Effect of the ratio of wheat flour and cassava and process parameters on the pellet qualities in low starch feed recipe extrusion

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Effect of the ratio of wheat flour and cassava and process parameters on the pellet qualities in low starch feed recipe extrusion Shifeng Ma a, e, Hao Wang b, Junguo Li a, c, Min Xue a, *, Hongyuan Cheng a, d, *, Yuchang Qin b, Christophe Blecker e a

Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China c The Key Laboratory of Feed Biotechnology of Ministry of Agriculture, Beijing, 100081, China d Process Engineering Consultancy, Horsholm, 2970, Denmark e Food Science and Formulation, University of Li`ege, Gembloux Agro-Bio tech, Avenue de la Facult´e d’ Agronomie, 2B, B-5030, Gembloux, Belgium b

A R T I C L E I N F O

A B S T R A C T

Keywords: Extrusion Low-starch feed Floating feed Cassava Process parameter optimization

The purpose of the investigation was to study the effects of the ratio of wheat flour and cassava and extrusion process parameters on the physical qualities of low-starch content floating feed in a pilot extrusion plant in order to transfer the production characteristics to scale manufacture. The results show that with an increase of cassava content in formulations, the feed pellet bulk density decreases. When the cassava content is ≥ 54.3 g/kg (wt), the floatability of feed pellet reaches 100 %. The feed pellet hardness depends significantly on the starch sources (P < 0.05). The ratio of wheat flour and cassava in a recipe has significant but irregular impacts on the feed pellet water stability. A wheat flour/cassava ratio (54.3/54.3) was selected as an optimal formulation. For the optimal recipe, a pilot extrusion trial shows that an increase of moisture content and die temperature gives a decrease in the feed pellet bulk density, then an increase in pellet floating possibility. Based on Response Surface Methodology, the optimal parameters for processing the selected recipe are moisture content 32 % (wt), screw speed 247 rpm and die temperature 135 ℃. A theoretical model was also applied to cross-check the results from Response Surface Methodology.

1. Introduction The extruded fish feed has many advantages in aquaculture applications, such as high physical and nutritional quality (Sørensen, 2012). In a fish feed recipe, the main nutrients are protein, starch, fat, crude fiber, etc. Among the nutrients, starch provides not only energy for fish but also as a binder to facilitate the expansion of the feed (Sørensen et al., 2010). However, fishes, especially carnivorous fishes, are more suitable for metabolizing protein in a feed recipe rather than carbohydrate. High dietary digestible carbohydrate causes excessive hepatic glycogen and fat accumulation that leads to lipid and glucose metabolism disorder, chronic inflammation, Abbreviations: RSM, Response Surface Methodology; RVA, rapid visco analyzer; SME, specific mechanical energy; WAI, water absorption index; WSI, water solubility index. * Corresponding authors at: Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China. E-mail addresses: [email protected] (M. Xue), [email protected] (H. Cheng). https://doi.org/10.1016/j.anifeedsci.2020.114714 Received 9 November 2019; Received in revised form 16 August 2020; Accepted 12 October 2020 Available online 9 November 2020 0377-8401/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: Shifeng Ma, Animal Feed Science and Technology, https://doi.org/10.1016/j.anifeedsci.2020.114714

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apoptosis, and finally results in liver damage (Goodwin et al., 2002; Maher, 2016). Thus, the starch content has to be limited or minimized in diets for the carnivorous fish. However, reducing the starch levels in the formula not only increases feed cost but also makes the feed processing difficult (Yu et al., 2019). Moreover, the low-starch extruded feed pellets often have a low expansion ratio or high bulk density and lack of floating capability. Such feed pellets cannot meet the feeding habits of carnivorous fish, such as large-mouth bass (Micropterus Salmoides) that is used to eat floating feed. Thus, it is a challenge for fish feed industry to produce low-starch floating feed pellets. The production of floating aquafeeds requires optimization which is time-consuming (Kannadhason and Muthukumarappan, 2010). Because the starch level, starch composition (amylopectin and amylose), extrusion processing parameters, and their complex interactions may impact the production of floating feed (Ayadi et al., 2010; Chevanan et al., 2010; Kannadhason et al., 2011). To minimize the starch content in the formula, starch sources with good binding and expansion properties are needed. The amylose-amylopectin ratio is critical in determining the properties of starch-based extruded products (Colonna, 1989), and the higher the amylopectin content in the starch, the higher the expansion of the product. Kannadhason et al. (2011) have proved that cassava is a suitable ingredient for floating fish feed because of its high amylopectin content. Wheat flour is extensively used in the feed industry due to its functional and nutritional properties. To our knowledge, no studies have been published investigating differences between cassava and wheat flour in low-starch extruded floating feed. Processing conditions, including temperature, screw speed, and water content (Draganovic et al., 2011; Singh and Muthukumarappan, 2016; Kamarudin et al., 2018), could influence the expansion of the product, thereby affecting the floatability of extruded feed. Thus, the optimization of process parameters is also an effective method to optimize the production of low-starch extruded floating feed. In the fish feed extrusion process, the Response Surface Methodology (RSM) is often used to investigate the effects of process parameters on the qualities of products. However, the RSM cannot reflect the fundamental physical and chemical changes in an extrusion process, which may create misunderstanding for a process. Cheng and Hansen (2016) developed a phenomenological equation (named as “Bulk Density Model” in this article) to describe extrudate bulk density, which may suitable for the extrusion process analysis before a theoretical model is well developed (Ma et al., 2018). In this work, the RSM and the Bulk Density Model are selected to optimize the extrusion process parameters for the production of low-starch content floating feed. The investigation aimed to study the effects of the wheat flour/cassava ratio on the pellet qualities of low-starch extruded floating fish feed and to select an optimal formula according to the feed qualities. Besides, the effect of moisture content (24 %–32 %), screw speed (190− 290 rpm) and die temperature (110–150 ℃) on the quality of the optimal formula feed was studied using a pilot-scale extruder. 2. Materials and methods 2.1. Experimental materials Wheat flour was purchased from Beijing Nankou Flour Mill, Beijing, China. The starch content of the wheat flour is 575.5 g/kg and the ratio of amylose to amylopectin in the flour is 0.320. Cassava was purchased from Haid Group Co., Ltd, Guangdong, China. The starch content of the cassava is 719.2 g/kg and the ratio of amylose to amylopectin in the cassava is 0.215. Soybean protein concentrate was purchased from Yihai Kerry Investment Co. Ltd, Qinhuangdao, China. Cottonseed protein concentrate was purchased from SinoLeader Biotech Co. Ltd, China. The proximate chemical composition of these ingredients was shown in Table 1. The mixed material was ground through a hammer mill (JYNU30-15, Qingdao Jieyina Machinery Science & Technology Co., Ltd., Qingdao, China) and passed through a 0.177 mm screen and stored at room temperature (approximately 25 ◦ C). 2.2. Experimental design Five recipe formulations were designed with controlling starch content at 73.0 ± 3.0 g/kg for each recipe. The ratio of wheat flour and cassava flour was adjusted in the five recipes (Table 2). The five recipes were processed at the same extrusion parameters like moisture content: 30 ± 1 % (wt), screw speed: 300 ± 5 rpm, die temperature: 147 ± 2 ℃ (as shown in Table 3). One optimal recipe was selected according to the pellet properties. For the selected recipe, a central composite design was used to evaluate the effects of moisture content, screw speed and die temperature on the pellet qualities. A total of 20 experimental settings were used with 8 cube points, 6 star points and 6 replications of the centre point. The moisture content was varied between 24–32 % (wt). The screw speed was set between 190− 290 rpm. The die temperature was changed between 110–150 ℃ (as shown in Table 5). The experiments were Table 1 Proximate chemical composition of different ingredients, g/kg, wet basis. Ingredients

