Preparation and evaluation of low glycemic index reconstituted rice

Abstract

This article describes the optimization and fuzzy logic sensory evaluation of the reconstituted rice manufacturing by a twin-screw extruder. An improved fuzzy logic sensory evaluation system was established, including an evaluation index set and its weight set, a fuzzy transform rule, and an observation value. The result of the fuzzy logic sensory evaluation was used to investigate the dependent variables used in the optimization process. The three factors affecting product quality, together with the optimal level of each in brackets were extruder screw speed (190 rpm), barrel temperature (76 °C), and material moisture (26%). The glycemic index (GI) of the reconstituted rice was 53.6, classifying it as a low GI food. The low GI reconstituted rice was demonstrated to more slowly increase the blood sugar level.

Introduction

Extrusion technology is widely used in food processing, including the processes of mixing, heating, extrusion, and molding (Riaz, 2000). Food extrusion technological design can be divided into three categories: single screw, twin screw, and multiple screws. Twin-screw extrusion is a widely-used method of exploiting the chemical effect of mechanical force. This works by using extremely high temperatures combined with strong shear forces, such that the high-frequency vibration and friction change the physical, chemical, and structural properties of a material (Ali, 2017). A variety of reconstituted foods can be extruded, including brown rice, soybean, cereals, vegetables, and other raw materials (Kato et al., 2006). After crushing, mixing, and tempering, the recombined raw material enters the extruder for extrusion and granulation. After drying, the final product has a grain size and shape similar to that of rice, hence the term reconstituted rice. In the process of extruding reconstituted rice, nutritional enhancers can be added according to the needs of specific populations or customers (Su et al., 2007; Saadat et al., 2019; Harrow et al., 1982). This not only provides a way of processing and using agricultural products, but also provides a new food source to fulfill the nutritional needs of specific populations, and greatly increases the economic value of the product (Sitakalin et al., 2000).

Glycemic index (GI) reflects the ability of food to raise the blood glucose level. The rate and frequency of increases in blood glucose level are important in the prevention and control of many chronic diseases (Jenkins et al., 1981). Low GI foods can slow down the absorption of carbohydrates, helping to control the appetite and delay hunger, and are beneficial for glycemic control in diabetics (Brand-Miller et al., 2003; Wang et al., 2015). Clinically, the GI of foods is used to inform the diets of diabetic patients (Wang et al., 2009). Low GI refers to a GI value of less than 55, while food with a GI of higher than 70 belongs to in the high GI category (Kumar et al., 2014). Low GI foods can help prevent and alleviate the onset and severity of type 2 diabetes, coronary heart disease, and other conditions, while high GI foods often cause insulin resistance and other diseases in humans (Breymeyer et al., 2016). Many day-to-day foods have a high GI, including cake, muffins, bread, breakfast cereals, porridge, and pastry (Jenkins et al., 2012). As such, these foods pose a potential threat to public health. Low GI reconstituted rice has been recognized as a form of fresh-pressed rice, which has been made from rice as the main raw material, and supplemented with plant powder products that have hypoglycemic effects. Carbohydrates could be divided into rapidly available glucose (RAG) and slowly available glucose (SAG), and that this property was related to GI (Englyst et al., 1966; Englyst et al., 1999). The GI of some foods increased after heating such as by cooking or baking (Allen et al., 2012).

There have typically been two methods used for evaluating rice products: human sensory evaluation (cooking and tasting) (Adebisi et al., 2020) and electronic sensory evaluation (Buratti et al., 2007). The former refers to “GB/T15682-2008_ Inspection of cereals and oils, cooking and sensory evaluation of rice” used in China, whereas the latter describes several instrument evaluations, for example, the electronic nose (Tian et al., 2019), texture analyzers (Meullenet et al., 2000; Sesmat et al., 2001), electron microscopes, and the electronic tongue (Schlossareck and Ross, 2019). The evaluation of food quality is a complex process, mainly based on sensory inspection. Such results are often affected by human factors and cannot be described accurately using mathematical equations (Debjani et al., 2013; Chakraborty et al., 2015). Fuzzy logic sensory evaluation applies fuzzy logic mathematics to the evaluation and analysis of products to establish a set of rules for operation and transformation based on fuzzy set theory (Singh et al., 2018; Vivek et al., 2019). This comprehensively reproduced the uncertainty and fuzziness in the process of product evaluation by using fuzzy logic and made an accurate and objective evaluation of product processing (Inan et al., 2011).

