# From printability to palatability: A sensory and hedonic study of 3D-printed cereal-and legume-based products

Xincheng Liu, David Blumenthal, Valérie Guénard-Lampron

► To cite this version:

Xincheng Liu, David Blumenthal, Valérie Guénard-Lampron. From printability to palatability: A sensory and hedonic study of 3D-printed cereal-and legume-based products. Innovative Food Science & Emerging Technologies / Innovative Food Science and Emerging Technologies, 2025, 104, pp.104078. 10.1016/j.ifset.2025.104078. hal-05158713

HAL Id: hal-05158713

https://hal.science/hal-05158713v1

Submitted on 11 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# From printability to palatability: A sensory and hedonic study of 3D-printed cereal- and legume-based products

Xincheng Liu<sup>ab</sup>, David Blumenthal<sup>a</sup>, Valérie Guénard-Lampron<sup>a</sup>

<sup>a</sup>Université Paris-Saclay, INRAE, AgroParisTech, UMR Sayfood, 91120, Palaiseau, France

<sup>b</sup>Present address: Circulegg, 21 Rue Charles Lindbergh, 35150, Janzé

*Corresponding author: valerie.guenardlampron@agroparistech.fr

## Highlights

* Sensory perceptions and liking were mainly affected by the type of flour used.

* Soy-based products were harder, grainier and with a stronger cereal aroma.

* Rye and wheat-based products were sweeter, moister, and more deformable.

* Wheat-based 3D printed products were the most preferred by consumers.

* Rye and soy flour showed great potential for mixed formulations with wheat flour.

## Abstract

This study focused on generating formulas with high 3D-printability, nutritious values and appreciated by consumers. Earlier studies developed a thermomechanical treatment to make wheat flour printable, which was then optimized for rye and soy flours to vary the nutritional composition of the printed products, in particular their protein and fiber content. However, few studies have looked at consumers' sensory perceptions and preferences towards printed products. In this study, recipes based on wheat, rye or soy flour were taste-enhanced by the addition of cinnamon and apple puree. A sensory and hedonic characterization approach was then implemented to better understand the sensory criteria associated with each of these flours. By evaluating 7 products with two descriptive methods, we found that rye-based products are less compact and with cinnamon smell moderately pronounced. Soy-based products have less deformation, they are more compact and grainier with less sweetness. Wheat-based products are less hard, more humid, moderately compact and have a stronger cinnamon smell. Hedonic evaluation showed that soy products have the best visual appreciation but the worst textural appreciation. By using PREFMAP method combining sensory description by panelists and hedonic evaluation, we noticed that wheat flour products are consumers favorite (80-95%), followed by rye (35-50%) and soy (10-20%). There is little difference in appreciation of products with or without apple puree (< 10%). This approach provides sensory and hedonic information that will be crucial in establishing a reasoned approach to flour blend formulation in a future study.

Keywords :

3D food printing, cereal, legume, sensory analysis, consumer appreciation
36
37

# 1. Introduction

3D food printing is an emerging technology that offers new possibilities for personalized nutrition, culinary innovation, and sustainable food production. By allowing the precise layering of ingredients, this technology has the potential to be applied in various sectors, from customized meal plans in healthcare to novel food experiences in high-end restaurants. To date, some of the most commonly printed foods include chocolate (Mantihal et al., 2019; Rando & Ramaioli, 2021), gels system (Chen et al., 2021; Sartori et al., 2023), pureed foods (Armstrong et al., 2022; Hassan et al., 2024; Pant et al., 2021) and various starch-based structures such as wheat, rice, potatoes, etc. (Tang et al., 2024). One of the main constraints in food 3D printing is the formulation of printable materials. These materials must exhibit specific rheological properties to ensure smooth extrusion, structural stability, and layer-by-layer adhesion (Godoi et al., 2016; Nijdam et al., 2021). Achieving this requires the right balance between solid and liquid components, which can often conflict with nutritional goals. For example, adding fibers may enhance the healthiness of a product, but this modification can have a negative impact on printability and alter sensory properties such as texture (Keerthana et al., 2020; Krishnaraj et al., 2019; Lille et al., 2018). Thus, designing recipes that are both printable and nutritious while appealing to consumers is a complex challenge.

The development of 3D-printed foods from cereals such as wheat is being widely studied, as it enables the generation of different products with a wide range of textures and can be adapted for both sweet and savory foods. These include cookies, snacks, bread, pizza which are popular due to their simple formulation and wide consumer acceptance (Lipton et al., 2015; Pulatsu et al., 2020; Severini et al., 2018). For example, 3D-printed pizza dough has been explored for applications in space travel, where fresh, customizable food is essential for long missions (Santhoshkumar et al., 2024). Similarly, bread and crackers have been printed for use in healthcare settings, particularly for patients with dysphagia who require modified textures (Hemsley et al., 2019; Mirazimi et al., 2022; Sun et al., 2015). The most of these previous studies are focused on wheat-based recipes and a few of other cereal-based recipes or recipes rich in starch. These printed products offer a reliable texture and structural integrity, making them ideal candidates for 3D printing. For wheat flour, a thermomechanical process during dough treatment in 3D printing allows the starch granules to swell and gluten protein to form a continuous closely-packed network embedding swollen starch granules. This structure gives a good viscoelastic property and stickiness to the dough, which are important for the extrudability, adhesion inter-layers and the maintenance of the form in 3D printed products (Masbernat et al., 2021). Whatever the matrix based on, starch and gluten are important components thanks to their water holding capacity. When the gluten is partially replaced by other hydrophobic proteins such as mealworm powder, the water holding capacity of the dough could be influenced, reducing the viscosity of the dough (Pérez et al., 2019). Similar phenomenon can also appear on recipes with cereal without gluten. For example, for rice flour having other proteins than gluten and
for tapioca flour containing nearly no proteins, the texture of printed products is more fragile and fluid. But this problem can be corrected by adding hydrocolloids or adjusting water, sugar and lipid content in the dough as rice and tapioca flour are at least both rich in starch (Pulatsu et al., 2020). These mechanisms make starch-rich cereals frequently used in 3D printing studies. However, despite the versatility of starch-rich cereal-based recipes, challenges remain in ensuring the nutritional quality of these products without compromising their printability, texture, sensory appeal and consumer acceptance.

Some studies are also looking at the printing of cereal- or legume-based foods with little or no starch, and offering different nutritional benefits (Auyeskhan et al., 2023; Keerthana et al., 2020; Krishnaraj et al., 2019; Lille et al., 2020; Pulatsu et al., 2020). For example, in our previous study (Guénard-Lampron et al., 2023), 25 flours of cereal, pseudo cereal, legume, nut and root categories were screened and compared by HAC and PCA methods. After a first selection with starch, protein, fiber, calcium, ash and several vitamins' contents, 5 flours (rye, chestnut, soy, lupine, chickpea) were selected. A second selection with textural and sensorial observation allowed the final choice of soy and rye flour thanks to their relatively good sensorial perception compared to the other 3 flours. Then with an experimental design based on 10 trials, optimized formulation and thermomechanical treatment parameters were calculated and validated, proving that these two nutritious flours allow a good printing quality. Despite these advancements, there is still limited research on how 3D-printed foods are perceived by consumers, particularly in terms of sensory attributes such as taste, texture, and visual appeal. Most studies to date have focused on the technical aspects of printability and nutrition, placing less emphasis on the importance of consumer experience (Čukelj Mustač et al., 2023). In several recent studies, different ingredients were tested in 3D printing dough formulation, such as wheat and amaranth bran, but the sensorial perception of these formulations were limited in the publications, and the intensity of each descriptor were not always associated to hedonic evaluation and consumer acceptability or vice versa, or without comparison with a control recipe (Habuš et al., 2022; Keerthana et al., 2020; Krishnaraj et al., 2019; Lille et al., 2020). Yet, for 3D-printed foods to gain widespread acceptance, it is crucial to design matrices that not only meet health and technical requirements but also satisfy consumer preferences. In the case of soy flour and rye flour which were validated for their printability (Guénard-Lampron et al., 2023), soy flour is known to be rich in proteins and essential amino acids, offers significant nutritional benefits, particularly for vegetarian or vegan diets. It is also a valuable source of vitamins and minerals such as iron and calcium. However, soy-based products often face sensory barriers such as beany flavors or gritty textures, which can deter consumer acceptance, especially in European countries where soy-based foods are less embodied in the culture than in Asian area (Mirazimi et al., 2023; Shin et al., 2013; Tu, 2010). Rye flour, known for its high fiber content and beneficial bioactive compounds like lignans, poses similar challenges. These sensory drawbacks must be addressed to increase consumer appreciation of such nutritious ingredients.
Thus, this study aims to fill this gap by investigating the sensory properties of three different flours (wheat, soy, and rye) used in 3D food printing. Each flour has unique nutritional profiles and sensory characteristics that may influence their performance in 3D printing and their acceptance by consumers. By exploring the relationship between these flours, their printability, and sensory attributes, this research seeks to develop new strategies for creating printable foods that align with consumer expectations for taste, texture, and visual appeal.