FM

SPC

CPC

WG

WF

Cassava

DM Crude protein Crude lipid Total ash Starch content

899.0 618.7 90.4 186.0 –

947.0 652.0 – 71.0 –

947.5 615.1 23.6 69.0 –

911.0 770.4 53.0 10.8 64.5

896.0 115.5 23.0 20.0 575.5

885.0 17.2 9.90 20.2 719.2

DM: dry matter, FM: fishmeal, SPC: soy protein concentrate, CPC: cottonseed protein concentrate, WG: wheat gluten, WF: wheat flour. 2

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Table 2 Composition and nutrients of the diet (air-dry basis, g/kg). Ingredients

LS-1

LS-2

LS-3

LS-4

LS-5

Fish meal Soy protein concentrate Cottonseed protein concentrate Wheat gluten meal Soybean oil Wheat flour Cassava Kelp meal Total Nutrient compositions (dry matter basis, g/kg) Dry matter Crude protein Crude lipid Starch

300.1 246.9 246.9 54.0 18.0 118.1 – 16.0 1000

301.3 248.7 248.7 54.0 18.0 86.3 27.0 16.0 1000

303.9 249.8 249.8 54.0 18.0 54.3 54.3 16.0 1000

305.4 250.9 250.9 54.0 18.0 28.5 76.3 16.0 1000

307.1 252.1 252.1 54.0 18.0 – 100.7 16.0 1000

892.5 553.7 56.5 71.4

892.5 553.6 56.2 72.6

892.3 553.3 56.0 73.7

892.2 553.1 55.8 74.8

892.0 552.8 55.6 75.9

Table 3 Extrusion processing parameters of the experimental diets. Processing parameters

LS-1

LS-2

LS-3

LS-4

LS-5

Extrusion zone 1, ℃ Extrusion zone 2, ℃ Extrusion zone 3, ℃ Die temperature, ℃ Conditioning temperature, ℃ Moisture content, % Screw speed, rpm Feed rate, kg/h

98 138 132 147 99 31 300 70

98 130 131 147 98 31 300 71

98 131 129 146 100 29 300 68

98 137 130 149 99 30 300 72

98 130 131 148 97 29 300 71

Table 4 Effects of the ratio of wheat flour and cassava on the pellet properties and SME (means ± SEM). Items Bulk density, g/L Expansion ratio Hardness, N Floatability (%) WAI WSI, % Water solubility, % SME, kJ/kg

LS-1

LS-2 a

510 ± 2 1.47 ± 0.05e 44.52 ± 6.46a 81 ± 5b 3.35 ± 0.14ab 14.47 ± 0.14a 5.36 ± 0.15ab 28.88 ± 1.25b

LS-3 b

LS-4 c

499 ± 1 1.51 ± 0.04d 33.56 ± 4.21c 99 ± 1a 3.10 ± 0.07b 14.69 ± 0.04a 5.05 ± 0.24b 27.45 ± 1.33c

LS-5 d

444 ± 1 1.61 ± 0.06c 39.34 ± 7.71b 100 ± 0a 3.42 ± 0.27ab 13.45 ± 0.47b 5.44 ± 0.14ab 29.70 ± 1.39b

420 ± 2e 1.69 ± 0.05a 45.90 ± 5.66a 100 ± 0a 3.61 ± 0.07a 14.23 ± 0.51a 5.74 ± 0.12a 28.82 ± 1.21b

