Extrusion‐Based 3D Food Printing of Gluten‐Free Quinoa Biscuits: Formulation and Printing Parameter Optimization

ABSTRACT

Extrusion‐based three‐dimensional (3D) printing technology offers a novel approach for developing nutritionally fortified, gluten‐free cereal‐based foods. Although quinoa is nutrient‐dense, its lack of gluten network in traditional processing results in loose texture, however, 3D printing leverages material rheological properties to construct robust structures, effectively addressing this limitation. This study employed pure quinoa flour as the primary ingredient and conducted single‐factor experiments to investigate the effects of sucrose, butter, and milk on dough rheology and texture properties. Response surface methodology was subsequently applied to optimize sensory quality, yielding an optimal formulation (10 g quinoa flour: 3.04 g sucrose, 3.09 g butter, and 6.84 g milk), with predicted and validated sensory scores of 84.4 and 83.7, respectively (p < 0.0001). On the basis of this optimal formulation, critical printing parameters were systematically optimized (nozzle diameter 0.8 mm, printing speed 40 mm/s, and infill density 65%). Using the optimized formulation and process, four three‐dimensional models of varying complexity were successfully printed, demonstrating excellent shape retention and dimensional accuracy post‐baking. This study confirms that quinoa is a promising gluten‐free food ink with superior 3D printability, establishing a technological foundation for personalized manufacturing of healthy grain‐based snacks.

1. Introduction

Three‐dimensional (3D) printing technology, also known as “additive manufacturing,” is profoundly transforming the food processing sector as a revolutionary digital manufacturing technology (Yang et al. 2017). Food 3D printing technology utilizes computer control to layer‐by‐layer deposit edible “food inks” according to preset digital models, ultimately creating foods with complex geometric shapes and customized nutritional compositions (Singhal et al. 2020). The printability of food inks is a core factor determining the success of printing, as it comprehensively reflects the material's extrusion performance and its self‐supporting ability to maintain structural integrity after deposition. This is primarily governed by the material's rheological properties, such as viscosity, shear‐thinning behavior, and yield stress (J. Zhang, Pandya, et al. 2022). Ideal food inks should exhibit significant “shear‐thinning” and thixotropic characteristics, meaning that viscosity decreases under the high shear forces at the nozzle, facilitating easy extrusion. After extrusion, viscosity rapidly recovers, ensuring that deposited layers can support the weight of subsequent layers without collapsing (Y. Liu et al. 2024). Additionally, there exists a complex interaction between printing parameters (such as nozzle diameter, printing speed, layer height, and fill rate) and material formulations, collectively determining the precision, structural stability, and sensory quality of the final product (Pradhan et al. 2025).

In recent years, research on 3D printing food inks has expanded from simple thermally fusible materials, such as chocolate and candy (M. Wang et al. 2022), to a variety of complex food systems, including starch‐based gels (Maniglia et al. 2020), protein systems (L. Liu et al. 2020), fruit and vegetable purees (Kim et al. 2018), and meat pastes (Lee et al. 2025). These studies have significantly enriched the variety of 3D‐printed foods and opened new avenues for their functionalization and nutritional enhancement (Zhao et al. 2021). Among the many emerging printing materials, Chenopodium quinoa Willd, a nutritionally complete “pseudo‐cereal,” has been recommended by the Food and Agriculture Organization (FAO) as an ideal whole‐nutrient food for humans (Kumar et al. 2025). It is rich in high‐quality protein, a balanced composition of amino acids, unsaturated fatty acids, dietary fiber, vitamins, and various minerals (Batool et al. 2025). Additionally, quinoa contains abundant bioactive compounds, such as phenolics and flavonoids, which offer multiple physiological functions, including antioxidant and anti‐inflammatory effects (Penidez et al. 2023). More importantly, quinoa is naturally gluten‐free, making it an ideal raw material for developing gluten‐free foods (S. Wang and Zhu 2016). However, in traditional baking, the absence of gluten proteins to form a gluten network often results in products made with pure quinoa flour or high proportions of quinoa flour being hard, crumbly, and less acceptable in terms of sensory qualities. Extrusion‐based 3D printing technology relies primarily on the rheological properties of materials rather than a gluten network to construct structures, offering a new solution for the application of highly nutritious grains such as quinoa (Kewuyemi et al. 2022). Biscuit‐like products, which have lower dependency on gluten networks, thus serve as ideal carriers for developing quinoa‐based products using 3D‐printing technology. Therefore, combining the nutritional advantages of quinoa with the personalized manufacturing capabilities of 3D printing technology to develop quinoa‐based functional foods that are healthy, delicious, and engaging holds significant practical importance.

Although previous studies have explored the application of quinoa in traditional biscuits (Pourshahidi et al. 2020), systematic development and optimization of quinoa‐based biscuit formulations for 3D printing have not yet been reported. To address this, this study aims to develop a 3D‐printed quinoa biscuit formulation that combines excellent printability and sensory quality. Using pure quinoa flour as the raw material, the study first identified the influence range of key auxiliary ingredients such as white sugar, butter, and milk on the printability of quinoa dough through single‐factor experiments. Subsequently, response surface methodology was employed to optimize the formulation, achieving the best quinoa dough formulation with ideal sensory properties. Finally, by fine‐tuning the printing process parameters and conducting multi‐model printing verification, a stable production process was established. This study aims to provide a theoretical basis and technical support for the development of high‐value‐added quinoa products, promoting innovation and advancement in personalized functional foods.

2. Experimental Method

2.1. Experimental Materials

Quinoa flour (Mugu, Huaizhi Coarse Cereals Co. Ltd., Zhuluke Town, Jianping County, China); white granulated sugar (Sugarman, Guangzhou Fuzheng Donghai Food Co. Ltd., China); Anchor butter (Anchor, Fonterra Trading Co. Ltd., New Zealand); and whole milk (Mengniu, Inner Mongolia Mengniu Dairy Co., Ltd., China) all these ingredients were used within their shelf life.