In the present study, twin-screw extrusion was used to produce low GI reconstituted rice, and a sensory evaluation scheme for evaluating low GI reconstituted rice was established using fuzzy logic theory. The preparation process for making low GI reconstituted rice was determined and optimized based on GI measurement and clinical testing. The results of this study make a significant impact on the industrialization of low GI reconstituted rice and other kinds of reconstituted rice.

Materials and Methods

Materials    round-shaped rice (white, 7.5% protein, 0.5% fat, 76.3% carbohydrate, and 14.7% moisture) and Tartary buckwheat powder (9.5% protein, 2.6% fat, 62.3% carbohydrate, and 15.3% moisture) were purchased from a local market in Shanghai, China. Pumpkin powder (10% moisture), soybean protein isolate (90% protein and 8.7% moisture), resistant dextrin (14.7% moisture), inulin (98% purity), and konjac flour (10% moisture) were provided by Beijing Baoderui Health Industry Group Co., Ltd. (Beijing, China), Shandong Wandefu Industrial Group Co., Ltd. (Dongying, Shandong Province, China), Cargill Investment Co., Ltd. (Shanghai, China), Sensus B.V. (Roosendaal, Netherlands), and Shantou Jiecheng Biotechnology Co., Ltd (Shantou, Guangdong Province, China), respectively.

Preparation of reconstituted rice samples    The recipe was based on the preliminary experimental extrusion results. The specific formula was as follows: round-shaped rice flour 42%, Tartary buckwheat flour 15%, soybean protein isolate 15%, inulin 10%, pumpkin powder 8%, resistant dextrin 5%, and konjac flour 5%. The reconstituted rice samples were processed in the extrusion pilot laboratory at China Food and Fermentation Industry Research Institute Co., Ltd. Preparation comprised four steps. First, each 50 g sample was weighed and put into a steaming dish, and the number of samples was prepared according to the number of evaluators. Second, the weighed sample was poured into the drainer, the drainer placed in the basin, and 300 mL water was added quickly before the rice was stirred in a circular motion, 10 times clockwise and 10 times counterclockwise. Then, again quickly, the water was changed and the operation was repeated. Then, the rice was rinsed with 200 mL distilled water, drained and placed in a steamer dish. Third, an appropriate water addition for batching was added into the steamer (a 26 cm diameter stainless steel steamer, Zhejiang Aishida Electric Appliance Co., Ltd., Ningbo, Zhejiang Province, China) and heated with an electric element until boiling. Also, 40 mL distilled water was added into the steamer dish with the sample before the dish was placed in the steamer, covered, and allowed to cook for 25 min. The sample was then removed for tasting.

Optimization of the extrusion process    The experiments used a 62 Series extruder (Shanxi Juncheng Machinery Co., Ltd., Xi'an, Shanxi Province, China) with a screw diameter of 62 mm, a length-to-diameter ratio of 19:1. Steaming temperature, main motor, and were feeder all controlled using frequency conversion. The drying oven was a JCDW series (Shanxi Juncheng Machinery Co., Ltd. with steam heating, providing temperature control of ± 1 °C.

Single factor experiments    The factors affecting the texture characteristics of extruded reconstituted rice include the thickness, extruder speed, opening rate, water addition for batching (Gomez et al., 1983), feeding rate, barrel temperature (Lawton et al., 1972), cutter speed, drying temperature, and drying rate. According to a large number of previous experiments, the results show that the feeding rate will affect the capacity of the equipment and the expansion degree of the extruded reconstituted rice, and then affect the texture. The size and expansion degree of reconstituted rice were affected by the cutting speed. The drying temperature mainly affects the morphology of the reconstituted rice after drying, and it is easy to crack and the appearance is not beautiful. The effect of drying rate and drying temperature is similar. The process parameters corresponding to the fixed formula can be determined through experiments. In addition, a large number of experiments were carried out to verify the opening rate of the extrusion film and the thickness of the material used.A large number of pilot experiments were used to establish the optimal values for each factor: feed rate = 75 kg/h, cutter speed = 600 rpm, drying temperature = 60 °C, drying rate = 1 m/min, and fixed mold and batching fineness with a raw grain powder of 60–80 mesh, spray or crystallized without special requirements. Based on this, this paper focuses on the water addition for batching, the extruder screw speed and the barrel temperature.This article describes the optimization of the remaining factors of water addition for batching (X₁), extruder screw speed(X₂), and barrel temperature (X₃) by assessing their effects on the texture characteristics of the rice.