436 ± 1 1.64 ± 0.06b 37.91 ± 5.97b 100 ± 0a 3.34 ± 0.13ab 14.57 ± 0.21a 5.03 ± 0.19b 31.35 ± 0.99a

Within the same row, values with different superscripts are significantly different (P < 0. 05). WAI, water absorption index; WSI, water solubility index; SME, specific mechanical energy. Bulk density (g/L), n = 3; Expansion ratio, n = 10; Hardness (N), n = 30; Floatability (%), n = 3; WAI, n = 3; WSI (%), n = 3; Water solubility (%), n = 3; SME (kJ/kg), n = 3.

run in random order. 2.3. Extrusion process The extrusion process was carried out with a pilot-scale co-rotating twin-screw extruder (Muyang, Jiangsu, China), equipped with a pre-conditioner. The extruder had a barrel diameter of 56 mm, with a length to diameter (L/D) ratio of 20:1. The mixed material was fed into the pre-conditioner by a screw feeder. The feed flow rate was set at 70 kg/h and maintained as a constant in all trials. The Table 5 Coded and actual levels for the experimental design variables. Numerical variables

Symbol

Coded variable levels

Moisture content (%) Screw speed (rpm) Die temperature (℃)

X1 X2 X3

− 1.682 24 190 110

− 1 25.6 210 118

3

0 28 240 130

1 30.4 270 142

1.682 32 290 150

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moisture content of the mixture was adjusted by adding water into the pre-conditioner, while the steam flow injected into the preconditioner was kept as a constant. The outlet raw material temperature in the pre-conditioner was kept at 95 ± 3 ◦ C. Two 3 mm diameter dies were applied in our trials. The extrusion variables, including die temperature, moisture content and screw speed, were adjusted according to an experimental design plan. After the process reached a steady state for approximately 15 min, samples were collected. The extrudates were cut into approximately 5 mm long pellets. The extrusion process parameters, including water feed flow rate, steam flow rate, screw speed, barrel temperatures in different zones and torque, shown in the control panel were stored every 4 s on a PC system. Specific Mechanical Energy (SME; kJ/kg) was calculated according to Riaz (2000). Approximately 25 kg batches of each replicate were extruded and all treatments were run in triplicate for the five formulations, while the central composite design had six replicates in the central point. The samples were allowed to cool and dry for approximately 24 h under ambient conditions to equilibrate the final moisture to a level between 4 and 6 (% wet base) and stored in sealed polyurethane bags at 4 ◦ C for sample analysis. 2.4. Pellet quality measurement 2.4.1. Hardness Pellet hardness was measured by a Texture Analyser (TA-XY2i; Stable MicroSystems, Blackdown Rural Industries, Surrey, UK), which was equipped with a 25-kg load cell. The pellets were broken between a flat-ended cylindrical probe and the base plate at a test speed of 1 mm/s. The force used to break the pellets was presented as a function of force and time and analysed using the TEXTURE EXPERT EXCEED for Windows (version 2.54, Stable Micro Systems, Blackdown Rural Industries, Surrey, UK). The peak force was calculated from the height of the first peak and presented as Newton (N). The hardness values were recorded from 30 pellets for each feed sample. 2.4.2. Expansion ratio The radial expansion ratio of a pellet was found by measuring the extrudate diameter randomly with a Vernier caliper and dividing by the die diameter (3 mm). 10 pellets were applied in each sample. 2.4.3. Floatability At room temperature, pour 10 extruded pellets (Fi) into a 100 mL beaker filled with distilled water. The number of floating pellets (Ff) that remained suspended on the water surface after 20 min was recorded. The floatability (F) was calculated using the following equation: ( ) Ff × 100 (1) F= Fi 2.4.4. Water solubility A total of 10 g feed pellets were placed into the net wire basket. Put the baskets containing the samples into 600 ml beakers. 300 mL of tap water was added into each beaker. The beakers were incubated in a water bath at 25 ℃ and shaken for 20 min. After incubation, the baskets were drained and placed in a heating cabinet at 105 ◦ C for 18 h. Then the baskets were weighed to determine the residual dry matter (DM) in each basket. The water solubility was calculated as the difference in DM weight before and after incubation in water divided by DM weight of the feed before incubation. 2.4.5. Water absorption index and water solubility index The determination of water absorption index (WAI) and water solubility index (WSI) was based on an experimental procedure developed by Anderson (1969). Pellets were ground and sieved through a 200 μm sieve. The fine powder (Wds, 2.5 g) were then suspended in distilled water (30 mL) at 30 ◦ C for 30 min and stirred intermittently in a 50 mL centrifuge tube. Then, the suspended samples were centrifuged at 3000 g for 10 min. The supernatant was poured into a tared aluminium dish and dried at 135 ◦ C for 2 h. The weight of the gel (Wg) remaining in the centrifuge tube was calculated as WAI according to the following equation: WAI =

Wg Wds

(2)

The weight of dried solid supernatant (Wss) recovered from the aluminium dish after evaporation was calculated as WSI from the following equation: Wss WSI = ( ) × 100 Wds

(3)

2.4.6. Bulk density The pellet bulk density was determined by pouring the pellets into a 1000 mL cylinder. When the pellets reach 1000 mL, the weight of the cylinder was recorded. The ratio of the pellet weight to the volume of the 1000 mL cylinder was expressed as the pellet bulk density, g/L (1 g/L = 1 kg/m3). Each measurement was carried out in triplicate. 4