2.2. Dough Preparation

Sift the quinoa flour through a 150‐mesh sieve to ensure uniform particle size. Melt the butter in a water bath at 50°C. Then, add the melted butter, white sugar, and milk to a mixing bowl. Using a mixer (ZG‐LZ908, Zhongshan Anxin Life Appliance Co. Ltd., China), mix at medium speed for 2–3 min until a uniform emulsion forms. Gradually add the sifted quinoa flour to the mixture and continue mixing at medium speed for 3 min to form a dough with a uniform texture and no dry flour particles, without using any additional additives. Then, load the dough into the barrel of a 3D printer and print the desired model. Finally, place it in an oven (Lechuang Double‐Layer Four‐Tray, Lechuang Kitchen Appliance Equipment Co. Ltd., China) with a bottom heat of 150°C and a top heat of 165°C and bake for 20 min to produce 3D‐printed quinoa cookies.

2.3. Single‐Factor Experimental Design

On the basis of preliminary exploration, the basic formulation of quinoa dough was determined as follows: 10 g of sifted quinoa flour, 3 g of sugar, 3 g of Anchor butter, and 6.8 g of milk. To investigate the effects of key components on the printability of quinoa dough, a single‐factor experimental method was employed.

The levels for each factor were determined through preliminary trials and literature review. Specifically, sugar content (1–5 g) and butter content (1–5 g) were selected on the basis of typical ranges reported for gluten‐free bicuits doughs (Z. Zhang et al. 2024). Excessively low or high ingredient addition would lead to extreme dough state, so five gradient levels were set within the feasible processing range for single‐factor screening. While keeping the amounts of other components fixed, the effects of sugar content (1, 2, 3, 4, and 5 g), butter content (1, 2, 3, 4, and 5 g), and milk content (5.8, 6.3, 6.8, 7.3, and 7.8 g) on the properties of quinoa dough were examined. For 3D printing parameters, the gradient settings referred to the fixed common parameter of food extrusion 3D printers to fully reflect food ingredients parameter effects on print quality. All experimental dough samples were printed under fixed 3D printing parameters (nozzle diameter: 0.8 mm, printing speed: 40 mm/s, and infill density: 65%). The effects of different key component contents on the rheological properties, textural properties, and printing accuracy of quinoa dough were systematically evaluated (Sosa et al. 2019).

2.4. Study on the Printability of Quinoa Dough

2.4.1. Rheological Measurement of Quinoa Dough

Referring to previous research (Z. Zhang et al. 2024), a rotational rheometer (HR10, TA Instruments Waters, USA) was used to measure the rheological properties of quinoa dough (Avila‐de la Rosa et al. 2015). A parallel plate with a diameter of 40 mm was selected, with a measurement gap set at 2500 µm and a test temperature of 25°C. Steady‐state shear scanning was performed within a shear rate range of 0.1–100 s⁻¹, and the apparent viscosity curve as a function of shear rate was recorded to characterize the rheological behavior of the dough under shear.

2.4.2. Texture Analysis of Quinoa Dough

Referring to previous research (Z. Zhang et al. 2024), the mechanical properties of the dough were measured using a TA.XTC‐18 texture analyzer (Baosheng Technology Co. Ltd., Zhejiang, China) equipped with a TA/0.75S spherical probe. The test parameters were set as follows: pre‐test, test, and post‐test speeds were 3, 1, and 1 mm/s, respectively; compression ratio was 20%; interval time was 5 s; and trigger force was 5 N. Using the TPA (Texture Profile Analysis) mode, the hardness, adhesiveness, springiness, and resilience of the quinoa dough were determined. Each sample group was measured three times, and the results were averaged.

2.4.3. Printing Accuracy Assessment

To evaluate the printability of the dough, a rectangular frame model measuring 40 mm × 40 mm × 8 mm was designed and printed. After printing, a digital vernier caliper (accuracy 0.1 mm) was used to measure the length, width, and height of the printed structure. Three different positions were measured for each dimension, and the average value was taken.

2.4.4. Response Surface Analysis for Optimizing Dough Formulation

On the basis of the single‐factor experimental results, the influence range of each component on printing performance was clarified, and the optimization intervals for key factors were determined. Using Design‐Expert 13 software, a Box‐Behnken design was employed, with the addition amounts of white sugar (A), butter (B), and milk (C) as independent variables, and the sensory score of baked quinoa cookies after printing as the response value. A three‐factor, three‐level response surface optimization experiment was conducted (Z. Zhang et al. 2024). The experimental design and coding levels are shown in Table 5. By constructing a quadratic polynomial model, the effects of each factor and their interactions on the response value were analyzed, and the optimal formulation combination was determined.

TABLE 5.

Response surface analysis test results of three‐dimensional (3D) printed quinoa biscuit formulation.

Test number	Amount of white sugar added (g)	Butter addition amount (g)	Milk addition amount (g)	Sensory score (points)
1	3	3	6.8	84
2	2	4	6.8	74
3	3	3	6.8	83
4	3	4	6.3	67
5	3	3	6.8	85
6	2	3	7.3	71
7	3	4	7.3	66
8	4	4	6.8	72
9	4	3	7.3	71
10	4	2	6.8	69
11	3	3	6.8	85
12	4	3	6.3	69
13	3	2	6.3	58
14	3	2	7.3	70
15	2	3	6.3	66
16	2	2	6.8	65
17	3	3	6.8	84

2.4.5. Sensory Evaluation Analysis

A professional sensory panel was established for sensory evaluation. A total of 12 trained panelists (6 males and 6 females, aged 22–35 years) were recruited from the university's food science department. All panelists, previously completed basic sensory evaluation training courses, had experience in descriptive analysis of bakery products.

Five sensory attributes were evaluated with a total possible score of 100 points. Form (10 points) and organizational structure (15 points) were assigned lower weights, as these appearance‐related attributes are largely governed by the 3D printing parameters. In contrast, taste (30 points) and texture (30 points) were given higher weights because they are primarily formulation‐dependent and serve as the key drivers of consumer acceptability. Color and luster were assigned an intermediate weight (15 points). The scoring criteria for each attribute were defined at three quality levels, with corresponding score ranges detailed in Table 1. The descriptive terms used in the criteria (e.g., “uniform thickness,” “clear patterns,” “distinct quinoa grain aroma,” and “crispy texture”) were derived from preliminary discussions among the panelists and reference to established lexicon for biscuit products.