The single factor experiment was carried out at five levels. Specifically, the water addition for batching of the mixture (X₁) was varied at 10%, 15%, 20%, 25%, and 30%. The extruder screw speed (X₂) was varied at 120, 150, 180, 210, and 240 rpm. The barrel temperature (X₃) was set at 60, 70, 80, 90, and 100 °C. In this study, the human sensory evaluation was simulated by texture instrument and the hardness is the maximum force peak of the first extrusion cycle using this instrument. The hardness of the cooked rice was taken as the index to preliminarily determine the level of each element and measured using a texture analyzer (TA-XT PLUS, SMS, UK). The hardness of the cooked rice was measured using a 30 kg force sensor with a P/36 probe. For each test, a grain of rice was used for texture analysis and repeated three times. The pre-test, mid-test, and post-test rates were all 2 mm/s, and the compression ratio was always 70%.

Orthogonal experiment    Optimum values for the water addition for batching (X₁), extruder screw speed(X₂), and barrel temperature (X₃) were determined individually from the results of the single-factor experiments. An orthogonal L9 (3⁴) design was used to optimize these parameters and the fuzzy logic sensory evaluation was used to evaluate the products.

Establishing the fuzzy logic sensory evaluation method    According to GB/T 15682–2008 sensory evaluation method of cooking and cooking rice and rice, the evaluation index set was defined before and after cooking. The set before cooking was defined as A_i = {A₁, A₂, A₃}, where A1 indicates the appearance, including normal color A₁₁ and uniform particles A₁₂; A₂ indicates the odor, including natural flavor A₂₁ and an absence of mildew flavor A₂₂; and A₃ indicates the texture, including hard A₃₁ and intact A₃₂. Similarly, the evaluation index set after cooking was defined as B_i = {B₁, B₂, B₃, B₄, B₅}, where B₁ was the odor, including aroma purity B₁₁ and aroma richness B₁₂; B₂ was the appearance structure, including color B₂₁, gloss B₂₂, and integrity B₂₃; B₃ was the palatability, including viscosity B₃₁, elasticity B₃₂, and soft hardness B₃₃; B₄ was the taste, including taste purity B₄₁ and taste persistence B₄₂; and B₅ was the texture of cold rice, including viscoelastic B₅₁ and non-regenerative B₅₂. The weighting coefficients of each evaluation index set were calculated using a questionnaire survey method. This included a professional survey (completed by professionals, n = 7) and a network questionnaire survey (completed by members of the public, n = 72).

The respondents ranked according to the importance of each evaluation set index, and finally made statistical calculation according to the survey results, and gave the weight of each evaluation set index. The number of people who gave the same level of importance was divided by the total number of people to get the weight X_i of an index. Due to the different confidence levels of the surveys, a mandatory decision-making method was adopted to give different weights to the evaluation results of respondents in the two channels, such that the professionals were weighted 0.6 and the members of the public 0.4.

The set of products to be evaluated was defined as Y, and Y_i was the set of evaluation objects. Here Y_i generated by orthogonal experimental design was {Y₁, Y₂, Y₃, Y₄, Y₅, Y₆, Y₇, Y₈, Y₉}, representing the evaluation of 9 samples.

By setting a specific score area corresponding to the evaluation set, the final result can be quantified. For instance, for an observation set V = {V₁, V₂, V₃, V₄}, where V₁, V₂, V₃, and V₄ indicate “very good” (a score of 90–100), “good” (80–90), “normal” (60–80) and “poor” (0–60), respectively. According to the boundary fuzzy logic method of clear quality grade, the score was obtained by taking the center values of each range, i.e. 95, 85, 70, and 30, respectively. The 7 professionals evaluated the products obtained from the orthogonal experiment using the above product evaluation index set. The results were summarized in a statistical table of rice quality evaluation, and then the membership degree was obtained by dividing the number of people making the same evaluation by the total number of people, forming the evaluation matrix Rij, as below:   

According to the principle of fuzzy transformation, it is possible to obtain Y=X○R, where Y

According to the principle of fuzzy transformation, it is possible to obtain Y=X○R, where Y is the set of evaluation objects as in the comprehensive evaluation set of samples above, X is the weighting set, R is the evaluation matrix, and ○ is a wide area fuzzy operator. It follows that:   

where Y_i=(X₁×r_1i) ∨ or ∧ (X₂×r_2i)…(X_n×r_n), " ∨ " means the larger value after comparison, and "∧" means the smaller value. According to the relationship between the sensory evaluation indexes, " ∨ " was selected using the maximizing algorithm for the fuzzy transformation of secondary and tertiary factors, and " ∧ " was selected according to the minimizing algorithm for the fuzzy transformation between the pre-cooking and post-cooking factors applied to the outermost layer (Jenkins et al., 2012; Allen et al., 2012). Finally, the score in the table corresponding to the comment set and the scoring area provided a comprehensive evaluation that was converted into a score, which was then taken into the orthogonal experiment as the result.

