TY - JOUR
AU - Quek, Siew, Young
AB - Abstract Objectives Rice has been identified as a high glycemic index (GI) food associated with obesity and diabetes. Current study investigated the replacement of rice flour and its effect on product properties using extrusion technology. Materials and Methods High selenium yam and konjac flours were used to increase fibre and selenium contents, whereas sorghum and soy protein were added to improve protein content as well as textural properties of product. The following variables were optimized: yam flour (20% to 60%), feed moisture content (25% to 35%), and extrusion temperature (100°C to 120°C) by evaluating the breaking strength, firmness, colour, bulk density, and water absorption index (WAI) of the extruded products. Results Results show that the extrusion temperature has a significant relationship with the products’ breaking strength, WAI, and bulk density, whereas the feed moisture content influenced the WAI, bulk density, and firmness (P < 0.05). The inclusion of yam flour significantly increased the firmness and yellowness (b* value) of the products (P < 0.05). Sensory profiling revealed that the enriched product has comparable textural properties (stickiness, firmness, and graininess) with the commercial rice. Comparing with commercial rice, the enriched product has significantly higher antioxidant activity (163.53 vs. 10.33 μmol Trolox/100 g, DPPH assay), protein (13.4% vs. 9.4%), fibre (12% resistant starch, 9% glucomannan), and a lower GI value (69.6 vs. 95.4). The enriched product also contains 15.62 µg/100 g selenium, providing additional health benefit as Se-enriched functional food. Conclusions This work has demonstrated the suitability of applying extrusion to produce a healthier alternative rice product by nutrient enrichment via rice flour substitution. extrusion, nutrient enrichment, rice product, selenium, fibre, glycemic index Introduction Rice (Oryza sativa L.) is a major staple food, especially in Asia. However, starch, as the main component of rice (72% to 82% dry weight), has shown to impart high glycemic index (GI) associated with health issues including obesity and cardiovascular disease (Pfeiffer and Keyhani-Nejad, 2018). GI value is associated with postprandial blood glucose production after starch digestion and is an important characteristic of carbohydrate food (Jenkins, 2007). Substitution of rice flour to produce a healthier rice product is therefore significant to give consumers with alternative choice. The factor to consider is to increase dietary fibre, reduce the GI value, and adding protein content to increase the nutritional value of the rice product. To achieve this, a few ingredients including yam, konjac, and sorghum could be considered as rice flour substitution. Yam is a tuber crop consisted of 40% resistant starch and has a lower GI of 52 compared with 82 in brown rice (Lin et al., 2010; Huang et al., 2016). Konjac is also from tuber and contains approximately 80% soluble fibre in a form of glucomannan which has been reported to decrease the risk of type II diabetes and cardiovascular disease (Shah et al., 2015). Sorghum comprised of up to 65% resistant starch and 15% protein (Moraes et al., 2015) and has been used to improve the textural properties and protein content of pasta and gluten-free bread (Phattanakulkaewmorie et al., 2011; Patel et al., 2016). Soy protein is a good source of protein and has been shown to enhance the textural properties of extruded product (Detchewa et al., 2016). Selenium (Se) is an important micronutrient required for thyroid hormone metabolism, DNA synthesis, reproduction, and protection from infection in human. It has been reported as having antioxidant, anti-inflammatory, and anticancer activities and is a cofactor of glutathione peroxidases enzyme to decrease lipid peroxidation and cellular damages (Li et al., 2017). The recommended Se daily intake is 55 µg/day based on a reference dose of 0.005 mg/kg body weight/day, with the maximum safety level of 400 µg/day (WHO, 2011). Se deficient in soil is still causing health concern in parts of the world including Finland, India, New Zealand, and some regions in China (Fordyce, 2013). Because of the importance role of Se in human health, ensuring adequate consumption especially in Se-deficient areas is a necessity. Enrichment of food products by incorporating high Se food ingredients in product formulation served as an effective method to achieve the daily Se consumption. For this reason, high Se yam flour sourced from Enshi (Hubei Province, China) was used to fortify the rice product in the current study. As the only place in the world that have accumulation of selenium up to 8,000 mg/kg in soil, Enshi has been recognized as the “World Capital of Selenium” (Yuan et al., 2012). The high Se content in soil has led to rich Se content in agricultural products produced from Enshi, for example, sweet potato and rice have been reported to contain Se of 0.96 and 0.36 mg/kg, respectively (Huang et al., 2013). Extrusion is a continuous mixing, cooking, and structure forming process by passing feed materials through dies by pressure. It has been applied to produce a variety of food produces including snack food, breakfast cereals, and pastas (Alam et al., 2016). Fortification of food products by extrusion has recently been studied including triple-fortified rice grain with zinc, iron, and vitamin A to decrease nutritional deficiency in children from developing countries (Pinkaew et al., 2013), and protein- and fibre-enriched products (Beck et al., 2018). There are advantages of using extrusion in food processing as the process gives high productivity yield, lower production cost, and material loss during processing (Alam et al., 2016). It can be scaled up for different feed ingredients and production parameters, making it useful for product development (Seth and Rajamanickam, 2012). This research studied the enrichment of Se, fibre, and protein in extruded rice product by substituting rice flour with high Se yam flour, konjac, sorghum, and soy protein, and the resulted effects on the product properties. Processing parameters (extrusion temperature, yam flour percentage, and feed moisture content) were studied in relation to the physicochemical properties of the extruded samples, including breaking strength, firmness, colour, bulk density, and water absorption index (WAI). The chemical, textural, and sensory characteristics as well as the in vitro starch digestion of the enriched products were studied in comparison to the commercial rice. Materials and Methods Materials Yam flour (Enshi, China), konjac flour (Yunnan, China), rice flour (Zhenjiang, China), sorghum (Liaoning, China), and soy protein (Shandong, China) were collected and stored in sealed plastic bags in cool dry place before used. All chemicals used were of analytical grade. Preparation of samples Optimization of processing parameters The extrusion processing experiments were optimized using response surface methodology (RSM) based on central composite