TABLE 1.

Sensory evaluation form.

Scoring item	Full marks	Scoring criteria	Score
Form	10	The shape is complete, with uniform thickness, clear patterns, no deformation, no connected edges on the cake body, and no breakage	8–10
		The appearance is basically intact, thickness is generally uniform, patterns are largely clear, with slight deformation, and the cake body has no connected edges	5–7
		The shape is incomplete, thickness uneven, patterns blurred, severely deformed, heavily broken, and the cake body is connected to the edges	1–4
Organizational structure	15	The cross‐section structure is clear, with uniform pore sizes, distinct layers, and a fine, loose texture	11–15
		The cross‐sectional structure is basically clear, with uneven pore sizes and distribution, and the tissue is relatively coarse	6–10
		The cross‐section structure is unclear, and the texture is rigid, with few pores, lack of fineness or absence of pores, and surface cracks	1–5
Color and Luster	15	The biscuits exhibit a uniform light yellow color, with even coloration throughout, and the edges are neither overly browned nor too pale, free from any scorch marks	11–15
		The cake has a darker or lighter color, uneven coloration, and darker edges, with no obvious signs of burning	6–10
		The cake has a darker or overly pale color, severely uneven coloring, excessively burnt or too pale edges, and noticeable scorch marks	1–5
Taste	30	It has the distinctive grain aroma of quinoa, with a moderate and pure sweetness, free from any off‐flavors or bitter aftertaste, offering a refreshing taste experience	21–30
		Slightly has the characteristic grain aroma of quinoa, with a sweetness that is neither too strong nor too faint, no obvious off‐flavors, and a slight lingering bitterness	11–20
		No characteristic quinoa grain aroma, with off‐flavors, sourness, bitterness, rough texture, and severe lingering bitterness	1–10
Flavor	30	Crispy texture, not greasy, no gritty feeling, and not sticky to the teeth	21–30
		The texture is relatively crispy, slightly oily, with a somewhat sandy feel, and it sticks to the teeth a bit	11–20
		The texture is either stiff or soft, greasy, gritty, and sticky	1–10

All panelists were required to avoid spicy, greasy, and irritating food 2 h before evaluation and to rinse their mouths with pure water between different samples. Samples were served at room temperature in randomized order and coded with three‐digit random numbers to avoid bias. The total sensory score for each sample was calculated as the sum of the five attribute scores, and the final result was expressed as the mean score of all panelists. The sensory evaluation procedure was designed with reference to the national standard (GB/T 20980‐2021, General Rules for Biscuits).

2.5. Optimization of 3D Printing Process

On the basis of the optimal dough formulation determined by the response surface methodology, the key process parameters for extrusion‐based 3D printing were systematically optimized. Using a desktop food 3D printer (FOODBOT‐S2, Shiyin Technology Co. Ltd., Hangzhou, China), the printing model was a transparent rectangular prism measuring 40 mm × 40 mm × 8 mm. The optimization process employed the controlled variable method as follows:

1. Nozzle diameter optimization: With a fixed speed of 40 mm/s and a fill density of 65%, the effects of different nozzle diameters (0.4, 0.6, 0.8, 1.0, and 1.2 mm) on printing quality were investigated.

2. Fill density optimization: With a fixed speed of 40 mm/s and a nozzle diameter of 0.8 mm, the effects of different fill densities (20%, 35%, 50%, 65%, and 80%) on printing quality were investigated.

3. Printing speed optimization: With a fixed nozzle diameter of 0.8 mm and a fill density of 65%, the effects of different printing speeds (20, 40, 60, 80, and 100 mm/s) on printing quality were investigated. The evaluation criteria for printing quality were consistent with those in the experimental method 2.4.3, comprehensively considering printing fidelity and structural integrity (such as uniformity of lines and the presence of breaks or excessive accumulation).

2.6. Post‐Baking Treatment

Place the 3D‐printed quinoa cookies into the preheated oven, set the bottom heat to 150°C and the top heat to 165°C, and bake for 20 min. After baking, remove them and let them cool to room temperature before storing for later use.

2.7. Best 3D Printing Process Validation

To verify the stability and universality of the determined optimal dough formulation and printing process parameters, models with varying levels of complexity and three‐dimensionality were introduced for printing validation, including (1) Doraemon, (2) Iron Man, (3) Desert Eagle, and (4) Ghost. A comprehensive evaluation of the final products was conducted, focusing on molding accuracy, structural integrity, and appearance, to confirm the feasibility of this process scheme in manufacturing foods with complex geometric shapes.

2.8. Statistical Analysis

All experiments were conducted in triplicate, and experimental data were expressed as mean values ± standard deviation (SD). One‐way analysis of variance (ANOVA) was performed using SPSS 27.0 software (IBM, USA) to evaluate significant differences among groups. The significance level was set at p < 0.05 for statistical difference and p < 0.01 for highly significant difference. Multiple comparisons were carried out using the Duncan's post hoc test.

For response surface optimization, Box‐Behnken design and quadratic polynomial regression analysis were implemented via Design‐Expert 13.0 software (Stat‐Ease, USA). The goodness‐of‐fit of the regression model was evaluated by coefficient of determination (R ²), adjusted coefficient of determination (R ² _Adj), and coefficient of variation (CV). All figures and tables were plotted and arranged using Origin 2024 software.

3. Results and Discussion

3.1. Single‐Factor Study of Dough

3.1.1. Effect of White Sugar Addition on the Printability of Quinoa Dough

In extrusion‐based 3D printing, the formulation of food ink is a critical factor determining the final product quality (Wilms et al. 2021). As a common sweetener and structural regulator in biscuit formulations, the addition level of white sugar can influence the rheological properties, texture characteristics, and final printing accuracy of the dough (Li et al. 2012). Rheological tests (Figure 1) indicate that quinoa dough at all addition levels exhibits shear‐thinning behavior, where apparent viscosity decreases with increasing shear rate, which is an ideal characteristic for extrusion‐based printing materials (Herrada‐Manchón et al. 2023). However, within the concentration range of this study, no significant differences in apparent viscosity were observed among the groups, suggesting that white sugar has a limited effect on the macroscopic flowability of quinoa dough.