Establishing a GI test    The method was based on ISO26642:2010 and the Shanghai local standard DB31/T399-2008. Thirteen people were recruited who did not have any glucose-metabolism-related diseases and were not taking any medication. They were asked to provide their basic information, such as age, sex, body weight, height,history of disease,and to sign an informed consent form. Participants were requested not to eat or drink after 10 p.m. the night before the test, and not to take strenuous exercise on the morning of the test. Blood samples were collected from the fingers of each participant after their finger had been disinfected with an alcohol-soaked cotton wool ball and allowed to dry. The sample was taken using a sterile sampling needle and analyzed using a Johnson & Johnson Wenhao blood glucose meter. Fasting blood glucose levels were measured twice within a maximum of 5 min. Participants were either given glucose water containing 50 g glucose (in 250 mL water), or the test sample prepared according to the above method, taken with 250 mL warm water. All food was consumed within 12–15 min. Blood samples were then collected and recorded at time 15, 30, 45, 60, 90, and 120 min after the initial sample.

Use time as X-axis, spot blood glucose value as Y-axis to make the blood glucose response curves, and calculate the IAUC (Zhang et al., 2019). The GI was calculated with the reference glucose whose IAUC set as 100, the GI value of the product is I_mean:

I_mean = Test food IAUC / Glucose IAUC × 100

Clinical test    To study the effect of food intervention on insulin resistance in patients with type-II diabetes mellitus, a randomized, double-blinded, controlled clinical trial was conducted using the low GI reconstituted rice. The diagnostic criteria for type-II diabetes were in line with the 1999 WHO criteria for the diagnosis and classification of diabetes: impaired glucose tolerance and a clear clinical record of fasting plasma glucose (FPG) ≥ 7 mmol/L and ≥ 11.1 mmol/L after oral glucose for 2 h, or a history > 1 year. The exclusion criteria were: acute cardiovascular or cerebrovascular events or myocardial infarction within 6 months; liver function Aspartate aminotransferase (AST) and/or Alanine aminotransferase (ALT) ≥ 2.5 × ULN; renal function C_r > 1.2 × ULN; severe gastrointestinal disease; surgical history to change the structure of the gastrointestinal tract; hypertension with poor blood pressure control (SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg); severe hematological disease; any other endocrine disease (such as hyperthyroidism or hypercortisolism); stress or secondary hyperglycemia (such as those on glucocorticoids); pregnant or breastfeeding women, or women who were unwilling to use contraception during the study period; users of recreational drugs; drug allergies; having participated in another drug trial within the past 3 months; mental illness or an inability to cooperate; or any other reason the research team determined they would not be suitable for the study.

Sixty patients were randomly divided into an experimental group and a control group. SAS software was used to automatically generate a random coding table with the number of experimental groups and centers. In the diet plan, the experimental group was given low GI reconstituted rice in combination with lifestyle guidance, as per the standard management of diabetes. Instead of 100 g of the usual staple food that would be prescribed each day, patients consumed the low GI reconstituted rice independently and did not consume any other staple food in the same meal over the 4 weeks testing period. The control group was given regular rice with lifestyle guidance. Instead of 100 g of staple food every day, patients consumed regular rice independently and did not consume any other staple food in the same meal for the 4 weeks.

Participants completed the following visits: a screening visit for trial inclusion, a week 0 visit, a week 2 visit, and a week 4 visit. Dynamic monitoring was conducted using a dynamic blood glucose monitoring system. At each visit, all indicators were measured and any adverse reactions were recorded. Instantaneous blood glucose data were recorded in units of one day, and blood glucose responses for week one, week two, week three, and week four were attributed to days 3, 10, 17, and 27, respectively.