design (Montgomery, 2017). Preliminary experiments were conducted to select the most influential experimental parameters for optimization. Three selected independent variables were percentages of yam flour (YF%), feed moisture content, and extrusion temperature (Tables 1 and 2). A second-order polynomial model (Eq. 1) was applied to fit the experimental data (Ganorkar and Jain, 2015). Analysis of variance (ANOVA) was conducted using the Design Expert 7.0 (Stat Ease, Inc., Minneapolis, MN), Table 1. Optimization of experimental parameters using response surface methodology with central composite design: independent variables and their levels for central composite design Independent variables Symbol coded Coded factor level −1.68 −1 0 +1 +1.68 Yam flour (%) x1 6.7 20 40 60 73.6 Feed moisture content (%) x2 21.6 25 30 35 38.4 Extrusion temperature (°C) x3 93.1 100 110 120 126.3 Independent variables Symbol coded Coded factor level −1.68 −1 0 +1 +1.68 Yam flour (%) x1 6.7 20 40 60 73.6 Feed moisture content (%) x2 21.6 25 30 35 38.4 Extrusion temperature (°C) x3 93.1 100 110 120 126.3 View Large Table 1. Optimization of experimental parameters using response surface methodology with central composite design: independent variables and their levels for central composite design Independent variables Symbol coded Coded factor level −1.68 −1 0 +1 +1.68 Yam flour (%) x1 6.7 20 40 60 73.6 Feed moisture content (%) x2 21.6 25 30 35 38.4 Extrusion temperature (°C) x3 93.1 100 110 120 126.3 Independent variables Symbol coded Coded factor level −1.68 −1 0 +1 +1.68 Yam flour (%) x1 6.7 20 40 60 73.6 Feed moisture content (%) x2 21.6 25 30 35 38.4 Extrusion temperature (°C) x3 93.1 100 110 120 126.3 View Large Table 2. Optimization of experimental parameters using response surface methodology with central composite design: central composite experimental design, actual and coded variables Run Yam flour (%) Feed moisture content (%) Extrusion temperature (oC) 1 20 (−1) 25 (−1) 100 (−1) 2 60 (+1) 25 (−1) 100 (−1) 3 20 (−1) 35 (+1) 100 (−1) 4 60 (+1) 35 (+1) 100 (−1) 5 20 (−1) 25 (−1) 120 (+1) 6 60 (+1) 25 (−1) 120 (+1) 7 20 (−1) 35 (+1) 120 (+1) 8 60 (+1) 35 (+1) 120 (+1) 9 6.36 (−1.68) 30 (0) 110 (0) 10 73.64 (+1.68) 30 (0) 110 (0) 11 40 (0) 21.59 (−1.68) 110 (0) 12 40 (0) 38.41 (+1.68) 110 (0) 13 40 (0) 30 (0) 93.1 (−1.68) 14 40 (0) 30 (0) 126.28 (+1.68) 15 40 (0) 30 (0) 110 (0) 16 40 (0) 30 (0) 110 (0) 17 40 (0) 30 (0) 110 (0) 18 40 (0) 30 (0) 110 (0) 19 40 (0) 30 (0) 110 (0) 20 40 (0) 30 (0) 110 (0) Run Yam flour (%) Feed moisture content (%) Extrusion temperature (oC) 1 20 (−1) 25 (−1) 100 (−1) 2 60 (+1) 25 (−1) 100 (−1) 3 20 (−1) 35 (+1) 100 (−1) 4 60 (+1) 35 (+1) 100 (−1) 5 20 (−1) 25 (−1) 120 (+1) 6 60 (+1) 25 (−1) 120 (+1) 7 20 (−1) 35 (+1) 120 (+1) 8 60 (+1) 35 (+1) 120 (+1) 9 6.36 (−1.68) 30 (0) 110 (0) 10 73.64 (+1.68) 30 (0) 110 (0) 11 40 (0) 21.59 (−1.68) 110 (0) 12 40 (0) 38.41 (+1.68) 110 (0) 13 40 (0) 30 (0) 93.1 (−1.68) 14 40 (0) 30 (0) 126.28 (+1.68) 15 40 (0) 30 (0) 110 (0) 16 40 (0) 30 (0) 110 (0) 17 40 (0) 30 (0) 110 (0) 18 40 (0) 30 (0) 110 (0) 19 40 (0) 30 (0) 110 (0) 20 40 (0) 30 (0) 110 (0) Data are shown as actual values (coded values). View Large Table 2. Optimization of experimental parameters using response surface methodology with central composite design: central composite experimental design, actual and coded variables Run Yam flour (%) Feed moisture content (%) Extrusion temperature (oC) 1 20 (−1) 25 (−1) 100 (−1) 2 60 (+1) 25 (−1) 100 (−1) 3 20 (−1) 35 (+1) 100 (−1) 4 60 (+1) 35 (+1) 100 (−1) 5 20 (−1) 25 (−1) 120 (+1) 6 60 (+1) 25 (−1) 120 (+1) 7 20 (−1) 35 (+1) 120 (+1) 8 60 (+1) 35 (+1) 120 (+1) 9 6.36 (−1.68) 30 (0) 110 (0) 10 73.64 (+1.68) 30 (0) 110 (0) 11 40 (0) 21.59 (−1.68) 110 (0) 12 40 (0) 38.41 (+1.68) 110 (0) 13 40 (0) 30 (0) 93.1 (−1.68) 14 40 (0) 30 (0) 126.28 (+1.68) 15 40 (0) 30 (0) 110 (0) 16 40 (0) 30 (0) 110 (0) 17 40 (0) 30 (0) 110 (0) 18 40 (0) 30 (0) 110 (0) 19 40 (0) 30 (0) 110 (0) 20 40 (0) 30 (0) 110 (0) Run Yam flour (%) Feed moisture content (%) Extrusion temperature (oC) 1 20 (−1) 25 (−1) 100 (−1) 2 60 (+1) 25 (−1) 100 (−1) 3 20 (−1) 35 (+1) 100 (−1) 4 60 (+1) 35 (+1) 100 (−1) 5 20 (−1) 25 (−1) 120 (+1) 6 60 (+1) 25 (−1) 120 (+1) 7 20 (−1) 35 (+1) 120 (+1) 8 60 (+1) 35 (+1) 120 (+1) 9 6.36 (−1.68) 30 (0) 110 (0) 10 73.64 (+1.68) 30 (0) 110 (0) 11 40 (0) 21.59 (−1.68) 110 (0) 12 40 (0) 38.41 (+1.68) 110 (0) 13 40 (0) 30 (0) 93.1 (−1.68) 14 40 (0) 30 (0) 126.28 (+1.68) 15 40 (0) 30 (0) 110 (0) 16 40 (0) 30 (0) 110 (0) 17 40 (0) 30 (0) 110 (0) 18 40 (0) 30 (0) 110 (0) 19 40 (0) 30 (0) 110 (0) 20 40 (0) 30 (0) 110 (0) Data are shown as actual values (coded values). View Large y =b0+b1x1+b2x2+b3x3+b12x1x2+b13x1x3+b23x2x3+b11x12+b22x22+b33x32 (1) where y is the response function; x1 is YF%; x2 is the feed moisture; x3 is the extrusion temperature; b0 is the constant; b1, b2, and b3 are linear terms; b12, b13, and b23 are interactions; and b11, b22, and b33 are quadratic regression terms. Extrusion The extrusion was performed using a commercial twin screw extruder (DS56-III, Jinan Saixin Machinery Co., Ltd, Jinan, China) with 65 mm diameter by 1050-mm long screws. The feed consisted of konjac flour, sorghum flour, soy protein, and yam flour to replace rice flour. The yam flour percentages were as verified following RSM (Table 1). The percentages of the konjac (10%), sorghum (5%), and soy protein (3%) flours were selected based on preliminary experiments. The barrel zone temperatures were set constant at 40oC and 80oC in zones 1 and 2, whereas the barrel temperature in zone 3 was verified following RSM (Table 2). The extruded products produced from an extruder die were dried in a hot air oven at 60oC for 5 h according to Park et al. (2012). Prior to extrusion, the moisture content of feed materials was analysed (AOAC, 2012) and adjusted according to Ganorkar and Jain (2015). Measurements of responses Breaking strength Breaking strength of raw extruded sample was measured using Texture analyser (TA.XT plus Stable Micro systems Ltd., Surrey, UK) according to Huang et al. (2007). Firmness Extruded samples were cooked in a rice cooker (Midea Co., model WFS3018SN, Guangdong, China) with a rice to water ratio of 2:1. The cooked samples were filled into a cylinder container (30 mm diameter, 15 mm height). Firmness of the samples was analysed using Texture analyser according to Huang et al. (2007). Colour The extruded sample was placed on plastic plate with 10 cm diameters and 1 cm height, and colour was measured by a colorimeter and calculated as a mean of 5 replicates. Bulk density Bulk density of the raw extruded samples was determined by the relationship between weight and volume (equation 3) of 20 randomly selected kennels (Chikkanna et al., 2015), BD =4Wπd2l (2) where BD is the bulk density (g/cm3), W is the weight (g), d is the diameter (cm), l is the length (cm), and d and l are measured by a Vernier calliper. Water absorption index Ten grams of raw extruded sample were boiled in 50 ml of water for 3 min, drained with a 20 mesh