## 2. Material and Methods

### 2.1. Ingredients and preparation of the printable dough

The three flours used in this study were: rye flour (Type 170, Moulins de Versailles, France), soy flour (toasted and whole type, Alnatura, Germany) and wheat flour (Type 55, Villa Bleue, Minoterie Bourseau, France). The choice of using these 3 flours is linked to our previous study (Guénard-Lampron et al., 2023), in which soy, rye, lupin, chestnut and chickpea were selected in a first screening step with nutritional, economic and ecological criteria. Then, only soy and rye were retained after printability pre-tests for having better texture and sensory characteristics. Also, legumes are not commonly used in bread-type products, but the more and more legume-based products such as lupine, pea, chickpea and soy-based ingredients start to be widely used for their protein high content (Aslan & Bilgiçli, 2022; Halm et al., 2024). Soybean flour is also already used in the preparation of certain cakes (Sanful et al., 2010; Sedem Dzah et al., 2016; Ugwuona et al., 2012) which shows the interest in working with these flours.

Tap water, cinnamon (Ducros, France) and apple puree without added sugar (Cora, France) were also used to prepare the doughs from these flours. The water content (Table 1) of apple puree was determined by weighing the dried matter after 5 h at 103°C (Etuve EM10, Chopin Technologies, Villeneuve-la-Garenne, France) and a reference method for cereals-based products (NF V03-707) was used for the other ingredients.

<caption>**Table 1**</caption>

Water content measured for each ingredient.

<table><thead><tr><th></th><th>Rye (T170)</th><th>Soy</th><th>Wheat (T55)</th><th>Apple puree</th><th>Cinnamon</th></tr></thead><tbody><tr><th>Water content (%)</th><td>11.2 ± 0.2</td><td>7.4 ± 0.1</td><td>9.5 ± 0.1</td><td>87.7 ± 0.1</td><td>11.8 ± 0.1</td></tr></tbody></table>

Doughs made with wheat flour were prepared as described by (Guénard-Lampron et al., 2021) using the same total water content (65 % w/w). A planetary mixer with a leaf device (KSS45 EOB CLASSIC, KitchenAid, USA) was used for the mechanical treatment (120 rpm, 12 min) and then a multifunction robot (Vorwerk, Thermomix TM6, FR) for the thermomechanical treatment (85°C, 100 rpm, 15 min). Doughs made with rye or soy flour were prepared according to the process parameters optimized (water content: 59 % for soy and 60 % for rye, thermomechanical treatment: 27 for soy and
22 min for rye) previously (Guénard-Lampron et al., 2023). To adapt these recipes for sensory analysis, a consumer survey was conducted with 21 participants. Cinnamon flavor was chosen from among eight sweet flavors and a ninth "no flavor" option. Its quantity was then determined at 2% with preliminary trials and the result of hedonic appreciation of 20 consumers combined with the result of another profile analysis (not described in this study) and reverses-engineering method. Meanwhile, to enhance the sensory experience, the addition of 20% apple puree was chosen because it has the least impact on dough viscosity among 5 vegetable and fruit purees tested. Additionally, it lowers the mean force of the dough in back extrusion compared to other purees (celery, broccoli, carrot or mango) and may help soften soy-based products, improving texture perception (Masbernat, 2021). Thus, in this study, each dough was flavored with 2% (w/w) cinnamon and a version with 20% (w/w) apple puree for each flour was prepared. After preparation and cooling, the doughs were kept at 4°C overnight.

## 2.2. Texture analysis of printable dough

A texture analysis of each dough at room temperature was performed (Guénard-Lampron et al., 2023). The mean force (N) was measured between 10 and 20 s of the back extrusion method (piston probe with annulus gap of 1.5 mm, crosshead speed of 1mm/s, 30 mm of depth) using a texture analyzer (TaHD, Stable Micro Systems, Surrey, UK). Triplicate were realized for each formulation (after dough preparation and after overnight storage at 4°C) to determine the average firmness of the doughs.

## 2.3. Printing and baking

The 3D food printer prototype built by Dagoma (Roubaix, France) was used in our study (Guénard-Lampron et al., 2021, 2023b; Masbernat, 2021). The day after the dough was produced, it was poached in the syringe of the prototype and was printed (20 ± 1 °C) on silicone baking mat. Printing and baking parameters were determined by preliminary tests and fixed for all printed products. We used a nozzle diameter of 3.4 mm, a filling rate of 55 % and a printing speed of 10 mm.s⁻¹ to print six cylindrical model (diameter: 3 cm, height: 1.5 cm) for each batch of dough. 3D-printed products were then baked at 175 ± 5 °C during 17 min directly after the printing. Oven baking time and temperature were determined during preliminary tests after assessing the general appreciation of 10 products among 20 consumers. The percentage of water loss was measured by comparing the mass of printed products before and after baking.

## 2.4. Experimental design

A full factorial design was used to study two factors: the type of flour (wheat, rye or soy) and the apple puree (0 or 20%) leading to the production of 6 different products (Table 2). However, in order to check the reliability of the panelists and consumers assessments, a duplicate product based on wheat flour and without apple puree was generated.
## 2.5. Sensory analysis

This study was carried out in accordance with the international ethical guidelines for research involving humans established in the Declaration of Helsinki. A longstanding internal code of conduct for sensory science has also been respected.

Two descriptive methods (description by panelists and then by consumers) and a hedonic test were carried out. Four panelists experienced in evaluating cereal- and legume-based printed products and used to participating in sensory analysis were recruited to describe the products. 56 consumers were recruited to evaluate the products at home, using an online questionnaire including hedonic and CATA tests. These consumers were recruited via email invitations from the school network, with an exclusion criterion based on gluten and soy allergies. The inclusion criterion was that individuals were able to follow the tasting instructions, including the predefined order, and complete the questionnaire. Among the 56 consumers, 2 were under 20 years old, 29 between 20 and 30, 8 between 30 and 40, 3 between 40 and 50, and 4 between 50 and 59. Thirty-six were female (64%) and 20 were male (36%), 39 were university students or doctoral/postdoctoral students, and 17 were school employees.

The instructions were to carry out the evaluation alone, on the same day as the products were distributed. All participants were acknowledged at the end of their evaluation with a chocolate bar.

