time (temperature: 1100C, 1150C and 1210C, time: 5min, 7min, 10min and 15min) on the color and microbiological properties of products was evaluated In term of color, the three samples which sterilized at 1100C in 5, and 10 achieved the highest score of color The sample sterilized at 1100C in 10 was the best which successfully met requirements about microbiological criteria according National technical regulation for soft drinks, 2010 (QCVN 6-2:2010/BYT) The result of sensory evaluation by panel indicated that the average scores of both quinoa milk and Korean rice milk that were in the range from “like extremely” to “dislike extremely” were 6.25 and 6.22 respectively Although quinoa milk is a new product in Viet Nam, it was slightly liked by the panel Finally, the process of producing quinoa milk was built with the attached technological parameters vii CHAPTER INTRODUCTION 1.1 INTRODUCTION Drinks play an important role in human’s life It provides body not only a big amount of water involved in the metabolism of the cell but also a huge amount of nutrients, vitamins and minerals to compensate for the loss of energy and nutrients have been consumed in the activity of human (Hien, 2006) Nowadays, because of busy lifestyle, the time spending for consuming food in daily life is very short Furthermore, people pay more attention about their health Therefore, the ready to drink providing full of nutrition for body is quite necessary In Vietnamese beverage industry, consumers tend to use natural products such as green tea or fruit juice although the price is higher than that of carbonated beverage Besides, many kinds of seed is also nutritious source of raw material used in manufacturing of drinking product for the consumer such as soy beans, rice, corn Quinoa grains have presented in the world 7000 years ago at South America (Bazile et al., 2016) but it is quite new in Viet Nam Despite of early appearance, the potential and benefits of quinoa have recently been known by researchers in other countries Studies have been performed in an increasing number of countries The number of countries growing quinoa has risen rapidly from in 1980 to 75 in 2014, with a further 20 countries which sowed quinoa for the first time in 2015 (Bazile and Baudron, 2015) In Vietnam, quinoa plants were grown and developed in the period of time between 1986 and 2000 with HV1 specie in many provinces, the yield from 14.0 to 20.6 kg / (Trinh Ngoc Duc, 2001) Nowadays, they are grown in Quang Tri, An Giang province and Viet Nam University of Agriculture This kind of seed attracts the attention of many researches in different countries because of its nutritional value (James, 2009) especially protein Its protein levels are similar to those found in milk and higher than those present in cereals such as wheat, rice and maize (James, 2009) Furthermore, quinoa seed has highest content of bioactive compounds compared to other cereals and pseudo-cereals (Hirose et al., 2010) Therefore, we conduct the research on the topic: “Development of drinking product from the quinoa seed (Chenopodium quinoa)” to diversify of beverage products in the market 1.2 OBJECTIVE 1.2.1 General objective The aim of this study was to develop a drinking product from