FIGURE 1.

Apparent viscosity of quinoa dough with different amounts of white sugar added.

In terms of texture characteristics (Figure 2), the addition of white sugar showed no significant differences in the hardness, elasticity, or resilience of the dough (p > 0.05), indicating that within the selected range, white sugar is not a key factor influencing the skeletal structure of the dough. However, the adhesiveness of the dough reached its lowest value at an addition of 2 g, showing a significant difference compared to the 5 g group (p < 0.05). The texture characteristics of the dough are not only influenced by a single component but are also closely related to other components, such as flour and fat (Pavicic et al. 2021).

FIGURE 2.

Texture profile distribution of quinoa dough with different white sugar addition levels: (a) hardness (g); (b) adhesiveness (g s); (c) springiness (%); and (d) resilience. Different lowercase letters above the bars indicate significant differences among treatments (p < 0.05)

Although white sugar has no significant impact on the macroscopic indicators of rheology and texture, its effect on the final printing accuracy shows notable differences. As shown in Figure 3, when the white sugar addition is 3 g, the dough exhibits moderate hardness, and the extrusion process is stable and continuous. The deviations in the X‐ and Y‐axis directions from the preset model dimensions are minimal, whereas there is a slight deviation in the Z‐axis direction, which remains within an acceptable range and shows no significant difference compared to the 2 and 5 g groups (p > 0.05). In summary, the influence of white sugar addition on texture characteristics is relatively limited. However, a 3 g addition ensures optimal printing accuracy while maintaining structural stability (Table 2).

FIGURE 3.

Photos of 3D printing with different amounts of white sugar added: (a) 1 g; (b) 2 g; (c) 3 g; (d) 4 g; and (e) 5 g.

TABLE 2.

Three‐dimensional (3D) printing accuracy of quinoa dough with different amounts of white sugar added.

Amount of white sugar added (g)	Length in the X‐axis direction (mm)	Length in the Y‐axis direction (mm)	Length in the Z‐axis direction (mm)
Preset model dimensions	40	40	8
1	39.0 ± 0.1a	38.9 ± 0.1a	6.0 ± 0.4a
2	39.9 ± 0.1b	39.3 ± 0.4ab	8.0 ± 0.0c
3	40.0 ± 0.1b	40.0 ± 0.1bc	8.6 ± 0.1d
4	41.1 ± 0.7c	40.7 ± 0.6c	7.0 ± 0.1b
5	40.0 ± 0.1b	39.5 ± 0.7ab	9.0 ± 0.0d

Note: The specifications of the 3D model are 40 mm × 40 mm × 8 mm. Different lowercase superscript letters within the same column indicate significant differences (p < 0.05).

3.1.2. Single‐Factor Experiment With Different Single‐Factor Butter Addition Levels

As shown in Figure 4, the apparent viscosity of quinoa dough with varying butter addition levels decreases with increasing shear rate, exhibiting typical “shear‐thinning” behavior. This characteristic is crucial for extrusion‐based 3D printing, as it ensures smooth material flow under high shear forces within the print head, whereas viscosity rapidly recovers after deposition due to the disappearance of shear forces, thereby maintaining the stability of the printed structure (Z. Liu et al. 2017). Additionally, it was observed that as the butter addition increased, the apparent viscosity of the dough gradually decreased. This is because butter contains fat components, and the lubricating effect of fat acts as a lubricant in the dough system, effectively reducing friction and interactions among starch granules, protein molecules, and other components (Pavicic et al. 2021). This finding aligns with research results indicating that increasing fat content in chocolate systems significantly reduces viscosity (Molard et al. 2026).

FIGURE 4.

Apparent viscosity of quinoa dough with different butter addition levels.

The textural properties are directly related to the extrusion performance of the material and the self‐supporting capability of the printed structure. As shown in Figure 5, with the increase in butter content, the hardness, adhesiveness, and elasticity of the printed samples all showed a significant decreasing trend (p < 0.05), reaching their peak at an addition of 2 g; no significant difference was observed in resilience. This result further confirms that the lubricating and plasticizing effects of fat weaken the internal structural strength of the dough network, and the printing conformity is highly consistent with the textural analysis results.

FIGURE 5.

Texture profile distribution of quinoa dough with different butter addition levels: (a) hardness (g); (b) adhesiveness (g s); (c) springiness (%); and (d) resilience. Different lowercase letters above the bars indicate significant differences among treatments (p < 0.05).

When the butter addition is too low (1 g), the dough hardness is excessively high, resulting in unsatisfactory printed products. Conversely, when the addition is too high (5 g), the texture becomes overly soft, leading to internal depressions and a decline in print formability. With a 3 g addition, although the printed sample's dimensions do not fully achieve the ideal target model size, the sample exhibits a complete appearance and uniform lines, with no occurrences of collapse or filament breakage. At additions of 1 and 5 g, significant deviations in the Z‐axis length are observed, likely due to the dough's excessive hardness and elasticity causing post‐extrusion rebound and diameter reduction (In et al. 2022). In summary, an appropriate amount of butter can enhance printing accuracy and shape fidelity by modulating the dough's textural properties while ensuring continuous extrusion (Table 3, Figure 6).

TABLE 3.

Three‐dimensional (3D) printing accuracy of quinoa dough with different butter additions.

Butter addition amount (g)	Length in the X‐axis direction (mm)	Length in the Y‐axis direction (mm)	Length in the Z‐axis direction (mm)
Preset model dimensions	40	40	8
1	40.0 ± 0.0a	40.0 ± 0.2a	9.1 ± 0.0c
2	40.0 ± 0.0a	39.9 ± 0.1a	6.7 ± 0.2a
3	40.4 ± 0.2b	40.2 ± 0.3a	7.0 ± 0.1ab
4	40.3 ± 0.2b	39.8 ± 0.3a	7.4 ± 0.4b
5	40.0 ± 0.0a	39.7 ± 0.4a	9.2 ± 0.3c

Note: The specifications of the 3D model are 40 mm × 40 mm × 8 mm. Different lowercase letters above the bars indicate significant differences among treatments (p < 0.05).

