Effect of water feed rate and temperature on physicochemical and viscosity properties of fresh extruded rice-shaped kernels (FER) containing naked oat, rice and defatted flaxseed flour

Abstract

Naked oat, rice and defatted flaxseed flour blends were extruded to prepare fresh extruded rice-shaped kernels (FER). Response surface methodology was adopted to evaluate influence of independent variables, that is, water feed rate (16–20 L/h) and zone 3 barrel temperature (90–110 °C) on the terminal product. Through results of the regression analysis, the predicted models were reasonable. Results showed that both the variables affected the responses studied, Overall, water feed rate was positively correlated with bulk density (BD) and hardness, whereas negatively with expansion ratio (ER), whereas temperature had the opposite relationship. FER with Run 5 (18 L/h, 124 °C) presented the lowest hardness (1 028 g) and Run 11 (18 L/h, 96 °C) the highest cohesiveness (0.767). FER with center points (18 L/h, 110 °C) had the highest lightness (50.70) and Run 4 (20 L/h, 100 °C) the highest retention rate of lignans (88.08%). The viscosity analysis showed that FER presented typical characteristics of extrudates, namely gelatinized starch. FER with Run 9 (21 L/h, 110 °C) presented the highest breakdown (586 cP), and Run 5 (18 L/h, 124 °C) presented the lowest setback (233 cP).

Introduction

As a modern grain processing technology, extrusion is commonly used for varieties of food manufacture, such as breakfast cereals, baby food, flat bread, snacks, and modified starch (Sayanjali et al., 2019; Su and Kong, 2007). The advantage of extrusion technology is high-efficient, low cost and multi-function such as the combination of mixing, cooking and forming (Gopirajah and Muthukumarappan, 2018). There are many variables influence extrusion processing and the end product (Vainionpää, 1991). When the extruder is in running status, some parameters for instance screw structure, length to diameter ratio and rotating knife design may be not easy to change, while other variables such as water feed rate, barrel temperature distribution, screw speed and rotating knife cutting speed may be conveniently altered according to the vision of the end product (Sandrin et al., 2018).

Rice is a main food for more than half of the global population because of rich in starch and good palatability, especially in the East, South and Southeast Asia. However, due to excessive processing of dehulling, milling, washing, and cooking, polished rice is a poor source of dietary fiber (DF) and other micronutrients (de Pee, 2014). Moreover, cooked polished rice is quickly digested in the gastrointestinal tract into glucose, which is detrimental to the prevention and maintenance of diabetes, obesity, and colorectal cancer and is not desirable from human health standpoint (Tao et al., 2020). Oat is a good food material being rich of nutrients, such as protein, unsaturated fatty acids and DF. Oat also has specific function for human health, especially in hypolipidemic effect. Peng et al. (2013) had indicated that oat effectively reduced both the serum and hepatic lipids, and reduced LDL-C for adults with overweight and obesity. Because of rich in nutrients, oat is considered a healthy cereal and often have been used in extruded food (Sayanjali et al., 2019; Sandrin et al., 2019). Flaxseed is considered a superior source of n-3 fatty acids compared to other oil crops. Defatted flaxseed meal, the main by-product of the flaxseed oil extraction process, used to be wasted as simple animal feed in China. Nowadays, defatted flaxseed meal is known for its abundance of functional dietary fiber, and high quality protein gradually (Wu et al., 2019). Besides, flaxseed also is found to be the richest source of plant lignans today (DeLuca et al., 2018). Lignans are polyphenolic compounds found in fiber-rich plant products and previous studies have shown that lignans are capable of showing a wide spectrum of health-benefcial effects including antioxidative, antiviral and antitumorigenic (Kour et al., 2019), and the main lignan in flaxseed is secoisolariciresinol diglucoside (SDG) (Pilar et al., 2017). However, little research has been done about extruded food making with naked oat, rice and defatted flaxseed flour.

In this paper, we produced a kind of fresh extruded rice-shaped kernels (FER) using naked oat, rice and defatted flaxseed flour, and effect of water feed rate (W) and barrel temperature (T) on quality attributes including expansion ratio (ER), bulk density (BD), hardness, cohesiveness, color values (L^*), retention rate of lignan (RL), and viscosity properties of FER were studied using Response surface methodology (RSM).