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2.4.7. Pasting properties of recipe formulations A Rapid Visco-Analyzer (RVA-4/ZM100) was used to measure the apparent viscosity of the mixture. Add 3.5 g sample and 25 ml (plus moisture adjustment) distilled water to the sample canister and shake for 5 s to disperse the sample. The heating-cooling profile was as follows: the temperature was held constant at 50 ◦ C for 1 min. Then, it was raised to 95 ◦ C at a rate of 12 ◦ C/min and then held for 2.5 min. Finally, the temperature was cooled to 50 ◦ C at a rate of 12 ◦ C/min and held for 2 min. The slurry was stirred at 960 rpm for 10 s before the shear rate was set constant to 160 rpm. Each treatment was tested in triplicate. 2.5. Statistical analysis The experimental data of the five formulations were assessed using SPSS Statistics 17.0 (IBM, New York, USA). Differences among treatment means were analysed using one-way ANOVA and Duncan’s multiple range test with significance set at P < 0.05. The experimental data of central composite design were fitted to a second-order polynomial (Eq. 4) by means of multiple linear regression (Myers et al., 2002) by using the design expert software (Version 8.0.6, STAT-EASE Inc., Minneapolis, USA). In the models, Yi is the estimated response, b0 is the intercept, bi, bii and bij are the regression coefficients of linear, quadratic and interaction term, respectively, X is the predictor variables and ε is the residual (error). The different values of each replicate (30 for hardness, 10 for expansion ratio and 3 for other physical properties) were averaged. ∑n ∑n ∑n ∑n Yi = b0 + bi Xi + bii Xii2 + bij Xi Xj + ε (4) i=1 i=1 i=1 j=i+1 Analysis of variance (ANOVA) was used to represent the relationship between the equation and actual results, involving significant variables and the response shown by the equation. The probability-value (p-value) and Fisher’s test value (F-value) were used to verify the significance of the coefficient term at the significance level of 0.05. According to Derringer and Suich (1980), Derringer’s desir­ ability function was used for multiple response optimizations. In this work, the parameters were optimized by fixing a maximum target of floatability and a minimum target of bulk density. The statistical analysis was conducted using the design expert software (Version 8.0.6, STAT-EASE Inc., Minneapolis, USA). An equation proposed by Cheng and Hansen (2016) was also used to model the extrudate bulk density, which is as following: ( )α T ΔE ρB = K1 die (Xw )β Nsγ exp( + cXw ) (5) RTdie T0 where K1,α, β, γ, ΔE/R, c are the experimentally determined dimensionless coefficients, which can be estimated by regression analysis of experimental bulk density data, ρB is the pellet bulk density, g/L, Tdie is the die temperature, ◦ C, T0 is the raw material initial temperature and assigned a fixed value of 25 ◦ C, Xw is the moisture content of the mixture in the extruder, g/g (wet base), Ns is the screw speed, rpm, ΔE is the activation energy, J/mol, R is the gas constant, 8.314 J/(mol*K). 3. Results The apparent viscosity of the five formulations increases with an increase in the cassava proportion (Fig. 1). When the cassava content was more than 54.3 g/kg, the feed floatability reached 100 %, and the cassava content of the LS-3 group was the lowest among the three formulas, which was selected as the best formula (Table 4). Response surface methodology (RSM) (Eq. 4) is used to analyse the relationship between the feed qualities and the process parameters of the recipe LS-3 (Table 6). The regression equation coefficients are presented in Table 7. In all cases, the lack of fit is not significant (P > 0.05), and the regression models are significant except for WAI and WSI. In addition, the fitness of these models is greater than 0.7 (Table 7). Thus, the regression models can satisfactorily describe

Fig. 1. RVA measurement results for five different recipes, the wheat/cassava ratios, W/C, are: LS-1, W/C = 11.81/0, LS-2, W/C = 3.20, LS-3, W/C = 1, LS-4, W/C = 0.37, LS-5, W/C = 0/10.7. 5

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Table 6 Extruder and pellet responses (means ± SEM). ENo

Moisture content (%, wt)

Screw speed (rpm)

Die temperature (℃)

Bulk density (g/L)

Expansion ratio

Hardness (N)

Floatability (%)

WAI

WSI (%)

Water solubility (%)

SME (kJ/kg)

