Advanced
Determining the Optimal Recipe for Long-Grain Jasmine Rice with Sea Tangle Laminaria japonica, and Its Effect on the Glycemic Index
Determining the Optimal Recipe for Long-Grain Jasmine Rice with Sea Tangle Laminaria japonica, and Its Effect on the Glycemic Index
Fisheries and aquatic sciences. 2014. Mar, 17(1): 47-57
Copyright © 2014, The Korean Society of Fisheries and Aquatic Science
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Received : October 10, 2013
  • Accepted : December 12, 2013
  • Published : March 31, 2014
Download
PDF
e-PUB
PubReader
PPT
Export by style
Share
Article
Author
Metrics
Cited by
TagCloud
About the Authors
Jiting Zeng
Department of Nutrition and Food Science, Pukyong National University, Busan 608-737, Korea
Nam-Do Choi
Department of Nutrition and Food Science, Pukyong National University, Busan 608-737, Korea
Hong-Soo Ryu
Department of Nutrition and Food Science, Pukyong National University, Busan 608-737, Korea
hsryu@pknu.ac.kr
Abstract
Thai Jasmine rice ( Oryza sativa , long grain Indica var.) is popular in southeastern Asia and China due to its non-glutinous, fluffy texture and fragrant smell. However it has a high starch digestibility, which leads to an increased glycemic index (GI). Therefore it may require modified cooking methods for diabetes patients. The objectives of this study were to optimize the ratio of Thai Jasmine rice, sea tangle, and olive oil (CLTR) based on consumers’ acceptance. The GI of plain cooked Thai Jasmine rice (CLR) was measured as a control. Sensory evaluation and response surface methodology were used to determine the optimal ratio. Texture analysis and nutritional evaluation were also performed on the optimal recipe of cooked Jasmine rice with sea tangle. A multiple regression equation was developed in quadratic canonical polynomial models. We used 26 trained Chinese panelists in their forties to rate color, flavor, adhesiveness, and glossiness, which we determined were highly correlated with overall acceptability. The optimal CLTR formula was 34.8% rice, 2.8% sea tangle, 61.9% water, and 0.5% olive oil. Compared to CLR, CLTR had a lower hardness, but a higher springiness and cohesiveness. However, CLR and CLTR had the same adhesiveness and chewiness. The addition of sea tangle and olive oil delayed retro-gradation of starch in CLTR and increased total dietary fiber, and protein and ash contents. The degree of gelatinization, and in vitro protein and starch digestibility of CLTR were lower than those of CLR. Based on Wolver’ method, the GI of CLTR (52.9, incremental area under the glycemic-response curve, ignoring the area below fasting, as used for calculating the GI [Inc]) was lower compared with that of CLR (70.94, Inc), which indicates that CLTR is effective in decreasing and stabilizing blood glucose level, owing to its lower degree of gelatinization and starch digestibility. Our results show that CLTR can contribute to the development of a healthier meal for families and the fast food industry.
Keywords
Introduction
Rice is a primary food crop and edible grain, and is used by 65% of the Chinese population as a staple food. China is the world’s largest rice producer and consumer, accounting for more than 30% of the total produced and consumed. However, with recent reforms and economic development, the diet and food habits of the Chinese population are diversifying. As meat consumption grows continuously, per capita rice consumption has declined from 85.9 kg in 1991 to 67.6 kg in 2008 ( Feng et al., 2010 ).
Thai Jasmine rice has historically been popular in China because of its non-glutinous, fluffy texture and fragrant smell. Additionally, this rice has a high nutritional value, which makes it attractive to more and more Chinese consumers. At present, China imports ~300,000 tons of Thai rice annually, a large part going to southern coastal areas (Guangdong province); in particular, Thai rice represents more than 70% of rice consumed in Hong Kong. However, a diet based on Western-style fast food is becoming more common in China. The high energy and sugar levels in these foods lead not only to obesity but also to diabetes, coronary heart disease, and various types of cancer. In contrast, the traditional Oriental diet, based on grain and plants with rice as a main element, seems to be more healthy and balanced. It is therefore becoming increasingly important to enhance the nutritional value of white rice and to improve its taste.
This research focused mainly on addition of seaweed and olive oil during cooking rice; we investigated formulas and explored their nutritional values according to food nutrition theory and statistical analyses. Refined white rice lacks dietary fiber—adding sea tangle can make up for this deficiency. Sea tangle contains alginic acid (11-45%), fiber, and mannitol, which cannot be digested, in addition to kelp elements ( Lee et al., 2002 ). All components have multiple health benefits, such as regulating blood lipids, lowering blood glucose and blood pressure, and anti-coagulation, anti-tumor, anti-virus, anti-radiation, and immunity-enhancing functions. In addition, sea tangle is rich in protein, vitamins, and minerals, especially iodine, calcium, selenium, and other nutrients beneficial to humans ( Choi et al., 2003 ).
