Textural characteristics and color analyses of 3D printed gluten-free pizza dough and crust

Abstract

A new methodology was developed to print pizza dough with a gluten free flour blend or commercial gluten whole wheat flour using extrusion-based 3-D printing technology. Their physical properties were compared to commercially available pizza dough and crust. The optimized nozzle size, print speed, ingredient flow speed, and line thickness for the 3-D printing of pizza dough were: 0.04 cm, 800 cm/minutes, 1.8, and 0.34 cm, respectively. The printed gluten-free pizza dough required 120 min of fermentation to obtain a comparable color and textural profile (P < 0.05) to that of the gluten whole wheat flour dough fermented for 60 min. The 3-D printed gluten free, whole-wheat pizza and commercially available wheat flour dough and standard crusts demonstrated identical $\mathrm{\Delta }{E}_{\mathit{ab}}^{\ast }$ values of 0.14 and 0.13, respectively with brownness index (BI) values of 1.47 and 1.62, respectively. Textural profile analysis (TPA) of 3-D printed gluten free and whole wheat pizza dough, crust and the commercial standard wheat flour pizza dough and crust demonstrated significant (P < 0.05) correlations in terms of hardness, fracturability, adhesiveness, springiness, cohesiveness, chewiness, and resilience. An optimized method was developed to prepare gluten-free pizza dough and crust with similar functional properties to that of gluten whole wheat flour dough and crust.

Introduction

Three-dimensional printing of food is an emerging technology that allows printing of complex, diversified food products layer by layer (bottom up), with many benefits such as customizable nutritional value, automation of food manufacturing, and reduced food wastage (Lipton et al. 2015; Pallottino et al. 2016). It has the potential to simplify the supply chain, increase the utilization of existing food materials, and extend shelf-life of food while allowing customizable, personalized food designs and nutrition (Holland et al. 2018). A wide array of healthier 3-D printable food materials can lead to a sustainable, industrially viable way for production of food for the future (Zhao et al. 2020). The prevalence of gluten related disorders such as celiac disease and nonceliac gluten sensitivity are leading to an increasing number of people adopting a gluten-free diet for a variety of signs and symptoms. Remission of symptoms (serologic and histological aspects) can only be achieved via a life-long adherence of gluten-free diet (Caruso et al. 2013; Silva et al. 2020). Pizza is one of the most popular food options throughout the world as evidenced by the forecasted market cap of over 233 billion dollars by 2023 (Bartelme 2016). Healthier, gluten-free, 3-D printed pizza dough can offer a viable alternative for industrial scale food production in the future.

The objective of this study was to develop an optimized method using extrusion-based 3-D printing technology to prepare sustainable and healthy gluten-free pizza dough and crust with comparable functional properties to that of gluten whole wheat flour pizza dough and crust. Textural profile and color analyses of the produced gluten-free pizza crust has been performed to compare with the gluten whole wheat flour and the commercially available standard pizza dough and crust.

Materials and methods

Materials

Gluten free flour blend, brown rice flour, plantain flour, quinoa flour, and monoglyceride emulsifier were purchased from Namaste foods LLC (Namaste foods perfect flour blend, Spokane Valley, USA), Bob’s Red Mill Inc. (Bob’s red mill Natural foods, Inc. Milwaukie, USA), Iya foods LLC (Iya Foods LLC, North Aurora, USA), and Danisco (Copenhagen, Denmark, product# 844,124), respectively. Ingredients purchased from a local grocery store were: wheat flour (Great value self-rising flour, Walmart, USA), sugar (Great value granulated sugar, Walmart, USA), salt (Morton sea salt fine, Walmart, USA), oil (best choice sunflower oil, Walmart, USA), and Yeast (Fleischmann’s active dry yeast, Ach food companies, Memphis, USA).

Preparation of pizza dough

Optimization of the formulation of the pizza dough

Gluten free flour blend (sweet brown rice flour 30.9%, tapioca starch 25%, brown rice flour 19%, sorghum flour 25%, xanthan gum 0.1%), Brown rice flour (100%), Plantain flour (100%), and Quinoa flour (100%) were used independently as well as in a combination (1:1) to simulate the 3-D printed dough using the whole wheat flour, and the standard whole wheat dough purchased from the supermarket. The resulting dough and crust were visually compared to the traditional dough and crust by the color, dough forming ability, texture, and presence of dough like network inside. Equal amounts of individual or a mixture (1:1) of flour were used for the optimization while all other ingredients remained un-changed.

Gluten free flour

In the optimized dough formulation, 150 g gluten free flour was weighed in a mixing bowl followed by the addition of 1.22 g, 2.22 g, 0.72 g, and 5 mL of salt, sugar, monoglyceride and oil, respectively and mixed using a stand mixer (Hamilton Beach Brands, Southern Pines, USA) (Table 1). To this mixture, 200 mL of warm water (37 °C) was added and mixed for 5 min at speed setting 1, followed by 5 additional minutes at speed setting 3 to obtain a smooth dough. To this dough, 1 g active dry yeast was added and mixed thoroughly for 5 additional minutes. (Table 2).