1

25.6

210

118

565 ± 1

1.38 ± 0.06

52.99 ± 9.96

0±0

13.03 ± 0.25

4.91 ± 0.04

23.71 ± 1.45

2

25.6

210

142

517 ± 1

1.43 ± 0.07

26.14 ± 7.22

0±0

13.28 ± 1.54

8.40 ± 0.34

24.64 ± 1.90

3

25.6

270

118

562 ± 2

1.38 ± 0.06

47.78 ± 9.80

0±0

13.58 ± 0.74

4.97 ± 0.03

28.47 ± 0.82

4

25.6

270

142

522 ± 2

1.43 ± 0.09

81.01 ± 11.41

0±0

14.08 ± 0.07

5.62 ± 0.13

30.25 ± 1.33

5

30.4

210

118

559 ± 2

1.37 ± 0.11

88.15 ± 9.85

14 ± 4

13.45 ± 0.39

4.35 ± 0.28

21.06 ± 1.37

6

30.4

210

142

503 ± 2

1.46 ± 0.09

56.11 ± 8.69

97 ± 2

13.94 ± 0.13

4.48 ± 0.13

21.25 ± 1.57

7

30.4

270

118

538 ± 1

1.44 ± 0.17

59.63 ± 10.32

40 ± 4

13.70 ± 0.22

4.78 ± 0.24

25.40 ± 1.90

8

30.4

270

142

509 ± 1

1.46 ± 0.15

50.33 ± 9.14

85 ± 4

13.99 ± 0.01

4.73 ± 0.06

26.36 ± 1.90

9

24

240

130

530 ± 4

1.40 ± 0.11

57.90 ± 10.67

0±0

14.24 ± 0.03

4.93 ± 0.17

22.22 ± 2.59

10

32

240

130

488 ± 1

1.49 ± 0.16

60.09 ± 9.53

98 ± 1

14.38 ± 0.07

4.59 ± 0.34

22.02 ± 1.45

11

28

190

130

532 ± 1

1.39 ± 0.10

29.30 ± 6.13

1±1

13.58 ± 0.68

5.05 ± 0.11

20.09 ± 0.97

12

28

290

130

530 ± 1

1.43 ± 0.08

30.63 ± 5.02

7±2

13.03 ± 0.15

5.03 ± 0.09

31.50 ± 1.67

13

28

240

110

572 ± 1

1.33 ± 0.09

28.76 ± 8.56

0±0

13.47 ± 0.26

4.44 ± 0.16

25.01 ± 1.33

14

28

240

150

519 ± 3

1.44 ± 0.09

36.72 ± 9.50

9±0

13.72 ± 0.04

5.22 ± 0.29

25.55 ± 0.88

15

28

240

130

518 ± 3

1.43 ± 0.09

16.91 ± 4.43

31 ± 3

13.63 ± 0.17

5.63 ± 0.16

27.23 ± 2.67

16

28

240

130

515 ± 1

1.38 ± 0.11

16.53 ± 3.61

27 ± 2

13.74 ± 0.13

5.75 ± 0.15

27.62 ± 1.33

17

28

240

130

519 ± 1

1.46 ± 0.08

27.61 ± 8.60

33 ± 4

13.72 ± 0.17

5.45 ± 0.13

27.82 ± 0.71

18

28

240

130

520 ± 3

1.43 ± 0.06

27.98 ± 4.45

32 ± 4

13.43 ± 0.12

5.68 ± 0.11

28.31 ± 1.34

19

28

240

130

530 ± 2

1.40 ± 0.05

34.83 ± 6.62

20 ± 3

13.63 ± 0.13

5.45 ± 0.13

27.22 ± 1.36

20

28

240

130

510 ± 3

1.41 ± 0.04

15.18 ± 5.33

40 ± 5

3.51 ± 0.07 3.48 ± 0.10 3.13 ± 0.18 3.27 ± 0.09 3.19 ± 0.04 3.10 ± 0.03 3.40 ± 0.12 2.97 ± 0.02 3.16 ± 0.06 3.18 ± 0.03 3.29 ± 0.26 3.59 ± 0.02 3.41 ± 0.11 3.49 ± 0.06 3.49 ± 0.06 3.43 ± 0.05 3.52 ± 0.06 3.53 ± 0.02 3.49 ± 0.03 3.52 ± 0.02

13.72 ± 0.11

5.68 ± 0.14

26.33 ± 1.23

WAI, water absorption index; WSI, water solubility index; SME, specific mechanical energy. Bulk density (g/L), n = 3; Expansion ratio, n = 10; Hardness (N), n = 30; Floatability (%), n = 3; WAI, n = 3; WSI (%), n = 3; Water solubility (%), n = 3; SME (kJ/kg), n = 3. 6

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the effects of moisture content, screw speed and die temperature on most of the pellet properties. 3.1. Effects of wheat flour/cassava ratio on the pellet properties and SME There are significant differences in bulk density and expansion ratio between different formulations (P < 0.05) (Table 4). With an increase of cassava proportion, the pellet bulk density decreases. Meanwhile, the pellet floatability increases. When the cassava content is ≥ 5.43 %, the floatability reaches 100 %. The pellet hardness of the recipe LS-1 (wheat flour/ cassava ratio = 118.1/0) and the recipe LS-5 (wheat flour/ cassava ratio = 0/100.7) is significantly higher than that of the other three recipes (P < 0.05). The pellet WAI of the recipe LS-5 has a maximum value. The pellet WSI of the recipe LS-3 (wheat flour/ cassava ratio=54.3/54/3) is much lower than that of others (P < 0.05). The pellet water solubility of the recipe LS-5 is greatly higher than that of the recipe LS-2 (wheat flour/ cassava ratio=86.3/27.0) and the recipe LS-4 (wheat flour/ cassava ratio=28.5/76.3). The system SME of the recipe LS-2 is signifi­ cantly lower than that of others (P < 0.05). The SME of the recipe LS-4 is significantly higher than that of others (P < 0.05). 3.2. RSM model results for the recipe LS-3 The relationship between the pellet bulk density and moisture content or die temperature is linearly correlated. But they are inversely related. The effects of the quadratic term of screw speed and die temperature are significant (P < 0.05) (Table 7). The pellet expansion ratio is linearly correlated with moisture content and die temperature (P < 0.05) (Table 7). Moisture content and die temperature have significant impact on floatability (P < 0.05) (Table 7). The RSM cannot well correlate the feed pellet water ab­ sorption index (WAI) and water solubility index (WSI). For water solubility, the impacts of moisture content and die temperature play important roles (P < 0.05) (Table 7). In the case of hardness, the effects of the interaction term of moisture content, screw speed and die temperature, and the quadratic term of moisture content and die temperature are significant (P < 0.05) (Table 7). For SME, the effects of moisture content, screw speed and the quadratic term of moisture content are significant (P < 0.05) (Table 7). 3.3. Optimization of extrusion process parameters for the recipe LS-3 Based on the RSM models, a multiple response optimization of the pellet physical properties was performed by Derringer’s desirability function. The optimization criteria were to maximize the floatability and to minimize the bulk density. One of the best optimum parameters with desirability 0.650 as suggested by the statistical software was chosen. The obtained optimal conditions are moisture content: 32 %, screw speed: 247 rpm and die temperature: 135 ℃. Under the optimal conditions, the predicted physical qualities of the feed pellet and SME are bulk density: 492 g/L, expansion ratio: 1.49, hardness: 62.53 N, floatability: 100 %, water solubility: 4.35 %, SME: 22.10 kJ/kg. 3.4. Bulk Density Model results for the recipe LS-3 Based on Eq. (5), the experimental bulk density data can be fitted. The extruder was treated as a torque rheometer and the acti­ vation energyΔE was evaluated through the data obtained during processing. The activation energy is estimated by the following equation: Table 7 Regression equation coefficients of models (coded factors) for bulk density, expansion ratio, floatability, water absorption indices (WAI), water solubility indices (WSI), water solubility, hardness and SME. Regression Coefficients