sieve, and left for 5 min followed by weighing. The WAI was calculated by the increased weight of the cooked sample as a percentage of the original dry weight (Yoo et al., 2013). Physicochemical properties of samples Proximate composition Proximate analysis of the selected ingredients and extruded products was conducted according to AOAC (2012). Moisture content was determined gravimetrically at 105°C, ash content at 550°C, lipid content by Soxhlet extraction with hexane, protein content by Kjeldahl method, and carbohydrate content was estimated by difference. Total selenium content Total Se content of the extruded samples was determined according to Castineira et al. (2001). A total of 200 mg of ground sample was microwave digested with 5 ml of 68% nitric acid (Merck Millipour, Germany). Total Se content in the digested sample was determined by ICP-MS (7700 ICP-MS, Agilent Technologies, USA). Antioxidant capacity Antioxidant capacity of the samples was determined using DPPH (Hsu et al., 2004; AOAC, 2012) and ABTS methods (Kim et al., 2002; Shen et al., 2009). Briefly, 1 g of samples were extracted with 70% methanol at 45°C for 5 h. For DPPH, 10 µl of extract was mixed with 200 µl of DPPH solution, incubated in the dark at room temperature for 60 min, and absorbance measured at 517 nm. For ABTS, 10 µl of extract was mixed with 190 µl of ABTS solution, incubated in the dark at room temperature for 60 min, and absorbance measured at 734 nm. Standard calibration curves were prepared with Trolox standard. The free radical scavenging capacity of sample was determined as Trolox equivalent antioxidant capacity (µmol Trolox/100 g sample). Textural properties Texture analyser was used to determine the breaking strength of the raw extruded samples togather with the firmness and adhesiveness of the cooked samples as described in Breaking strength and Firmness sections. Adhesiveness was the instrumental measurement of product stickiness based on an amount of force required to remove product that adheres to teeth (Wagoner et al., 2016). Microstructure analysis Microstructure of the samples was observed via scanning electron microscopy. Samples were cut in half (diameter was ~0.2 cm) and coated with platinum before observation. In vitro starch digestion Starch digestion was conducted according to Eleazu et al. (2016) with modification. Cooked sample (0.05 g) was digested with 10 ml pepsin solution in HCl-KCl buffer (pH 1.5) at 40oC for 60 min. Then α-amylase (2.6 IU) in 30 ml Tris-Maleate buffer (pH 6.9) was added and the mixture was incubated at 37oC. Digested sample (0.1 ml) was collected every 30 min, immediately boiled in water for 5 min to inactivate α-amylase. This was followed by addition of 0.4M sodium acetate (1 ml, at pH 4.75) and amyloglucosidase (30 µl) and incubation at 60oC for 180 min. The digested sample was hydrolysed in 2M KOH at room temperature for 1 h. The glucose content was analysed using a glucose assay kit. The GI value of the digested sample was calculated according to Goñi et al. (1997). Sensory characteristics of samples Seven trained panellists (3 females, 4 males) aged between 20 and 35 were recruited for sensory profiling of the extruded products using descriptive analysis. Training was conducted using ballot method begun with a list of descriptors and references presented to the panellists. The panellists discussed and chose the sensory terms used to describe the products through consensus. Sensory panellists were then trained on using the line scale to achieve good consistency. After training, sensory evaluation of raw and cooked samples was conducted in separate sessions. Approximately 10 g of each sample was served to the panellists, one sample at the time, for rating of the selected sensory attributes using a 10-cm line scale. Data were collected and analysed. Statistical analysis All analyses were conducted in triplicate and the results were reported as mean ± standard deviation. ANOVA with Duncan’s multiple range post hoc test was conducted using SPSS software Version 17 (SPSS, Inc., Chicago, IL). Results and Discussion Optimization of processing parameters Table 3 shows the ANOVA and response coefficients of the enriched extruded products as fitted by the multiples regression model (equation 1). Results show that the R2 values of the fitted models were in the range of 0.7520 to 0.8802 for the responses studied, i.e. breaking strength, firmness, colour (b*value), bulk density, and WAI. These responses were selected based on their influences on the overall acceptability of extruded product from literature (Petitot et al., 2010; Sereewat et al., 2015; Singh et al., 2016). The result also shows that the probability (P value) of the regression models was small (in the range of 0.0016 to 0.0218), with no significant lack of fit for model fitting (P > 0.05) except for L* value. This indicated that the generated models could be applied to explain the experimental data adequately except for L* value. In addition, these empirical models could be used to describe valid relationships between the processing parameters and the responses (except L* value for colour) which were represented by the selected physiochemical properties as below. Table 3. Analysis of variance and response coefficients of the extruded rice products Variables Breaking Strength (g) Firmness (g) L value b value Bulk density (g/cm3) WAI (g water/g sample) Model 1.499 × 10−7 1.466 × 106 89.46 11.93 0.35 0.72 Model (F value) 7.97 3.94 8.17 7.08 4.27 3.37 Model (P value) 0.0016 0.0218 0.0015 0.0026 0.0166 0.0360 R2 0.8777 0.7802 0.8802 0.8644 0.7936 0.7520 Adjusted R2 0.7676 0.5823 0.7724 0.7423 0.6078 0.5287 Lack of fit (P value) 0.1748 0.2575 0.0016 0.5274 0.1740 0.1671 Coefficients  b0 64482.109 −4712.907 53.388 −11.824 13.260 22.121  b1 45.334 −6.310** −0.223*** 0.011*** −0.033 3.641 × 10−3  b2 −322.784 375.589*** 0.364*** 0.737*** −0.047*** −0.277***  b3 −981.729*** 15.939 0.346 0.382 −0.201 −0.290  b12 0.311 −0.826 5.0 × 10−4 −3.00 × 10−4 4.635 × 10−4 4.102 × 10−4  b13 −0.584 0.226 2.498 × 10−3 4.2 × 10−4 1.648 × 10−4 −1.511 × 10−4  b23 1.488 −0.126 0.012 −2.71 × 10−3 −7.386 × 10−4 4.937 × 10−4  b11 0.126 0.180 −1.518 × 10−3* −1.184 1.225 × 10−5 1.046  b22 2.280 6.151** 0.011 −8.528 2.167 × 10−3* 2.883 × 10−3  b33 4.783*** 0.113 −4.627 × 10−4 −1.474 × 10−3 9.753 × 10−4*** 1.258 × 10−3** Variables Breaking Strength (g) Firmness (g) L value b value Bulk density (g/cm3) WAI (g water/g sample) Model 1.499 × 10−7 1.466 × 106 89.46 11.93 0.35 0.72 Model (F value) 7.97 3.94 8.17 7.08 4.27 3.37 Model (P value) 0.0016 0.0218 0.0015 0.0026 0.0166 0.0360 R2 0.8777 0.7802 0.8802 0.8644 0.7936 0.7520 Adjusted R2 0.7676 0.5823 0.7724 0.7423 0.6078 0.5287 Lack of fit (P value) 0.1748 0.2575 0.0016 0.5274 0.1740 0.1671 Coefficients  b0 64482.109 −4712.907 53.388 −11.824 13.260 22.121  b1 45.334 −6.310** −0.223*** 0.011*** −0.033 3.641 × 10−3  b2 −322.784 375.589*** 0.364*** 0.737*** −0.047*** −0.277***  b3 −981.729*** 15.939 0.346 0.382 −0.201 −0.290  b12 0.311 −0.826 5.0 × 10−4 −3.00 × 10−4 