quinoa seed (quinoa milk) belonging to favorable product family in recent years which help to diversify of beverage products in the market 1.2.2 Specific objectives - Enzymatic hydrolysis optimization of quinoa starch - Determining the appropriate condition of sterilization - Determining degree of preference of consumer toward product - Building the process of producing quinoa milk APPENDIX Response surface analysis of total sugar content Response Total sugar content ANOVA for Response Surface Quadratic model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 9047,25 1005,25 10,58 0,0091 A-Enzyme concentration (%) 124,66 124,66 1,31 0,3039 B-Temperature (0C) 6262,48 6262,48 65,90 0,0005 C-Time (min) 3,93 3,93 0,041 0,8468 AB 22,94 22,94 0,24 0,6440 AC 72,25 72,25 0,76 0,4231 BC 319,52 319,52 3,36 0,1262 A2 718,49 718,49 7,56 0,0403 B2 1596,99 1596,99 16,80 0,0094 C2 183,63 183,63 1,93 0,2232 Residual 475,17 95,03 Lack of Fit 338,34 112,78 Pure Error 136,82 68,41 Cor Total 9522,42 14 1,65 0,3992 significant not significant The Model F-value of 10,58 implies the model is significant There is only a 0,91% chance that an F-value this large could occur due to noise Values of "Prob > F" less than 0,0500 indicate model terms are significant In this case B, A^2, B^2 are significant model terms Values greater than 0.1000 indicate the model terms are not significant If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model The "Lack of Fit F-value" of 1,65 implies the Lack of Fit is not significant relative to the pure error There is a 39,92% chance that a "Lack of Fit F-value" this large could occur due to noise Non-significant lack of fit is good we want the model to fit 50 Std Dev 9,75 R-Squared Mean 70,25 Adj R-Squared 0,8603 C.V % 13,88 Pred R-Squared 0,3992 PRESS 5721,33 Adeq Precision 9,303 -2 Log Likelihood 94,40 0,9501 BIC 121,48 AICc 169,40 The "Pred R-Squared" of 0,3992 is not as close to the "Adj R-Squared" of 0,8603 as one might normally expect; i.e the difference is more than 0.2 This may indicate a large block effect or a possible problem with your model and/or data Things to consider are model reduction, response transformation, outliers, etc All empirical models should be tested by doing confirmation runs "Adeq Precision" measures the signal to noise ratio A ratio greater than is desirable Your ratio of 9,303 indicates an adequate signal This model can be used to navigate the design space Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High Intercept 92,55 5,63 78,08 107,01 A-Enzyme concentration (%) 3,95 3,45 -4,91 12,81 B-Temperature (0C) -27,98 3,45 -36,84 -19,12 1,00 C-Time (min) 0,70 3,45 -8,16 AB -2,40 4,87 -14,92 10,13 1,00 AC 4,25 4,87 -8,28 1,00 BC -8,94 4,87 -21,47 3,59 1,00 A2 -13,95 5,07 -26,99 -0,91 1,01 B2 -20,80 5,07 -33,84 -7,76 1,01 -7,05 5,07 -20,09 5,99 1,01 C Final Equation in Terms of Coded Factors: Total sugar content = +92,55 +3,95 *A -27,98 *B 51 9,56 16,78 VIF 1,00 1,00 +0,70 *C -2,40 * AB +4,25 * AC -8,94 * BC -13,95 * A2 -20,80 * B2 -7,05 * C2 The equation in terms of coded factors can be used to make predictions about the response for given levels of each factor By default, the high levels of the factors are coded as +1 and the low levels of the factors