Table 1.

Pizza dough formulation based on the optimization

Serial. No	Whole Wheat Pizza Dough	Gluten Free Pizza Dough
1	Wheat flour (50.0%)	Gluten free flour* (41.6%)
2	Sugar (0.7%)	Sugar (0.6%)
3	Salt (0.4%)	Salt (0.3%)
4	Emulsifier (0.2%)	Emulsifier (0.2%)
5	Oil (1.7%)	Oil (1.4%)
6	Yeast (0.3%)	Yeast (0.3%)
7	Water (46.7%)	Water (55.6%)

Table 2.

Optimization of the formulation for the preparation of 3D printable pizza dough and crust

Ingredient	Dough (appearance)	Crust (appearance)
Brown rice flour	Dough did not resemble traditional pizza. White in color, not dough forming, no hold, spreadable, not network forming, poor water retention	Crust did not resemble traditional pizza. Brownish white in color, cracked, hard, no gluten like network inside
Plantain flour	Dough did not resemble traditional pizza. Yellowish brown in color, not dough forming, no hold, spreadable, not network forming	Crust did not resemble traditional pizza. Dark-white in color, crust breaks apart, crumbles easily, no gluten like network inside
Quinoa flour	Dough did not resemble traditional pizza. White in color, not dough forming, no hold, spreadable, not network forming	Crust did not resemble traditional pizza. Yellow in color, thin hard crust, cracks on the surface
Gluten-free flour blend	Dough resembled closely to the traditional gluten dough.White in color, dough forming, Soft, squishy, network forming	Crust resembled traditional pizza. Golden brown in color, soft, bread like network inside
Gluten-free flour blend + Brown rice flour (1:1)	Dough did not look like traditional pizza. Yellowish-white in color, partially dough forming, limited hold, limited network forming	Crust did not resemble traditional pizza. White in color, soft crust, limited gluten like network
Gluten-free flour blend + Plantain flour (1:1)	Dough did not look like traditional pizza. Yellowish-white in color, partially dough forming, limited hold, spreadable, limited network forming	Crust did not resemble traditional pizza. Dark white in color, soft crust, limited gluten like network inside
Gluten-free flour blend + Quinoa flour (1:1)	Dough did not look like traditional pizza. Yellowish-white in color, partially dough forming, limited hold, limited network forming	Crust did not resemble traditional pizza. White in color, soft crust, limited gluten like network
Brown rice flour + Plantain (1:1)	Dough did not resemble traditional pizza. Not dough forming, no hold, spreadable, not network forming	Crust did not resemble traditional pizza. Dark white in color, crust crumbled, no gluten like network
Brown rice flour + Quinoa flour (1:1)	Dough did not resemble traditional pizza. White in color, not dough forming, no hold, spreadable, not network forming, poor water retention	Crust did not resemble traditional pizza. White in color, hard, cracked easily, no gluten like network
Plantain flour + Quinoa flour (1:1)	Dough did not resemble traditional pizza. Yellowish-brown in color, no dough formation, spreadable, no polymerization, poor water retention	Crust did not resemble traditional pizza. Dark white in color, hard, crumbled easily, no gluten like network

Whole wheat flour

In the optimized formulation, 150 g of gluten whole wheat flour was used and 140 mL of water at 37 °C was added. All other ingredients, their respective amounts, and procedures in the above mentioned gluten-free flour formulation remained unchanged. To the dough, 0.84 g Fleischmann’s active dry yeast was added and mixed thoroughly for 5 additional minutes.

Preparation of the dough for 3-D printing

A circle and a ring (10 cm diameter) from the clip-art section was used to model the pizza dough, the heights and the layers were adjusted thereafter. The factors considered for this setting are listed in Table 3. The printer settings used are listed in Table 4. The pizza was printed on the silicone mat inside the 3-D printer. Nozzle height (the distance between the nozzle and the printing bed) critically determined the quality of the resulting printed product. The following equation is used to estimate the critical nozzle height (hc) (equation 1)

$${\mathbf{h}}_{\mathbf{c}}=({\mathbf{V}}_{\mathbf{d}}/{\mathbf{v}}_{\mathbf{n}}{\mathbf{D}}_{\mathbf{n}})\text{X 1}00$$ 1

where, V_d is the volume of the material extruded rate (cm³/s), V_n the nozzle moving speed (cm/s) and D_n the nozzle size (cm) (Table 4) (Czyżewski et al. 2022).

Table 3.

Optimization of the 3-D printing parameters, their function, and effect on the 3-D printed pizza

3D printing parameters	Function of the 3D printing parameters	Effect of the 3-D printing parameters
Print speed	Speed of X axis (movement of the extruder horizontally), Y-axis (movement of the printing bed), Z-axis (displacement of the extruder vertically)	Coherent printing and gelation (Physico-chemical)
Nozzle size	Pressure and speed of flow product	Self-supporting layers (Textural)
Line thickness	Homogeneity	Color (Sensory)
Distance between layers	Infill layer, extruded layer height	Firmness, hardness, cohesiveness, roughness (Mechanical)
Extruder temperature	Ingredient hold	Viscosity, elastic modulus (Rheological)
Ingredient flow speed	Infill density	Respective ratio and mixing of the ingredients (Compositional)

Table 4.