It is the custom when cooking Thai Jasmine rice to add animal fat. This can improve the taste of rice, and make it soft and smooth in texture. However, animal fat contains a high level of cholesterol and saturated fatty acids, which may cause health problems such as cardiovascular diseases, high blood sugar, and high cholesterol. Olive oil may be a suitable alternative. While some components of the Oriental diet may protect against heart problems, the higher sodium content of sauces counters any benefit. Many cooks use salt when cooking rice dishes to prevent them from boiling over, but using a dash of olive oil and reducing the heat slightly is an alternative solution. Olive oil can improve metabolic function because it has marked antioxidant activity and is rich in vitamins and unsaturated fatty acids. It has important effects in lowering blood glucose, cholesterol, and in preventing cardiovascular disease, as well as in improving digestive system functions and controlling obesity.
The glycemic index (GI) reflects the rise in blood sugar after eating. It can track changes in blood glucose caused by the digestion and absorption of food. In addition, it reflects the effect of food on blood sugar. Food with a high GI can be digested rapidly and absorbed into the blood, causing blood sugar to peak ( Wolever et al., 1991 ). Rice is important in blood sugar regulation if used as a staple food. A rise in blood sugar can increase the sugar intake of muscle, liver, and adipose tissue, and can also inhibit glycogen release during hepatic lipolysis. Consumption of food with a high GI can accelerate this process and lead to fat and sugar being absorbed rapidly. The decrease in fat and sugar can lead to reactive hypoglycemia and 48higher free fatty acid levels. This may result in the sensation of hunger. If this trend continues, chronic metabolic disease will emerge slowly. The GI of food is a quantitative value that represents food glycemic effects compared to those of a standard control food (glucose). It is a physiological parameter that reflects the carbohydrate level of a food ( Brand-Miller et al., 1999 ). All carbohydrates in the human body are digested and degraded to monosaccharides, leading to an increase in blood sugar and inducing the body to produce signs of satiety. The secretion of insulin allows blood glucose to ceil to restore normal blood glucose levels. A rapid reduction in insulin level may lead to sudden hunger. Therefore, it is preferable to maintain blood glucose at a stable level in both normal individuals and those with diabetes. Food with a low GI can cause the blood glucose level to decline slowly, while food with a high GI facilitates absorption by the tissues of blood glucose. The carbohydrate content does not chemically reflect the degree of utilization and the GI enables a new, nutritional evaluation method for carbohydrate-rich food.
We used response surface methodology (RSM) on sensory evaluations to optimize the cooking conditions of cooked rice long-grain Jasmine rice with sea-tangle patch and olive oil mixture (CLTR). Using RSM, we determined the factors affecting kelp rice quality, including the amounts of sea tangle, added water, and olive oil. The goal of the present study was to systematically evaluate various long-grain rice formulas with high sea tangle content and to investigate the effects of CLTR cooking conditions on the GI. We were interested in whether the GI can accurately reflect blood sugar levels after eating, which can help to control the postprandial blood glucose level ( Ryu et al., 2004 ).
Materials and Methods
- Materials and standard recipe
We used a fragrant type of Thai Jasmine rice (Golden Elephant Brand, Tsing yi, Hong Kong) produced by Tresplain Investments, Ltd., Hong Kong (10 kg). This is a non-elutriating (vacuumed) rice series and did not need to be washed before cooking. We additionally used dried sea tangle Laminaria japonica , manufactured by the Garipo Sea Food Korea Company (300 g). Finally, we purchased Spanish extra virgin olive oil (pressed) from a local supermarket (Namcheon Megamart, Busan, Korea).
The standard recipe was as follows:
  • 1. Weigh 300 g of Thai Jasmine long-grain rice.
  • 2. Add water, sea-tangle, and olive oil according to the central composite design.
  • 3. Steep for 20 min.
  • 4. Cook using electric cooker under low-pressure conditions.
- Cooked long-grain Jasmine rice (CLR)
CLR was prepared using an electric cooker (0.9-1.0 kg/cm 2 pressure; Cuckoo Electronics Co., Ltd., Yangsan, Korea).
- Cooked long-grain Jasmine rice containing sea tangle and olive oil (CLTR)
CLTR was prepared using an electric cooker. The ratios of CLR, sea tangle, water, and olive oil followed those in Tables 1 and 2 (the central composite design).
Independent variables and their levels for central composite design
PPT Slide
Lager Image
X1, amount of sea-tangle patch; X2, amount of added water; X3, amount of added olive oil.
Arrangement of the three-variable, five-level response surface design
PPT Slide
Lager Image
X1, amount of sea-tangle patch; X2, amount of added water; X3, amount of added olive oil.