Bulk density

Expansion ratio

Floatability

WAI

WSI

Water solubility

Hardness

SME

Intercept Moisture content (X1)

518.010 − 9.350*

1.420 0.019*

29.020 29.400*

3.470 − 0.051

13.610 0.098

5.120 − 0.450*

21.260 3.660

Screw speed (X2) Die temperature (X3) Moisture content (X21)

− 1.200 − 19.190* − 2.230

0.010 0.029* 0.010

1.800 10.530* 9.060*

0.054 0.140 0.230*

− 0.150 0.410* 0.000

1.290 − 1.580 16.240*

Screw speed (X22) Die temperature (X23) Moisture content * Screw speed (X1X2)

5.550* 10.680* − 2.120

− 0.002 − 0.011 0.009

− 6.970* − 6.680* 1.670

0.000 − 0.019 − 0.120 * − 0.027 − 0.024 0.084

− 0.130 − 0.026 − 0.130

0.000 0.000 0.430

Moisture content * Die temperature (X1X3) Screw speed * Die temperature (X2X3) R2 Lack of fit P

0.380

0.001

16.080*

− 0.078

0.002

− 0.510*

5.980 6.960* − 10.490 * − 5.960

27.380 − 0.980 * 2.860* 0.350 − 1.700 * − 0.400 − 0.580 − 0.110

4.380 0.94 0.46 0.0001

− 0.009 0.82 0.86 0.0188

− 4.670 0.94 0.14 0.0001

− 0.021 0.67 0.09 0.1538

0.006 0.71 0.05 0.0942

− 0.380 0.77 0.06 0.0270

10.350* 0.86 0.28 0.0056

*

Parameter is significant to the predictive regression model (P < 0.05). 7

− 0.190 0.200 0.93 0.07 0.0004

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log10 (torque) = α +

ΔE RT

(6)

where torque is the measured value during extrusion, Nm; α is an adjustable parameter; ΔE is the activation energy, J/mol; R is the gas constant, 8.314 J/(mol K) and T is the die temperature, K. In the estimation, ΔE/R is regarded as one parameter. TheΔE/R value is estimated as -89.41 K based on torque and die temperature data. Using the experimental pellet bulk density data (Table 6), the model parameters, i.e. K1, c, α, β, γ, were determined with the following function: n [ ∑

cal ρexp B,i − ρB,i

obj = min

]

(7)

i=1

To search for a possible global minimum, random initial values were used in the regression. The obtained model parameters are as shown in Table 8. The average absolute deviations (AAD%) of the model regression is 1.29 %. The AAD% is calculated as: ⃒] [⃒ exp ⃒ 1 ∑ ⃒ρB − ρcal B AAD(%) = × 100% (8) exp n n ρB where n is the number of experimental runs. As the Eq. (5) is a sound theoretical model which could represent the pellet bulk density with extrusion process parameters, we can cross-check the regression results of the RSM. Based on the regression equation coefficients in Table 7 and the model coefficients in Table 8, we plot the relationship between extrudate bulk density and extrusion process parameters as shown in Fig. 2. From Fig. 2, we can see that the bulk density decreases with the increase of moisture content and die temperature. However, the predicted bulk density from the RSM is lower than the experimental values, while the predicted bulk density from Eq. 5 is closer to the observed values. The Bulk Density Model can replace the RSM to predict the extrudate bulk density. Moreover, as Eq. (5) has a clear derivation procedure, it can be applied in another extrusion system for the same recipe and provide a physical explanation for the interactions between pellet bulk density and process parameters, which cannot be obtained from the RSM results. Taking the optimization results of the RSM, we set the bulk density ≤ 492 g/L as an objective to search optimal extrusion parameters by Eq. (5). The optimization results are shown in Fig. 3. As shown in Fig. 3, with an increase in moisture content, the available operation window increases gradually. 4. Discussion 4.1. Effects of the wheat flour/cassava ratio on the pellet properties and SME As shown from the RVA curves in Fig. 1, the viscosity of different recipes increases with an increase of cassava content. This is likely due to the increase of the amylopectin content in the formulations. Blazek and Copeland (2008) also reported that peak viscosity decreases with increasing amylose content (opposite to amylopectin effect) in a recipe. In an extrusion process, however, it is the melt viscosity other than the viscosity obtained in dilute solution from RVA, which determines the feed pellet expansion. If we can assume the amount of cassava content is the dominating factor to affect the viscosity of different recipes in the RVA measurement and extend the assumption to the melt viscosity in an extrusion process, we may use the cassava content to adjust the melt viscosity during extrusion processing and then adjust the feed quality at the same set of extrusion process parameters. As can be seen from Table 2, the wheat flour/cassava ratio is the main difference in the five recipe formulations. For the five different recipes, the pellet expansion values are almost linearly decreased with adding more cassava ingredient, as shown in Table 4. The functionality of starch is affected by the ratio of amylose to amylopectin (Visser et al., 1997). The ratio of amylose: amylopectin content in cassava and wheat flour was approximately 18:82 and 24:76 respectively. The swelling power of cassava starch is higher than that of the wheat starch (Anggraini et al., 2009; Singh et al., 2010). Wheat starch is more stable because it is dominated by type A starch with higher crystallinity (Liu, 2005). Wheat starch has moderate swelling power characterized by a low peak viscosity, while the cassava starch has high swelling power characterized by a high peak viscosity. The lower expansion observed for the wheat-based feeds may be explained by a lower viscosity. Therefore, we may use the ratio of amylose to amylopectin to adjust pellet bulk density in practice. Table 8 Model coefficients of Eq. (5). Coefficients K1