Materials and Methods

Raw materials and reagents Naked oat (Avena nuda L.) flour, and rice (japonica) flour were purchased from Tianjin Jinyuanbao trading market (China), with moisture, crude protein, and fat content of 12.67%, 11.89%, and 5.81% and 12.83%, 7.80%, 0.62%, respectively. Defatted flaxseed flour was purchased from Xi'an Youshuo Biotech Co. Ltd, China. All the raw materials were vacuum packaging (Aode, Co. Ltd., Beijing) in polyethene bags respectively and stored at 4 °C. HPLC-grade methanol was purchased from Merck Co. (Darmstadt, Germany), and secoisolariciresinol diglucoside (SDG, 95% purity) was purchased from Sigma-Aldrich Co. (St.Louis, USA).

Extrusion process of FER In pre-experiment, we determined that naked oat, rice and defatted flaxseed flour were blended with the ratios of 64: 35: 1. The mixed powders were treated with an industrial mixer (HWT 20, Jinan Sanguan Food Machinery Co., Ltd., China) for 10 min to homogeneity, then the co-rotating intermeshing twin-screw extruder was used to carry out extrusion processing (DSE32-I, Jinan Shengrun Technology Development Co., Ltd., China) with three barrel sections. The screw length-to-diameter (L/D) ratio was 20, and the extruder adopted 1.25 mm wide rice-shaped mold. The temperature of the extruder barrel in the first two zones was kept at 60 and 80 °C, respectively, whereas the third zone followed the experimental design. Feed rate of blend were constant at 8 Hz. Screw speed and cutting speed was kept constant at 18 Hz and 35 Hz, respectively. The water feed rate also referred to the experimental design. Finally, the FERs were dried at room temperature (25 °C) for 5 h to 13.0%–14.5% moisture content, then vacuum packaging in polyethene bags and stored at 4 °C for analysis.

Experimental design and statistical analysis The experiment was designed by Response Surface Methodology (RSM) using Design-expert 10 software (Stat-Ease Inc., USA), which was also used for statistical analysis of experimental results. The extrusion process was studied by means of two factors and three levels central composite design (CCD) including 5 center points and 8 non-center points. Two independent variables including water feed rate (W) and zone 3 barrel temperature (T) were coded at the level of -1.41, -1, 0, +1 and +1.41 (Table 1). Expansion ratio (ER), bulk density (BD), hardness, cohesiveness, color values (L^*), retention rate of lignan (RL) and viscosity properties of FERs were the response variables.

Table 1. Experimental design and effect of independent variables on the response value for FERs

Run	Actual levels and Coded levels					Response value
	Water feed rate (L/h)	Temperature of zone 3 barrel (°C)	ER	BD (g/L)	Hardness (g)	Cohesiveness	L*	SDG (mg/kg)	RL (%)
1	16(−1)	120(1)	2.47	624	1 052	0.371	27.41	83.52	52.2
2	18(0)	110(0)	1.41	766	1 288	0.676	48.57	137.98	86.24
3	18(0)	110(0)	1.59	758	1 262	0.684	49.20	133.95	83.72
4	20(1)	100(−1)	1.22	872	1 421	0.635	38.37	140.93	88.08
5	18(0)	124(+1.41)	2.23	643	1 028	0.499	20.16	61.44	38.4
6	16(−1)	100(−1)	1.56	848	1 252	0.619	37.11	113.09	70.68
7	18(0)	110(0)	1.61	755	1 246	0.721	49.14	133.76	83.6
8	20(1)	120(1)	1.78	782	1 146	0.579	27.59	84.10	52.56
9	21(+1.41)	110(0)	1.38	841	1 331	0.742	39.12	135.23	84.52
10	18(0)	110(0)	1.64	789	1 224	0.694	50.70	132.03	82.52
11	18(0)	96(−1.41)	1.32	866	1 376	0.767	35.53	108.29	67.68
12	18(0)	110(0)	1.65	767	1 248	0.696	47.63	129.15	80.72
13	15(−1.41)	110(0)	2.25	646	1 120	0.437	32.57	99.26	62.04

ER: expansion ratio, BD: bulk density, RL: retention rate of lignans

Each response variable was expressed respectively by the following binary quadratic equation.

  

In this equation, Y was the response with different factor level combination; b₀ was a constant; b₁ to b₅ were the coefficients of regression; W and T were coded variables of water feed rate and zone 3 barrel temperature, respectively.