The postprandial blood glucose curve was used to measure the blood sugar control ability of diabetic patients has it has clinical significance in this regard. Due to differences in dietary habits and nutritional requirements across patients, there were unavoidable differences in the calorie and carbohydrate content of meals across participants. As such, the ability to directly compare postprandial blood glucose responses across patients was limited. We, therefore, recorded the dietary and calorie intake of participants in both groups on days 3, 10, 17, and 27, and recorded blood glucose values at time 15, 30, 45, 60, 75, 90, 105, 120, 150, and 180 min after meals as captured using the dynamic blood glucose monitoring, in order to calculate the area under the curve (AUC) of blood glucose by comparing the changes in blood glucose response within and between groups.

Ethical evaluation    In the study, the sensory evaluation, GI test and clinical test were approved by the Ethics Review Committee of the Peking Union Medical College Hospital of the Chinese Academy of Medical Sciences; the Ethics Review Document No. is HS-1763 (Supplemental-Appendix).

Statistical analysis    All experiments were conducted in triplicate. In the orthogonal array and clinical test, statistical analyses were performed using the SPSS software (SPSS version 16.0; SPSS Inc, Chicago, IL) , and the statistical significance was defined as p < 0.05. One-factor ANOVA was used to compare changes in the AUCs between participants taking the reconstituted rice compared withe participants taking regular rice.

In the GI test, t-test, according to the standard specification of ISO 26642: 2010, there are following requirements for the test of blood glucose and the calculation of GI value in food: the laboratory's inter-assay CV (i.e. analytical variation) on standard solutions should be < 3.6%; the mean within-subject CV for the reference food for the group of subjects tested shall be ≤ 30%; outliers can be excluded from the calculation, but a minimum of 10 subjects should still be available for the tests to have statistical validity. The statistical analysis was performed using t-test with the significance at p < 0.05.

Results and Discussion

Optimization of the single factor experiment    As shown in Fig. 1(A), the hardness of the reconstituted rice increased with the water addition for batching added, and then decreased slowly again after a certain of water addition for batching. A preliminary interpretation of this may be that the addition of water up to the critical amount improves the melting ability of the ingredients due to the increase in moisture, making the mixture better for phase transition. After this critical water addition for batching, further increases in water mean that more mechanical energy was absorbed by this moisture and that a flash phenomenon occurs after shaping, resulting in a poor product quality. After fitting the equation, the hardness of the reconstituted rice reached the maximum when the water addition for batching was about 26%. Through a large number of early experiments, the results show that too low water will cause the extrusion machine internal torque is too large, more than the equipment load and locked; The high water content will greatly reduce the mechanical energy of the material in the extrusion cylinder, and then hinder the material melting and tissue reconstruction. Therefore, the optimum water content was selected for the optimization experiment.Therefore, the water addition for batching in the orthogonal experiment was determined to be X₁ = (21%, 26%, 31%).

Fig.1.

Effect of water addition for batching (A), extruder screw speed (B) and barrel temperature (C) on the hardness of reconstituted rice

With increasing extruder speed, the hardness of the reconstituted rice increased slowly at first, increasing more rapidly after reaching a certain hardness, and then following a parabolic downward decline, as represented in Fig. 1(B). Preliminary analysis showed that the slow increase in the first stage was due to insufficient input of mechanical energy at this extruder speed. Then, when the mechanical energy exceeded a certain threshold, the quality of the reconstituted rice improved. As the extruder screw speed continued to increase, the pressure in front of the die became far greater than atmospheric pressure, resulting in a rapid decline in product quality. By fitting the equation, the maximum hardness of the product was found to be obtained when the extruder screw speed was about 190 rpm. Therefore, the extruder screw speed factor (X₂) was determined to be (160, 190, 220 rpm).

As the barrel temperature increased, the hardness of the product increased gradually up to a point, before decreasing sharply for a while, and then tending to be flat, as illustrated in Fig.1(C). In the first stage of heating, the phase change of the product allowed the structure of the reconstituted rice to form. As the product was heated continuously, it expanded greatly, with the degree of expansion being more intense above a certain temperature. By fitting the equation and solving it, the hardness of the product was found to be highest when the barrel temperature was about 76 °C. The hardness tested in this experiment is the hardness index of reconstituted rice after cooking. Hardness means good taste and chewy. Of course, in the investigation, the reconstituted rice must be fully steamed under the premise of testing.Therefore, the barrel temperature factor (X₃) was determined to be (66, 76, 86 °C).

Optimization of the orthogonal experiment    The three levels of each factor were obtained from the results of the single-factor experiment. A three-factor and three-level L9 (3⁴) orthogonal experiment was then carried out. Water addition for batching was set at 21%, 26%, and 31% (w/w), extruder screw speed was 160, 190, and 220 rpm, and barrel temperature was 66, 76, and 86 °C. Each combination was measured three times in parallel.