Setting for 3D food Printer for pizza dough extrusion

Parameter	Condition	Parameter	Condition
Nozzle Size	4 mm	Line Thickness	3.4 mm
Print Speed	8000 mm/min	Ingredient flow speed	1.8
Distance between layers	3.5 mm	Fill factor	1
First Ingredient flow	6.25 mm	First Ingredient hold	4.2 mm
First layer speed	100%	First layer nozzle height	3.4 mm
Jump Height	15 mm	Ingredient hold	3 mm
Resume Ingredient flow	0.01 mm	Min. hold distance	10 mm
Pre-heat Temp	NA	Pre-heat time	NA

Prepared pourable dough was loaded into the standard stainless-steel capsule (volume of each capsule = 100 ml) avoiding any air-bubbles. The capsule was fitted with a 0.4 cm nozzle. The stainless-steel capsule was placed in the bay for printing.

Fermentation of the extruded pizza dough

In the optimized procedure, the extruded pizza doughs using gluten-free flour were fermented for 120 min using a digital timer (VWR International, Radnor, USA) at 37 °C. The extruded pizza doughs using whole wheat flours were fermented for 40 min at 37 °C.

Baking of the pizza dough

The 3-D printed pizza dough was transferred to the baking trays for baking. In the optimized procedure, the extruded pizza doughs were baked for 10 min at 204 °C in the smart countertop convection oven. The baked samples were removed from convection oven (Weber.Inc, San Francisco, USA) and cooled for 40 min on wire racks at ambient temperature. The length, width, and height of the printed pizzas were measured using a Vernier caliper (VWR International, Radnor, USA), to the nearest 0.003 cm, in triplicate.

Color measurement of pizza crust

A Minolta CR-300 Chroma meter (Minolta Co., Ltd, Japan) was used to analyze the color parameters: lightness/darkness (L*), greenness/redness (a*) and blueness/yellowness (b*) of the pizza. The color parameters consist of a luminance or lightness component (L*) and two chromatic components: the (a) component (from green to red) and the (b) component (from blue to yellow). The photoshop lab mode (the asterisks are dropped from the name) has a lightness component L* that can range from 0 to 100. In the color picker the a* component (green–red axis) and the b* component (blue–yellow axis) can range from + 128 to  − 128. In the color palette the a* component and the b* component can range from + 120 to − 120.

Variables of L*, a*, b* or E* are represented as ΔL, Δa, Δb or ΔE_ab, where: (Equations 2,3,4)

$$\mathrm{\Delta }{E}_{\mathit{ab}}^{\ast }={\left[{\left(\mathrm{\Delta }{L}^{\ast }\right)}^2+{\left(\mathrm{\Delta }{a}^{\ast }\right)}^2+{\left(\mathrm{\Delta }{b}^{\ast }\right)}^2\right]}^{1/2}$$ 2

This represents the magnitude of the difference in color but does not indicate the direction of the color difference. Therefore, the brownness index (BI) was calculated according to the following equations (Pathak et al. 2017):

$$BI=100\times \left(x-0.31\right)/0.17$$ 3

$$x=\frac{\left(a^{\ast },+,1,·,75,\times ,L^{\ast }\right)}{\left(5,·,645,L^{\ast },+,a^{\ast },-,3.012,b^{\ast }\right)}$$ 4

Texture profile analysis (TPA)

Texture profile analyses were carried out using a Texture Analyzer (Model TA-XT2i, Stable Micro Systems Ltd, Godalming, U.K.), equipped with a 50,000 g load cell and a round 7.5 cm diameter compression platen probe). Each sample was placed under the probe and compressed to 50% of the original height at a constant speed of 6 cm/minute. After the initial compression, the probe withdrew for 0.08 min, followed by a second compression of 50% of the original height. The computer software Texture exponent 32 (Stable Micro Systems Ltd, Godalming, U.K.) was used both for the experimental phase and to compute textural parameters from the TPA curve (Jiang et al. 2019; Maetens et al. 2017).

Statistical analysis

All tests were performed in triplicate. The data were compared using analysis of variance (ANOVA) at different stages between pizza dough and crust. When significant differences were found (P < 0.05), post hoc comparisons using Tukey’s Honestly significant difference (HSD) test were performed to determine the differences among wheat flour pizza dough and gluten free flour extruded pizza dough means. The JMP 15 software (SAS Institute Inc., Cary, NC, USA) was used to perform the statistical analyses.

Results and discussion

Optimization of the ingredients, printing parameters for the extrusion-based 3-D printing, and fermentation and baking of pizza dough prepared from wheat and gluten free flour are reported. Color, brownness index, and textural profile analysis of the resulting baked gluten free pizza crust were compared with a wheat flour and a commercially available dough and pizza crust, used as a control (Fig. 1).

Fig. 1.