Analytical methods

Expansion ratio (ER) The ratio of FERs' cross sectional diameter (d) to the die diameter (d₀=1.25 mm) was adopted to express ER (Yuryev et al., 1995). The diameter of FERs were measured with a vernier caliper (Zhejiang BOSI Industrial Co., Ltd., China), and ER of FER was calculated by Eq-2:   

Where d was FERs' cross sectional diameter (mm), d₀ was the die diameter (mm).

Bulk density (BD) 250 mL graduated cylinder was selected and filled up with FER to scale, then poured out the FER from graduated cylinder and weighed them. Bulk density of FER was calculated according to Eq-3.

  

Where m was weight of FER (g).

Hardness and cohesiveness 200 g FERs were soaked in 300 g distilled water for 10 min. Then an automated electric rice cooker (Changhong, China) was used for FERs cooking (about 20 min). Cooked FER samples were cooled to 37 °C, then hardness and cohesiveness of FERs were measured using a texture analyzer (TA. XT plus, Stable Micro System Corp., UK). The tests were performed using the P/36R probe and twocycle compression tests. Three grains of cooked FERs were selected and carefully placed side by side, and then were compressed to 70% at 1.0 mm/s. The time between chews was 5s.

Lightness (L*) The lightness (L*, L* 0 to 100 means black to white) of FERs were measured using colorimeter (WR-18, Shenzhen Wave Photoelectric Technology Co., Ltd., China).

Retention rate of lignan (RL) Extraction of SDG followed the method of Hyvärinen et al. (2006) and made appropriate change. Sample powder (2.5 g) was hydrolyzed by 50 mL of 1 mol/L NaOH in absolute methanol overnight with a magnetic stirrer at room temperature (25 °C). After hydrolysis, the pH was adjusted to slightly acidic with 6 mol/L HCl, then the slurry was centrifuged at 5 000 g for 5 min. The supernatant was evaporated at 40 °C to near dryness with a rotary evaporator, and the residue was dissolved again with 5 mL absolute methanol, then was filtered by 0.22 µm organic filter disk and stored at −18°C until HPLC analysis.

The SDG in samples were analyzed by Shimazu LC-20A Series (Kyoto, Japan), equipped with a Shimazu C18 column (250 mm × 4.6 mm, 5 µm). All solvents were of HPLC grade and filtered with a 0.22 µm filter disk. The mobile phase consisted of mobile phase A, water/acetic acid (99.5 : 0.5, v/v) and mobile phase B, methanol. The flow rate was 1.0 mL/min and the gradient program followed the method of Li et al. (2008). UV spectra were recorded from 210 to 400 nm, whereas the chromatograms were registered at 280 nm. The injection volume was 10 µL. All analyses were carried out at room temperature (25 °C). SDG (95% purity) was used as the standard, and RL of FERs were calculated by Eq-4:   

Pasting properties Pasting properties of FER were measured by Rapid Visco Analyzer (RVA 4500, Perten Instruments). Just before analyzing, the FERs were ground using a high-speed grinder (XL-10B, XuLang Machinery, GuangZhou, China) and passed through 80-mesh sieve. Weighing 3 g samples powder in a small aluminum cup and adding 25 mL distilled water, and each sample was then tested under the same temperature-time conditions: the equilibration time was at 50 °C for 1 min, and then heating to 95 °C in 3.75 min and maintain for 2.5 min, next the temperature was lowered to 50 °C within 3.75 min and last for 2 min. After an equilibration time at 960 rpm for 10 s, maintaining a constant paddle rotating speed of 160 rpm, and cP units was used to express the absolute viscosity. TCW software of RVA was applied for the pasting properties, including initial viscosity (IV), peak viscosity (PV), trough viscosity (TV), breakdown, final viscosity (FV) and setback. Raw blend was used as the control (CK).

Results and Discussion

Expansion ratio (ER) ER was showed as the ratio of FERs' cross sectional diameter to the die diameter, which was adopted to describe the degree of expansion of the sample under extrusion (Ding et al., 2006). As shown in Table 2, 48.30 for F-value mean the model was highly significant (p < 0.01). The lack of fit of the model for ER was 0.42, which implied it is not significant (p > 0.05). On account of low coefficient of variation (CV = 4.99%) and high correlation coefficient (R² = 0.9814), this model may be considered suitable for predicting the influence of scaled variables on ER, and the difference between Pred R² (0.9187) and Adj R² (0.9517) was less than 0.2 (Table 2). Both linear and quadratic terms of water feed rate and temperature had a highly significant (p < 0.01) effect on ER of FERs.