From the range analysis (Table 1), it can be seen that the order in which the three factors affected the quality of the reconstituted rice was X₂ (extruder speed; greatest effect), then X₃ (barrel temperature), then X₁ (water addition for batching; least effect). According to the mean scores given, the middle values of each factor were optimal, with the best combination being 26% water, 190 rpm extruder speed, and a barrel temperature of 76 °C. From the ANOVA in Table 1, it can be seen that the extruder screw speed had the most significant impact on the quality of the reconstituted rice.The experimental results showed that the speed of extruder was the most significant factor affecting the quality of reconstituted rice. The analysis is that because the different speed of the extruder represents the different time that the material stays in the barrel of the extruder, the mechanical energy and heat energy are greatly different.

Table 1. Orthogonal array design, corresponding results and variance analysis

Experiment number	Water addition for batching (%)	Extruder screw speed(rpm)	Barrel temperature (°C)	error	result
1	1(21)	1(160)	1(66)	1	72.2
2	1(21)	2(190)	2(76)	2	85.1
3	1(21)	3(210)	3(86)	3	76.3
4	2(26)	1(160)	2(76)	3	75.2
5	2(26)	2(190)	3(86)	1	83.4
6	2(26)	3(210)	1(66)	2	75.6
7	3(31)	1(160)	3(86)	2	73.8
8	3(31)	2(190)	1(66)	3	82.6
9	3(31)	3(210)	2(76)	1	75.9
Mean value 1	77.867	73.733	76.800	77.167
Mean value 2	78.067	83.700	78.733	78.167
Mean value 3	77.433	75.933	77.833	78.033
R	0.634	9.967	1.933	1.000
Sum of square of deviation	0.629	164.496	5.616	1.769
Freedom	2	2	2	2
F ratio	0.201	52.681	1.799	0.567
F critical value	6.940	6.940	6.940	6.940
Significance		*

For the index “appearance” A₁, according to the fuzzy transformation rule, the evaluation results were obtained as follows:   

=[(0.43×0.143)∨(0.57×0.143), (0.43×0.429)∨(0.57×0.571), (0.43×0.286)∨(0.57×0.286), (0.43×0.143)∨(0.57×0.000)] =(0.082, 0.325, 0.163, 0.061),

which can be normalized to Y_A1=(0.130, 0.515, 0.258, 0.097). Similarly,

Y_A2=(0.388, 0.515, 0.097, 0.000), Y_A3=(0.096, 0.474, 0.214, 0.216).

The evaluation matrix R_A of pre-cooking membership indices is thus   

The evaluation results before cooking were then calculated as follows:   

=(0.074, 0.204, 0.098, 0.093),

which was normalized to Y_A=(0.158, 0.435, 0.209, 0.198). Similarly, the evaluation after cooking was calculated as Y_B=(0.114, 0.418, 0.253, 0.215).

The evaluation matrix R₁ of sample 1 was obtained as follows:   

Using the minimum calculation method, the evaluation set of sample 1 was obtained using the "∧" operation:   

=(0.059, 0.209, 0.000, 0.095),

which can be normalized to Y₁=(0.163, 0.576, 0.000, 0.262).

According to the evaluation set mentioned in Materials and methods 2.4, the score of sample 1 was: 95 × 0.163 + 85 × 0.576+70 × 0.000+30 × 262 = 72.2 (points). The scores of other samples were calculated similarly, and the scores of the 9 experimental samples were:

Y_i={Y₁, Y₂, Y₃, Y₄, Y₅, Y₆, Y₇, Y₈, Y₉}

= {72.2, 85.1, 76.3, 75.2, 83.4, 75.6, 73.8, 82.6, 75.9}.

(3) Evaluation of the low GI reconstituted rice

(a)The evaluation index set and weightings

In the fuzzy sensory evaluation, statistical analysis of the survey data was used to evaluate the factors and weights of the sensory evaluation of the low GI reconstituted rice (Table 2). Each weighting was calculated using the following formula:   

Table 2. Evaluation criteria and weights calculation results.