The 3D printed gluten free pizza dough and crust. a Printing of gluten-free pizza dough. b Gluten-free dough after fermentation. c Gluten flour dough after fermentation. d A slice of baked gluten crust. e A slice of baked gluten-free crust

Formulation of the pizza dough

The optimized formulation of the 3-D printed pizza dough using whole wheat and gluten-free flour are listed in Table 1. The flour-blends: 1. Gluten free flour blend (gluten-free flour mix), 2. Brown rice flour, 3. Plantain flour, and 4. Quinoa flour were used to optimize the gluten-free flour alternative for 3-D printing pizza dough Table 2. The flours were used independently as well as in a combination (1:1) to simulate the 3-D printed dough using the whole wheat flour, and the standard whole wheat dough purchased from the supermarket. The resulting dough and crust were visually compared to the traditional dough and crust by the color, dough forming ability, texture, and presence of dough like network inside. Equal amounts of individual or a mixture (1:1) of flour were used for the optimization while all other ingredients remained un-changed Table 2.

The formulation using pure brown rice, plantain, and quinoa flour produced a thicker than usual, sticky, and spreadable dough with poor water retention, which was not suitable for 3-D printing. The resulting baked crusts were brownish to yellow in color, hard, cracked or crumbled easily, and lacked the texture and gluten like network, making the formulations unsuitable for preparation of pizza crust. Gluten free flour blend by itself produced a dough and crust which had the color and texture closest to the gluten flour pizza. Pizza dough produced from the combination flour blends (1:1) between brown rice flour, plantain flour and quinoa flours produced sticky and highly viscous mixtures with limited hold and gluten like network, making them unsuitable for preparing 3-D printable pizza dough. The resulting crusts were dark white to brown in color, hard, and crumbled easily, making the abovementioned mixtures unsuitable for the preparation of pizza dough and crust. The combination formulations (1:1) using the gluten free flour and either of the following (1:1): brown rice flour, plantain flour, and quinoa flour resulted in a yellowish to white colored dough with limited hold, and gluten like network. The resulting crusts were dark white to golden brown in color, soft, and presented with limited gluten like network Table 2.

Gluten free flour blend was chosen as the ingredient for 3-D printing of gluten free pizza dough and crust due to its maximum resemblance to the 3-D printed gluten pizza produced in the laboratory.

Amounts of water, yeast, salt, and emulsifier were optimized to mimic the gluten-like functional properties in gluten-free pizza dough and crust (Padalino et al. 2011). The ingredients in the gluten free flour blend were: sweet brown rice flour, tapioca starch, brown rice flour, arrowroot powder, sorghum flour, and xanthan gum. Homogeneous viscoelasticity of dough is an important rheological parameter to determine before 3-D printing. Using 1% of food additives such as lecithin and monoglyceride helps to retain the rheological properties of printable pizza dough. To maintain the homogeneous viscoelasticity of pizza dough, 1% monoglyceride was used as a food additive emulsifier (Table 1). The gluten-starch interactions dictate the formulation of cereal-based products such as pizza. The total starch content in the tapioca starch, arrowroot powder, and xanthan gum helped this flour-blend to simulate the properties of gluten, found in traditional whole wheat flour, to produce a soft crust with gluten-like network and structure.

Components in the gluten wheat flour formulation were homogeneously blended and hydrated to form the hydrogen bonding and disulfide cross-linking resulting from the thorough kneading (Yano 2019). Fermentation, followed by the trapping of the resulting CO₂ gas in the heat-induced bio-polymerized gluten-rich dough-matrix, made it indispensable for the mechano-chemical and viscoelastic properties of the pizza dough and crust (Jekle et al. 2016).

In the optimized formulation, the gluten flour dough was prepared using gluten flour, sugar, salt, emulsifier, oil, yeast, and water; 50.0%, 0.7%, 0.4%, 0.2%, 1.7%, 0.3%, and 46.7%, respectively (Fig. 1, Table 1). In the optimized formulation, the gluten-free dough was prepared using gluten-free flour, sugar, salt, emulsifier, oil yeast, and water; 41.6%, 0.6%, 0.3%, 0.2%, 1.4%, 0.3%, 55.6%, respectively (Fig. 1, Table 1). Gluten-free flour required a larger (55.6%) amount of water compared to the whole wheat flour (46.7%) to achieve comparable end products. Starch molecules are known to accelerate the absorption of water into the matrix (Padalino et al. 2016). The xanthan gum is known to increase the yield and softness of a bread. Other ingredients frequently used in gluten-free formulations included: flour made of corn starch, rice, potato (or other tubers), additional proteins, gums, and emulsifiers (Padalino et al. 2016). Xanthan gum has been previously reported as a hydrocolloid to simulate the sticky nature of gluten in a similar fashion (Salehi 2019). Arrowroot powder has also been reported as a source of starch in gluten-free formulations (Horstmann et al. 2017). Flaxseed powder and gum containing neutral and acidic hetero polysaccharides was also reported in dough preparation to mimic properties of gluten (Sapozhnikov et al. 2021).