FIGURE 6.

3D printed photos with different butter addition amounts: (a) 1 g; (b) 2 g; (c) 3 g; (d) 4 g; and (e) 5 g.

3.1.3. Single‐Factor Experiment With Varying Amounts of Milk Added

Milk not only regulates the moisture content of the dough as a liquid component, but its rich proteins and fats also interact with the starch and proteins in quinoa flour, forming a complex protein–polysaccharide network. This profoundly affects the extrusion behavior, interlayer bonding, and molding stability of the dough during extrusion‐based 3D printing (Aparnna et al. 2025, 2026). As shown in Figure 7, the apparent viscosity of all quinoa doughs with varying milk additions decreases with increasing shear rate, exhibiting typical shear‐thinning behavior. As the milk addition increases, the overall apparent viscosity of the dough shows a declining trend, indicating that milk effectively acts as a plasticizer, enhancing the fluidity of the system. The textural properties are directly related to the extrudability of the material and the structural stability after printing (buildability) (Holkunde et al. 2025).

FIGURE 7.

Apparent viscosity of quinoa dough with different milk addition levels.

Texture analysis (Figure 8) showed that as the amount of milk added increased, the hardness, adhesiveness, and elasticity of the quinoa dough exhibited a significant decreasing trend (p < 0.05), whereas no significant change was observed in resilience (p > 0.05). The study suggests that the reduction in hardness implies a decrease in the driving force required for extrusion, which is beneficial for improving printing fluency. However, excessively low hardness and elasticity may cause the deposited lines to be unable to support the weight of subsequent layers, leading to structural collapse (Yang et al. 2021).

FIGURE 8.

Texture profile distribution of quinoa dough with different milk addition levels: (a) hardness (g); (b) adhesiveness (g s); (c) springiness (%); and (d) resilience. Different lowercase letters above the bars indicate significant differences among treatments (p < 0.05)."

On the basis of the printing results, the amount of milk added has a decisive influence on the shape fidelity and dimensional accuracy of the printed product. When the milk addition is 6.8 g, the printed structure is complete with uniform lines, and the printed dimensions are highly consistent with the target model. An insufficient amount of milk (5.8 g) results in overly stiff dough, making extrusion difficult and leading to undersized prints. Conversely, an excessive amount of milk (7.8 g) causes excessive fluidity, making it difficult for the extrudate to maintain its shape and reducing structural stability. In summary, the milk addition is the dominant factor in regulating the rheological properties of the dough. By adjusting the moisture content and intermolecular interactions within the system, it finely controls the rheological and textural characteristics of the dough. In this experimental system, a milk addition of 6.8 g is the optimal choice for achieving high‐fidelity 3D printing, as it ensures both extrudability and sufficient structural support (Table 4, Figure 9).

TABLE 4.

Three‐dimensional (3D) printing accuracy of quinoa dough with different milk addition levels.

Milk addition amount (g)	Length in the X‐axis direction (mm)	Length in the Y‐axis direction (mm)	Length in the Z‐axis direction (mm)
Preset model dimensions	40	40	8
5.8	38.7 ± 0.4a	39.0 ± 0.0a	5.9 ± 0.4a
6.3	39.3 ± 0.2ab	39.2 ± 0.7a	5.7 ± 0.4a
6.8	40.0 ± 0.1b	39.9 ± 0.1b	6.9 ± 0.1b
7.3	40.0 ± 0.0b	39.9 ± 0.1b	6.8 ± 0.4b
7.8	39.3 ± 1.2ab	39.9 ± 0.1b	6.7 ± 0.2b

Note: The specifications of the 3D model are 40 mm × 40 mm × 8 mm. Different lowercase superscript letters within the same column indicate significant differences (p < 0.05).

FIGURE 9.

Photographs of 3D printing with different milk addition amounts: (a) 5.8 g; (b) 6.3 g; (c) 6.8 g; (d) 7.3 g; and (e) 7.8 g.

3.2. Response Surface Model Analysis

3.2.1. Response Surface Experimental Results

To optimize the formulation of 3D‐printed quinoa cookies, the amounts of white sugar (A), butter (B), and milk (C) were taken as independent variables, with sensory score as the response value. A three‐factor, three‐level response surface experiment was conducted using the Box‐Behnken design. Sensory evaluation is a core indicator for measuring food acceptability and is crucial for new product development. As shown in Table 5, the sensory scores of the 17 test groups ranged from 58 to 85 points. The significant variation in scores indicates that the cookie formulation factors substantially influenced the sensory quality of the cookies, providing a foundation for further optimization and analysis (Table 6).

TABLE 6.

Response surface regression coefficients table for three‐dimensional (3D) printed quinoa biscuit formulation.

Item	Coefficient
Constant	84.2
A—White granulated sugar addition amount	0.625
B—Butter Addition Amount	2.13
C—Milk addition amount	2.25
AB	−1.5
AC	−0.75
BC	−3.25
A 2	−5.1
B 2	−9.1
C 2	−9.85

Using Design‐Expert 13 software, the experimental data were fitted with a quadratic multiple regression, resulting in the regression equation for sensory score (Y) with respect to the amounts of white sugar (A), butter (B), and milk (C)

$$\begin{array}{ccc}\hfill Y& =& -5.1A^2-9.1B^2-9.85C^2-1.5AB-0.75AC-3.25BC\hfill \\ & & +0.625A+2.13B+2.25C+84.2.\hfill \end{array}$$

As shown by the response surface analysis of variance results (Table 7), the regression model is highly significant (p < 0.0001), and the lack‐of‐fit term is not significant (p > 0.05), indicating that the model accurately reflects the actual experimental conditions without systematic bias. The high coefficient of determination (R ² = 0.9900) and adjusted coefficient of determination (R ² _Adj = 0.9771) demonstrate that the model explains 97.71% of the variation in response values, with excellent correlation between predicted and actual values. The low coefficient of variation (CV = 1.72%) indicates that the experiment has good precision and reliability (Mohammadi et al. 2024). In summary, this quadratic polynomial model can effectively predict sensory scores under different formulation combinations and can be used for subsequent formulation optimization.