### Table 2

<table><caption>Overview of the preparation of the six printable doughs.</caption><thead><tr><th></th><th>Rye (T170)</th><th>Soy</th><th>Wheat (T55)</th></tr></thead><tbody><tr><td><em>Flour composition<fnref n="1" /></em></td><td></td><td></td><td></td></tr><tr><td>Protein (%)</td><td>15.9</td><td>41.0</td><td>9.6</td></tr><tr><td>Starch (%)</td><td><strong>18.7</strong></td><td><strong>5.4</strong></td><td><strong>69.3</strong></td></tr><tr><td>Gluten (%)</td><td>NA</td><td>0</td><td>7.4</td></tr><tr><td>Fiber (%)</td><td>23.8</td><td>19</td><td><strong>2.5</strong></td></tr><tr><td>Sugar (%)</td><td>2.3</td><td>3.1</td><td><strong>0.4</strong></td></tr><tr><td><em>Product composition</em></td><td></td><td></td><td></td></tr><tr><td>Total water content (%)</td><td>60.0</td><td>59.0</td><td>65.0</td></tr><tr><td>Apple puree (%)</td><td>0 20</td><td>0 20</td><td>0 20</td></tr><tr><td>Cinnamon (%)</td><td>2 2</td><td>2 2</td><td>2 2</td></tr><tr><td><em>Recipe Name</em></td><td>Rye Rye + Apple</td><td>Soy Soy + Apple</td><td>Wheat Wheat<fnref n="2" /></td></tr><tr><td><em>Process</em></td><td></td><td></td><td></td></tr><tr><td>Mechanical treatment (min) at 120 rpm</td><td>12</td><td>12</td><td>12</td></tr><tr><td>Thermo-mechanical treatment (min) at 100 rpm</td><td>22</td><td>27</td><td>15</td></tr></tbody></table>

### 2.5.1. Sensory description

<footnote n="1">¹ The theoretical values (Ciqual database) are in bold, other values were obtained by the supplier or NA: Not available.</footnote>

<footnote n="2">² A duplicate of the wheat-based recipe has been produced and will be named “wheat*” in the results.</footnote>
To determine the descriptors to be evaluated, a list of 15 descriptors pre-established by 6 panelists who had evaluated printed rye flour products flavored with cinnamon was used. This initial list of 15 descriptors resulted from a 3-stage process inspired by the ISO 11035 method with the 6 judges: descriptor generation (total of 72), qualitative sorting (reduction to 47 descriptors) and then quantitative sorting (0-1 method). In our approach, the choice of descriptors was already obvious after the 0-1 sort, so we chose not to do the 0-5 sort. We added information about this methodology. This list of 15 descriptors was then discussed among the 4 panelists recruited and adapted to be relevant to the 7 products under study (Table 3). The panelists reached a consensus on the definition and the protocol evaluation of 14 descriptors for visual appearance, smell, texture (by touching and tasting) and taste (Table 4). During another session in sensory cabins, one evaluation of each product (2 samples of each) in random order was carried out using an unstructured linear scale of 10 cm (ISO 4121:2003) (Figure 1).

Descriptor: Sweet

Definition: Perception of sweetness via the tongue

Assessment Protocol: Take a bite and assess the intensity of the sweetness on the tongue.

<figure><img src="image_2.png" /></figure>

Not sweet

Very sweet

<caption>**Figure 1.** Example of protocol and scale used to assess the descriptor “sweet”.</caption>

<caption>**Table 3**</caption>

Changes made to the list of descriptors after discussion among panelists.

<table><thead><tr><th></th><th>Descriptors retained</th><th>Descriptors removed</th><th>Descriptors added</th></tr></thead><tbody><tr><td>Visual aspect</td><td>color, deformation, swelling, compact</td><td>none</td><td>space between printed lines</td></tr><tr><td>Texture (by touching)</td><td>hard</td><td>grainy, brittle</td><td>none</td></tr><tr><td>Odor</td><td>cereal, cinnamon</td><td>none</td><td>none</td></tr><tr><td>Texture (by tasting)</td><td>moisture</td><td>crispy, doughy</td><td>sticky, grainy</td></tr><tr><td>Taste/flavor</td><td>cinnamon, sweet</td><td>burnt</td><td>cereal</td></tr></tbody></table>
<table><caption>**Table 4**</caption><thead><tr><th>Type of descriptor</th><th>Descriptor</th><th>Definition of the scale</th><th>Evaluation protocol</th></tr></thead><tbody><tr><td colspan="4">* Use the first sample</td></tr><tr><td rowspan="4">Visual</td><td>Color</td><td>Color intensity from beige to brown</td><td>Observe the entire surface of the product</td></tr><tr><td>Deformation</td><td>Modification of the cylindrical shape from not to very deformed</td><td>Observe the cylindrical shape the product by the bottom surface</td></tr><tr><td>Swelling</td><td>Modification of the heigh of the cylinder from not to very swollen</td><td>Observe the height of the product</td></tr><tr><td>Space between print lines</td><td>Visibility of the space between the print lines from not to very visible</td><td>Observe the space between the print lines on the top surface</td></tr><tr><td>Handmade texture</td><td>Hard</td><td>From not to very hard</td><td>Compress the product between your fingers and assess the force needed to crush the product</td></tr><tr><td colspan="4">* Use the second sample and divide product into two parts</td></tr><tr><td>Visual</td><td>Compact</td><td>From not to very compact</td><td>Observe the proximity of components inside the product</td></tr><tr><td rowspan="2">Odor</td><td>Cereal</td><td>Cereal odor from not to very intense</td><td rowspan="2">Smell the inside of the product</td></tr><tr><td>Cinnamon</td><td>Cinnamon odor from not to very intense</td></tr><tr><td rowspan="3">Taste</td><td>Cereal</td><td>Cereal taste from not to very intense</td><td rowspan="3">Chew the product and then assess the intensity of the tastes</td></tr><tr><td>Cinnamon</td><td>Cinnamon taste from not to very intense</td></tr><tr><td>Sweet</td><td>Sweet intensity from not to very sweet</td></tr><tr><td rowspan="3">Texture during tasting</td><td>Moisture</td><td>from not to very moist</td><td>Chew the product then assess the amount of humidity in the mouth</td></tr><tr><td>Sticky</td><td>from not to very sticky</td><td>Chew the product then assess the amount of product sticking to the teeth</td></tr><tr><td>Grainy</td><td>from not to very grainy</td><td>Chew the product then rub the tongue on the palate to assess the graininess</td></tr></tbody></table>

Definition and evaluation protocol of sensory descriptors in order of evaluation.
219

## 2.5.2. Hedonic appreciation and CATA

220

For the consumer study, 56 consumers (70 % of students and 30 % of university employees, 36 women and 20 men) were recruited to evaluate the 7 products. Each consumer received one printed and baked cylinder of each of the 7 products. The coded products were evaluated in the order of a Latin square. Consumers were asked to give three scores based on their general appreciation of the product, its visual appearance and its perception in the mouth. A 9-point hedonic scale was used, where 1 represented a low appreciation and 9 a high appreciation.

221

222

223

224

225

226

After giving their hedonic appreciation of the product, consumers answered a CATA questionnaire. Terms used in the CATA questionnaire were selected according to sensory descriptors used in the sensory test and following a discussion between the authors and the panelists. The CATA question included 10 terms related to sensory aspects (hard, soft, crispy, moisture, dry, sticky, brittle, grainy, cereal and sweet). Consumers were asked to read the entire list of terms before beginning the evaluation, and the terms were always presented in the same order to make it easier for them to identify the terms to be checked off.

227

228

229

230

231

232

233

## 2.6. Statistical analysis

234

The experimental design and statistical analysis (PCA, CFA, desirability function) were performed using JMP software (version 16.0, SAS Institute Inc., Cary, SC, USA). ANOVA and multiple pairwise comparisons (Tukey (HSD)) were performed using XLSTATS (version 2024.3.0, Addinsoft, Paris, France). Preference mapping was realized using MATLAB version: 9.13.0 (R2022b), Natick, Massachusetts: The MathWorks Inc.