Table 2. Analysis of variance of different models and regression coefficients of the binary quadratic equation for the response surface of FERs

	ER	BD	Hardness	Cohesiveness	L*	RL
Model (F-value)	48.3**	59.37**	93.17**	13.43*	116.74**	56.07**
Lack of Fit (F-value)	0.42ns	3.77 ns	0.14 ns	15.37*	2.41 ns	4.89 ns
CV (%)	4.99	2.77	1.52	7.28	3.66	4.57
R2	0.9718	0.9519	0.9852	0.9181	0.9881	0.9756
Adj R2	0.9517	0.9359	0.9746	0.8596	0.9797	0.9582
Pred R2	0.9187	0.8416	0.9693	0.4538	0.9391	0.8557
Adeq Precision	21.755	23.844	30.919	11.574	29.382	22.143
Constant
b0	1.58	765.92	1253.60	0.69	49.05	83.36
Linear
b1	−0.28**	57.22**	70.17**	0.082**	1.34*	6.19**
b2	0.34**	−78.67**	−120.89**	−0.085**	−5.28**	−11.93**
Interaction
b3	−0.088ns	33.50**	−18.75ns	0.048ns	−0.27ns	−4.26*
Quadratic
b4	0.11**	—	−13.05ns	−0.067**	−6.41**	−4.36**
b5	0.088**	—	−24.80**	−0.046*	−10.41**	−14.48**

*  significant at p < 0.05

**  significant at p < 0.01

ns, not significant at p > 0.05

ER: expansion ratio, BD: bulk density, RL: retention rate of lignans

ER values for FERs ranged from 1.22 to 2.47 for Run 4 (20 L/h, 100 °C) and Run 1 (16 L/h, 120 °C), respectively (Table 1 and Fig. 1A). The linear coefficient of water feed rate showed a negative effect while temperature showed a positive effect on ER. Increasing water feed rate reduced ER of FER, which may be due to moisture reduce the temperature of the material and thus the ER of FER (Liu et al., 2000). However, temperature was much more efficient in providing a higher ER than water feed rate. Sandrin et al. (2018) studied the influence of screw speed and temperature on oat-rice extrudates' properties, which also turned out that temperature was the more important parameter for ER. The temperature modified the rheological properties of the paste, and the higher degree of superheating of water in the extruder, the more likely to form bubble, which in turn affected ER (Moraru and Kokini, 2003; Ding et al., 2006).

Fig. 1.

Response surface plot for the effect of water feed rate and temperature on the expansion ratio (A), bulk density (B), hardness (C), cohesiveness (D), L* value (E), and retention rate of lignan (F).

Bulk density (BD) Except for ER, bulk density (BD) also is regarded as an indicator of puffing degree. As illustrated in Table 2, 59.37 for F-value implied the model was highly significant (p < 0.01), and lack of fit of the model was not significant (p > 0.05). Due to low CV (2.77%) and high R² (0.9519) (Table 2), this model may be considered suitable for forecasting the influence of scaled variables on BD. However, BD only was highly significant (p < 0.01) affected by linear and interaction terms of scaled variables.

The BD measured for all experimental samples ranged from 624 to 872 g/L for Run 1 (16 L/h, 120 °C) and Run 4 (20 L/h, 100 °C), respectively (Table 1 and Fig. 1B). Quite the opposite of ER, the linear coefficient of water feed rate showed a positive effect while that of temperature showed a negative effect on BD, so increasing in water feed rate may increase BD nevertheless increasing in barrel temperature may decrease BD. In addition, the interaction between water feed rate and temperature also had highly (p < 0.01) significant effect on BD (Table 2). Borah et al. (2016) took low amylose rice flour with seeded banana and carambola pomace as raw material, and also reported similar impact of temperature on BD of extrudates.

Hardness and cohesiveness Texture property plays an important role in consumers' preferences (Shen et al., 2019), and up to 16 dimensions can be used to describe texture at different stages of sensory evaluation. Among all factors, hardness and stickiness were the two most commonly used texture property of cooked rice (Li and Gilbert, 2018), and most of East Asians like cooked rice with relatively low hardness and high sticky mouthfeel.