TABLE 7.

Analysis of variance results for response surface.

Factor	Sum of squares	Degree of freedom	Mean square	F value	p value	Salience
Model	1092.71	9	121.41	76.91	<0.0001	**
A—White granulated sugar	3.13	1	3.13	1.98	0.2022
B—Butter	36.13	1	36.13	22.88	0.0020	**
C—Milk	40.5	1	40.5	25.66	0.0015	**
AB	9	1	9	5.7	0.0483	*
AC	2.25	1	2.25	1.43	0.2714
BC	42.25	1	42.25	26.76	0.0013	**
A 2	109.52	1	109.52	69.38	<0.0001	**
B 2	348.67	1	348.67	220.88	<0.0001	**
C 2	408.52	1	408.52	258.79	<0.0001	**
Total lack of fit term	11.05	7	1.58
Lack‐of‐fit term	8.25	3	2.75	3.93	0.1097	Not significant
Pure error	2.8	4	0.7
Total	1103.76	16
R 2 = 0.9900, R2 Adj = 0.9771, CV = 1.72%

Note: “*” indicates significance (p < 0.05); “**” indicates high significance (p < 0.01).

According to the analysis of variance results, in the linear terms, the addition amounts of butter (B) and milk (C) have an extremely significant impact on the sensory score (p < 0.01), indicating that butter and milk are key direct factors affecting the sensory quality of the biscuits. In the interaction terms, the BC (butter × milk) interaction is extremely significant (p < 0.01), and the AB (butter × white sugar) interaction is significant (p < 0.05), suggesting that the interaction effects between different fat and liquid components significantly influence the final sensory outcome. In the quadratic terms, A ², B ², and C ² are all extremely significant (p < 0.0001), which clearly indicates a nonlinear quadratic relationship between each factor and the sensory score. This implies the existence of an optimal addition range, where deviations above or below this range may lead to a decline in sensory scores. On the basis of the magnitude of the F values, the order of influence of each factor on the sensory score is: milk addition amount (C) > butter addition amount (B) > white sugar addition amount (A).

3.2.2. Analysis of Response Surface Interaction Results

As shown in Figure 10a,b, the sensory scores of 3D‐printed quinoa cookies are jointly influenced by the amounts of added white sugar and butter. The response surface model exhibits a distinct parabolic shape, indicating the existence of an optimal region for sensory scores within the selected factor level ranges (Kokkinidou and Peterson 2013). When the white sugar addition is within the range of 2.5–3.5 g and the butter addition is within 2.5–3.5 g, the sensory score can be maintained at a relatively high level above 75 points. This is primarily because white sugar not only provides sweetness but also contributes significantly to the color and crispness of the cookies. Meanwhile, butter, as the main source of fat, imparts a rich flavor, soft texture, and good mouthfeel solubility to the product (Jiang et al. 2019). Excessive (e.g., exceeding 3.5 g) or insufficient (e.g., below 2.5 g) addition of either component disrupts the balance of taste, leading to a significant decline in scores, such as excessive sweetness, greasiness, bland flavor, or a dry, hard texture. From the contour plot, the elliptical shape of the contours indicates a significant interaction between the two factors—white sugar and butter addition—suggesting that the effect of one factor on sensory scores changes with variations in the level of the other factor. Identifying such interaction effects is crucial for optimizing formulations, as the synergistic and antagonistic relationships among ingredients must be comprehensively considered when developing novel foods (such as 3D‐printed foods) with high consumer acceptability (R. Liu et al. 2025).

FIGURE 10.

Contour plot and surface plot of white sugar addition amount versus butter addition amount (a and b); contour plot and surface plot of white sugar addition amount versus milk addition amount (c and d); and contour plot and surface plot of butter addition amount versus milk addition amount (e and f).

The combined effects of sucrose and milk addition on sensory scores are shown in Figure 10c,d. The response surface similarly exhibits a parabolic distribution, indicating that sensory scores initially increase and then decrease with changes in the levels of the two factors (Kokkinidou and Peterson 2013). When sucrose addition ranges from 2.8 to 3.2 g and milk addition ranges from 6.5 to 7.1 g, sensory scores reach their peak (above 80 points). However, unlike Figure 10a,b, the contour lines appear nearly circular, suggesting that the interaction between sucrose and milk addition is not statistically significant (p > 0.05). This indicates that, within the experimental range of this study, the effects of sucrose and milk addition on sensory scores are largely independent of each other (Erem 2025). This finding simplifies the process of formula optimization and suggests that adjustments to certain ingredients can be made independently when developing palatable and healthy foods.

The interaction between the amounts of butter and milk has the most pronounced effect on the sensory score, as shown in Figure 10e,f. The response surface plot exhibits the steepest curvature and the greatest slope, indicating that the sensory score is highly sensitive to variations in the levels of these two factors (Pandey and Mishra 2015). The sensory score of the cookies increases rapidly with the addition of butter and milk, reaching its peak at approximately 3.2 g of butter and 7.0 g of milk, after which it declines sharply. This strong interaction arises from the critical roles of butter (fat) and milk (primarily moisture and protein) in structuring the food ink and the final product texture (Jantrawut et al. 2023). The contour lines form distinct ellipses, with the highest density observed around 3 g of butter and 6.8 g of milk, suggesting that even minor changes in factor levels within this range lead to significant fluctuations in sensory scores. This also confirms the presence of an optimal sensory score near this region. In summary, the balance between butter and milk is the most crucial factor determining the sensory quality of 3D‐printed quinoa cookies.

3.2.3. Validation Test of the Optimal Formulation for 3D Printed Quinoa Cookies

Using Design‐Expert 13 software, the response surface experimental data were fitted and analyzed. The resulting quadratic multiple regression equation accurately predicts the sensory scores of 3D‐printed quinoa cookies under different formulation combinations. Through software calculation and analysis, the optimal formulation parameters for 3D‐printed quinoa cookies were determined as follows: white sugar 3.04 g, butter 3.09 g, and milk 6.84 g, with a predicted sensory score of 84.4 points. To verify the consistency between the predicted and actual values and to test the reliability and practicality of the model, three parallel validation experiments were conducted using the aforementioned optimal formulation. The validation experiments strictly adhered to the previously established dough preparation process, 3D printing parameters, and post‐processing conditions to ensure consistency with the response surface experimental conditions. The results showed that the average sensory score of the three parallel experiments was 83.7 points, which closely matched the predicted value. This indicates that the quadratic multiple regression model established in this study has an extremely high degree of fit, accurately reflecting the intrinsic relationship between the amounts of white sugar, butter, and milk and the sensory score. It can be effectively used for the optimization and prediction of 3D‐printed quinoa cookie formulations.