- Experiment plan for RSM
A response surface design was used to investigate the relative contributions of different variables to rice quality, and to determine optimal CLTR formulas. The objectives of this study were to optimize the mixture ratio for CLTR containing sea tangle and olive oil, and to compare CLTR to CLR based on taste and nutritional value ( Ryu et al., 2004 ).
- Central composite design for RSM
RSM was used in this trial to investigate the simultaneous effects of three compositional variables: sea tangle (0-24 g), water (420-450 g), and olive oil (0-8 g) ( Table 1 ). These three factors were expressed as X 1 , X 2 , and X 3 , respectively ( Ryu et al., 2004 ). Five content levels of each variable were used in accordance with the principles of the central composite design. For statistical analysis, the five levels of the three variables were coded as –2, –1, 0, 1, and 2. We also analyzed the effects of any interactions among the variables on the quality of CLTR using RSM. The arguments were coded according to the equation:
xi = (Xi -X0)/ΔX,
where x i is the coded value of the independent variable, X i is the true value of the independent variable, X 0 is the true value of the experimental center point from the variable, and ΔX is the step change of the variable. The average sensory evaluation score, Y, is the response value. The relationships between coded variable levels (x 1 ) and true values (X 1 ) are as follows:
  • x1= ( X1– 4)/2
  • x2= ( X2– 480)/30
  • x3= ( X3– 4)/2
- Sensory evaluation
We used the statistical software Minitab version 16 in this study. The number of formulas in the optimizing design was 16, as shown in Table 2 .
Twenty-six Chinese panelists, ~40 years old and from various professions and social classes, were divided into two teams and completed sensory evaluation questionnaires independently. A questionnaire with a nine-point hedonic scale was used to record results regarding color, glossiness, flavor, adhesiveness, and overall acceptability, as follows: very good (9), good (7), neutral (5), bad (3), unacceptable (1). Panelists first evaluated the smell of rice when hot, and then observed its color and shape. Lastly, they evaluated taste by chewing the rice. All panelists received sensory evaluation education before performing this evaluation. Clean drinking water was provided for rinsing between samples ( Wu et al., 2009 ). Scores were then summarized and averaged.
- Textural analysis
The various CLTR and CLR formulas were cooked using a rice cooker. The texture of cooked rice was then measured with a texture analyzer (model TA-XT2; Stable Microsystems, Surrey, UK) using the compression test (5). One gram of cooked rice was arranged in a single-grain layer on a base plate. A compression plate was set 5 mm above the base. A two-cycle compression force versus distance program was used to allow the plate to travel 4.9 mm, return, and repeat, with a test speed of 1 mm/s. A cylindrical plunger, 50 mm in diameter, was employed. We used hardness (height of the force peak on the first cycle), adhesiveness, cohesiveness, springiness, and gumminess as parameters ( Pitiphunpong et al., 2011 ).
- Statistical analysis
We aimed to determine the optimal cooking conditions for CLTR from contour and response surface plots of overall acceptability; we then used Minitab to analyze the sensory evaluation results. Furthermore, we used SPSS version 11.5 (SPSS Inc., Chicago, IL, USA) for multivariate analysis. Significant differences between means were detected by Duncan’s multiple-range test at α = 0.05 ( Ryu et al., 2004 ). The results were measured by variance analysis and effectiveness. Duncan’s multiple range tests were used to determine the effectiveness of those projects which have effective values. Incremental area under the curve (IAUC) was calculated using the geometric method, and the GI of the other two groups was calculated by assuming that the IAUC of glucose is 100% ( Wolever et al., 1991 ).
- Experimental procedures
- Proximate composition
The proximate compositions of all samples were determined using the following Association of Official Analytical Chemists (AOAC) procedure ( AOAC, 1990 ). Sample moisture was determined after drying at 105℃ to constant weight. Crude protein was calculated by multiplying the nitrogen in the samples by a factor of 6.25, using the semi-micro Kjeldahl method (Gerhardt Vapodest 30). Crude lipid (Soxhlet extraction) and ash (gravimetric) contents were also determined using AOAC methods ( AOAC, 1990 ). The carbohydrate concentration was calculated by subtracting the concentrations of moisture, crude protein, crude lipid, and ash from the sample. The concentration of total dietary fiber (TDF) was measured using the Prosky method ( Prosky, 2003 ).
- Determiningin vitroprotein digestibility
The in vitro protein digestibility values of all samples were determined by the following methods of Oduro et al. (2011) modified from the AOAC method ( AOAC, 1982 ). The procedure typically uses a four-enzyme method, but we used three proteolytic enzymes. We determined the correlation coefficient between the results of the two methods and found a high correlation ( r 2 = 0.9955). The three-enzyme method used α-chymotrypsin (Sigma 38 units/mg solid; Sigma, St. Louis, MO, USA), trypsin (Sigma 13, 390 BAEE units/mg solid), and protease ( Streptomyces griceus , Sigma 46 units/mg solid). We used ANRC casein as a reference protein. Digestibility was calculated as follows:
  • % Digestibility (3-enzyme method) = 234.84 – 22.56 x, where x is the sample pH at 20 min.