Primary factor	Secondary factor	Third level factors	Standard requirements
Before cooking A (0.48)	Appearance A1(0.38)	Color and lustre A11(0.43)	The color was normal
		Grain A12(0.57)	The particles were uniform
	Smell A2(0.19)	Aroma A21(0.37)	The fragrance was normal
		No mildew A22(0.63)	No mildew
	Texture A3(0.43)	It's hard A31(0.59)	Rice grains have a certain hardness
		Integrity A32(0.41)	Particle integrity
After cooking B (0.52)	Smell B1(0.16)	Aroma purity B11(0.55)	It has unique aroma
		Strong aroma B12(0.45)	Rich aroma
	Appearance B2(0.18)	Color B21(0.31)	The color was normal
		Gloss B22(0.39)	The surface was bright
		Integrity B23(0.30)	Compact structure, no crack
	Palatability B3(0.30)	Viscosity B31(0.33)	It has a certain viscosity
		Elastic B32(0.33)	It was elastic and chewy
		Hardness and softness B33(0.34)	Appropriate hardness and softness
	Taste B4(0.25)	Taste purity B41(0.61)	It has a special taste
		Taste persistence B42(0.39)	The fragrance lasts long
	Cold rice texture B5(0.11)	Viscoelasticity B51(0.43)	good viscoelasticity after cooling
		Anabiosis B52(0.57)	no rebirth after cooling

where X_i was the weight of the i-th index, a_j was the index assignment, n_ij was the number of people who attributed an importance of j to the i-th index, and N was the total number of participants.

(b) Membership degree of the fuzzy evaluation

Table 3 shows the evaluation of sample 1 by 7 professionals and the membership degree of the evaluation. The evaluation of the remaining 8 samples was conducted in the same way.

Table 3. Membership degree of each index of sample 1.

Primary factor	Secondary factor	Weight	Third level factors	Weight	Evaluation membership
					Very good	Good	Commonly	Poor
Before cookingA (0.48)	A1	0.38	A11	0.43	0.143	0.429	0.286	0.143
			A12	0.57	0.143	0.571	0.286	0.000
	A2	0.19	A21	0.37	0.143	0.714	0.143	0.000
			A22	0.63	0.429	0.571	0.000	0.000
	A3	0.43	A31	0.59	0.143	0.286	0.429	0.143
			A32	0.41	0.143	0.714	0.143	0.000
After cooking B (0.52)	B1	0.16	B11	0.55	0.143	0.143	0.571	0.143
			B12	0.45	0.143	0.286	0.571	0.000
	B2	0.18	B21	0.31	0.286	0.571	0.143	0.000
			B22	0.39	0.143	0.714	0.143	0.000
			B23	0.3	0.143	0.714	0.143	0.000
	B3	0.3	B31	0.33	0.143	0.286	0.429	0.143
			B32	0.33	0.143	0.429	0.143	0.286
			B33	0.34	0.000	0.714	0.143	0.143
	B4	0.25	B41	0.61	0.143	0.286	0.286	0.286
			B42	0.39	0.143	0.286	0.286	0.286
	B5	0.11	B51	0.43	0.286	0.429	0.143	0.143
			B52	0.57	0.143	0.429	0.286	0.143

The fuzzy sensory evaluation (Singh et al., 2018) used fuzzy logic to comprehensively analyze the uncertainty and fuzziness in the process of product evaluation, in order to accurately, objectively, and scientifically evaluate the product processing. This improved the on product evaluation index set based on “GB/T15682-2008_ Inspection of cereals and oils, cooking and sensory evaluation of rice”. As the sensory characteristics of the low GI reconstituted rice change before and after cooking, some evaluation indices were refined, and the differences between before and after cooking were characterized. As such, the evaluation index weighting coefficients were calculated. In addition, three levels from inside to outside were involved in the process of the fuzzy transformation. Due to the similarity and correlation between indices, the internal three-level and two-level fuzzy transformations followed the principle of maximum membership, so "∨" was used. Similarly, the fuzzy transformations before and after cooking of the outermost layer followed the minimum algorithm, "∧", due to the relative independence of the indexes and the short acting effect of defects.

GI of reconstituted rice    As stipulated in ISO26642:2010 and the Shanghai local standard DB31/T399-2008, the GI of the low-GI reconstituted rice was measured to be 53.6, which is much lower than that of ordinary rice (GI = 88). The rate at which it increased and decreased the blood glucose levels of patients were also lower (Kumar et al., 2014), and it has a lesser impact on the postprandial blood glucose. Low GI reconstituted rice can help to maintain the stability of postprandial blood glucose and is likely to be more suitable for patients with chronic diseases such as diabetes (Udani et al., 2009).

Clinical test    The postprandial blood glucose responses within the experimental and control groups were compared on days 3, 10, 17, and 27 after the intervention. Note that the experimental group consumed the low GI reconstituted rice, and the control group consumed regular rice, with all other factors being kept the same between the two groups.