4.635 × 10−4 4.102 × 10−4  b13 −0.584 0.226 2.498 × 10−3 4.2 × 10−4 1.648 × 10−4 −1.511 × 10−4  b23 1.488 −0.126 0.012 −2.71 × 10−3 −7.386 × 10−4 4.937 × 10−4  b11 0.126 0.180 −1.518 × 10−3* −1.184 1.225 × 10−5 1.046  b22 2.280 6.151** 0.011 −8.528 2.167 × 10−3* 2.883 × 10−3  b33 4.783*** 0.113 −4.627 × 10−4 −1.474 × 10−3 9.753 × 10−4*** 1.258 × 10−3** WAI is water absorption index. *Significant at 0.1; **Significant at 0.05; ***Significant at 0.01. b0 is the constant; b1, b2, and b3 are linear; b12, b13, and b23 are interactions; and b11, b22, and b33 are quadratic regression terms in the equation: y = b0+ b1x1+ b2x2+ b3x3+ b12x1x2+ b13x1x3+ b23x2x3+ b11x12+ b22x22+ b33x32. x1 = Yam flour (%); x2 = Feed moisture content (%); x3 = Extrusion temperature (oC). View Large Table 3. Analysis of variance and response coefficients of the extruded rice products Variables Breaking Strength (g) Firmness (g) L value b value Bulk density (g/cm3) WAI (g water/g sample) Model 1.499 × 10−7 1.466 × 106 89.46 11.93 0.35 0.72 Model (F value) 7.97 3.94 8.17 7.08 4.27 3.37 Model (P value) 0.0016 0.0218 0.0015 0.0026 0.0166 0.0360 R2 0.8777 0.7802 0.8802 0.8644 0.7936 0.7520 Adjusted R2 0.7676 0.5823 0.7724 0.7423 0.6078 0.5287 Lack of fit (P value) 0.1748 0.2575 0.0016 0.5274 0.1740 0.1671 Coefficients  b0 64482.109 −4712.907 53.388 −11.824 13.260 22.121  b1 45.334 −6.310** −0.223*** 0.011*** −0.033 3.641 × 10−3  b2 −322.784 375.589*** 0.364*** 0.737*** −0.047*** −0.277***  b3 −981.729*** 15.939 0.346 0.382 −0.201 −0.290  b12 0.311 −0.826 5.0 × 10−4 −3.00 × 10−4 4.635 × 10−4 4.102 × 10−4  b13 −0.584 0.226 2.498 × 10−3 4.2 × 10−4 1.648 × 10−4 −1.511 × 10−4  b23 1.488 −0.126 0.012 −2.71 × 10−3 −7.386 × 10−4 4.937 × 10−4  b11 0.126 0.180 −1.518 × 10−3* −1.184 1.225 × 10−5 1.046  b22 2.280 6.151** 0.011 −8.528 2.167 × 10−3* 2.883 × 10−3  b33 4.783*** 0.113 −4.627 × 10−4 −1.474 × 10−3 9.753 × 10−4*** 1.258 × 10−3** Variables Breaking Strength (g) Firmness (g) L value b value Bulk density (g/cm3) WAI (g water/g sample) Model 1.499 × 10−7 1.466 × 106 89.46 11.93 0.35 0.72 Model (F value) 7.97 3.94 8.17 7.08 4.27 3.37 Model (P value) 0.0016 0.0218 0.0015 0.0026 0.0166 0.0360 R2 0.8777 0.7802 0.8802 0.8644 0.7936 0.7520 Adjusted R2 0.7676 0.5823 0.7724 0.7423 0.6078 0.5287 Lack of fit (P value) 0.1748 0.2575 0.0016 0.5274 0.1740 0.1671 Coefficients  b0 64482.109 −4712.907 53.388 −11.824 13.260 22.121  b1 45.334 −6.310** −0.223*** 0.011*** −0.033 3.641 × 10−3  b2 −322.784 375.589*** 0.364*** 0.737*** −0.047*** −0.277***  b3 −981.729*** 15.939 0.346 0.382 −0.201 −0.290  b12 0.311 −0.826 5.0 × 10−4 −3.00 × 10−4 4.635 × 10−4 4.102 × 10−4  b13 −0.584 0.226 2.498 × 10−3 4.2 × 10−4 1.648 × 10−4 −1.511 × 10−4  b23 1.488 −0.126 0.012 −2.71 × 10−3 −7.386 × 10−4 4.937 × 10−4  b11 0.126 0.180 −1.518 × 10−3* −1.184 1.225 × 10−5 1.046  b22 2.280 6.151** 0.011 −8.528 2.167 × 10−3* 2.883 × 10−3  b33 4.783*** 0.113 −4.627 × 10−4 −1.474 × 10−3 9.753 × 10−4*** 1.258 × 10−3** WAI is water absorption index. *Significant at 0.1; **Significant at 0.05; ***Significant at 0.01. b0 is the constant; b1, b2, and b3 are linear; b12, b13, and b23 are interactions; and b11, b22, and b33 are quadratic regression terms in the equation: y = b0+ b1x1+ b2x2+ b3x3+ b12x1x2+ b13x1x3+ b23x2x3+ b11x12+ b22x22+ b33x32. x1 = Yam flour (%); x2 = Feed moisture content (%); x3 = Extrusion temperature (oC). View Large Breaking strength Breaking strength indicates the porosity and density of the products (Yu et al., 2012). The breaking strength of the raw samples was found to be significantly influenced by the extrusion temperature (x3) (P < 0.01, for linear and second order of the variables, Table 3; Figure 1A). This result may be attributed to the starch gelatinization and the changes of starch granule structure during changes of processing temperature. Literature has reported that cross-linking between starch chains could be increased at higher temperatures (90–120°C) at moisture content of 10% to 30%, leading to the higher gel hardness of gelatinized starch due to crystallization (da Rosa Zavareze and Dias, 2011). This was also reflected in Wang et al. (2016)’s study on extruded brown rice pasta. However, the presence of other components in current formulation including konjac glucomannan and protein may also affect the macromolecular interactions in the matrix together with starch, contributing to the structure formation during extrusion processing, thus, influencing the breaking strength of the resulted product. Figure 1. View largeDownload slide Contour plot of response surface related to breaking strength (A); firmness (B); L* value (C); b* value (D); bulk density, g/cm3 (E); and WAI (F) of the extruded rice product as influenced by yam flour percentage, feed moisture content, and extrusion temperature. Figure 1. View largeDownload slide Contour plot of response surface related to breaking strength (A); firmness (B); L* value (C); b* value (D); bulk density, g/cm3 (E); and WAI (F) of the extruded rice product as influenced by yam flour percentage, feed moisture content, and extrusion temperature. Firmness Firmness is an important textural characteristic correlated to product density (Detchewa et al., 2016). Table 3 shows that firmness of the cooked samples has significant relationships with the YF% (P < 0.05) and with feed moisture (P < 0.01 at a linear level; P < 0.05 at a second-order level). Firmness of the extruded rice products increased with an increase in YF% and the feed moisture content at 23.5% reached the maximum level (Figure 1B). As affected by the fibre content in yam flour (∼3%), increasing yam flour would result in less expansion resulting in a firmer product (Chiu et al., 2013). The result is in agreement with the study of Bouasla et al. (2016) on extruded rice–pea pasta. However, it does not mean that the highest firmness would be the ideal firmness for the product as sensory aspect will need to be considered. Colour Colour is a major parameter affecting the sensory acceptability and purchase intent of consumer (Baiano et al., 2011; Sereewat et al., 2015). Regression data show that the b* value was influenced by YF% and feed moisture (P < 0.01, Table 3). Extruded samples become more yellow (b* value increased) at feed moisture around 24% to 25% and as yam flour content increased (Figure 1D). Higher YF% is also found to decrease the brightness (L*value) of the product, especially when feed moisture content is at the higher level (Figure 1C). These results may be caused by the nonenzymatic browning reaction during thermal processing. Literature has shown that the increase of YF% could induce darker colour (increased b* value, decreased L* value) in product due to Maillard reaction of amino acid and reducing sugar in the flour during thermal processing (Krishnan et al., 2010; Falade and Omiwale, 2015; Djeukeu et al., 2017). Apart from Maillard reaction, the sugar presence in the formulation could also be contributed to caramelization during extrusion. Bulk density Bulk density is an important physical attribute related