  • % Digestibility (4-enzyme method) = 1.03 × (3-enzyme digestibility) – 0.34.
- Determiningin vitrostarch digestibility
The in vitro starch digestibility was determined using freeze-dried CLTR and CLR samples (50 mg/mL of 0.2 M phosphate buffer, pH 6.9) after amylolysis with 0.5 mL of pancreatic amylase (500,000 U/mg) suspension (0.44 mg/mL of 0.2 M phosphate buffer, pH 6.9) at 37℃ for 2 h, according to methods used by Alonso et al. (2000) . At the end of the incubation period, 4 mL of 3, 5-dinitrosalicylic acid reagent were added, and the mixture was boiled for 5 min. After cooling, the absorbency of the filtered solution was measured at 550 nm with maltose as a standard. In vitro starch digestibility was expressed as a percentage ( Ordonez-Ramos et al., 2012 ).
- Measurement of degree of gelatinization
The degree of starch gelatinization was measured by malt diastase, using the Yamasita method ( Yamasita, 1968 ).
- Glycemic index
This study was conducted using internationally recognized GI methodology. The function of the GI in preventing chronic diseases has been confirmed since the 1990s ( Wolever et al., 1991 ). Subjects fasted overnight prior to each study day. We used 8-12 subjects assigned in random order to each group. We evaluated the test meal by measuring blood glucose levels in the group subjects over 2 h. The carbohydrate content of CLTR and CLR, equivalent to 50 g glucose for each meal, was calculated by proximate composition. We used glucose (50 g) as a control food in this study. The GI of the test meal was calculated using the method of Wolever et al. (1991) . To minimize day-to-day variation in glucose tolerance, the reference food was tested three times for each subject. Ten healthy subjects 20-30 years of age were recruited. All test and reference foods were served with 200 mL of water. Each subject was asked to consume 50 g available carbohydrate portions of test food and reference food. Finger capillary blood samples were collected at the start of eating and 15, 30, 45, 60, 90, and 120 min after food consumption. A glucose meter was used to collect finger capillary blood samples (1-2 μL; ACCU-CHEK Active Test Strips, Mannheim, Germany). We tested each group at 3-d intervals ( Yuan et al., 2011 ). We compared CLTR with CLR and glucose, to determine whether CLTR decreased and stabilized blood glucose.
  • Test food GI = (Test food IAUC/ Reference food IAUC) × 100%.
  • Glucose GI (reference food) = 1.
Results
- Sensory evaluation
A response surface regression model can be built to optimize response factor levels using Minitab version 16. Based on the 16 CLTR formulas according to the central composite design, the average sensory evaluation score can be considered as the response value ( Table 3 ). We used a quadratic canonical polynomial model for the 16 formulas ( Ryu et al., 2004 ). The experimental data in Table 3 were processed using quadratic regression fitting and tested using lack-of-fit and significance tests of regression coefficients for the mathematical model predicting sensory scores of CLTR with different formulas. The experimental data were processed based on polynomial regression analysis, using sensory evaluation as the response variable. This analysis determined the polynomial equation model ( Table 4 ).
Central composite design arrangement and responses by Chinese forties
PPT Slide
Lager Image
Y1, color; Y2, flavor (taste); Y3, adhesiveness; Y4, glossiness; Y5, overall acceptability. Sensory scores: very good (9), good (7), general (5), bad (3), worst (1).
Response surface methodology program-derived polynomial equation by the Chinese forties
PPT Slide
Lager Image
*Coefficient of determination.
The F -value of the responses of the five test variables (color, flavor, adhesiveness, glossiness, and overall acceptability) was 64.21. Our data indicate that the height of the model is obvious. The P -value was <0.0001, meaning that the factor level of the model is obvious in the mass. The r 2 (coefficient of determination) value was 0.145-0.247, meaning that the experimental method is reliable and the level range of each factor was sufficient to estimate the sensory evaluation ( Ryu et al., 2004 ). The results of sensory evaluation by the panelists were analyzed using SAS; the output of the resulting ANOVA table is shown in Table 5 .