are coded as -1 The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients Final Equation in Terms of Actual Factors: Total sugar content = -1850,40000 +1286,01667 * Enzyme concentration (%) +38,69088 * Temperature (0C) +6,45277 * Time (min) -4,79000 * Enzyme concentration (%) * Temperature (0C) +4,25000 * Enzyme concentration (%) * Time (min) -0,044688 * Temperature (0C) * Time (min) -5579,83333 * Enzyme concentration (%)2 -0,20797 * Temperature (0C)2 -0,017630 * Time (min)2 Response surface analysis of score of color Response Score of color ANOVA for Response Surface Quadratic model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 78,82 8,76 52 7,78 0,0180 Significant A-Enzyme concentration 0,93 0,93 0,83 0,4046 B-Temperature 6,14 6,14 5,46 0,0667 C-Time 37,76 37,76 33,55 0,0022 AB 9,15 9,15 8,13 0,0358 AC 0,40 0,40 0,35 0,5784 BC 2,79 2,79 2,48 0,1762 A2 1,48 1,48 1,31 0,3035 B2 6,56 6,56 5,83 0,0605 15,98 15,98 14,20 0,0130 Residual 5,63 1,13 Lack of Fit 2,99 1,00 Pure Error 2,64 1,32 Cor Total 84,45 14 C 0,75 0,6133 not significant The Model F-value of 7,78 implies the model is significant There is only a 1,80% chance that an F-value this large could occur due to noise Values of "Prob > F" less than 0,0500 indicate model terms are significant In this case C, AB, C^2 are significant model terms Values greater than 0.1000 indicate the model terms are not significant If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model The "Lack of Fit F-value" of 0,75 implies the Lack of Fit is not significant relative to the pure error There is a 61,33% chance that a "Lack of Fit F-value" this large could occur due to noise Non-significant lack of fit is good we want the model to fit Std Dev 1,06 R-Squared Mean 5,35 Adj R-Squared 0,8134 C.V % 19,85 Pred R-Squared 0,3638 PRESS 53,72 Adeq Precision 7,379 -2 Log Likelihood 27,86 BIC AICc 0,9334 54,94 102,86 The "Pred R-Squared" of 0,3638 is not as close to the "Adj R-Squared" of 0,8134 as one might normally expect; i.e the difference is more than 0.2 This may indicate a large block effect or a possible problem with your model and/or data Things to consider are 53 model reduction, response transformation, outliers, etc All empirical models should be tested by doing confirmation runs "Adeq Precision" measures the signal to noise ratio A ratio greater than is desirable Your ratio of 7,379 indicates an adequate signal This model can be used to navigate the design space Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High Intercept 7,50 0,61 5,93 9,08 A-Enzyme concentration 0,34 0,38 -0,62 1,31 1,00 B-Temperature 0,88 0,38 -0,088 1,84 1,00 C-Time -2,17 0,38 -3,14 -1,21 1,00 AB 1,51 0,53 0,15 2,88 1,00 AC -0,32 0,53 -1,68 1,05 1,00 BC -0,84 0,53 -2,20 0,53 1,00 A2 -0,63 0,55 -2,05 0,79 1,01 B -1,33 0,55 -2,75 0,086 1,01 C2 -2,08 0,55 -3,50 -0,66 1,01 Final Equation in Terms of Coded Factors: Score of color = +7,50 +0,34 *A +0,88 *B -2,17 *C +1,51 * AB -0,32 * AC -0,84 * BC -0,63 * A2 -1,33 * B2 -2,08 * C2 54 VIF The equation in terms of coded factors can be used to make predictions about the response for given levels of each factor By default, the high levels of the factors are coded as +1 and the low levels of the factors are coded as -1 