Optimization of 3-D printing parameters

A commercially available 3-D printer was used to investigate the effect of the 3-D printing parameters and their effect on the physio-chemical, textural, mechanical, rheological, compositional, and sensory attributes of the resulting pizza dough and crust. The results of the optimization process were used to achieve the final printing conditions. The outcomes of the optimization process are listed in Table 3, and the optimized 3-D printing parameters are listed in Table 4. Speed of the 3-D printing capsule in the X, Y, and Z axis, nozzle size, distance between the layers, extruder temperature, ingredient flow speed, and line thickness were optimized to achieve the optimized gluten-free pizza dough (Table 3).

The quality of pizza samples was dependent on physio-chemical characteristics such as chewiness, cohesiveness, ability to be swallowed, and efficient maintenance of the hardness. The physio-chemical attributes in the 3-D printed pizza were significantly affected by the printing speed of the 3-D printing capsule in the X axis (movement of the extruder horizontally), the Y-axis (movement of the printing bed), and the Z-axis (displacement of the extruder vertically). They were optimized to achieve a coherent printing and gelation in the final 3-D printed pizza crust (Table 3). Efficient, self-supporting layers are an essential structural attribute for a 3-D printed product to maintain its denseness, and structure. Line thickness and nozzle size were optimized to control the product flow, providing the optimum interlayer adhesion to maintain the textural and sensory attributes such as firmness, color, and flavor of the resulting pizza. Lower nozzle size resulted in a higher degree of refinement of the printed sample. However, it increased the printing time, caused discontinuous deposition, and instigated rising of the feed pressure in the machine. Rising feed pressure could cause overloaded and machine wear, which will be counterproductive to the efficient printing at an industrial scale. Mechanical properties such as firmness, hardness, and cohesiveness of the 3-D printed pizza were essential for its edibility and desirability. The appearance of the 3-D printed pizza dough also turned rougher as the distance between layers was increased. 3-D printed pizza with desired mechanical properties was obtained by optimization of the distance between the layer parameters in the 3-D printer. It affected the infill layer and the extruded layer height in the 3-D printed pizza dough to optimize the firmness, hardness, and cohesiveness of the pizza. Extrusion speed and the print speed had a severe effect on the efficiency of the printing, with the nozzle size and the distance between the layers unchanged. High extrusion speed (10 on the scale of 0.1–50) with a low print speed (80 cm/minutes) resulted in an inaccurate extrusion with too much dough extruded per unit time. On the other hand, a slow extrusion speed (1 on the scale of 0.1–50) and a faster printing speed (800 cm/minutes) resulted in an incomplete pizza dough. Hence, the nozzle size, extrusion speed and the print speed were optimized together to obtain the pizza with desirable textural properties. The rheological properties such as viscosity and the elastic modulus of the pizza were optimized by changing the temperature of the extruding capsule. It was found that relatively higher temperature of the 3-D printing capsule (45 °C) decreased the yield-stress and the consistency of the dough. However, the structural integrity of the resulting 3-D printed pizza dough was optimum at room temperature. Ingredient flow speed dictated the infill density of the dough, which determined the respective ratio and mixing of the ingredients (Table 3). Increase in the distance between the layers and ingredient flow speed at the same time resulted in longer layer depositions, which were unsuitable to obtain high quality pizza dough and crust. The 3-D printing parameters were coordinated to achieve optimized printing condition of pizza dough.

In the optimized procedure, the 3-D printing parameters were as follows: nozzle size, print speed, distance between the layers, first ingredient flow, first layer speed, jump height, resume ingredient flow, line thickness, ingredient flow speed, fill factor, ingredient hold, nozzle height, ingredient hold, min, and hold distance such as 0.4 cm, 800 cm/minutes, 0.35 cm, 0.625 cm, 100%, 1.5 cm, 0.001 cm, 0.34 cm, 1.8, 1, 0.42 cm, 0.34 cm, 0.3 cm, and 1.0 cm, respectively (Table 4).

Had previously reported that optimization of extrusion speed, nozzle movement speed, nozzle size, and distance between the layers were beneficial for 3-D printing of chocolate (Feng et al. 2019). Yang et.al. reported that nozzle size, print speed, ingredient flow speed, and printing temperature must be coordinated to attain an accurate 3-D printed food (Yang et al. 2018). Wang et.al. reported that a decrease in the nozzle size increased the printing time while also increasing the refinement, quality and the feed pressure (Wang et al. 2018). Attalla et. al. reported that distance between the layers was important to obtain accurate 3-D printing of food (Attalla et al. 2016). Lanaro et. al. studied 3-D printing of chocolate and reported that the extruder temperature affected the rheological properties of the printing ink, thereby affecting the properties of the product (Lanaro et al. 2017). Derossi et. al. reported that high flow levels (130%) improved the structural uniformity and integrity of the samples, whereas low flow levels (extrusion speed 70 and 100%) displayed irregular shapes with interrupted material lines and undesired oversized pores due to the insufficient amount of deposition during printing to cover the whole path. They have also studied the effect of print speed and ingredient flow speed on the quality of printed materials and correlated them with a linear relationship to ensure accurate final shapes with less pore size formations (Derossi et al. 2018). Yang et. al. reported that nozzle size and ingredient flow speed are very closely related. Choosing the optimum nozzle size is dependent on the type of the material printed and the compositional and textural properties of the extruded material are dependent on the ingredient flow speed (Yang et al. 2018).