SAS output of ANOVA table for overall acceptability by the forties
PPT Slide
Lager Image
MSE, ; DF,
The experimental data were processed using Minitab version 16, and plotted using statistical analysis software. The best CLTR recipe was analyzed using the response-surface methodology, including contour and response surface plots. The RSM graph shows a specific response variable, Y, and the corresponding arguments comprise a three-dimensional diagram. The graph reflects any influence of the arguments on the response variables. Figs. 1 and 2 reflect the impact of various factors on the response value. In a contour map, the extreme condition should be at the center of the circle. The response surface chart shows the impacts of various interactions (sea tangle and water, sea tangle and olive oil, and water and olive oil) on sensory evaluation scores. The sensory score for all five variables increased with increasing amounts of sea tangle, and the score was maximal when the amount of sea tangle peaked. Fig. 1 shows the contour map of the overall acceptability of all response variables for 6 g, 12 g, and 18 g sea tangle.
PPT Slide
Lager Image
Contour plots of overall acceptability by the forties.
PPT Slide
Lager Image
Response surface plots of 5 response variables by the twenties (X1 = 1).
Fig. 1 reflects the fact that the subjects did not mind the taste and texture of sea tangle and that its acceptance was quite high. The average age of the 26 panelists was 40 years, putting them in a middle-aged category of people, who tend to pay more attention to health because the incidences of diabetes and cardiovascular disease are higher in middle-aged individuals. This category of person also has more nutritional knowledge, and is aware that sea tangle is high in dietary fiber, as well as a variety of healthy bioactive substances. Thus, they may consume more sea tangle in their diets than other groups. Therefore, the sensory evaluation may have been somewhat subjective, possibly leading to higher scores.
This study also includes the sensory evaluations of college students, ~20 years old. The diet of such young people is more biased toward Western-style fast food that includes fried food. A meat-based diet is predominant, and the acceptance of staple lant-based foods is relatively low. Our test results showed that the scoring of the five variables by young people declined with the addition of sea tangle. The highest average score given by this group was to rice without sea tangle. It will get more praise if olive oil is added. As these results did not match the goals of our study, this group was excluded from further analysis or discussion. Fig. 2 shows the response surface plot of the five variables when the sea tangle content was maintained at 18 g (X 1 = 1).
We found that color, flavor, adhesiveness, and glossiness strongly correlated with overall acceptability. According to the response surface plot of sensory scores, the sea tangle peak appeared when the water and olive oil contents were constrained. The best CLTR recipe was determined using the Minitab response optimizer, assuming a sensory score goal of eight points. With this recipe, responses for the five variables using sensory evaluation are optimized. The maximum point of the comprehensive response surface map, also called the optimal level of the three main factors, can be determined by deriving a regression equation and setting the response surface map to zero. The goals, purposes, on-line, weights, and importance of the five variables are shown in Table 6 ( Cao et al., 2009 ). The Minitab response optimizer analysis result is shown in Fig. 3 .
Response value optimization settings
PPT Slide
Lager Image
Response value optimization settings
PPT Slide
Lager Image
Response optimization curves of five attributes. *When X1 = 2; X2=1.8384; X3 = 0.2628, the maximum of the forecast available sensory evaluation score is 0.79602.
The optimal CLTR formula was 34.8% rice, 2.8% sea tangle, 61.9% water, and 0.5% olive oil. According to our analysis of the Minitab response optimizer, the optimum CLTR recipe occurred when the amount of sea tangle was the maximum of the target scope while the amounts of water and olive oil were in a horizontal scope, which is consistent with experimental targets. Early in the study, we used the central composite design to identify the key indicator of the optimum CLTR recipe and to determine the horizontal range of the optimum amounts, on the basis of preliminary experiments and review of the literature. The amount of sea tangle to be added should be determined based on (i) the fact that the primary CLTR cooking implement may be a general household rice cooker, and (ii) that 300 g of rice are usually set as the standard. We conducted preliminary experiments and determined that at a sea tangle content ≥ 30 g, the overall appearance of the rice becomes unappetizing and CLTR palatability decreases. Therefore, the optimal amount of sea tangle in CLTR recipes is 24 g.
- Nutritional evaluation
Using RSM, we established a quadratic mathematical model of the key factors affecting CLTR quality. The model was statistically tested and the optimum CLTR recipe was determined, as follows: 34.8% rice, 2.8% sea tangle, 61.9% water, and 0.5% olive oil. For analysis of texture and nutritional quality of CLTR recipes, rice was cooked alone and with sea tangle and olive oil under the same conditions ( Ryu et al., 2004 ). The textural analysis of CLTR using optimized recipes is shown in Table 7 .
Texture profile analysis of cooked Thai Jasmine rice, long grain Indicavar(CLR) and CLR containing grainy sea-tangle patch (CLTR)
PPT Slide
Lager Image
*Mean in the same column with different superscripts is significantly different.
- Texture properties
Compared to CLR, CLTR had a lower hardness, but higher springiness and cohesiveness. However, CLR and CLTR had identical adhesiveness and chewiness ( P < 0.01).