1203505.00 − 0.49 2.74 − 0.03 10.92 − 89.41 1.29

α β γ c △E/R, K AAD%

8

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Fig. 2. Response surface plot (A) and Bulk Density Model plot (B) for the low-starch extruded feed. H2O: moisture content, %, Td: die temperature, ◦ C, Ns: screw speed, rpm.

In an extrusion process, the SME is related to the change of material viscosity. As can be seen from Table 4, when the cassava is added into the system, the SME is decreased and then slightly increased with adding more cassava. Finally, the SME is decreased again when the wheat flour ingredient disappears. It can be concluded that the material viscosity is controlled by the interaction between wheat flour and cassava, and the interaction between starch and other nutrients when the extrusion process parameters are fixed. In practice, more complicated behaviours can happen as the extrusion parameters are adjusted from time to time. The varied but irregular hardness observed for the five formulas underlines that starch sources with different functions can affect the physical quality of the feed. In contrast to previously reported studies, there was no correlation between feed hardness and expansion ratio in the present studies. Previous studies have shown an inverse relationship between hardness and diameter of feed (Sørensen et al., 2009). The results in the present study suggested that using cassava as a single starch source, like the LS-5 group, not 9

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Fig. 3. Optimization of extrusion process according to Bulk Density Model in different moisture content (A: 28 %, B: 30 %, C: 32 %). The shade area is the available operation window.

10

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only increased expansion but also improved the feed quality. As shown in Table 4, other pellet properties are also changed with different wheat/cassava ratio. However, no specific pattern can be found out from our observation for the obtained experimental data, which may be due to the interaction between starch and other nutrients. However, this needs further investigation. From fish nutrition point of view, the high ratio of amylose to amylopectin may lead to slow digestion of starch, which may contribute to improving the growth performance of fish (Rawles and Lochmann, 2003; Liu et al., 2014), especially in carnivorous fish species. In practice, therefore, the content of cassava in a formulation should be minimized because of its high amylopectin content. Floatability is an important quality index of extruded feed. In commercial production, the floatability of extruded floating feed should reach 100 %. As shown in Table 4, the floatability LS-3 group was 100 % with the minimum cassava content, which was selected as the optimal formula. The cassava content of LS-2 group was lower than that of LS-3 group, but the floatability was 99 %, which indicated that there was still a risk of feed sinking, which could not be accepted by feed mills. 4.2. Effects of extrusion conditions on the bulk density, expansion ratio and floatability of the recipe LS-3 The expansion or the bulk density is the best property to describe product porosity. Moreover, the bulk density determines the floatability of fish feed pellets. The data in Table 6 shows that an increase of the moisture content and the die temperature in the extrusion process causes an increase of the pellet expansion ratio and a decrease in the pellet bulk density. Draganovic et al. (2011) investigated the effects of fish meal, wheat gluten, soy protein concentrate and feed moisture on extruder system parameters and fish feed pellet qualities. They observed that with an increase of feed moisture from 26 % to 32 %, the expansion ratio of feed pellet (having 8.8 % starch) increases, and the specific density decreases. Because the recipe in the investigation of Draganovic et al. (2011) is not the same as ours, their results are not directly comparable. But it is approximately consistent with the findings in our study, i.e. the moisture content is a sensitive factor in the systems. It can be suggested that the increase of moisture content may be beneficial to gelatinization which could lead to an increase of expansion ratio and the decrease of bulk density. In similar research for fish feed recipe, Samuelsen and Oterhals (2016) studied the impacts of the water-soluble protein content, steam and water content in an extrusion process. The starch content of the studied diet was about 10 %. They found out that an increase in steam amount results in an increase of die temperature and then pellet expansion. They also suggested that an increase of moisture content may reduce the resistance for expansion and decrease extrudate flow-starting temperature (Tf) and melt viscosity. As can be seen from the row ENo1 to ENo8 for the pellet floatability in Table 6, the feed floatability increases with an increase of moisture content and increases with the die temperature at the moisture content of 30.4 %. It indicates that the floatability of pellets is greatly related to the bulk density of the pellets (Glencross et al., 2012). 4.3. Effects of extrusion conditions on WAI and WSI of the recipe LS-3 Starch gelatinization and degradation can be observed by WAI and WSI because the data obtained can explain how the swollen gelled particles remain their integrity in aqueous dispersion (Anderson, 1982; Mason and Hoseney, 1986). The ability of the pellets to float for some time is related to hydration properties (WAI and WSI). It can be seen from the row ENo5 to ENo8 for WAI in Table 6 that WAI decreases with an increase of the die temperature. As shown in WSI of Table 6, WSI increases with an increase in the die tem­ perature. These are in agreement with the results of Williams (1977) who observed that higher temperature could result in higher dextrinization, which could lead to a decreased extrudate water absorption index and an increased extrudate water solubility index. 4.4. Effects of extrusion conditions on water solubility of the recipe LS-3 Pellet water solubility indicates how the product can withstand water dissolution (Ayadi et al., 2011). Lower water solubility indicates that feed pellets have better water stability. Starch plays an important role in water stability (Vijayagopal, 2004), where its dextrinisation results in the products being able to absorb water fairly well. It can be seen from the row ENo1 to ENo6 for water solubility in Table 6 that water solubility increases with an increase of die temperature. This may be due to the high dextrinization at high temperature. Tyapkova et al. (2016) reported that slightly higher abrasion resistance and water stability were measured for the pellets produced in a lower temperature condition. As shown from the row ENo1 to ENo8 for water solubility in Table 6, water sol­ ubility decreases with an increase of moisture content in the experiment. Similar findings were also reported by Umar et al. (2013) and Foley and Rosentrater (2013) who found an increase in water stability with an increase in moisture content. Water is a kind of solvent. The water-soluble components and gelatinized starch in the mixture may be used as binders to improve the water stability of feed. Therefore, with the increase of moisture content, more binders can be used under high-temperature and high-pressure extrusion conditions, resulting in better water stability of feed. 4.5. Effects of extrusion conditions on the pellet hardness of the recipe LS-3 Pellets need to have a basic form of physical quality in terms of hardness and durability to withstand the rigours of transportation. Pellet hardness is the force necessary to crush a pellet or a series of pellets at a time (Thomas and Van der Poel, 1996). As can be seen from the row ENo1-ENo3 and row ENo5-ENo7 for pellet hardness in Table 6, the hardness of the feed pellets increases as the moisture content increases, which might be due to lower rate of starch degradation at higher moisture content (Seth et al., 2015). The die temperature impact can be observed from the row ENo1-ENo2 and ENo5-ENo8 for hardness in Table 6. The feed pellet hardness tends 11