235

236

237

238

239

# 3. Results and discussion

240

241

## 3.1. Textural properties of printable dough

242

The firmness of formulated dough varies according to flour type and apple puree content (0 or 20%), but for the same product the firmness is similar after preparation and after 1 night's storage, except for wheat-based dough with and without puree which are less firm after 1 night's storage (Table 5). Dough firmness is much higher for soy flour-based pasta (~ 35 N) than for rye (~ 20 N) and wheat (~ 17 N) flour-based dough. Measurements after dough preparation have enabled us to compare results with other projects and define an acceptable firmness range for printing (~ 10 to 40 N). However, the firmness values are different from those in our previous article (Guénard-Lampron et al., 2023), which could be explained by the addition of 2% cinnamon to the recipe. Measurements after overnight storage are used to relate print quality to textural properties. Apple puree slightly reduces firmness for all dough and this change is significant for rye and wheat doughs, while for soy dough the decrease is not significant, which might be explained by the firm texture initially observed in the soy dough (Table 5). These differences had no impact on the printability of the products, but we did observe visual differences after baking the printed products. For example, rye-based products are

243

244

245

246

247

248

249

250

251

252

253

254
the darkest, which is not necessarily linked to its main composition (protein, starch, fibre, etc.) but rather to phenolic compounds, which are known to influence colour (Heiniö et al., 2008). Also, the difference in colour due to the addition of the apple puree is only visible in the soy flour-based product. This higher coloration observed for soy flour products when apple puree was added could be explained by a stronger Maillard's reaction enhanced by the reducing sugar contained in apple puree and the large quantity of available proteins in soy-based dough. For rye and wheat flour products, the addition of apple puree resulted in greater deformation of the 3D model during baking.

<caption>**Table 5**</caption>

Firmness (mean force) of printable dough after preparation and after 1 day.

<table><thead><tr><th>Type of flour</th><th>Apple puree (%)</th><th>Firmness after preparation (N)</th><th>Firmness after 1 day (N)</th><th>Printed product</th><th>Baked product</th></tr></thead><tbody><tr><td rowspan="2">Rye (T170)</td><td>0</td><td>19.8 ± 1.5 B</td><td>19.2 ± 1.6 B</td><td rowspan="2"></td><td rowspan="2"></td></tr><tr><td>20</td><td>14.8 ± 1.4 CD</td><td>15.0 ± 1.1 CD</td></tr><tr><td rowspan="2">Soy</td><td>0</td><td>35.0 ± 2.6 A</td><td>36.8 ± 2.4 A</td><td rowspan="2"></td><td rowspan="2"></td></tr><tr><td>20</td><td>33.7 ± 0.7 A</td><td>34.8 ± 1.7 A</td></tr><tr><td rowspan="2">Wheat (T55)</td><td>0</td><td>17.0 ± 0.6 BC</td><td>13.4 ± 1.2 D</td><td rowspan="2"></td><td rowspan="2"></td></tr><tr><td>20</td><td>13.0 ± 1.0 D</td><td>8.8 ± 1.5 E</td></tr></tbody></table>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

Different superscript letters indicate significant differences (Tukey's HSD test, p < 0.05)

## 3.2. Sensory map of printed products

Figure 2 shows that products containing apple puree were slightly different with darker color (soy) and more distorted shape (rye). Soy flour products were more compact, grainier and harder (higher dough firmness), with a stronger cereal smell, cereal taste and very visible spaces between the print lines (good hold of the printing model). Rye and wheat flour products were sweeter, but cinnamon smell and taste were stronger for wheat flour products, followed by rye flour products, both well
pronounced compared to soy flour products. Wheat flour products were also more humid inside and moderately compact, while rye flour products were darker and stickier, more swollen, airy and deformed. Interestingly, as the only non-cereal flour among the three, soy flour gives a stronger cereal smell (7.4 for Soy and 6.5 for Soy + Apple) and taste (7.6 for Soy and 7.9 for Soy + Apple) to final products then rye flour does (for cereal smell: 6.2 for Rye and 5.5 for Rye + Apple and for cereal taste: 5.7 for Rye and 5.4 for Rye + Apple), while wheat-based products have the weakest cereal smell (~3.1 for Wheat and 1.4 for Wheat + Apple) and taste (~2.9 for Wheat and 2.5 for Wheat + Apple). This may be related to the high protein content in soy flour that enhances Maillard reactions and produces cereal-like flavors and tastes. Another explanation is the confusion between the descriptors "cereal" for grains and "nutty" for lipid-containing ingredients such as soy or nuts. For example, a whole-grain wheat bread could be associated with "nutty" attribute (Bhasin, 2020).

<figure><img src="image_9.png" /><figcaption>**Figure 2.** Sensory map of each product made from a) rye, b) soy or c) wheat flour according to the 14 sensory descriptors. AP indicates products with apple puree. The wheat sensory map (without apple puree) represents an average of the product and its duplicate.</figcaption></figure>

To quantify the observations based on the sensory map, an ANOVA analysis was carried out to study the impact of judge and product on the sensory descriptors (Table 6). The results shown in Table 6 demonstrate that among the 14 descriptors, only "cereal (taste)" is not significant in distinguishing
different products, which means that the perceived intensity of cereal taste is not different for all 7 products. This suggests that the difference of the cereal taste isn't significant because of the strong disagreement among judges, which could be related to the difficulty to distinguish “cereal” and other similar attributes such as “nutty”. Apart from the "cereal (taste)" descriptor, the products result in a significant difference on the 13 other descriptors even if removing the effect of the judges who are not necessarily always aligned on certain descriptors such as deformation, swelling, spaces between printed lines, sweet and grainy. A multivariate analysis (not shown) also confirmed that the panelists agreed with each other. In order to analyze more precisely the origin of the significant difference, additional data with the estimated means and the classes of the multiple pairwise comparisons for product (Tukey (HSD)) are available (supplementary data). These results enabled us to validate the judges' assessment, since the duplicates of the wheat-based product without apple puree were always in the same group. These classes also allow us to conclude whether the difference comes from the flour or the presence of apple puree. For all descriptors except color and across all flours, there is no significant effect between the product with and without apple puree. This allows us to conclude that only the flour type, and not the addition of puree, influences these sensory descriptors. As for the color aspect, the difference is only significant for soy-based products (class abc versus d). Meanwhile, soy products don't share the same classes with neither rye nor wheat products for spaces between printed lines, compact, cinnamon (taste), sweet and grainy aspects. At least one rye product shares almost always the same classes with at least one wheat product apart from cinnamon (odor), which demonstrates their similarity.
310 Table 6

311 ANOVA summary for all sensory descriptors

<table><thead><tr><th></th><th>Color</th><th>Deformation</th><th>Swelling</th><th>Spaces between printed lines</th><th>Hard</th><th>Compact</th><th>Cereal (odor)</th><th>Cinnamon (odor)</th><th>Cereal (taste)</th><th>Cinnamon (taste)</th><th>Sweet</th><th>Moisture</th><th>Sticky</th><th>Grainy</th></tr></thead><tbody><tr><td>R<sup>2</sup></td><td>0.796</td><td>0.901</td><td>0.852</td><td>0.909</td><td>0.851</td><td>0.935</td><td>0.622</td><td>0.891</td><td>0.472</td><td>0.808</td><td>0.775</td><td>0.910</td><td>0.682</td><td>0.818</td></tr><tr><td>F</td><td>7.812</td><td>18.224</td><td>11.552</td><td>19.903</td><td>11.455</td><td>28.707</td><td>3.284</td><td>16.330</td><td>1.790</td><td>8.391</td><td>6.871</td><td>20.143</td><td>4.282</td><td>8.997</td></tr><tr><td>Pr > F</td><td>0.000</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.015</td><td>&lt;0.0001</td><td>0.140</td><td>&lt;0.0001</td><td>0.000</td><td>&lt;0.0001</td><td>0.004</td><td>1</td></tr><tr><td>Judge</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.000</td></tr><tr><td>Pr > F</td><td>0.000</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.015</td><td>&lt;0.0001</td><td>0.140</td><td>&lt;0.0001</td><td>0.000</td><td>&lt;0.0001</td><td>0.004</td><td>1</td></tr><tr><td>Product</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.000</td></tr><tr><td>Pr > F</td><td>0.000</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.015</td><td>&lt;0.0001</td><td>0.140</td><td>&lt;0.0001</td><td>0.000</td><td>&lt;0.0001</td><td>0.004</td><td>1</td></tr></tbody></table>