- Proximate composition
The proximate compositions of dried sea tangle and CLTR and CLR recipes are shown in Table 8 . Dried sea tangle is high in dietary fiber; therefore the CLTR recipes containing sea tangle (2.8%) had more dietary fiber and health benefits than CLR. Furthermore, CLTR is higher in protein and ash than CLR.
Proximate composition and total dietary fiber content of sea-tangle, CLR and CLTR
PPT Slide
Lager Image
Values are presented as % (dry basis). CLR: cooked long-grain Jasmine rice. Pressure cooked at atm (0.9-1.0 kg/cm2) with electric cooker; CLTR: cooked rice long-grain Jasmine rice with sea-tangle patch and olive oil mixture. Pressure cooked at atm (0.9-1.0 kg/cm2) with electric cooker. *Significantly different compare CLR with CLTR (P < 0.05).
- Protein and starch digestibility
In vitro protein and starch digestibility, and the degree of gelatinization of CLTR and CLR are shown in Table 9 . The protein digestibility of CLTR was lower than that of CLR because the added sea tangle contained some viscous, soluble dietary fiber, which would restrict protease activity and lower digestion and absorption rates.
In vitroprotein and starch digestibility, and gelatinization degree of CLR and CLTR (%)
PPT Slide
Lager Image
CLR, cooked long-grain Jasmine rice. Pressure cooked at atm (0.9-1.0 kg/cm2) with electric cooker; CLTR, cooked rice long-grain Jasmine rice with sea-tangle patch and olive oil mixture. Pressure cooked at atm (0.9-1.0 kg/cm2) with electric cooker.
The starch digestibility and the degree of gelatinization of CLTR were also lower because the dietary fiber and active substances in kelp can constrain the activity of α-amylase. Likewise, physical attributes, such as size, temperature, and cooking time, are influenced by the added olive oil. Under these conditions, the degree of gelatinization is reduced.
- Postprandial glucose responses of CLTR and CLR
We tested CLTR, CLR, and glucose as a reference food in 50-g portions of available carbohydrates ( Yuan CS et al., 2011 ) ( Table 8 ). The formula was as follows:
Test food quality = 100 × 50/CHO (Test food %).
When testers consumed 148 g of CLTR, 145 g of CLR and 50 g of glucose, glucose response times differed. Blood glucose levels increased rapidly in the first 30 min, when testers consumed CLR and glucose, and then declined substantially. However, the blood glucose levels of testers who consumed CLTR stabilized after 30 min, and the rate of decline was slow. For testers who consumed CLTR, the highest blood glucose level was 7.4 mmol/L; for those who consumed CLR, the highest value was 10.1 mmol/L; and for those who consumed glucose, the highest value was 10.9 mmol/L. Therefore, the blood glucose levels of testers who consumed CLTR was lower than those of testers who consumed CLR and glucose ( Fig. 4 ).
PPT Slide
Lager Image
Mean glucose concentrations elicited by glucose, cooked longgrain Jasmine rice (CLR) and cooked rice long-grain Jasmine rice with sea-tangle patch and olive oil mixture (CLTR) in healthy subjects. *Data are expressed as the change in plasma glucose concentration from the fasting baseline concentration. **The quantities of foods to be measured were computed based on an equivalent content of 50 g carbohydrates. By using 50 g glucose as a reference, each 10 volunteers as one group were measured for fasting blood glucose, and then the levels of blood glucose at various time spots within 2 h after consumption of a specified experimental food.
- Influences of CLTR and CLR on GI
Testers were required to eat 148 g of CLTR, 145 g of CLR, or 50 g of glucose. Then, for 2 h, we tested the IAUC to determine the GI. Given a glucose GI of 100%, we compared the GI of CLTR and CLR. The IACU and GI of glucose and CLR were markedly higher than those of CLTR ( P < 0.01) ( Table 10 ). When CLTR was consumed, the increase in blood glucose was lower than that resulting from a normal diet. Several methods have been used to calculate the area under the glycemic-response curve. Given the same blood glucose data, different methods may result in markedly different areas and GI values ( Fig. 5 ). The GI is based on the area under the blood glucose-response curve above the baseline only ( Wolever et al., 1986 ). The GI of CLTR (52.9, incremental area under the glycemic-response curve, ignoring the area below fasting, as used for calculating the GI [Inc]) was lower compared to that of CLR (70.94, Inc), which indicates that CLTR was effective in decreasing and stabilizing the blood glucose level owing to its lower degree of gelatinization and starch digestibility ( P < 0.01).
The influences of CLTR, CLR and glucose on GI
PPT Slide
Lager Image
CLR, cooked long-grain Jasmine rice; CLTR, cooked rice long-grain Jasmine rice with sea-tangle patch and olive oil mixture; GI, glycemic index.*Significantly different compare CLR with CLTR (P < 0.05).