to the expansion ratio and textural properties of extruded products (Singh et al., 2016). Results show that bulk density is dependent on the feed moisture content (at liner order) and the extrusion temperature (at the second order of variables) (P < 0.01; Table 3). The lowest bulk density was obtained when feed moisture content was at around 25.5% and barrel temperature at 115°C (Figure 1E). Previous literature has discussed the relations of bulk density to feed moisture content and extrusion temperature (Severini et al., 2016). Chaiyakul et al. (2009) reported that at higher temperature, feed material with higher moisture content could create porosity structure during extrusion process and thus lower the density of product. Ding et al. (2006), on the other hand, reported that increasing feed moisture content would increase the density of extruded wheat product. Generally, lower product bulk density was due to enhanced expansion of the extruded products creating higher porosity microstructure. The effect of temperature and feed moisture content on bulk density is also related to the composition of feed material such as the presence of fibre and protein content as well as starch gelatinization. Increasing fibre content in feed mixture might increase the bulk density of starch-based extruded product because water binding ability of fibre could inhibit water loss of starch granule at the die and lower the expansion (Natabirwa et al., 2018). Increasing protein content between 10% and 25% was found to increase the expansion ratio of rice-based extruded product (Beck et al., 2018). On the other hand, other study reported that increasing the degree of starch gelatinization at higher extrusion temperature could contribute to the increase of bulk density in extruded product (Hagenimana et al., 2006). Water absorption index WAI is a property of extruded product attributed to starch dispersion in water as affected by porosity and degree of starch gelatinization-induced damage (Yağcı and Göğüş, 2008). Lower water absorption capacity affects cooking quality of extruded product, contributing to poor texture acceptability of the product (Wang et al., 2012; Bouasla et al., 2016). The current study (Table 3) shows that the WAI of the extruded products has negative relationship with the linear effect of feed moisture content (P < 0.01) but is directly related to the quadratic effect of extrusion temperature (P < 0.05). The lowest WAI could be obtained at feed moisture around 35.5% and extrusion temperature of 110°C as shown in contour plot of WAI (Figure 1F). Lower feed moisture content could generally impart higher feed viscosity which would increase the WAI of extruded products, and this was thought to be related to starch gelatinization (Ghumman et al., 2016). The intactness of starch granules could also influence the WAI. Lower water absorption capacity of extruded product has been related to destroyed starch granules and polymer chains because of higher shear rate inside the extruder barrel (Ghumman et al., 2016; Pęksa et al., 2016). From previous studies, increasing the extrusion temperature from 100°C to 110°C was found to increase the WAI of extruded yam starch-based pasta (Sobukola et al., 2013) which was slightly different from the results in the current study where WAI only increased with temperature of >105°C. This implies that the relation of WAI and extrusion temperature can be complicated due to the variation in feed material composition. The ingredients used in current formulation (yam flour, konjac flour, sorghum, and soy protein) could interact to form a complex three-dimensional structure among protein, fibre, and starch during the extrusion process at high temperature, and this could influence the WAI of the resulted products. Overall, the results from optimization show that the extrusion temperature has a significant relationship with the products’ breaking strength, WAI, and bulk density, whereas the feed moisture content influences the WAI, bulk density, and firmness (P < 0.05). On the other hand, the inclusion of yam flour significantly increased the yellowness (b* value) and firmness of the products (P < 0.05). Physicochemical properties of samples Proximate composition The proximate composition of the selected ingredients, the enriched product produced at the optimized extrusion processing conditions, and the two control samples (namely, the extruded product produced from 100% yam flour and the commercial rice sample) are presented in Table 4. Results showed that rice flour have significantly higher moisture content than other ingredients (P < 0.05). Yam flour and sorghum flours used to replace rice flour both contained significant higher total protein content than rice flour (P < 0.05). As expected, soy protein has the highest protein content at ~86%. Considering the cost, the flavour impact, and physical properties of the enriched product, it was kept at 3% in the formulation. Sorghum flour gave the highest lipid content at 2.32%, therefore increase the total fat content in the extruded rice product, and could influence the product shelf life if use at a higher percentage. Furthermore, sorghum flour was found to affect the colour of the product if incorporated at high percentages. From preliminary study, 5% of sorghum flour was the appropiate amount to be used. Konjac and soy protein gave the highest ash contents (~4.7%), followed by yam flour at 3.89%. Rice and sorghum flours both have significantly lower ash content (<1%, P < 0.05). Ash content is an important quality attribute for nutritional evaluation of food products as higher ash content implies larger amount of mineral content in the samples. By calculation from the differences of other nutrient components, the carbohydrate content of the flours was in the range of 71% to 78%. Despite konjac flour has the highest amount of carbohydrate (78%), their major carbohydrate composition is in the form of glucomannan, a water-soluble fibre that has shown to reduce the risks of type II diabetes and cardiovascular disease (Shah et al., 2015). Table 4. Proximate composition of selected ingredients and samples Moisture content (%) Protein (%) Lipid (%) Ash (%) Carbohydrate* (%) Ingredients  Rice flour 14.02 ± 0.76a 9.05 ± 0.15c 1.40 ± 0.33b 0.38 ± 0.05c 75.16  Yam flour 11.85 ± 0.75b 11.70 ± 0.00b 1.36 ± 0.32b 3.89 ± 0.43b 71.19  Konjac flour 11.47 ± 0.22b 5.03 ± 0.13d 0.79 ± 0.16bc 4.70 ± 0.06a 78.00  Sorghum flour 9.53 ± 0.13c 12.26 ± 0.13b 2.32 ± 0.47a 0.62 ± 0.08c 75.27  Soy protein 7.35 ± 0.08d 85.92 ± 0.74a 0.32 ± 0.15c 4.66 ± 0.01a 1.75 Samples  Enriched extruded rice product 13.39 ± 0.31b 13.37 ± 0.31a 1.15 ± 0.32ns 2.74 ± 0.30a 69.53  Extruded rice product 14.61 ± 0.27a 9.34 ± 0.11b 1.05 ± 0.17ns 0.47 ± 0.10b 74.53  Commercial jasmine rice 14.26 ± 0.58a 9.37 ± 0.34b 1.28 ± 0.27ns 0.06 ± 0.04b 75.05 Moisture content (%) Protein (%) Lipid (%) Ash (%) Carbohydrate* (%) Ingredients  Rice flour 14.02 ± 0.76a 9.05 ± 0.15c 1.40 ± 0.33b 0.38 ± 0.05c 75.16  Yam flour 11.85 ± 0.75b 11.70 ± 0.00b 1.36 ± 0.32b 