312
## 3.3. Relationship between sensory and nutritional properties of printed products

A Principal component analysis was carried out to establish the correlations between the sensorial descriptors of each product, the nutritional composition of each dough's formula, process-related information (water loss (%)) and textural characteristic (dough firmness (N)) of each dough (Figure 3). As the gluten content of rye flour was not available (measured or theoretical) and can vary widely (between 3.0 and 7.8 g/100 g when protein content varies between 5.5 to 11.2 g/100 g in 32 cultivars) (Xhaferaj et al., 2023), this data was not taken into account in the PCA. But we can point out that the gluten content of our products differs mainly for soya-based products, which are gluten-free. Components 1 and 2 of the PCA explain 95.2% of the total variance. Most of the variance was explained by component 1 (68.9%) and was related to all the sensorial descriptors studied and most of the nutritional and textural characteristics of the doughs. The content of starch, fiber, sugar and water and the percentage of water loss of the dough, with several sensorial descriptors (color, sticky, cereal smell, moisture e.g.) also contributed to the variance explained by component 2 (26.3%). The explanatory variables sugar and apple puree appear as relatively short vectors near the origin, indicating that their individual contributions to the variance explained by the first two principal components are limited. However, their orientation suggests a moderate association with sensory attributes such as sweetness, moisture and cinnamon smell and taste.

<figure><img src="image_10.png" /><figcaption>**Figure 3.** Principal component analysis (PCA map and correlation circle) of 3D-printed and baked products. Principal variables (red lines) were 13 significant sensorial descriptors; explanatory variables (blue dashed lines) were nutritional composition and technical characteristics of each dough.</figcaption></figure>

Doughs on the left of the plane are high in protein, ash and are firmer, related to a harder, grainier, more compact product with more obvious spaces between printed lines and stronger cereal smell. These are characteristics of soy-based products. These products are also drier, less colored, less sticky, less swollen with less deformation. In terms of smells and tastes, they are less sweet and have milder cinnamon taste and smell. In the other hand, this observation also suggests the correlation between the mean force during back extrusion of the dough and the hard and compact aspects of the
final products, giving the possibility to predict sensorial description based on textural properties of the doughs. In addition, the link between a perception of a hard final product and a firm dough texture can be traced back to the nutritional composition, which implies a higher content of ash and protein and lower water content, as well as that of starch. Since starch has the role of retaining water in the dough process, this explains the correlation between a higher starch content giving a less firm dough and a moist final product, and conversely, by using a more protein-rich recipe therefore containing less starch, the finished product is harder.

Meanwhile, doughs on the right part, where rye and wheat-based products can be found, are richer in starch and water. Rye doughs in the upper right part are correlated to a more colored, sticky, deformed, swollen and sweeter product while wheat doughs have stronger characteristics on higher moisture and sweetness with pronounced cinnamon taste and smell. Actually, rye products are also known as colored compared to other cereal-based products like oat and millet and have sweeter taste than these two cereals and sorghum-based products (Torbica et al., 2019).

Another observation is that the loss of water during the drying of the product is not correlated to the moisture sensation in the final product. It is however correlated to a more sticky and less compact product, which can be explained by a loss of water greater in products that are initially very humid, and this loss of water does not completely counterbalance the humidity initially present in the dough. The finished products therefore remain more sticky and less compact compared to others.

## 3.4. Consumer perception of printed products

The hedonic scores evaluated by the 56 consumers shown in Figure 4 show consumer preference for the 7 printed products. The highest scores remained around 5.5, indicating moderate acceptance. There are 3 possible explanations: 1) the awareness that the products were 3D-printed, potentially influencing perception, 2) simple 3D shapes were proposed, with room for more appealing designs in future projects, 3) flavoring choices that may not have suited all consumers, warranting further exploration. These could be partially confirmed by comments of consumers: 8 participants mentioned a strange texture as some found the soy-based products too dry and compact, while rye-based ones were considered too soft and incomparable with classic snacks like biscuits. At least 2 participants disliked the cinnamon flavor, and 8 participants noted insufficient sweetness.

For overall ratings, wheat-based products are the most popular while soy-based products are the least popular. Looking in more detail, soy-based products are nevertheless appreciated at the visual level, it is the texture in-mouth aspect which is not appreciated by consumers with an average score of 2.5 (class D) and 2.2 (class D) for products with and without apple puree. For wheat and soy products, the difference in appreciation with and without apple puree is not highlighted. For rye-based products, only the visual score makes the difference between products with or without apple puree: 4.6 (class B) versus 2.9 (class C), probably due to the important deformation of the product with apple puree. The low global and in-mouth appreciation of soy products is probably related to their hard, compact and
dry mouthfeel, as well as beany flavor (not included in this study) and taste which are characteristic of
raw soybean flour, which can be improved by pretreatment like steaming or roasting (Shin et al.,
2013).

<figure><img src="image_11.png" /><figcaption>**Figure 4.** Global, visual and in-mouth appreciation of the 7 products with classes of multiple pairwise comparisons for product (Tuckey (HSD)). Different letters of significance indicate that products are significantly different within one of the three ratings (overall, visual or in-mouth).</figcaption></figure>

In order to understand the correlation between consumers' appreciation of taste, texture and overall appearance and their perception of the various descriptors of the printed products, a CATA test was also carried out and the results were analyzed using the CFA method. This test shows whether consumers describe products in the same way as the sensory description by panelists, so that we can confirm whether their hedonic appreciation is based on the same basis as previous descriptions concluded by PCA method.

<figure><img src="image_12.png" /><figcaption>**Figure 5.** CFA of 3D-printed and baked products obtained from the CATA evaluation.</figcaption></figure>
According to the CFA results (Figure 5), wheat products are related to moist, sweet, soft, not crispy, not brittle, not dry and not grainy characteristics. Rye products are related to sticky and soy products are in the opposite position of products based on wheat. These results show that CATA results are close to the sensory description by panelists despite lack of intensity, which is not surprising as CATA was demonstrated nearly as efficient as descriptive analysis of flash profile in other sensory studies (Hunaefi & Marusiva, 2021; Kim et al., 2023). Compared to the principal component analysis, the characteristics are less represented in the CFA, the coordinates of the vectors are less close to 1. Even if the descriptors are not the same, it is still possible to find the correlation between the moisture, sweet and soft aspects (the opposite of hard in the PCA), and these characteristics are anticorrelated to grainy and dry aspects. The sticky descriptor correlates with neither soft nor moist and is highly related to rye-based products. Two new descriptors, crispy and brittle, have been introduced and are mainly linked to soy-based products.

### 3.5. Relationship between sensory properties and consumer preferences

An external preference mapping using the principal component analysis on the sensory data and the global appreciation of the consumers was performed (Masson et al., 2016). Figure 6 shows the preference mapping where wheat flour products are consumers favorite (80-95%), followed by rye (35-50%) and soy (10-20%). In contrast to the averages presented in Figure 3, where only the visual aspect makes the difference in product appreciation with and without puree, and only for rye-based products, the difference in appreciation between products with and without apple puree are more visible in the preference mapping. It is possible to observe that products with apple puree are less appreciated than products without.