PPT Slide
Lager Image
Effect of different methods of calculating the area under the curve from the same blood glucose data on the glycemic indexof the cooked rice long-grain Jasmine rice with sea-tangle patch and olive oil mixture (CLTR) and cooked long-grain Jasmine rice (CLR). Inc, incremental area under the glycemic-response curve, ignoring the area below fasting, as used for calculating the glycemic index; Net, incremental area under the glycemic-response curve subtracting the area beneath the fasting amount; Total, total area under the glycemic-response curve.
The GI of CLTR was considerably lower than those of glucose and CLR, as was the glucose response level. Furthermore, the natural quality of CLTR was lower than that of CLR, considering primary nutrients. Therefore, as the main component, CLTR could improve the quality of the modern diet and lower postprandial glucose levels, which will ameliorate obesity.
Discussion
Thai Jasmine rice has a high starch digestibility, which leads to an increased GI. Therefore, diabetes patients may require modified cooking methods. Our study focused on adding sea tangle and olive oil as auxiliary ingredients, optimizing the recipe, and analyzing the nutritional value of the optimum formula according to food nutrition theory and statistical analysis. The optimal CLTR formula was 34.8% rice, 2.8% sea tangle, 61.9% water, and 0.5% olive oil. Compared to CLR, CLTR had a lower hardness, but higher springiness and cohesiveness. However, the two had identical adhesiveness and chewiness ( P < 0.01). The addition of sea tangle and olive oil delayed retro-gradation of starch in CLTR, and increased the TDF, protein, and ash contents ( P < 0.01).
The degree of gelatinization, in vitro protein, and starch digestibility of CLTR were lower than those of CLR ( P < 0.01). Based on Wolver’s method, the GI of CLTR (52.9, Inc) was lower than that of CLR (70.94, Inc), which indicates that CLTR is effective in decreasing and stabilizing the blood glucose level, owing to its lower degree of gelatinization and starch digestibility.
The GI of CLTR was considerably lower than those of glucose and CLR, as was the glucose response level. Furthermore, the natural quality of CLTR was lower than that of CLR, in terms of primary nutrients. Therefore, as an important component, CLTR could improve the quality of the modern diet and lower postprandial glucose levels, which will ameliorate obesity. Twenty years of research indicates that the glucose response in the body after ingesting starch-rich food is similar to the digestion rate of carbohydrates outside the body, when starch content is similar ( Englyst et al., 1996 ; Araya et al., 2002 ). Therefore, starch hydrolysis, based on enzymes, can be used to predict the glucose response of the body to food ( Gee and Johnson, 1985 ). Using in vitro starch digestion, we determined that CLR had a high GI (91). Generally, rice that was completely gelatinized had the highest GI values because gelatinization, in the absence of retro-gradation or structural changes, enhances starch digestibility ( Srikaeo and Sopade, 2010 ). The GI reflects the overall digestion and utilization condition of food, consolidating components and contents, types and structures of carbohydrates, physical condition, and the fabrication process. These factors can have an important effect on the GI ( Biliaderis, 1991 ).
Various ingredients affected CLTR pasting properties due to processing effects, types of ingredients, and differences in hydration/swelling behaviors. The physical state of rice, such as its size, as well as temperature and cooking time, are influenced by the abundant fiber in CLTR, and olive oil. Under these conditions, both the degree of gelatinization and the GI are reduced. As reported by Barclay et al. (2008) , ingestion of low-GI foods can reduce the risk of diabetes, coronary heart disease, breast cancer, and other chronic diseases because such food has less effect on blood glucose levels.
Instant noodles, developed for many years in China, have become common consumer goods. However, few types of instant rice are available, although more are under development. We propose that long-grain Thai white rice containing sea tangle can be sold as instant rice in the Chinese market, as stated by C.J. Hetbahn. Compared with common long-grain rice, CLTR can improve the organoleptic quality and, more importantly, the GI. Our results show that CLTR can contribute to development of a healthier meal for both families and the fast food industry. Moreover, CLTR may have the potential to replace instant noodles and be widely accepted in southern China, benefiting the economy.
Acknowledgements
This work was supported by a Research Grant of Pukyong National University (2013).