3.89 ± 0.43b 71.19  Konjac flour 11.47 ± 0.22b 5.03 ± 0.13d 0.79 ± 0.16bc 4.70 ± 0.06a 78.00  Sorghum flour 9.53 ± 0.13c 12.26 ± 0.13b 2.32 ± 0.47a 0.62 ± 0.08c 75.27  Soy protein 7.35 ± 0.08d 85.92 ± 0.74a 0.32 ± 0.15c 4.66 ± 0.01a 1.75 Samples  Enriched extruded rice product 13.39 ± 0.31b 13.37 ± 0.31a 1.15 ± 0.32ns 2.74 ± 0.30a 69.53  Extruded rice product 14.61 ± 0.27a 9.34 ± 0.11b 1.05 ± 0.17ns 0.47 ± 0.10b 74.53  Commercial jasmine rice 14.26 ± 0.58a 9.37 ± 0.34b 1.28 ± 0.27ns 0.06 ± 0.04b 75.05 Data shown are mean value ± standard deviation. *Carbohydrate content was determined by difference. Values with different letters in the same column are significantly different (P < 0.05) from the other; ns indicates nonsignificant difference between samples. View Large Table 4. Proximate composition of selected ingredients and samples Moisture content (%) Protein (%) Lipid (%) Ash (%) Carbohydrate* (%) Ingredients  Rice flour 14.02 ± 0.76a 9.05 ± 0.15c 1.40 ± 0.33b 0.38 ± 0.05c 75.16  Yam flour 11.85 ± 0.75b 11.70 ± 0.00b 1.36 ± 0.32b 3.89 ± 0.43b 71.19  Konjac flour 11.47 ± 0.22b 5.03 ± 0.13d 0.79 ± 0.16bc 4.70 ± 0.06a 78.00  Sorghum flour 9.53 ± 0.13c 12.26 ± 0.13b 2.32 ± 0.47a 0.62 ± 0.08c 75.27  Soy protein 7.35 ± 0.08d 85.92 ± 0.74a 0.32 ± 0.15c 4.66 ± 0.01a 1.75 Samples  Enriched extruded rice product 13.39 ± 0.31b 13.37 ± 0.31a 1.15 ± 0.32ns 2.74 ± 0.30a 69.53  Extruded rice product 14.61 ± 0.27a 9.34 ± 0.11b 1.05 ± 0.17ns 0.47 ± 0.10b 74.53  Commercial jasmine rice 14.26 ± 0.58a 9.37 ± 0.34b 1.28 ± 0.27ns 0.06 ± 0.04b 75.05 Moisture content (%) Protein (%) Lipid (%) Ash (%) Carbohydrate* (%) Ingredients  Rice flour 14.02 ± 0.76a 9.05 ± 0.15c 1.40 ± 0.33b 0.38 ± 0.05c 75.16  Yam flour 11.85 ± 0.75b 11.70 ± 0.00b 1.36 ± 0.32b 3.89 ± 0.43b 71.19  Konjac flour 11.47 ± 0.22b 5.03 ± 0.13d 0.79 ± 0.16bc 4.70 ± 0.06a 78.00  Sorghum flour 9.53 ± 0.13c 12.26 ± 0.13b 2.32 ± 0.47a 0.62 ± 0.08c 75.27  Soy protein 7.35 ± 0.08d 85.92 ± 0.74a 0.32 ± 0.15c 4.66 ± 0.01a 1.75 Samples  Enriched extruded rice product 13.39 ± 0.31b 13.37 ± 0.31a 1.15 ± 0.32ns 2.74 ± 0.30a 69.53  Extruded rice product 14.61 ± 0.27a 9.34 ± 0.11b 1.05 ± 0.17ns 0.47 ± 0.10b 74.53  Commercial jasmine rice 14.26 ± 0.58a 9.37 ± 0.34b 1.28 ± 0.27ns 0.06 ± 0.04b 75.05 Data shown are mean value ± standard deviation. *Carbohydrate content was determined by difference. Values with different letters in the same column are significantly different (P < 0.05) from the other; ns indicates nonsignificant difference between samples. View Large There was a significant different between the moisture content of the enriched product and the 100% rice flour extruded product, attributed by the significantly higher moisture content in the rice flour used as the raw ingredient (P < 0.05). However, the moisture content of both the extruded rice samples was lower than 15%, and thus, they could be classified as a low-moisture food (Hu, 2016). Selenium content Table 5 shows that the enriched extruded rice product gives 15.62 μg Se per 100 g sample, significantly higher than both the control samples (P < 0.05). Taking the usual serving size of rice as 1/2 cup (90 g of raw rice) per person, the enriched rice product will give 14.06 μg Se per serving of rice. This is reasonable given the daily recommended intake of Se is 55 μg per day, and at this level of enrichment, toxicity issue associated with over consumption will be unlikely to occur. Although Se deficiency may cause a range of symptoms such as fatigue, infertility, hair loss, affect cognatic performance, and weaken immune system and in extreme cases, contributing to Kashin-Beck disease (along with iodine deficiency) and cancer, over ingestion of Se over a period of time can cause toxicity (selenosis) (Fairweather-Tait et al., 2011). This point should be carefully considered when formulating Se-enriched functional food to avoid high Se content in a single food product. Overall, the Se deficiency issues outweigh the toxicity problem as recent report indicates that an estimation of one in seven people in the world population (a total of 1 billion) is still not having adequate Se intake (Jones et al., 2017). Therefore, there is a strong need for developing Se-enriched functional foods to target the above population. Table 5. Total selenium content, antioxidant capacity, and textural properties of the samples Samples Total Se content (µg/100 g) Antioxidant capacities Textural properties DPPH (μmol Trolox/100 g) ABTS (μmol Trolox/100 g) Breaking strength (g) Firmness (g) Adhesiveness (g/s) Enriched extruded rice product 15.62 ± 0.40a 163.53 ± 4.99a 70.52 ± 3.66a 10457.20 ± 395.30a 623.36 ± 83.67a 56.29 ± 10.10b Extruded rice product 5.90 ± 0.10b 44.32 ± 4.25b 14.89 ± 0.98b 9908.91 ± 605.44a 415.99 ± 80.55b 139.21 ± 6.33a Commercial jasmine rice 4.70 ± 0.60b 10.33 ± 2.48c 7.70 ± 0.88c 8925.36 ± 372.21b 377.32 ± 75.59b 118.43 ± 18.05a Samples Total Se content (µg/100 g) Antioxidant capacities Textural properties DPPH (μmol Trolox/100 g) ABTS (μmol Trolox/100 g) Breaking strength (g) Firmness (g) Adhesiveness (g/s) Enriched extruded rice product 15.62 ± 0.40a 163.53 ± 4.99a 70.52 ± 3.66a 10457.20 ± 395.30a 623.36 ± 83.67a 56.29 ± 10.10b Extruded rice product 5.90 ± 0.10b 44.32 ± 4.25b 14.89 ± 0.98b 9908.91 ± 605.44a 415.99 ± 80.55b 139.21 ± 6.33a Commercial jasmine rice 4.70 ± 0.60b 10.33 ± 2.48c 7.70 ± 0.88c 8925.36 ± 372.21b 377.32 ± 75.59b 118.43 ± 18.05a View Large Table 5. Total selenium content, antioxidant capacity, and textural properties of the samples Samples Total Se content (µg/100 g) Antioxidant capacities Textural properties DPPH (μmol Trolox/100 g) ABTS (μmol Trolox/100 g) Breaking strength (g) Firmness (g) Adhesiveness (g/s) Enriched extruded rice product 15.62 ± 0.40a 163.53 ± 4.99a 70.52 ± 3.66a 10457.20 ± 395.30a 623.36 ± 83.67a 56.29 ± 10.10b Extruded rice product 5.90 ± 0.10b 44.32 ± 4.25b 14.89 ± 0.98b 9908.91 ± 605.44a 415.99 ± 80.55b 139.21 ± 6.33a Commercial jasmine rice 4.70 ± 0.60b 10.33 ± 2.48c 7.70 ± 0.88c 8925.36 ± 372.21b 377.32 ± 75.59b 118.43 ± 18.05a Samples Total Se content (µg/100 g) Antioxidant capacities Textural properties DPPH (μmol Trolox/100 g) ABTS (μmol Trolox/100 g) Breaking strength (g) Firmness (g) Adhesiveness (g/s) Enriched extruded rice product 15.62 ± 0.40a 163.53 ± 4.99a 70.52 ± 3.66a 10457.20 ± 395.30a 623.36 ± 83.67a 56.29 ± 10.10b Extruded rice product 5.90 ± 0.10b 44.32 ± 4.25b 14.89 ± 0.98b 9908.91 ± 605.44a 415.99 ± 80.55b 139.21 ± 6.33a Commercial jasmine rice 4.70 ± 0.60b 10.33 ± 2.48c 7.70 ± 0.88c 8925.36 ± 372.21b 377.32 ± 75.59b 118.43 ± 18.05a View Large Antioxidant capacity The antioxidant capacities of the enriched product were 163.53 and 70.52 μmol Trolox per 100 g sample as determined by DPPH and ABTS methods (Table 5). Both antioxidant assays consistently give much higher values than those from the 100% rice flour extruded product and the commercial rice sample (P < 0.05). This might be due to the higher antioxidant activity of yam flour compared with rice flour, as previously studied in cell culture (Muntana and