As could be seen in Table 2, the model of hardness was highly significant (p < 0.01) and the lack of fit of the model was not significant (p > 0.05). The variable coefficient and R² values were 1.52% and 0.9852, severally (Table 2), which mean that 98.5% of the total variability in the hardness of FERs could be explained by this model. Analysis of variance showed that the selected models fully reflected the data for hardness attribute. In addition, the results showed in Table 2 illustrated that both linear and quadratic terms of temperature had a highly significant (p < 0.01) effect on hardness, however, only linear term of water feed rate has the same influence on hardness.

The hardness of FERs ranged from 1 028 to 1 421 g for Run 5 (18 L/h, 124 °C) and Run 4 (20 L/h, 100 °C), respectively (Table 1 and Fig. 1C). The linear coefficient of water feed rate was positive and that of temperature was inverse, thus increasing in water feed rate may increase hardness whereas temperature may reduce the extent of hardness. Hardness was increased with the rise of water feed rate, which may be due to the reduction of ER. Similar report also showed that increasing in feed moisture content decreased expansion and formed a dense product (Liu et al., 2000), and increasing in processing temperature signified better expansion and lower hardness values for extrudates (Yulini et al., 2006; Suksomboon et al., 2011; Kumar, 2018).

Most studies showed that the stickiness was negatively correlated with the hardness, while strongly positively correlated with cohesiveness (Li and Gilbert, 2018). So cohesiveness was adopted to represent stickiness. As shown in Table 2, the model of cohesiveness was significant (p < 0.05), low coefficient of variation (7.28%) and a relatively high correlation coefficient (R² = 0.9181). Thus, the model could be deemed as befitting to predict the influence of these variables on cohesiveness of cooked FERs.

Run 11 (18 L/h, 96 °C) and center points (18 L/h, 110 °C) presented higher cohesiveness, which were 0.767 and 0.676–0.721, respectively (Table 1 and Fig. 1D). On the contrary, Run 1 (16 L/h, 120 °C) and Run 13 (15 L/h, 110 °C) had lower cohesiveness, which were 0.371 and 0.437, respectively. Moreover, a positive linear coefficient were found in water feed rate while a negative relationship in that of temperature on cohesiveness of FERs (Table 2). In general, similar to natural rice, an appropriate textural indexes with good taste for cooked FERs should be low hardness and high stickiness (Shen et al., 2019).

L^* of FERs Color is an important physical characteristic of food that affects consumer acceptability (Sayanjali et al., 2019) and L^* was adopted to indicate the lightness of FERs. Because the model was highly significant (p < 0.01) and the lack of fit of the model was not significant (p > 0.05), the model could be regarded suitable to predict the influence of the variables on the L^* of FER. Low variable coefficient (CV% = 3.66) and high correlation coefficient (R² = 0.9881) were also found (Table 2). In addition, both linear and quadratic terms of temperature had a highly significant (p < 0.01) effect on L^* of FERs.

The L^* for FERs ranged from 20.16 to 50.70 for Run 5 (18 L/h, 124 °C) and Run 10 (18 L/h, 110 °C), respectively (Table 1 and Fig. 1E). L^* for FERs increased as water feed rate increased from 16% to 18% and declined with further increase in water feed rate. Overall, the temperature had a greater impact on L^* than water feed rate. For processing temperature lower than 110 °C, L^* ranged from 35.53 to 50.70 while for temperature between 110 °C and 124 °C, this response ranged from 49.14 to 20.16, i.e the L^* of FERs initially increased and then decreased with the increasing of temperature. Early ascent of L^* was because of a rise in ER. Comparatively higher temperature increased the ER hence bubble walls become thin, thus resulting in the rise of L^* (Fletcher et al., 1985). But as the temperature continues to rise, the browning reactions rate was also accelerating, which decreased L^* of FER. Rafiq et al. (2017) also reported that extrusion at low temperature produced pasta with high L^* value while extrusion at high temperature resulted pasta with low L* value. The L^* of FERs were significantly higher in the medium-level water feed rate and temperature (18 L/h, 110 °C) than other coded points (47.6–50.7 vs 20.2–39.1).

Retention rate of lignans (RL) in FERs Secoisolariciresinol diglucoside (SDG) is the main lignan found in flaxseed (Pilar et al., 2017), as a result, the retention rate of lignan was represented by the retention rate of SDG. Measurement results by HPLC showed that the absolute amount of SDG in blend before extrusion was 160 mg/kg.