References
Alonso R , Aguirre A , Marzo F 2000 Effects of extrusion and traditional processing methods on antinutrients and in vitro digestibility of protein and starch in faba and kidney beans Food Chem http://dx.doi.org/10.1016/S0308-8146(99)00169-7 68 159 - 165
1982 Calculated protein efficiency ratio (C-PER and DC-PER). Official first action J Assoc Off Anal Chem 65 496 - 499
1990 Official Methods of Analysis AOAC Washington DC, US
Araya H , Contreras P , Alviña M , Vera G , Pak N 2002 A comparison between an in vitro method to determine carbohydrate digestion rate and the glycemic response in young men Eur J Clin Nutr http://dx.doi.org/10.1038/sj.ejcn.1601386 56 735 - 739
Barclay AW , Petocz P , McMillan-Price J , Flood VM , Prvan T , Mitchell P , Brand-Miller JC 2008 Glycemic index, glycemic load, and chronic disease risk: a meta-analysis of observational studies Am J Clin Nutr 87 627 - 637
Barclay AW , Petocz P , McMillan-Price J , Flood VM , Prvan T , Mitchell P , Brand-Miller JC 2008 Glycemic index, glycemic load, and chronic disease risk: a meta-analysis of observational studies Am J Clin Nutr 87 627 - 637
Brand-Miller J , Wolever TMS , Colagiuri S , Foster-Powell K 1999 The Glucose Revolution: The Authoritative Guide to the Glycemic Index Marlowe and Co. New York 34 - 39
Cao XF , Gao YS , Zhao D , Lin J , Xie JL , Xu XS 2009 Optimization of process of Mozzarella cheese with soy milk by response surface methodology China Dairy Ind 37 24 - 27
Choi HG , Jang BH , Rhee JD , Yu BK , Yong CS 2003 The effect of Laminarica japonica diet on the pharmacokinetics of glipizide in rats J Korean Pharm Sci 33 113 - 120
Englyst HN , Veenstra J , Hudson GJ 1996 Measurement of rapidly available glucose (RAG) in plant foods: a potential in vitro predictor of glycemic response Br J Nutr http://dx.doi.org/10.1079/BJN19960137 75 327 - 337
Feng M , Lu Q , Lin JZ 2010 Elastic analysis of urban and rural consumption of rice Mod Prop Manage 3 035 -
Gee JM , Johnson IT 1985 Rates of starch hydrolysis and changes in viscosity in a range of common foods subjected to simulated digestion in vitro J Sci Food Agric http://dx.doi.org/10.1002/jsfa.2740360713 36 614 - 620
Lee JG , Lim YS , Joo DS , Jeong IH 2002 Effects of diet with sea tangle (Kjellemaniella crassifolia) on calcium absorption, serum composition and feces in rats J Korean Fish Soc http://dx.doi.org/10.5657/kfas.2002.35.6.601 35 601 - 607
Oduro FA , Choi ND , Ryu HS 2011 Effects of cooking conditions on the protein quality of chub mackerel Scomber japonicus Fish Aquat Sci http://dx.doi.org/10.5657/FAS.2011.0257 14 257 - 265
Ordonez Ramos LR , Choi ND , Ryu HS 2012 Effects of processing conditions on the protein quality of fried anchovy kamaboko Engraulis japonica Fish Aquat Sci http://dx.doi.org/10.5657/FAS.2012.0265 15 265 - 273
Pitiphunpong S , Champangern S , Suwannaporn P 2011 The Jasmine rice (KDML 105 Variety) adulteration detection using physico-chemical properties Chiang Mai J Sci 38 105 - 115
Prosky L 2003 What is dietary fiber? J AOAC Int 83 985 - 987
Ryu HS , Lee JH , Shin ES , Kim MS , Park HY , You BJ 2004 Optimizing mixture ratio of mungbean pancake using response surface methodology In: Proceedings of IFT Annual Meeting 12 1 - 16
Shin ES , Lee JH , Park KT , Ryu HS , Jang DH 2004 Optimizing cooking condition of short grain rice containing sea-tangle patch J Korean Soc Food Sci Nutr http://dx.doi.org/10.3746/jkfn.2004.33.10.1726 33 1726 - 1734
Shin ES , Lee JH , Park KT , Ryu HS , Jang DH 2004 Optimizing cooking condition of short grain rice containing sea-tangle patch J Korean Soc Food Sci Nutr http://dx.doi.org/10.3746/jkfn.2004.33.10.1726 33 1726 - 1734
Wolever TMS , Jenkins DJA 1986 The use of the glycemic index in predicting the blood glucose response to mixed meals Am J Clin Nutr 43 167 - 172
Wolever TM , Jenkins DJ , Jenkins AL , Josse RG 1991 The glycemic index: methodology and clinical implications Am J Clin Nutr 54 846 - 854
Wu W , Liu CM , Li T , Liu W , Wan J , Xu YJ 2009 Preparation of nutritional rice fortified with dietary fiber Food Sci 30 76 - 80
Yuan CS , Zhang WQ , Zhao XJ , Wang L , Meng J 2011 Measurement of glycemic index for whole-grain and coarse cereal foods in Shanxi Chin Rem Clin http://dx.doi.org/10.3969/j.issn.1671-2560.2011.04.001 11 365 - 367
Yamasita TR 1968 Determination of ɑ-starch Cook Sci Jpn 1 24 - 26