Optimization of the fermentation time of the pizza dough

The aim of this project was to use gluten-free flour and produce 3-D printed pizza dough with indistinguishable physio-chemical properties (color and texture) to that of a 3-D printed dough and crust using gluten flour. The fermentation time of the 3-D printed gluten and gluten-free dough dictated the former properties (Table 5). Fermentation is a traditional food processing practice, which transforms complex ingredients into consumable food. This process is usually aided by natural flora or microbial strains suitable for the biotransformation (Kewuyemi et al. 2021). Color and textural attributes were measured in triplicate to determine the optimal fermentation time for pizza dough using whole wheat and gluten free flour in a systematic analysis with 30, 60, 90, 120, 150, and 180 min of fermentation time. The fermentation process had initially been attempted on the pizza dough before the extrusion, however it made the printing process difficult. Hence, yeast was mixed during the dough preparation and printed quickly using the 3-D printer and then the fermentation process was executed.

Table 5.

$\mathbf{\Delta }{\mathit{E}}_{\mathit{a}\mathit{b}}^{\mathbf{\ast }}$ and brownness index (BI) calculated based on color measurement data generated from CR 300 chromo meter

Fermentation time	$\mathrm{\Delta }{E}_{\mathit{ab}}^{\ast }$	BI
	Wheat flour	Gluten free flour	P-value*	Wheat flour	Gluten free flour	P-value*
30 min	0.13 ± 0.00	0.13 ± 0.00	0.68	1.47 ± 0.13	1.34 ± 0.07	0.21
60 min	0.13 ± 0.00	0.13 ± 0.01	0.66	4.19 ± 0.47	1.85 ± 0.29	0.002
90 min	0.13 ± 0.00	0.14 ± 0.01	0.1	1.15 ± 0.08	1.30 ± 0.13	0.18
120 min	0.14 ± 0.00	0.14 ± 0.01	0.66	2.24 ± 0.23	1.62 ± 0.15	0.012

Data represented as mean ± standard deviation from three independent (n = 3) experiments

^*P-value > 0.05 represents no significant changes between the means

The browning index of the baked pizza crust demonstrated the most significant changes (P < 0.05) at the 60, and 120-min fermentation times (Table 5). The gluten-free dough required a longer fermentation time, due to the absence of gluten like network to trap the CO₂ bubbles. In the optimized procedure, the whole wheat crust required 60 min, while the gluten-free crust required 120 min of fermentation for a comparable color and texture profile analysis (Table 5). Gluten leads to a visco-elastic dough consisting of a continuous aqueous and gluten phase. After feeding on the supplemental sugar in the dough using the available oxygen, yeast shifts its metabolism to anaerobic fermentation, the end product of which is mainly composed of carbon dioxide (Liszkowska and Berlowska 2021). Formation of a continuous phase and texture in a dough in the absence of gluten (gluten-free flour) makes the process lengthy and difficult (Paulik et al. 2021).

Optimization of the baking time and temperature of pizza dough

While the starch molecules were gelatinized by heat, the inflation process in the gas cells resulted in an expansion, resulting in hardening of the enveloping gluten matrix and thus constructing the stable crumb framework (Paulik et al. 2021). During the optimization process, whole wheat gluten and gluten-free pizza doughs were baked at a combination of four different temperature settings (148.9 °C, 176.7 °C, 190.6 °C and 218.3 °C), and time periods (8, 10, 12 and 14 min) to make this baking process efficient. In the optimized procedure, extruded pizza dough was baked for 10 min at 204.4 °C in the smart oven. In the optimized procedure, the extruded pizza crust using gluten-free flour did not have any significant (P > 0.05) difference in color and textural profile between the 3-D printed gluten crust prepared in the laboratory and the pre-made pizza crust purchased from a grocery store (Product: MM Crust 7In, MIA 3PK 7, MM ANCNT 3Pk, Flatzza Buddy, Walmart, USA) (Tables 5, 6, 7). The Maillard reaction between the sugars and amino acids caused the browning and the hardening of the surface of the bread dough (Helou et al. 2016).

Table 6.