The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients Final Equation in Terms of Actual Factors: Score of color = -141,51500 -189,59167 * Enzyme concentration +2,51838 * Temperature +1,13079 * Time +3,02500 * Enzyme concentration * Temperature -0,31500 * Enzyme concentration * Time -4,17500E-003 * Temperature * Time -253,16667 * Enzyme concentration2 -0,013329 * Temperature2 -5,20104E-003 * Time2 Response surface analysis of score of appearance Response Score of appearance ANOVA for Response Surface Quadratic model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 82,77 9,20 8,73 0,0140 A-Enzyme concentration 0,61 0,61 0,57 0,4828 B-Temperature 5,28 5,28 5,01 0,0754 C-Time 41,22 41,22 39,11 0,0015 AB 9,36 9,36 8,88 0,0308 AC 0,40 0,40 0,38 0,5663 BC 3,72 3,72 3,53 0,1189 A2 1,30 1,30 1,24 0,3167 55 Significant B2 6,97 6,97 6,61 C2 16,27 16,27 15,44 0,0111 Residual 5,27 1,05 Lack of Fit 2,60 0,87 Pure Error 2,67 1,33 Cor Total 88,04 14 0,65 0,0499 0,6528 not significant The Model F-value of 8,73 implies the model is significant There is only a 1,40% chance that an F-value this large could occur due to noise Values of "Prob > F" less than 0,0500 indicate model terms are significant In this case C, AB, B^2, C^2 are significant model terms Values greater than 0.1000 indicate the model terms are not significant If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model The "Lack of Fit F-value" of 0,65 implies the Lack of Fit is not significant relative to the pure error There is a 65,28% chance that a "Lack of Fit F-value" this large could occur due to noise Non-significant lack of fit is good we want the model to fit Std Dev 1,03 R-Squared Mean 5,33 Adj R-Squared 0,8324 C.V % 19,25 Pred R-Squared 0,4588 PRESS 47,65 Adeq Precision 7,808 -2 Log Likelihood 26,88 BIC AICc 0,9401 53,96 101,88 The "Pred R-Squared" of 0,4588 is not as close to the "Adj R-Squared" of 0,8324 as one might normally expect; i.e the difference is more than 0.2 This may indicate a large block effect or a possible problem with your model and/or data Things to consider are model reduction, response transformation, outliers, etc All empirical models should be tested by doing confirmation runs "Adeq Precision" measures the signal to noise ratio A ratio greater than is desirable Your ratio of 7,808 indicates an adequate signal This model can be used to navigate the design space Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High Intercept 7,50 0,59 5,98 9,03 56 VIF A-Enzyme concentration 0,27 0,36 -0,66 1,21 1,00 B-Temperature 0,81 0,36 -0,12 1,75 1,00 C-Time -2,27 0,36 -3,20 -1,34 1,00 AB 1,53 0,51 0,21 2,85 1,00 AC -0,32 0,51 -1,63 1,00 1,00 BC -0,97 0,51 -2,28 0,35 1,00 -0,59 0,53 -1,97 0,78 1,01 A B -1,37 0,53 -2,75 7,333E- 1,01 004 C2 -2,10 0,53 -3,47 -0,73 1,01 Final Equation in Terms of Coded Factors: Score of appearance = +7,50 +0,27 *A +0,81 *B -2,27 *C +1,53 * AB -0,32 * AC -0,97 * BC -0,59 * A2 -1,37 * B2 -2,10 * C2 The equation in terms of coded factors can be used to make predictions about the response for given levels of each factor By default, the high levels of the factors are coded as +1 and the low levels of the factors are coded as -1 The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients Final Equation