As could be seen from Table 2, both linear and quadratic terms of variables had a highly significant (p < 0.01) effect on RL. 56.07 for F-value of the model indicated the model was highly significant (p < 0.01), and the lack of fit of the model for RL was not significant (p > 0.05). Based on high regression coefficient (R² = 0.9756) and low CV of 4.57%, it could be considered that RL data obtained from the model was appropriate.

After extrusion, the absolute amount of SDG in FERs and RL was shown in Table 1 and Fig. 1F. RL ranged from 38.4% to 88.08% for Run 5 (18 L/h, 124 °C) and Run 4 (20 L/h, 100 °C), respectively, namely the absolute amount of SDG in FERs was from 61.44 mg/kg to 140.93 mg/kg. Linear coefficients of water feed rate was positive and that of temperature negative, thus increase in water feed rate may be beneficial to preservation of lignans whereas temperature may be not good for retention of lignans. However, temperature had a greater impact on RL than water feed rate. Except for run 4, run 9 (21 L/h, 110 °C) and center points (18 L/h, 110 °C) also had high retention rate of lignan, which were 84.52% and 80.72–86.24% respectively. Higher RL availed to improve nutrition and function of FERs. Hyvärinen et al. (2006) added 300 mg/kg SDG to various bakery products, and found the average recovery was 263 mg/kg after baking, which may be due to the decomposition of SDG under the baking process, and may also be likely that SDG was bound to the product matrix and was not easily extractable.

Viscosity properties Viscosity properties have something to do with the changes in the apparent viscosity of starch during heating in excess water, which is usually measured by Rapid Visco Analyser (RVA) (Li et al., 2014). Starch is the main ingredient of grains, and the behavior change of starch in different processing conditions will be seen through the analysis of viscosity properties.

The viscosity properties for all assays in respect to initial viscosity (IV), peak viscosity (PV), trough viscosity (TV), breakdown, final viscosity (FV) and setback as summarized in Table 3. For natural rice, relatively high PV, breakdown and low setback is desirable for excellent palatability (Bhattacharya, 2009). As can be seen from the result, a decline in the overall viscosity properties was observed in FERs as compared with raw mixed flours, and there was a decrease in the PV, TV, breakdown, FV and setback except for IV, which may be concerned with the starch's gelatinization in the process of extrusion. Kour et al. (2019) also reported similar trends in the viscosity properties of corn and rice flour blend extrusion products.

Table 3. Viscosity values obtained in RVA analysis for FERs by changing process parameters

Run	IV (cP)	PV (cP)	TV (cP)	Breakdown (cP)	FV (cP)	Setback (cP)
1	222	690	422	268	658	236
2	146	927	451	476	805	354
3	156	938	461	477	814	353
4	219	1 100	606	494	1 026	420
5	277	713	459	254	692	233
6	145	944	482	465	809	327
7	133	914	439	475	793	354
8	307	830	534	296	801	267
9	69	1 041	455	586	783	328
10	124	905	429	476	784	355
11	209	1094	605	489	1 013	408
12	163	947	456	478	822	356
13	254	798	474	324	750	276
Raw blend	0	2 136	1 363	773	2 969	1 606

IV: initial viscosity, PV: peak viscosity, TV: trough viscosity, FV: final viscosity

The IV values of FERs ranged from 69 cP to 307 cP for Run 9 (21 L/h, 110 °C) and Run 8 (20 L/h, 120 °C), respectively, which were significantly higher than that of CK (0 cP), indicating that the starch was changed in the process of extrusion. High IV values illustrated that starch from FERs was able to form paste in low-temperature water, and starch was gelatinized to some degree in extrusion conditions. The higher the degree of gelatinization, starch granules became more hydrated, swollen, and were transformed in paste. They hydrated faster than CK because FERs were already partial gelatinized (Guha et al., 1998). In the presence of water, the starch was gelatinized by heating, which broke the hydrogen bond between the hydroxyl groups in the starch particles and formed a new hydrogen bond between the hydroxyl groups and water molecules (Li et al., 2014).