Lightness/darkness (L*), greenness/redness (a*) and blueness/yellowness (b*), and magnitude of the difference in color ΔE*_ab using wheat flour standard (WFS), gluten free flour standard (GFS), wheat flour extruded (WFE), gluten flour extruded (GFE), at the dough and baked states

Samples	L*	a*	b*	$\mathrm{\Delta }{E}_{\mathit{ab}}^{\ast }$
Color measurement of pizza dough
WFS Dough	42.6 ± 3.81bc	0.37 ± 0.00abc	0.36 ± 0.00ab	0.13 ± 0.002
GFS Dough	53.0 ± 3.26a	0.36 ± 0.01c	0.36 ± 0.00ab	0.13 ± 0.0003
WFE Dough	45.6 ± 2.02 ab	0.36 ± 0.00bc	0.36 ± 0.00ab	0.13 ± 0.003
GFE Dough	47.6 ± 3.80 ab	0.36 ± 0.01bc	0.36 ± 0.01ab	0.14 ± 0.003
Color measurement of baked pizza crust
WFS Baked	15.0 ± 1.87e	0.39 ± 0.01ab	0.35 ± 0.01b	0.13 ± 0.002
GFS baked	29.4 ± 3.25d	0.40 ± 0.00a	0.37 ± 0.00a	0.13 ± 0.006
WFE Baked	34.5 ± 4.15 cd	0.38 ± 0.02abc	0.37 ± 0.01a	0.14 ± 0.008
GFE baked	39.3 ± 2.71bc	0.38 ± 0.01abc	0.37 ± 0.01a	0.14 ± 0.005

^a−e Mean with different superscripts in the same column are significantly different (p < 0.05)

Data represented as mean ± standard deviation from three independent (n = 3) experiments

Table 7.

Textural Profile analysis values of extruded pizza crust using wheat flour standard (WFS), gluten free flour standard (GFS), wheat flour extruded (WFE), gluten flour extruded (GFE), at the dough and baked states

Samples	Hardness	Fracturability	Adhesiveness	Springiness	cohesiveness	Chewiness	Resilience
Texture profile analysis of pizza dough
WFS Dough	4840.9 ± 48.4a	18.9 ± 0.23 a	− 1.09 ± 0.06 a	0.96 ± 0.02 a	0.88 ± 0.02 a	4071.26 ± 133.52 a	0.54 ± 0.02 a
GFS Dough	4474.82 ± 14.48 a	25.31 ± 0.16 a	− 0.67 ± 0.03 a	0.96 ± 0.03 a	0.91 ± 0.01 a	3922.85 ± 91.53 a	0.46 ± 0.01 a
WFE Dough	1660.60 ± 38.77 a	17.23 ± 0.20 a	− 535.85 ± 44.74 a	0.98 ± 0.00 a	0.88 ± 0.02 a	1430.68 ± 35.89 a	0.06 ± 0.00 a
GFE Dough	1141.32 ± 47.34 a	16.74 ± 0.20 a	− 283.40 ± 3.39 a	0.94 ± 0.02 a	0.86 ± 0.01 a	924.49 ± 40.42 a	0.37 ± 0.02 a
Texture profile analysis of baked pizza crust
WFS Baked	7669.43 ± 16.08 a	27.76 ± 0.24 a	0.000 ± 0.00 a	0.96 ± 0.01 a	0.84 ± 0.02 a	6136.45 ± 149.02 a	0.41 ± 0.01 a
GFS baked	5632.68 ± 28.88 a	26.20 ± 0.18 a	− 0.004 ± 0.01 a	0.97 ± 0.01 a	0.73 ± 0.00 a	3986.49 ± 60.01 a	0.34 ± 0.02 a
WFE Baked	5339.49 ± 28.26 a	28.47 ± 0.75 a	0.000 ± 0.00 a	0.97 ± 0.01 a	0.90 ± 0.01 a	4666.68 ± 77.82 a	0.569 ± 0.01 a
GFE baked	4546.21 ± 19.57 a	22.51 ± 0.07 a	− 0.001 ± 0.00 a	0.99 ± 0.00 a	0.93 ± 0.01 a	4167.14 ± 51.99 a	0.544 ± 0.01 a

^a Mean with different superscripts in the same column is not significantly different (p > 0.05)

Data represented as mean ± standard deviation from three independent (n = 3) experiments

Measurement of color attributes of pizza dough

The lightness/darkness (L*), greenness/redness (a*) and blueness/yellowness (b*), magnitude of the difference in color ΔE*ab, and brownness index (BI) were measured for pizza dough and baked crusts using wheat flour standard (WFS), gluten-free flour standard (GFS), wheat flour extruded (WFE), gluten-free flour extruded (GFE) using a chromometer (Tables 5, 6). Color measurement was used to optimize the method to prepare 3-D printed pizza dough using gluten free flour. There is insufficient prior literature for characterization using 3-D printed gluten free pizza dough and crust. Therefore, gluten 3-D printed and commercially available pizza dough and crust were used as internal standards for comparison. The L*, a* and b* values for pizza dough and crusts ranged from 15.0 to 53.0, 0.36–0.40, 0.35–0.37, respectively. Most importantly the difference in color coordinates (ΔE*_ab) between the WFS, GFS, WFE, and GFE dough and crusts ranged from 0.13 to 0.14. The brownness index values for the 3-D printed gluten and gluten-free flours were comparable at 30 min (1.47, wheat flour baked pizza crust, Table 5) and 120 min (1.62, gluten-free flour baked pizza crust, Table 5), respectively. Lack of gluten could be the reason behind the longer baking time of gluten-free pizza dough to reach similar brownness index of whole wheat dough.