<figure><img src="image_13.png" /><figcaption>**Figure 6.** Preference mapping based on the global appreciation of products by consumers (n = 56) and sensory description of the 7 printed products.</figcaption></figure>
From this mapping, we were able to extract in the red zone of the map the ideal values to target for each of the sensory descriptors (Figure 7). This gives us a slightly distorted and domed product and the spacing between the lines doesn't have to be very visible. The product should be moderately colored with a moderately sticky texture, moderately compact, not hard or grainy but rather moist. In terms of smell and taste, it's ideal to have very little cereal smell, a moderate sweetness and a relatively strong cinnamon smell and taste. As for apple puree, the ideal product would contain none. However, in this study, the optimal product was not tested since our aim was rather to obtain sensory and hedonic information as well as links between this information in order to develop a knowledge base on the use of these flours in food 3D printing. These results show that it would be interesting to make a flour blend to obtain moderate values in certain aspects. For example, by comparing with the sensory maps of the products (Figure 1), we can hypothesize that a blend based on wheat and soy flour would result in a less firm and slightly moister product than one based solely on soy flour. Similarly, a mixture based on wheat and rye flour could improve the color and reduce the deformation of printed products.

<figure><img src="image_14.png" /><figcaption>**Figure 7.** Ideal sensory properties to target according to preference mapping.</figcaption></figure>
432

## 4. Conclusions

433

434

435

436

437

438

This study integrated nutritional composition, textural properties related to printability and sensory descriptions and appreciation of 3D printed products based on wheat, rye and soy flour. Textural analysis revealed that soy flour resulted in the highest dough firmness, while apple puree slightly reduced firmness across all the formulations. Although this did not affect printability, it influenced the appearance of final baked products, especially for rye-based ones. These findings align with sensory evaluation performed by panelists (descriptive analysis) and consumers (CATA).

439

440

441

442

443

444

445

446

447

448

Descriptive analysis highlighted that flour type had a stronger impact on sensory properties than apple puree. Principal Component Analysis (PCA) confirmed strong correlations between sensory descriptors, nutritional composition, and dough textural properties. Soy-based products were perceived as firmer, drier, grainier, and had stronger cereal aromas, while rye- and wheat-based products were described as sweeter, moister, and more deformable. Consumer preference analysis (PREMAP) showed a strong preference for wheat-based products (80-95%), followed by rye (35-50%) and soy (10-20%) among 56 consumers. The dry, firm and grainy texture of soy products could play a key role in this evaluation, as wheat and rye-based products were perceived as more appealing. Apple puree had little impact in overall appreciation, except for rye-based products, where it negatively impacted visual appeal.

449

450

451

452

These findings suggest opportunities to improve consumer acceptance of these 3D-printed products by optimizing product shape and enhancing flavor profiles. The observed correlation between the firmness of the dough and the sensory texture descriptors (compactness and hardness) indicates the potential to predict sensorial perception based on textural properties.

453

454

455

456

457

458

459

460

461

462

463

464

465

To further refine the formulation of 3D-printed foods that are both nutritionally relevant and sensorially appealing, several complementary research directions should be pursued. First, a more in-depth nutritional characterization of the products is necessary, with particular attention to minor bioactive compounds such as polyphenols, which may not only enhance the nutritional profile but also influence sensory properties (flavor). Second, the characterization of color changes after cooking should be expanded using instrumental colorimetry to better understand how flour composition and added ingredients impact Maillard reactions and pigment degradation, both of which play a role in visual appeal and perceived baking. Third, instrumental analysis of structural parameters, such as particle size distribution, should be deepened to assess its influence on dough rheology, printability, and final texture. Since granularity and viscosity directly affect the quality and stability of the printed structure, linking these parameters to consumer-perceived textural attributes will be critical. These investigations will allow for a more integrated understanding of how ingredient choice and processing parameters influence both the technical performance and sensory quality of 3D-printed foods.
Finally, while consumers showed a greater interest in wheat-based printed products, the other two flours, especially rye flour, presents sensory potential when incorporated into wheat-based formulations at partial substitution levels, which is also a common practice in food design. The sensory insights from this study provide a valuable foundation for a reasoned approach to flour blend formulation, enabling the optimization of both nutritional value and consumer enjoyment of 3D-printed products.
472 **Declaration of interests**

473 The authors declare that they have no known competing financial interests or personal relationships

474 that could have appeared to influence the work reported in this paper.

475

476 **Data availability**

477 Data will be made available on request.

478

479 **Funding sources**

480 This work was supported by a single interministerial fund (FR) and by regional co-funding (Hauts de

481 France et Auvergne Rhône Alpes) through a collaborative R&D project certified by Vitagora,

482 Euramaterials and Cimes clusters.
483 References

484 Armstrong, C. D., Yue, L., Deng, Y., & Qi, H. J. (2022). Enabling direct ink write edible 3D printing of food purees with cellulose nanocrystals. *Journal of Food Engineering, 330*(April), 111086.
https://doi.org/10.1016/j.jfoodeng.2022.111086

487 Aslan, M., & Bilgiçli, N. (2022). The comparison of lupin milk with soy milk as an alternative egg substitute for cake production. *Journal of Food Processing and Preservation, 46*(5).
https://doi.org/10.1111/jfpp.16572

490 Auyeskhan, U., Azhbagambetov, A., Sadykov, T., Dairabayeva, D., Talamona, D., & Chan, M. Y. (2023). Reducing meat consumption in Central Asia through 3D printing of plant-based protein—enhanced alternatives—a mini review. In *Frontiers in Nutrition* (Vol. 10). Frontiers Media SA. https://doi.org/10.3389/fnut.2023.1308836

494 Bhasin, T. (2020). Sensory characteristics of wholegrain. *Advances in Obesity, Weight Management & Control, 10*(6), 191–194. https://doi.org/10.15406/aowmc.2020.10.00327

496 Chen, Y., Zhang, M., & Phuhongsung, P. (2021). 3D printing of protein-based composite fruit and vegetable gel system. *Lwt, 141*(January), 110978. https://doi.org/10.1016/j.lwt.2021.110978

498 Čukelj Mustač, N., Pastor, K., Kojić, J., Voučko, B., Ćurić, D., Rocha, J. M., & Novotni, D. (2023). Quality assessment of 3D-printed cereal-based products. *Lwt, 184*(March).
https://doi.org/10.1016/j.lwt.2023.115065

501 Godoi, F. C., Prakash, S., & Bhandari, B. R. (2016). 3d printing technologies applied for food design: Status and prospects. *Journal of Food Engineering, 179*, 44–54.
https://doi.org/10.1016/j.jfoodeng.2016.01.025

504 Guénard-Lampron, V., Liu, X., Masson, M., & Blumenthal, D. (2023a). Screening of different flours for 3D food printing: Optimization of thermomechanical process of soy and rye flour dough. *Innovative Food Science and Emerging Technologies, 87*(December 2022).
https://doi.org/10.1016/j.ifset.2023.103394

508 Guénard-Lampron, V., Liu, X., Masson, M., & Blumenthal, D. (2023b). Screening of different flours for 3D food printing: Optimization of thermomechanical process of soy and rye flour dough. *Innovative Food Science and Emerging Technologies, 87*(December 2022).
https://doi.org/10.1016/j.ifset.2023.103394

512 Guénard-Lampron, V., Masson, M., Leichtnam, O., & Blumenthal, D. (2021). Impact of 3D printing and post-processing parameters on shape, texture and microstructure of carrot appetizer cake. *Innovative Food Science & Emerging Technologies, 72*(April), 102738.
https://doi.org/10.1016/j.ifset.2021.102738