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to decrease with an increase in die temperature, which might be due to higher expansion at elevated temperatures (Seth et al., 2015). Samuelsen and Oterhals (2016) also reported that a decrease of hardness and cutting strength was observed with increasing tem­ perature in the extrusion process, which is consistent with the results of this study. 4.6. Effects of extrusion conditions on SME of the recipe LS-3 SME is a measure of the sum of the total mechanical energy dissipated over the total length of the screw (Akdogan, 1996). SME is directly related to the changes of torque, screw speed and inlet flow rate. Meanwhile, it has been known that any variable which affects viscosity will also correspondingly affect the extruder torque and SME (Bhattacharya and Hanna, 1987). Different SME values have been obtained by adjusting moisture and screw speed in our trials. As shown from the row ENo1-ENo8 for SME in Table 6, SME decreases with the increase of moisture content and increased with the increase of screw speed. Water acts as a plasticizer in the extruder. High feed moisture reduces the melt viscosity and the viscous dissipation which leads to a decrease of SME. Samuelsen and Oterhals (2016) reported that an increase in water content causes a decline in SME in their extrusion process, which has the same effects in our observations. With the increase of screw speed, the viscous dissipation in the melt section leads to the increase of specific energy (Della Valle et al., 1993). 4.7. Optimization of extrusion process for the recipe LS-3 The floatability of pellets is greatly related to the bulk density of the pellets (Glencross et al., 2012). To optimize pellet floatability, we use the minimum of bulk density (predicted by the RSM model) as an objective function to search a set of suitable extrusion pa­ rameters by using Eq. 5. The optimization results show that the moisture content and die temperature are the dominated factors in the production of low-starch extruded floating feed and high moisture content and high die temperature are more conducive to the production of low-starch extruded floating feed. Li et al. (2007) suggested that soy protein concentrate reduced the water availability to starch in corn starch/soy protein concentrate composites, which resulted in higher transition temperature. Besides, the gelatini­ zation of corn starch in the composite was restricted by soy protein concentrate. In the present study, the gelatinization of starch may be reduced due to the inclusion of soy protein concentrate and cottonseed protein concentrate, so it is necessary to add more water and thermal energy to stimulate the starch gelatinization and increase the air cell size which makes a diet more expanded in diameter and floats. A high moisture content condition may reduce the need for specific mechanical energy. On the other side, however, an increase of the moisture content will produce high moisture content feed pellet in the extrusion process. This will result in more energy consumption in the drying of the pellets. 5. Conclusion The ratio of wheat flour and cassava ingredient in a formulation has a significant impact on the pellet properties of a low-starch extruded floating feed. The formulation having the wheat flour/cassava = 54.3/54.3 is an optimal selection in the study. The wheat flour/cassava ratio can be used to adjust feed pellet bulk density in practice. Following the feed quality requirements, a set of optimal extrusion process parameters are obtained for the selected optimal recipe LS-3 through Response Surface Methodology, which is: moisture content: 32 %, screw speed: 247 rpm, die temperature: 135 ℃. The corresponding feed pellet quality is bulk density: 492 g/L, expansion ratio: 1.49, floatability: 100 %, hardness: 62.53 N, water solu­ bility: 4.35 %, Specific Mechanical Energy: 22.10 kJ/kg. These optimal process parameters are only valid for the specific equipment that has been used in this experiment. Through comparing with the data analysis results from RSM and the Bulk Density Model for the feed pellet bulk density, it has been confirmed that high moisture content and high die temperature are more conducive to producing the low-starch extruded floating feed. Author statement Each of the co-authors has seen and agrees with each of the changes made to this manuscript in the revision and to the way his or her name is listed. Declaration of Competing Interest The authors report no declarations of interest. Acknowledgments This study was supported by the National Key R&D Program of China (2019YFD0900203 and 2018YFD0900400); the National Natural Science Foundation of China (31902382); The Agricultural Science and Technology Innovation Program (CAAS-ASTIP-2017FRI-08); and Beijing Technology System for Sturgeon and Salmoids (BAIC08-2020). 12

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