Textural properties of pizza dough

Preparation of 3-D printed gluten free dough was optimized based on the textural properties. Chewiness, crispness, cohesiveness, denseness, dryness, deformation ability, firmness and ability to pull apart of the printed crust were directly related to the swallowing, surface rupture of the crust, mass holding capability of the sample, degree of compactness, saliva to be absorbed, easiness of changing the form, hardness, and the force needed for separation, respectively. The textural profile analysis of the extruded pizza crust using wheat flour standard (WFS), gluten free flour standard (GFS), wheat flour extruded (WFE), gluten flour extruded (GFE), were performed at the dough and baked states. Textural profile analysis (TPA) was performed based on parameters such as hardness, fracturability, adhesiveness, springiness, cohesiveness, chewiness, and resilience (Herrada-Manchón et al. 2020) (Table 7). The hardness mean value in TPA analysis, which is defined as the force necessary to deform the food between the molar teeth, ranged from 4546.21 to 7669.43 (Table 7). The chewiness value in TPA, which is a measure of the length of time required to masticate a solid food until it ready for swallowing, ranged from 3986.48 to 6136.45. Adhesiveness, which is defined as the work required to overcome the attractive forces between the surface of the food and the surface of the other materials with which the food comes into contact, was not as important for pizza crusts. The fracturabilty value in a TPA is defined as the force necessary to crack or shatter the crust, springiness defined as the rate at which the deformed food goes back to its undeformed state once the deforming force has been removed, cohesiveness is defined as the strength of the internal bonds making up the body of the food. The resilience did not show a statistically significant difference between the wheat flour and gluten free pizza crust, after optimization. Results indicated that the TPA parameters of the wheat flour pizza crust and gluten free pizza crust did not show significant (P > 0.05) difference in standard and extruded pizza dough after optimization. However, GFE dough demonstrated a comparatively lower adhesiveness (GFS dough 0.670 ± 0.03, WFS dough 1.087 ± 0.06), lower chewiness and higher resilience value than the corresponding WFE dough (Table 7). Overall, the differences were insignificant statistically, and the absence of gluten can be attributed to these marginally lower values. Addition of xanthan gum and arrowroot powder has been reported to be responsible for the firmness of dough in gluten free recipes (Encina-Zelada et al. 2018). Modifying gluten-free recipes with xanthan gum and arrowroot powder can lead to the higher resilience, despite the marginally lower adhesiveness and chewiness values.

An optimized textural profile is very important to the experience of a food product (Jiang et al. 2019; Maetens et al. 2017). Components in the gluten wheat flour formulation are homogeneously blended and hydrated to form the hydrogen bonding and disulfide cross-linking resulting from the thorough kneading (Yano 2019). To our knowledge, there is insufficient prior literature for textural characterization with 3-D printed gluten-free pizza crusts. It has been previously reported that different additives (Starch and hydrocolloids) can affect the textural profile of a pizza dough and crust (Sagar and Pareek 2020).

In this research, gluten free pizza crust was successfully 3-D printed with no significant (P > 0.05) differences in color and textural properties in comparison to the equivalent wheat flour pizza crust. This optimized methodology could be followed to meet the increasing demand for healthier targeted and personalized food and offer a sustainable food supply with expanded spectrum of sources and a reduced amount of waste.

Conclusion

Gluten-free pizza dough was 3-D printed using innovative extrusion-based technology. Four types of flours were used to obtain the most optimized, comparable gluten-free pizza crust (color measurement and textural profile analysis) to the equivalent whole wheat analogue prepared in the lab and available in the market. Results in this experiment revealed that the production of high-quality pizza crust depended on the extrusion and print speed. The printing speed and baking parameters were determined experimentally to achieve the final 3-D printed product. The fermentation time demonstrated a significant influence on the textural attributes of the pizza. The optimization experiments revealed that gluten-free dough required 120 min of fermentation compared to 60 min for wheat flour dough to attain comparable textural quality. Both gluten free and wheat flour pizza were baked at 218.3 °C for 10 min in an oven. The 3-D printed pizzas using healthier gluten-free flour could potentially replace the wheat flour pizza with a comparable texture and color characteristic at an industrial scale, in future.

Instrumental techniques

TPA

Texture profile analysis

BI

Brownness index

Samples used

WFS

Wheat flour standard

GFS

Gluten-free flour standard

WFE

Wheat flour extruded

GFE

Gluten-free flour extruded

Author contributions

Sriloy Dey: writing original draft (Lead), Writing-review and editing (equal), Chandan Maurya: Investigation (Lead), Navam Hettiarachchy: (Corresponding author) conceptualization (Lead), Finding acquisition (Lead), Project administration (Lead), Supervision (Lead), Writing-review and editing (Supporting), Han-Seok Seo: Conceptualization (Supporting), Writing-review and editing (Supporting), Wenchao Zhou: Conceptualization (Supporting), Writing-review and editing (Supporting).

Funding

Funds provided by the office of research and innovation, University of Arkansas (0392 03971–22-2020) is appreciated.

Availability of data and material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Declarations

Conflict of interest

The authors declare that they have no interest or benefit arising from the direct applications of the research.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.