in Terms of Actual Factors: Score of appearance = 57 -148,27000 -197,16667 * Enzyme concentration +2,63475 * Temperature +1,19192 * Time +3,06000 * Enzyme concentration * Temperature -0,31500 * Enzyme concentration * Time -4,82500E-003 * Temperature * Time -237,66667 * Enzyme concentration2 -0,013742 * Temperature2 -5,24792E-003 * Time2 Evaluating the degree of liking of quinoa milk and rice milk Anova: Single Factor SUMMAY Groups Count Sum Average Variance Quinoa milk 60 375 6,25 1,47881 Rice milk 60 373 6,2166667 1,46073 Source of Variation SS df MS F Between Groups 0,03333333 0,0333333 33 0,02267 0,880551 Within Groups 173,433333 118 1,4697740 11 Total 173,466666 119 ANOVA P-value F crit 3,921478 Effect of sterilization on color of products Contrast Standardized Difference difference Critical value Pr > Diff Significant S1 vs S12 7,7500 3,3620 < 0.0001 Yes 54,9338 58 S1 vs S11 6,9375 49,1746 3,3620 < 0.0001 Yes S1 vs S10 6,0313 42,7509 3,3620 < 0.0001 Yes S1 vs S9 5,3125 37,6562 3,3620 < 0.0001 Yes S1 vs S8 2,9375 20,8217 3,3620 < 0.0001 Yes S1 vs S7 2,9375 20,8217 3,3620 < 0.0001 Yes S1 vs S6 2,4688 17,4991 3,3620 < 0.0001 Yes S1 vs S5 2,3750 16,8345 3,3620 < 0.0001 Yes S1 vs S4 0,3438 2,4366 3,3620 0,3940 No S1 vs S3 0,0938 0,6645 3,3620 0,9999 No S1 vs S2 0,0625 0,4430 3,3620 1,0000 No S2 vs S12 7,6875 54,4908 3,3620 < 0.0001 Yes S2 vs S11 6,8750 48,7316 3,3620 < 0.0001 Yes S2 vs J10 5,9688 42,3079 3,3620 < 0.0001 Yes S2 vs S9 5,2500 37,2132 3,3620 < 0.0001 Yes S2 vs S7 2,8750 20,3787 3,3620 < 0.0001 Yes S2 vs S8 2,8750 20,3787 3,3620 < 0.0001 Yes S2 vs S6 2,4063 17,0561 3,3620 < 0.0001 Yes S2 vs S5 2,3125 16,3915 3,3620 < 0.0001 Yes S2 vs S5 0,2813 1,9936 3,3620 0,6963 No S2 vs S3 0,0313 0,2215 3,3620 1,0000 No S3 vs S12 7,6563 54,2693 3,3620 < 0.0001 Yes S3 vs S11 6,8438 48,5101 3,3620 < 0.0001 Yes S3 vs S10 5,9375 42,0864 3,3620 < 0.0001 Yes S3 vs S9 5,2188 36,9917 3,3620 < 0.0001 Yes S3 vs S7 2,8438 20,1572 3,3620 < 0.0001 Yes S3 vs S8 2,8438 20,1572 3,3620 < 0.0001 Yes S3 vs S6 2,3750 16,8345 3,3620 < 0.0001 Yes S3 vs S5 2,2813 16,1700 3,3620 < 0.0001 Yes S3 vs S4 0,2500 1,7721 3,3620 0,8281 No S3 vs S12 7,4063 52,4972 3,3620 < 0.0001 Yes S4 vs S11 6,5938 46,7380 3,3620 < 0.0001 Yes S4 vs S10 5,6875 40,3143 3,3620 < 0.0001 Yes S4 vs S9 4,9688 35,2196 3,3620 < 0.0001 Yes S4 vs S7 2,5938 18,3851 3,3620 < 0.0001 Yes S4 vs S8 2,5938 18,3851 3,3620 < 0.0001 Yes S4 vs S6 2,1250 15,0625 3,3620 < 0.0001 Yes 59 S4 vs S5 2,0313 14,3980 3,3620 < 0.0001 Yes S5 vs S12 5,3750 38,0992 3,3620 < 0.0001 Yes S5 vs S11 4,5625 32,3400 3,3620 < 0.0001 Yes S5 vs S10 3,6563 25,9163 3,3620 < 0.0001 Yes S5 vs S9 2,9375 20,8217 3,3620 < 0.0001 Yes S5 vs S7 0,5625 3,9871 3,3620 0,0074 Yes S5 vs S8 0,5625 3,9871 3,3620 0,0074 Yes S5 vs S6 0,0938 0,6645 3,3620 0,9999 No S6 vs S12 5,2813 37,4347 3,3620 < 0.0001 Yes S6 vs S11 4,4688 31,6755 3,3620 < 0.0001 Yes S6 vs S10 3,5625 25,2518 3,3620 < 0.0001 Yes S6 vs S9 2,8438 20,1572 3,3620 < 0.0001 Yes S6 vs S7 0,4688 3,3226 3,3620 0,0557 No S6 vs S8 0,4688 3,3226 3,3620 0,0557 No S8 vs S12 4,8125 34,1121 3,3620 < 0.0001 Yes S8 vs S11 4,0000 28,3529 3,3620 < 0.0001 Yes S8 vs S10 3,0938 21,9292 3,3620 < 0.0001 Yes S8 vs S9 2,3750 16,8345 3,3620 < 0.0001 Yes S8 vs S7 0,0000 