Next, PV values showed to be higher than IV (690–1 100 cP vs 69–307 cP), meaning the presence of partially no gelatinized starch in FERs, and indicating that a number of granules had become swollen and continued gelatinization process. Lubrication of starch granules by lipids from oat may keep part of starch from high temperature and mechanical shearing caused hot extrusion, reducing the gelatinization degree of the starch granules. It was important to note that all FERs presented PV lower than raw mixed flour (690–1100 cP vs. 2 136 cP), which because of starch granule partial dextrinization and damage during hot extrusion processing. Amylose and amylopectin were partially destroyed at high temperature and high pressure conditions during extrusion, declining the starch swelling and further the viscosity (Gomez and Aguilera, 1984). However, high peak viscosity indicated a high ability of the starch granule to bind water via hydrogen bonds (Otegbayo et al., 2013) and less damage of starch granules. Run 4 (20 L/h, 100 °C) and Run 11 (18 L/h, 95.8 °C) had the highest PV values of 1 100 cP and 1094 cP, respectively, and followed closely by Run 9 (21 L/h, 110 °C) and center points (18 L/h, 110 °C) of 1041 cP and 905–947 cP, respectively.

Then, heating slurry of completely gelatinized materials reduced in the viscosity, resulting in the thinning of the slurry (Schweizer et al., 1986). The values of trough viscosity (TV) that depicted the minimum hot-paste viscosity among the FERs were found to vary from 422 cP to 606 cP for Run 1 (16 L/h, 120 °C) and Run 4 (20 L/h, 100 °C), respectively. The rupture of the swollen granules results in breakdown (the fall between peak and trough viscosity) of viscosity. The increased breakdown viscosity could be attributed to an increased rate of rupturing of the starch granules upon heating in RVA (Baxter et al., 2004). Breakdown viscosity is positively correlated with the taste of rice, and a higher of this parameter indicates more soft texture and less heat during cooking (Kraithong et al., 2018). Among the FERs, Run 9 (21 L/h, 110 °C) presented the highest breakdown of 586 cP, and center points (18 L/h, 110 °C) also had higher breakdown, which were 475 cP to 478 cP, respectively.

The again rise in viscosity (FV) at the end of the cooling phase gives an indication of the re-aggregation properties duing to retrogradation of the starch, namely the starch molecules recombine to form partially ordered structures which differ from native granules (Wang and Copeland, 2013). The gel formed in the final of the RVA cooling cycle is a three-dimensional network of intertwined amylose molecules incorporating dispersed swollen and ruptured starch granules in essence. Setback viscosity value is the difference between final and trough viscosity, which has been commonly used to describe the degree of retrogradation that occurred on cooling pasted starch (Fisher and Thompson, 1997). The higher setback value, the stronger ability of the gelatinized paste to reassociate and more serious in the retrogradation was, which was undesired for palatability of FERs. Lower retrogradation illustrated that starch in FERs had suffered thermal and mechanical degradation to a certain degree, which was not useful for retrogradation or gelification, and so the slurry still thin (Guha et al., 1998). The setback values of FERs ranged from 233 cP to 420 cP for Run 5 (18 L/h, 124 °C) and Run 4 (20 L/h, 100 °C), respectively, which far below raw mixed flour setback (1 606 cP), which illustrated that extrusion could improve the stability of the cold paste and make the FERs not easy to retrograde.

Conclusions

By mixing naked oat, rice and defatted flaxseed flour, a fresh extruded rice-shaped kernels (FER) could be made with extrusion processing technology, which would enrich the Asian people's choice of staple food with functional ingredients and could partially replace polished rice. This paper studied the effect of water feed rate and barrel temperature on physicochemical and viscosity properties of FER. Overall, water feed rate was positively correlated with BD and hardness, whereas negatively with ER, whereas temperature had the opposite relationship. FER with Run 5 (18 L/h, 124 °C) presented the lowest hardness and Run 11 (18 L/h, 96 °C) the highest cohesiveness. FER with center points (18 L/h, 110 °C) had the highest lightness and Run 4 (20 L/h, 100 °C) the highest retention rate of lignans. The viscosity analysis showed that FER presented typical characteristics of extrudates, namely gelatinized starch. FER with Run 9 (21 L/h, 110 °C) presented the highest breakdown, and Run 5 (18 L/h, 124 °C) presented the lowest setback.

Acknowledgements This study was supported by a grant from the Science and Technology Cooperation Program of University and Hebei Province (2019HB0101), the National Key Research and Development Program of China (2017YFD0401305) and National Innovation Methodology Program of China (2017IM010800).

Fig. S1.

Flow chart for the preparation of FER.

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