516 Habuš, M., Mykolenko, S., Iveković, S., Pastor, K., Kojić, J., Drakula, S., Ćurić, D., & Novotni, D. (2022). Bioprocessing of Wheat and Amaranth Bran for the Reduction of Fructan Levels and Application in 3D- Printed Snacks. *Foods, 11*(11), 1–15. https://doi.org/10.3390/foods1111649

519 Halm, J., Sahin, A. W., Nyhan, L., Zannini, E., & Arendt, E. K. (2024). Commercial Egg Replacers in Pound Cake Systems: A Comprehensive Analysis of Market Trends and Application. *Foods, 13*(2). https://doi.org/10.3390/foods13020292

522 Hassan, I., Gani, A., & Mudabir, S. (2024). Development and characterization of 3D printed fish gelatin and vegetable blends as nutritious meal for dysphagic patients. *Food Hydrocolloids, 157*(July), 110478. https://doi.org/10.1016/j.foodhyd.2024.110478

525 Heiniö, R. L., Liukkonen, K. H., Myllymäki, O., Pihlava, J. M., Adlercreutz, H., Heinonen, S. M., & Poutanen, K. (2008). Quantities of phenolic compounds and their impacts on the perceived
527 flavour attributes of rye grain. *Journal of Cereal Science*, 47(3), 566-575.

528 https://doi.org/10.1016/j.jcs.2007.06.018

529 Hemsley, B., Palmer, S., Kouzani, A., Adams, S., & Balandin, S. (2019). Review Informing the Design of 3D Food Printing for People with Swallowing Disorders: Constructive, Conceptual, and Empirical Problems. *Proceedings of the 52nd Hawaii International Conference on System Sciences*, 5735-5744. https://doi.org/10.24251/hicss.2019.692

533 Hunaefi, D., & Marusiva, W. (2021). SENSORY PROFILE OF 3 IN 1 INSTANT COFFEE USING EMOTIONAL-SENSORY MAPPING, FLASH PROFILE, AND CATA (CHECK-ALL-THAT-APPLY) METHODS. *Jurnal Teknologi Dan Industri Pangan*, 32(1), 169-180.
536 https://doi.org/10.6066/jtip.2021.32.2.169

537 Keerthana, K., Anukiruthika, T., Moses, J. A., & Anandharamakrishnan, C. (2020a). Development of fiber-enriched 3D printed snacks from alternative foods: A study on button mushroom. *Journal of Food Engineering*, 287(May), 110116. https://doi.org/10.1016/j.jfoodeng.2020.110116

540 Keerthana, K., Anukiruthika, T., Moses, J. A., & Anandharamakrishnan, C. (2020b). Development of fiber-enriched 3D printed snacks from alternative foods: A study on button mushroom. *Journal of Food Engineering*, 287(May), 110116. https://doi.org/10.1016/j.jfoodeng.2020.110116

543 Kim, D. E., Kwak, H. S., Lim, M., & Lee, Y. (2023). Comparison of Check-All-That-Apply (CATA), Rate-All-That-Apply (RATA), Flash Profile, Free Listing, and Conventional Descriptive Analysis for the Sensory Profiling of Sweet Pumpkin Porridge. *Foods*, 12(19).
546 https://doi.org/10.3390/foods12193556

547 Krishnaraj, P., Anukiruthika, T., Choudhary, P., Moses, J. A., & Anandharamakrishnan, C. (2019a).
548 3D Extrusion Printing and Post-Processing of Fibre-Rich Snack from Indigenous Composite Flour. *Food and Bioprocess Technology*, 12(10), 1776-1786. https://doi.org/10.1007/s11947-019-02336-5

551 Krishnaraj, P., Anukiruthika, T., Choudhary, P., Moses, J. A., & Anandharamakrishnan, C. (2019b).
552 3D Extrusion Printing and Post-Processing of Fibre-Rich Snack from Indigenous Composite Flour. *Food and Bioprocess Technology*, 12(10), 1776-1786. https://doi.org/10.1007/s11947-019-02336-5

555 Lille, M., Kortekangas, A., Heiniö, R. L., & Sozer, N. (2020). Structural and textural characteristics of 3D- Printed protein- and dietary fibre- Rich snacks made of milk powder and wholegrain rye flour. *Foods*, 9(11). https://doi.org/10.3390/foods9111527

558 Lille, M., Nurmela, A., Nordlund, E., Metsä-Kortelainen, S., & Sozer, N. (2018). Applicability of protein and fiber-rich food materials in extrusion-based 3D printing. *Journal of Food Engineering*, 220, 20-27. https://doi.org/10.1016/j.jfoodeng.2017.04.034

561 Lipton, J. I., Cutler, M., Nigl, F., Cohen, D., & Lipson, H. (2015). Additive manufacturing for the food industry. In *Trends in Food Science and Technology* (Vol. 43, Issue 1, pp. 114-123).
563 Elsevier Ltd. https://doi.org/10.1016/j.tifs.2015.02.004

564 Mantihal, S., Prakash, S., & Bhandari, B. (2019). Textural modification of 3D printed dark chocolate by varying internal infill structure. *Food Research International*, 121(December 2018), 648-657. https://doi.org/10.1016/j.foodres.2018.12.034

567 Masbernat, L. (2021). *Mise au point de matériaux alimentaires imprimables en 3D permettant la création de recettes innovantes Thèse de doctorat*. Paris-Saclay.

569 Masbernat, L., Berland, S., Leverrier, C., Moulin, G., Michon, C., & Almeida, G. (2021). Structuring wheat dough using a thermomechanical process, from liquid food to 3D-printable food material. *Journal of Food Engineering*, 110696. https://doi.org/10.1016/j.jfoodeng.2021.110696
572 Masson, M., Saint-Eve, A., Delarue, J., & Blumenthal, D. (2016). Identifying the ideal profile of French yogurts for different clusters of consumers. *Journal of Dairy Science*, 99(5), 3421–3433.
573 https://doi.org/10.3168/jds.2015-10119
574

575 Mirazimi, F., Saldo, J., Sepulcre, F., Gràcia, A., & Pujola, M. (2022). Enriched puree potato with soy protein for dysphagia patients by using 3D printing. *Food Frontiers*, 3(4), 706–715.
576 https://doi.org/10.1002/fft2.149
577

578 Mirazimi, F., Saldo, J., Sepulcre, F., & Pujolà, M. (2023). Study the correlation between the instrumental and sensory evaluation of 3D-printed protein-fortified potato puree. *European Food Research and Technology*, 249(6), 1669–1675. https://doi.org/10.1007/s00217-023-04246-5
579

580 Nijdam, J. J., LeCorre-Bordes, D., Delvart, A., & Schon, B. S. (2021). A rheological test to assess the ability of food inks to form dimensionally stable 3D food structures. *Journal of Food Engineering*, 291(June 2020), 110235. https://doi.org/10.1016/j.jfoodeng.2020.110235
581

582 Pant, A., Lee, A. Y., Karyappa, R., Lee, C. P., An, J., Hashimoto, M., Tan, U. X., Wong, G., Chua, C. K., & Zhang, Y. (2021). 3D food printing of fresh vegetables using food hydrocolloids for dysphagic patients. *Food Hydrocolloids*, 114(December 2020), 106546. https://doi.org/10.1016/j.foodhyd.2020.106546
583

584 Pérez, B., Nykvist, H., Brøgger, A. F., Larsen, M. B., & Falkeborg, M. F. (2019). Impact of macronutrients printability and 3D-printer parameters on 3D-food printing: A review. *Food Chemistry*, 287(February), 249–257. https://doi.org/10.1016/j.foodchem.2019.02.090
585

590 Pulatsu, E., Su, J. W., Lin, J., & Lin, M. (2020). Factors affecting 3D printing and post-processing capacity of cookie dough. *Innovative Food Science and Emerging Technologies*, 61, 102316. https://doi.org/10.1016/j.ifset.2020.102316
591