3.3. Printing Parameter Optimization

3.3.1. The Influence of Nozzle Diameter on Printing Quality

The nozzle diameter directly affects printing accuracy and surface smoothness by altering the material extrusion rate and line resolution, thereby influencing the structure and appearance of the printed part (J. Zhang, Li, et al. 2022). Under fixed printing conditions of 40 mm/s speed and 65% infill density, rectangular blocks measuring 40 mm × 40 mm × 8 mm were printed using nozzle diameters of 0.4, 0.6, 0.8, 1.0, and 1.2 mm, with the results shown in Figure 11. When the nozzle diameter was 0.4 mm (Figure 11a), the excessively small nozzle caused significant extrusion resistance, making it difficult for the dough to pass through smoothly. Severe filament breakage occurred, preventing the formation of a continuous structure, and the printing process was time‐consuming. With a nozzle diameter of 0.6 mm (Figure 11b), the lines were continuous, but interlayer adhesion was weak, and the printing process remained relatively slow, failing to achieve an ideal state. At a nozzle diameter of 0.8 mm (Figure 11c), extrusion was smooth, the lines were uniform, and the alignment with the target model was the highest, with moderate printing efficiency. For nozzle diameters of 1.0 and 1.2 mm (Figure 11d,e), the extruded lines were too thick, material overflow was evident, surface smoothness was low, and the samples exhibited slight collapse. This resulted in the actual printed dimensions exceeding the model dimensions (Severini et al. 2018; Zhu et al. 2023), compromising the fine‐forming advantage of 3D printing. Considering all factors, 0.8 mm was selected as the optimal nozzle diameter for balancing forming accuracy and extrusion efficiency within this material system.

FIGURE 11.

Quinoa dough models printed with different nozzle diameters: (a) 0.4 mm, (b) 0.6 mm, (c) 0.8 mm, (d) 1.0 mm, and (e) 1.2 mm.

3.3.2. The Impact of Printing Speed on Print Quality

Printing speed determines the deposition rate of material on the printing platform, directly affecting the interlayer bonding quality and printing continuity (Anukiruthika et al. 2020). With a fixed nozzle diameter of 0.8 mm and a printing infill of 65%, this study investigated the effects of five printing speeds (20, 40, 60, 80, and 100 mm/s) on printing quality, as shown in Figure 12. At a printing speed of 20 mm/s (Figure 12a), slight material accumulation occurred, filament extrusion was uneven, and efficiency was low. At printing speeds of 40 and 60 mm/s (Figure 12b,c), the printing speed and dough extrusion rate achieved optimal matching, resulting in smooth sample surfaces and stable, intact structures. At printing speeds of 80 and 100 mm/s (Figure 12d,e), slight filament breakage was observed, interlayer bonding strength weakened, irregular structures appeared at the edges, internal depressions formed in the samples, and significant deviations from the preset target model were noted, failing to achieve the ideal state. Considering the earlier nozzle diameter printing results, a printing speed of 40 mm/s was identified as the key parameter for achieving the optimal balance between extrusion volume and deposition rate, effectively ensuring printing continuity and structural integrity.

FIGURE 12.

Models of quinoa dough printed at different printing speeds: (a) 20 mm/s, (b) 40 mm/s, (c) 60 mm/s, (d) 80 mm/s, and (e) 100 mm/s.

3.3.3. The Impact of Print Fill Density on Print Quality

Printing infill density, which refers to the density of the internal support structure of the sample, not only directly affects printing time and material consumption but also influences the mechanical strength, structural stability, and forming quality of the product (Samaan et al. 2026). With a fixed nozzle diameter of 0.8 mm and a printing speed of 40 mm/s, five infill density gradients—20%, 35%, 50%, 65%, and 80%—were set to investigate their effects on the printing quality of quinoa dough. The results are shown in Figure 13. When the infill density was 20% and 35% (Figure 13a,b), the internal support structure of the samples was extremely sparse, failing to provide adequate support for the upper layers. This resulted in weak stability, insufficient mechanical strength, and a tendency for the samples to collapse or deform (Hu et al. 2026). At an infill density of 50% (Figure 13c), although structural stability improved, the relatively large line spacing still caused the upper lines to sink into the gaps of the lower layers, leading to blurred edges and suboptimal forming quality. When the infill density reached 65% (Figure 13d), the internal support structure became dense enough to effectively support the deposition of subsequent layers, resulting in high forming precision. At an infill density of 80% (Figure 13e), excessive material filling caused overcrowding, blurred lines, and increased extrusion resistance. Moreover, the high infill density significantly prolonged printing time and reduced production efficiency. In summary, a 65% infill density achieves an optimal balance among structural strength, forming precision, and printing efficiency, making it the best choice for this material system.

FIGURE 13.

Quinoa dough models printed under different printing fill densities: (a) 20%, (b) 35%, (c) 50%, (d) 65%, and (e) 80%.

3.4. Verification of Key Printing Parameters

To verify the stability and general applicability of the optimal dough formulation and printing process, four models with varying structural complexity and 3D dimensions—Doraemon, Iron Man, Desert Eagle, and Ghost—were selected for printing validation. This method of verifying parameter feasibility by printing complex geometric shapes is an effective approach for assessing “printability” (Z. Zhang et al. 2024). The printing results, as shown in Figure 14, demonstrate that all four models were successfully printed with intact structures, clear lines, and high fidelity in detail reproduction, showcasing excellent printing precision and shape fidelity (Table 8). After baking post‐processing, the models exhibited minimal dimensional changes and maintained good morphology. These dimensional changes are primarily attributed to structural alterations in the dough caused by moisture evaporation, starch gelatinization, and fat melting during the baking process (Pavicic et al. 2021). In summary, through multi‐model printing validation, the optimal quinoa biscuit formulation and printing parameters determined in this study demonstrate reliable universality and practicality, capable of meeting personalized printing requirements for models of varying complexity.