Prasong, 2010; Chen et al., 2017). Another earlier study also showed that a replacement of 20% wheat flour by yam flour in bread formulation would increase antioxidant activity from 25% to 42% in bread by DPPH assay (Hsu et al., 2004). These may be due to the free sulfhydryl group in yam protein (Dioscorin), which could reduce dehydroascorbate to produce ascorbate that has the ability to prevent oxidative damages. Literature has shown that protein can be an excellent antioxidant in foods and they may inhibit lipid oxidation via multiple pathways such as inactivation of reactive oxygen species, scavenging free radicals, chelation of prooxidative transition metals, reduction of hydroperoxides, and alteration of the physical properties of food systems (Elias et al., 2008). In addition to the significant higher protein in yam flour than rice flour (Table 4), the enriched sample contains 4% extra protein than the commercial rice, and these would result in higher antioxidant activity. Konjac glucomannan may also increase the antioxidant capacity contributed by its ability to inhibit reactive oxygen species (ROS) due to the presence of hemiacetal hydroxyl (Liu et al., 2015). Furthermore, the presence of Se in yam flour may also contribute to the antioxidant activity of the enriched product. Se has enhanced the antioxidant capacity of food products (Battin et al., 2006; Ilham and Fotedar, 2016). On the other hand, Dvorska et al. (2007) reported that chicken diet modified with organic Se and konjac glucomannan showed positive protective effect against antioxidant depletion in the chicken liver. Moreover, Se in the form of selenoprotein has been shown to have antioxidant ability to maintain reduction–oxidation balance and reduce oxidative stress and cell damages (Shen et al., 2010). Textural properties Looking at the texture properties (Table 5), the raw sample of the enriched product had higher breaking strength, and the cooked sample had higher firmness and lower adhesiveness (stickiness) compared with the commercial rice, as measured by instrument (P < 0.05). These are positive rice quality indicators to cooking and eating quality as cooked rice product with higher firmness and lower adhesiveness is perceived to have a better consumer preference (Thirumdas et al., 2015), whereas higher breaking strength ensures intact product shape during transportation. The results obtained are attributed by the composition of the products. The higher fibre content, as a result of the inclusion of yam and konjac flours, could facilitate interaction with protein to form fibre–protein matrix structure that enhanced firmness of the extruded product (Seth et al., 2015). Furthermore, increasing protein content has been shown to improve the hardness while lowering the adhesiveness of extruded product (Philipp et al., 2017; Beck et al., 2018), consistent with the results in this study. Microstructural analysis Figure 2 shows the cross-sectional images of the enriched product, the 100% rice flour extruded product, and the commercial jasmine rice. It can be seen that the enriched extruded rice product showed more porosity and less smooth microstructure compared with the two control samples, which have relatively homogeneous and compact inner structure. The rough surface as observed in the enriched extruded rice product was attributed to the ingredients used and the interaction of these ingredients during extrusion process. The inclusion of konjac may contribute to the formation of a porous structure in the extruded products. As a hydrocolloid, dehydration of konjac gel at high temperature extrusion would result in a porous structure as reported previously (Charoenrein et al., 2011; Xiao et al., 2016). Moreover, the presence of higher protein content in the enriched product, as contributed by soy protein and sorghum flour, could further reduce the size of air cell inside the extruded product, and this could lead to reduce adhesiveness of the product (de Mesa et al., 2009). The results correlate well with those from the textural analysis (Textural properties section). Figure 2. View largeDownload slide SEM images showing cross section surface of different samples: (A) enriched extruded rice product; (B) extruded rice flour product; and (C) commercial jasmine rice. Figure 2. View largeDownload slide SEM images showing cross section surface of different samples: (A) enriched extruded rice product; (B) extruded rice flour product; and (C) commercial jasmine rice. In vitro starch hydrolysis In vitro starch hydrolysis was conducted to evaluate the starch digestibility and the GI of the product. Results (Figure 3A) show that the starch hydrolysis equilibrium (C∞) of the enriched rice product was significantly lower than the commercial jasmine rice and the white bread control (P < 0.05) after 180-min digestion. The lower hydrolysis index (HI) and C∞ of the enriched product (Figure 3B) therefore led to significantly lower GI value (68.37) compared with the other samples (81.98 for the 100% rice flour extruded product and 86.43 for the commercial jasmine rice; P < 0.05). This is because yam flour which is used for rice flour substitution contains higher resistant starch compared with rice flour (Lin et al., 2010; Shah et al., 2015; Huang et al., 2016). Previous study also reported that the GI value of white yam was 65, lower than those of boiled white rice (GI = 73) and rice cracker (GI = 87) (Atkinson et al., 2008). Other studies reported a lower GI of 52 for yam compared with 82 in brown rice (Lin et al., 2010; Huang et al., 2016). The enriched product can be marginally classified as a moderate GI food (GI ranged from 56 to 69). Importantly, it also provides higher protein, resistant starch, and soluble fibre (glucomannan) contents. By calculation, the enriched product contains 12/100 g resistant starch and 9/100 g of glucomannan from yam and konjac flour substitution, respectively. Figure 3. View largeDownload slide Starch hydrolysis curve (A) and the estimated hydrolysis indices and glycemic index (B) of the enriched extruded rice product, extruded rice product, and commercial jasmine rice. For (B), C∞ and k are determined by the equation, C = C∞(1 – e−kt); hydrolysis index (HI) is obtained by the relation between area under the hydrolysis curve (AUC) of each sample and AUC of the reference sample (white bread); GI value is calculated by the equation proposed by Goni et al. (1997), where GI = 39.71 + (0.549 x HI). Data shown are mean value ± standard deviation; values with different letter in the same column are significantly different (P < 0.05) from the other(s). Figure 3. View largeDownload slide Starch hydrolysis curve (A) and the estimated hydrolysis indices and glycemic index (B) of the enriched extruded rice product, extruded rice product, and commercial jasmine rice. For (B), C∞ and k are determined by the equation, C = C∞(1 – e−kt); hydrolysis index (HI) is obtained by the relation between area under the hydrolysis curve (AUC) of each sample and AUC of the reference sample (white bread); GI value