595 Rando, P., & Ramaioli, M. (2021). Food 3D printing: Effect of heat transfer on print stability of chocolate. *Journal of Food Engineering*, 294(August 2020), 110415. https://doi.org/10.1016/j.jfoodeng.2020.110415
596

597 Sanful, R. E., Sadik, A., & Darko, S. (2010). Nutritional and Sensory Analysis of Soya Bean and Wheat Flour Composite Cake. *Pakistan Journal of Nutrition*, 8(9), 794–796.
598

600 Santhoshkumar, P., Negi, A., & Moses, J. A. (2024). 3D printing for space food applications: Advancements, challenges, and prospects. In *Life Sciences in Space Research* (Vol. 40, pp. 158–165). Elsevier Ltd. https://doi.org/10.1016/j.lssr.2023.08.002
601

602 Sartori, A. G. de O., Saliba, A. S. M. C., Bitencourt, B. S., Guedes, J. S., Torres, L. C. R., Alencar, S. M. de, & Augusto, P. E. D. (2023). Anthocyanin bioaccessibility and anti-inflammatory activity of a grape-based 3D printed food for dysphagia. *Innovative Food Science and Emerging Technologies*, 84(December 2022), 103289. https://doi.org/10.1016/j.ifset.2023.103289
603

607 Sedem Dzah, C., Mensah, C., & Mawunyo Kpodo, F. (2016). Sensory and Proximate Characteristics of Annatto-colored Soy-wheat Cake Formulations. *American Journal of Food and Nutrition*, 4(3), 78–82. https://doi.org/10.12691/ajfn-4-3-4
608

610 Severini, C., Azzollini, D., Albenzio, M., & Derossi, A. (2018). On printability, quality and nutritional properties of 3D printed cereal based snacks enriched with edible insects. *Food Research International*, 106(November 2017), 666–676. https://doi.org/10.1016/j.foodres.2018.01.034
611

612 Shin, D. J., Kim, W., & Kim, Y. (2013). Physicochemical and sensory properties of soy bread made with germinated, steamed, and roasted soy flour. *Food Chemistry*, 141(1), 517–523. https://doi.org/10.1016/j.foodchem.2013.03.005
613
616 Sun, J., Peng, Z., Zhou, W., Fuh, J. Y. H., Hong, G. S., & Chiu, A. (2015). A Review on 3D Printing for Customized Food Fabrication. *Procedia Manufacturing, 1*, 308–319.
617 https://doi.org/10.1016/j.promfg.2015.09.057
618

619 Tang, T., Zhang, M., Bhandari, B., & Li, C. (2024). Personalized transformation of 3D printing for traditional multi-material food with stuffing : A review. *Food Bioscience*, 59(April), 104112.
620
621 https://doi.org/10.1016/j.fbio.2024.104112

622 Torbica, A., Belović, M., & Tomić, J. (2019). Novel breads of non-wheat flours. *Food Chemistry*, 282, 134–140. https://doi.org/10.1016/j.foodchem.2018.12.113

624 Tu, V. P. (2010). “Pour moi, le goût du soja n’est pas une barrière à la consommation. Et pour vous ?”: Effet de la culture sur les croyances, attitudes et préférence vis-à-vis des produits à base de soja. https://theses.hal.science/tel-00750851v1

627 Ugwuona, F. U., Ogara, J. I., & Awogbenja, M. D. (2012). Chemical and sensory quality of cakes formulated with wheat, soybean and cassava flours. *IndiannJ.L.Sci*, 1(2), 1–6.

629 Xhaferaj, M., Muskovics, G., Schall, E., Bugyi, Z., Tömösközi, S., & Scherf, K. A. (2023).
630 Characterization of rye flours and their potential as reference material for gluten analysis. *Food
631 Chemistry*, 408. https://doi.org/10.1016/j.foodchem.2022.135148

632

633
634 Additional data

<caption>Table 1. ANOVA summary with estimated averages by product name and pairwise multiple comparisons for Product Name (Tukey (HSD))</caption>

636

<table><thead><tr><th>Modality</th><th>Color</th><th>Deformation</th><th>Swelling</th><th>Spaces between printed lines</th><th>Hard</th><th>Compact</th><th>Cereal (odor)</th><th>Cinnamon (odor)</th><th>Cereal (taste)</th><th>Cinnamon (taste)</th><th>Sweet</th><th>Moisture</th><th>Sticky</th><th>Grainy</th></tr></thead><tbody><tr><td>Rye + Apple</td><td>9,885 a</td><td>9,014 a</td><td>7,395 a</td><td>1,659 c</td><td>7,101 ab</td><td>0,854 d</td><td>5,462 abc</td><td>3,014 bc</td><td>5,364 a</td><td>5,910 a</td><td>5,625 a</td><td>1,628 b</td><td>7,692 a</td><td>3,151 b</td></tr><tr><td>Rye</td><td>9,021 ab</td><td>6,160 ab</td><td>7,323 a</td><td>2,564 c</td><td>6,440 abc</td><td>1,927 cd</td><td>6,213 abc</td><td>3,934 b</td><td>5,747 a</td><td>5,532 a</td><td>4,687 a</td><td>2,462 b</td><td>8,429 a</td><td>2,988 b</td></tr><tr><td>Wheat + Apple</td><td>6,530 abc</td><td>4,351 b</td><td>5,259 ab</td><td>3,643 bc</td><td>2,340 d</td><td>3,664 bc</td><td>1,391 c</td><td>6,979 a</td><td>2,514 a</td><td>7,689 a</td><td>5,696 a</td><td>6,498 a</td><td>4,392 ab</td><td>1,172 b</td></tr><tr><td>Wheat</td><td>5,280 cd</td><td>3,585 bc</td><td>3,467 bc</td><td>6,125 b</td><td>4,369 bcd</td><td>5,233 b</td><td>4,145 abc</td><td>7,285 a</td><td>2,984 a</td><td>7,628 a</td><td>4,435 a</td><td>7,446 a</td><td>5,216 ab</td><td>0,971 b</td></tr><tr><td>Wheat*</td><td>5,653 bcd</td><td>3,403 bcd</td><td>3,208 bc</td><td>3,824 bc</td><td>3,951 cd</td><td>4,957 b</td><td>2,128 bc</td><td>7,590 a</td><td>2,811 a</td><td>7,411 a</td><td>4,930 a</td><td>7,155 a</td><td>5,005 ab</td><td>2,312 b</td></tr><tr><td>Soy + Apple</td><td>6,728 abc</td><td>0,669 cd</td><td>0,146 c</td><td>9,808 a</td><td>9,142 a</td><td>9,170 a</td><td>6,522 ab</td><td>0,776 c</td><td>6,872 a</td><td>0,939 b</td><td>0,508 b</td><td>0,630 b</td><td>2,809 b</td><td>8,141 a</td></tr><tr><td>Soy</td><td>2,126 d</td><td>0,520 d</td><td>0,000 c</td><td>9,971 a</td><td>8,874 a</td><td>9,301 a</td><td>7,365 a</td><td>0,335 c</td><td>7,624 a</td><td>0,757 b</td><td>0,345 b</td><td>0,630 b</td><td>2,675 b</td><td>8,151 a</td></tr><tr><td>Product Pr > F (Modèle)</td><td>0,000</td><td>&lt;0,0001</td><td>&lt;0,0001</td><td>&lt;0,0001</td><td>&lt;0,0001</td><td>&lt;0,0001</td><td>0,015</td><td>&lt;0,0001</td><td>0,140</td><td>&lt;0,0001</td><td>0,000</td><td>&lt;0,0001</td><td>0,004</td><td>&lt;0,0001</td></tr></tbody></table>

637