FIGURE 14.

Top: Quinoa biscuit models printed using the optimal process: (1): Doraemon, (2): Iron Man, (3): Desert Eagle, and (4): Ghost; Bottom: 3D printed quinoa biscuits after baking.

TABLE 8.

3D model specifications, printed model, and post‐baking finished product size of quinoa cookies.

Model		3D modeling specifications (mm)	Print the obtained model (mm)	Finished product after baking (mm)
Doraemon	Length	37.0	37.2 ± 0.3	36.7 ± 0.3
	Width	54.0	54.0 ± 0.4	53.6 ± 0.4
	Height	21.0	21.2 ± 0.2	21.3 ± 0.2
Iron Man	Length	28.0	28.2 ± 0.3	27.8 ± 0.3
	Width	43.0	43.0 ± 0.4	42.7 ± 0.3
	Height	5.0	5.0 ± 0.2	4.7 ± 0.2
Desert Eagle	Length	80.0	80.2 ± 0.3	79.6 ± 0.4
	Width	45.0	45.2 ± 0.2	44.8 ± 0.3
	Height	11.0	10.9 ± 0.1	11.2 ± 0.3
Ghost	Length	46.0	46.0 ± 0.3	45.6 ± 0.2
	Width	41.0	41.0 ± 0.3	40.6 ± 0.3
	Height	6.0	6.1 ± 0.2	5.7 ± 0.3

3.5. Implications for the Food Industry and Economic Considerations

The findings of this study demonstrate the potential of quinoa as a viable gluten‐free ingredient for extrusion‐based 3D food printing, offering new opportunities for developing customized snacks targeting celiac patients and health‐conscious consumers. The optimized formulation and printing parameters established herein provide a technical basis for producing quinoa biscuits with desirable printability and sensory quality. The ability to manipulate printing parameters enables on‐demand shape design, which can be applied in personalized catering, gift confectionery, and creative bakery products, thereby enhancing the added value of conventional cereal‐based items.

From an economic perspective, quinoa is a widely cultivated crop with stable and accessible raw material costs. The optimized infill density (65%) minimizes material waste while maintaining structural integrity, reducing both ingredient consumption and product rejection rates. The selected printing parameters (0.8 mm nozzle, 40 mm/s speed) ensure consistent production efficiency and low equipment maintenance costs. Although 3D printing currently entails higher capital investment than conventional biscuit production, its inherent flexibility for small‐batch and customized manufacturing provides a clear competitive advantage in niche markets. As food‐grade 3D printing equipment becomes more accessible and cost‐effective, the overall production cost is expected to decline progressively, supporting its broader commercial viability.

3.6. Research Limitations

Despite the promising findings, several limitations of this study should be acknowledged. First, the storage stability, moisture migration characteristics, and shelf life of the 3D‐printed quinoa cookies were not evaluated, and these aspects warrant further investigation. Second, only a single variety of quinoa flour was used in this study; quinoa from different geographical origins or cultivars may exhibit variations in dough rheological properties and printing performance, which could affect the generalizability of the current results. Third, the validation experiments were conducted using a benchtop small‐scale 3D printer, and the applicability of the optimized parameters to large‐scale industrial equipment remains to be verified. Future research will focus on shelf‐life extension, raw material screening, and pilot‐scale production trials to facilitate the industrial translation of this technology.

4. Conclusion

This study successfully developed a novel printable gluten‐free food ink using pure quinoa flour as the main raw material and systematically optimized the formulation and printing parameters through single‐factor experiments and response surface methodology. The optimal formulation was determined as 3.04 g sucrose, 3.09 g butter, and 6.84 g milk per 10 g of quinoa flour, with printing parameters set at 0.8 mm nozzle diameter, 40 mm/s printing speed, and 65% infill density. Under these conditions, the 3D‐printed quinoa cookies exhibited excellent shape fidelity and dimensional accuracy across models of varying complexity, with good structural stability and desirable sensory quality after baking.

Beyond these technical specifications, this study demonstrates that quinoa, despite its lack of gluten, can be effectively processed into a printable food ink through formulation optimization, expanding the range of plant‐based materials suitable for additive manufacturing. These findings provide a scientific foundation for the development of personalized, nutritionally enhanced gluten‐free snacks targeting celiac patients and health‐conscious consumers, and contribute to the valorization of quinoa in the growing market for customized foods. Future research should focus on scaling up the process and evaluating long‐term storage stability to facilitate commercial translation.

Author Contributions

Xinyu Du: investigation, writing – original draft, visualization. Zhihong Zhang: conceptualization, writing – review and editing, supervision, funding acquisition, resources, project administration. Lan Zhan: investigation. Linjing Gong: writing – review and editing. Qiaoqiao Shi: data curation, validation. Dongxu Wang: writing – review and editing. Huanan Guan: writing – review and editing. Songtao Fan: writing – review and editing.

Funding

This work was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20251031), the Undergraduate Innovation and Entrepreneurship Training Program of Jiangsu University of Science and Technology (202510289026), Zhenjiang Municipal Social Development Guiding Project of 2024 (Grant No. Zhenke Nong [2024] 127), and the open project program of the State Key Laboratory of Food Science and Resources, Nanchang University (No. SKLF‐KF‐202424).

Ethics Statement

The study was conducted in accordance with appropriate protocols for protecting the rights of human participants. According to applicable regulations in China, formal ethical approval is not required for sensory evaluation studies involving only taste and quality testing of common food ingredients. This approach is also consistent with the US Federal Policy for the Protection of Human Subjects (45 CFR 46.104(d)(6)), which exempts “taste and food quality evaluation and consumer acceptance studies” involving wholesome foods without additives from formal IRB review. Informed consent was obtained from all sensory panelists prior to participation. All panelists were informed of their right to withdraw from the study at any time, and their privacy was fully protected throughout the research.

Conflicts of Interest

The authors declare no conflicts of interest.