As shown in Fig. 2, in the experimental group, the blood glucose rise had a latency compared with that of the control group, but there was no statistically significant difference in peak blood glucose values reached. Further, the AUCs of blood glucose at 3 h after the meal were compared. For the experimental group, the AUCs on days 3, 10, 17, and 27 were less than those of the control group, but this was not statistically significant.

Fig. 2.

Postprandial AUC in experimental group and control group.

From these results, it can be concluded that the experimental group was generally better than the control group in the key index of postprandial blood glucose control. In comparison to a diet containing regular rice, the consumption of low GI reconstituted rice was more conducive to blood glucose control. Due to the differences in calorie and carbohydrate intake in the same meal, the trend in postprandial blood glucose response was obvious.

Calorie intake and carbohydrate mass at each meal were defined as “food per kcal” and “carbohydrate per gram”, and indicated by the AUC per kcal of food and AUC per gram of carbohydrate, respectively (Englyst et al., 1966; Englyst et al., 1999). The postprandial blood glucose AUCs within the two groups were compared at 4 time-points (days 3, 10, 17, and 27). Carbohydrate mass and total calorie intake of each meal, as well as AUC per gram of carbohydrate and AUC per g of carbohydrate were analyzed (Fig. 3(A)). The carbohydrate AUC/g was significantly decreased in the experimental group on days 3, 17 and 27.

Fig. 3.

Postprandial AUC in the experimental and control groups. (A) and (C) show the cases of the experimental group, and “Y” and “N” indicate taking and non-taking the low GI reconstituted rice, respectively. (B) shows the case of the control group, and here “Y” and “N” indicate taking and non-taking the regular rice, respectively. p < 0.05 represents the statistically different significance.

The control group was divided into two subgroups: regular rice, and no rice, with all other factors being the same. As illustrated in Fig. 3(B), there was no statistically significant difference in AUC/g of carbohydrate between control subgroups days 3 and 27, but on days 10 and 17, the AUC/g of carbohydrate in the subgroup consuming rice was significantly higher than that in those not consuming rice. Considering the effect of energy intake on the blood glucose curve, the AUC/kcal of food in the experimental group was calculated, as shown in Fig. 3(C). On day 17, the AUC/kcal of the experimental group was significantly lower than that of the regular rice group, with differences on the other days being relatively smaller.

AUC comparisons between groups    The difference in the carbohydrate AUC/g between the experimental and control groups was compared (Fig. 4(A)). Compared with the control group, the postprandial blood glucose response in the experimental group was generally lower, which was more noticeable on days 3 and 17 (p = 0.036, p = 0.01, respectively). The difference in the AUC/kcal (Fig. 4(B)) was similarly lower in the experimental group compared to the control group, and this effect was more obvious on days 3 and 17 (p = 0.036 and p = 0.01, respectively).

Fig. 4

Postprandial AUC in the experimental and control groups (indicated by “A” and “B” on the abscissa axis respectively).

The AUC/kcal of food and the AUC/g of carbohydrate were significantly decreased in the experimental group compared to the control group. This demonstrates that the nutritional intervention of consuming low GI reconstituted rice as a staple diet can significantly improve the postprandial blood glucose response in diabetic patients and that the effect can remain stable over a sustained period (4 weeks).

Conclusion

By establishing and using a fuzzy sensory evaluation method, low GI reconstituted rice was evaluated, and an analysis was conducted on the basis of an orthogonally designed experiment. The extrusion process of the low GI reconstituted rice using a twin-screw extruder was explored and the optimal combination of three important factors affecting product quality was established as water addition for batching of 26%, an extruder screw speed of 190 rpm, and a barrel temperature of 76 °C. The low GI reconstituted rice manufactured in this experiment was shown to belong to the category of low GI food, making it suitable for diabetics. Through the clinical observation of a cohort of patients divided into groups who were given low GI reconstituted rice, and for comparison, regular rice, to consume, the low GI reconstituted rice was shown to significantly improve the postprandial blood glucose response in the diabetic patients. Further, this effect was shown to remain stable over a period of 4 weeks.

Acknowledgements    This research was funded by Key Research and Development Projects of Science and Technology Department of Hebei Province (Nos.:19223002D, 21327124D and 21327118D) and the Central Government Funds for Scientific and Technological Development of Xizang Province (No.: XZ202101YD0019C).

Conflict of interest    There are no conflicts of interest to declare.

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