is calculated by the equation proposed by Goni et al. (1997), where GI = 39.71 + (0.549 x HI). Data shown are mean value ± standard deviation; values with different letter in the same column are significantly different (P < 0.05) from the other(s). Sensory characteristics Figure 4 shows the sensory profiles of the raw and cooked extruded samples in comparison with the two controls (100% extruded rice product and commercial rice). Both the raw and cooked enriched samples were perceived as significantly darker in colour giving lower score in brightness and higher score in brownness than the controls (P < 0.05). This observation can also be seen from Figure 3B and is consistent with measurement of L* and b* values of the products (data not shown). The darker colour as observed could be caused by the nonenzymatic browning reaction when foods contained high level of carbohydrate are dry-cooked at high temperature such as in extrusion process (Colour section). Figure 4. View largeDownload slide Sensory profile of the raw (A) and cooked (B) enriched extruded rice product, extruded rice flour product, and commercial jasmine rice. Values with different letters are significantly different (P < 0.05) from other(s). The first, second, and third letters are associated with the enriched extruded rice product, the extruded rice flour product, and the commercial jasmine rice, respectively; ns indicates nonsignificant difference (P > 0.05). Figure 4. View largeDownload slide Sensory profile of the raw (A) and cooked (B) enriched extruded rice product, extruded rice flour product, and commercial jasmine rice. Values with different letters are significantly different (P < 0.05) from other(s). The first, second, and third letters are associated with the enriched extruded rice product, the extruded rice flour product, and the commercial jasmine rice, respectively; ns indicates nonsignificant difference (P > 0.05). The raw enriched sample has a significantly higher starch note and a distinctive konjac smell which was described by the panellists as ‘fishy’ or ‘seafood’ compared with the control samples (P < 0.05; Figure 4A). However, the konjac note was reduced markedly after cooking and it was not affecting the eating quality according to the sensory evaluation (Figure 4B). On the other hand, the commercial rice was described to have a significantly higher cereal note than the extruded samples (P < 0.05). The surface smoothness of the raw samples was in the order of commercial rice > 100% extruded rice sample > enriched extruded rice sample and the differences were statistically significant among the samples (P < 0.05). Rice grain has a few outer layers (pericarp, seed coat, nucellus, and aleuron layer) protecting the endosperm beneath while extruded product is basically formed by a matrix of ingredients (carbohydrate, protein, and lipid) which are mixed, pressurized, and ‘bound’ together creating a rather porous structure during high temperature extrusion. This explained the less surface smoothness in the extruded product. Both the raw and cooked enriched samples were also found to have less desirable rice shape than the commercial rice (P < 0.05). As the shape of the extruded products was dependent on the die used, the panellists thought that the shape was not as natural as the rice grain. However, there is no significant difference in term of stickiness (adhesiveness), firmness, and graininess for the cooked samples and grain hardness (breaking strength) in the raw samples compared with the commercial rice (P > 0.05), despite some of those properties were found to be higher than commercial rice by instrumental measurement (Textural properties section). These results indicate that the textural characteristics of the extruded product are comparable to those of the commercial rice. In fact, the stickiness (adhesiveness) of the cooked enriched sample was significantly lower than that of the 100% rice flour extruded product (P < 0.05), indicating that the addition of sorghum flour has successfully reduced the stickiness of the enriched sample to the level of commercial rice. To judge the consumer acceptability toward the enriched extruded product, a pilot consumer testing with 68 voluntary participants was also conducted. It was found that the overall acceptability score of the enriched extruded product was 5.48 ± 1.42 compared with 7.39 ± 1.20 for the commercial rice (data not shown). This indicates the potential marketability of the product. Improvement in flavour and appearance could be achieved by flavour enhancement and additional treatment such as surface polishing or coating of the extruded product. Conclusions Extrusion was successfully applied to study rice flour substitution using yam, konjac, and sorghum flours and soy protein. Extrusion processing variables including yam flour percentage, feed moisture content, and extrusion temperature were optimized according to product properties of significant importance to eating quality and consumer acceptability. The enriched product has higher protein and fibre (resistant starch and glucomannan) as well as Se, an important micronutrient for human health. The enrichment has led to significantly higher antioxidant activity and lower GI value than the commercial rice. From sensory perspective, the enriched product has good textural properties similar to commercial rice. Overall, extrusion has shown to be a useful processing method to produce enriched rice product and the selected ingredients were suitable to replace rice flour and to enrich the rice product. Development of Se-enriched rice product has significant importance to overcome the inadequate dietary Se intake for the affected population in Se-deficient areas. At the same time, having the added health benefits in the rice product such as fibre enrichment as shown in this study also offers an alternative preventive measure in combating the metabolic health issues facing the world, especially the Asian population, where rice is consumed as the main staple food. Funding This research was funded by the Enshi Tujia & Miao Autonomous Prefecture Academy of Agricultural Sciences, Enshi City, Hubei Province, P. R. China. References Alam , M. S , Kaur , J , Khaira , H , Gupta , K (2016) . Extrusion and extruded products: changes in quality attributes as affected by extrusion process parameters: a review . Critical Reviews in Food Science and Nutrition , 56 : 445 – 473 . Google Scholar Crossref Search ADS PubMed AOAC. (2012) . Official methods of analysis of AOAC International . 19 th edn. Gaithersburg, MD: AOAC International . Atkinson , F. S , Foster-Powell , K , Brand-Miller , J. C (2008) . 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TI - Selenium, fibre, and protein enrichment of rice product: extrusion variables and product properties
JF - Food Quality and Safety
DO - 10.1093/fqsafe/fyy028
DA - 2019-04-12
UR - https://www.deepdyve.com/lp/oxford-university-press/selenium-fibre-and-protein-enrichment-of-rice-product-extrusion-j0Vt3HXuGN
SP - 40
VL - 3
IS - 1
DP - DeepDyve
ER -