0,0000 3,3620 1,0000 No S7 vs S12 4,8125 34,1121 3,3620 < 0.0001 Yes S7 vs S11 4,0000 28,3529 3,3620 < 0.0001 Yes S7 vs S10 3,0938 21,9292 3,3620 < 0.0001 Yes S7 vs S9 2,3750 16,8345 3,3620 < 0.0001 Yes S9 vs s12 2,4375 17,2776 3,3620 < 0.0001 Yes S9 vs s11 1,6250 11,5184 3,3620 < 0.0001 Yes S9 vs s10 0,7187 5,0947 3,3620 0,0001 Yes S10 vs S12 1,7188 12,1829 3,3620 < 0.0001 Yes S10 vs S11 0,9063 6,4237 3,3620 < 0.0001 Yes S11 vs S12 0,8125 3,3620 < 0.0001 Yes 5,7592 0 S1: 110 C/5min, S2: 110 C/7min, S3: 110 C/10min, S4: 110 C/15min, S5: 150C/5min, S6: 1150C/7min, S7: 1150C/10min, S8: 1150C/15min, S9: 1210C/5min, S10:1210C/7min, S11: 1210C/10min, S12: 1210C/15min 60 Categor y LS means Standar -d error Lower bound (95%) Upper bound (95%) S1 8,7500 0,0998 8,5516 8,9484 A S2 8,6875 0,0998 8,4891 8,8859 A S3 8,6563 0,0998 8,4579 8,8546 A S4 8,4063 0,0998 8,2079 8,6046 A S5 6,3750 0,0998 6,1766 6,5734 B S6 6,2813 0,0998 6,0829 6,4796 B S7 5,8125 0,0998 5,6141 6,0109 C S8 5,8125 0,0998 5,6141 6,0109 C S9 3,4375 0,0998 3,2391 3,6359 S10 2,7188 0,0998 2,5204 2,9171 S11 1,8125 0,0998 1,6141 2,0109 S12 1,0000 0,0998 0,8016 1,1984 Groups D E F G Analyzing amino acid in quinoa seed Order of amino acid No Amino acid Asp Glu Ser Gly His Thr Arg Ala Pro 10 Cys 11 Tyr 12 Val 13 Met 14 Iso-Leu 15 Lys 16 L-Leu 17 Phe 61 C EU EU 9.065 10.00 10.00 10.257 15.00 9.00 9.00 10.00 5.00 0.00 8.00 60.00 40.00 20.00 0.00 8.00 8.986 10.354 11.00 11.00 12.00 12.00 13.00 13.00 15.00 15.00 15.149 13.788 14.00 14.00 13.648 15.291 16.00 16.00 18.589 19.00 19.00 8 21.00 21.00 21.177 19.653 19.897 20.00 20.00 19.529 19.779 17.099 18.00 Minutes 18.00 Minutes 18.452 17.00 17.00 16.924 21.282 22.00 22.00 23.00 23.00 23.424 10 24.013 24.330 12 13 26.115 26.263 26.386 15 16 24.979 13 27.238 17 28.00 28.00 17 12 26.175 26.322 26.446 27.00 27.00 27.187 11 14 26.00 26.00 14 24.089 24.408 25.00 25.00 24.906 23.517 24.00 24.00 11 10 62 15 16 EU 30.00 20.00 2.081 Tryptophan 10.00 EU 60.00 40.00 2.00 4.00 6.00 Minutes 8.00 10.00 4.00 6.00 Minutes 8.00 10.00 2.106 0.00 0.00 20.00 0.00 0.00 2.00 63 VIỆN KIỂM NGHIỆM VỆ SINH AN TOÀN THỰC PHẨM QUỐC GIA KHOA NGHIÊN CỨU THỰC PHẨM PHIẾU TRẢ KẾT QUẢ PHÂN TÍCH THÀNH PHẦN AXIT AMIN Tên mẫu: 01571783IDV Ngày nhận mẫu: 19/4/2017 STT Tên tiêu Hàm lượng (mg/g) Aspatic acid 22,0 Glutamic acid 44,1 Serine 13,0 Glycine 14,9 Arginine 0,79 Alanine 2,78 Proline 10,6 Cystin 2,78 Tyrosine 0,87 10 Valine 11 Histidine 7,21 12 Threonin 13,9 13 Methionine 4,47 14 Lysine 15,6 15 Iso-Leucine 10,1 16 Leucine 17,5 17 Phenyl Alanine 11,6 18 Tryphtophan 13 Các tiêu phân tích phương pháp HPLC Phụ trách Người phân tích 64 ... ? ?Development of drinking product from the quinoa seed (Chenopodium quinoa) ” to diversify of beverage products in the market 1.2 OBJECTIVE 1.2.1 General objective The aim of this study was to develop a drinking. .. Sterilisation Product Fig 3.1 Flow chart of producing quinoa milk Quinoa seed: As with numerous agricultural products, harvesting quinoa seeds will lead to co-mingling of the seeds with other components... higher value was determined The main characteristic of the quinoa seeds is the special quality of its amino acid composition The result of amino acid analysis in the seeds examined is presented