Effects of ingredients and pre-heating on the printing quality and dimensional stability in 3D printing of cookie dough

https://doi.org/10.1016/j.jfoodeng.2020.110412Get rights and content

Highlights

  • Flour and fat type in cookie dough affected the quality of deposited structures.

  • Ingredients and their interactions altered the dough rheology and microstructure.

  • Pre-heating of cookie dough greatly improved the quality of printed structures.

  • Rheological properties of doughs provided valuable insights for the printability.

  • Burgers model successfully captured the internal structural differences in doughs.

Abstract

This work aimed to investigate the effects of composition and the internal structure of cookie doughs on its printability and baking stability using an extrusion-based three-dimensional (3D) printer. Two types of fat (butter/shortening) and three types of flour (wheat/rice/tapioca) were used, and pre-heating was included in the dough preparation step. The mechanical, thermal, and structural properties of cookie dough were measured. The yield stress values of doughs were between 4.48 and 315.70 Pa. Results showed that pre-heating increased both the yield stress values and printing pressures significantly and the two parameters were positively correlated (r = 0.985). The power-law model parameters represent the internal structure; Af and z values were in between 2303 and 20067 Pa*s1/z and 5.35–13.27, respectively. The creep-recovery analysis indicates that the Burgers model behavior is essential for complex 3D shape deposition. FTIR analysis illustrated that the vibrational properties of dough samples were significantly affected by the fat and flour types.

Introduction

Three-dimensional (3D) printing is an emerging technology in various fields, including material and food science. This novel additive manufacturing technology adds materials layer-upon-layer to create a final 3D object. 3D printing technology is redefined, reimagined, and customized for specific applications in the relevant field (Gao et al., 2015). The use of 3D printing technology includes, but is not limited to, polymer (Wang et al., 2017), metal, ceramic, and glass (Lee et al., 2017), cell-culture ink or bio-printing (Ilkhanizadeh et al., 2007), and food printing (Sun et al., 2015b). The major advantages of 3D printing are the complexity in shape, variety in material supply, unlimited design space, and processing of the materials with less waste (Dankar et al., 2018a; Sun et al., 2015a, 2015b; Thompson et al., 2016). Among various kinds of 3D printing technologies, the extrusion-based method is the most popular and widely studied technology for 3D food printing (Liu, J. et al., 2018).

3D printing can also make foods with customized nutrition, shape, color, flavor, and texture, which can be popular among children, the elderly, and people with dysphagia (Cohen et al., 2009; Gong et al., 2014). Some of the food materials that have previously been used for additive manufacturing applications were ground meat (Lipton et al., 2015), potato puree (Dankar et al., 2018b), baking dough (Yang et al., 2018), cheese, fruits and vegetable blends (Severini et al., 2018b), chocolate (Hao et al., 2010; Lanaro et al., 2017), cookie dough (Pulatsu et al., 2020), and cereal-based snacks with edible insects (Severini et al., 2018a).

It is important to understand the 3D printing process and mechanism that affect the material properties throughout the process and have more options of printable food materials. During printing, the pressure is applied to extrude the material through the nozzle where the deformation occurs for extrusion-based 3D printing with a pneumatic type dispensing system. After the deposition of a layer, the subsequent layer is printed on top of the previous layer, and this continues until the desired shape is produced. In this regard, the relaxation behaviors of food materials become important that may be controlled by the ingredients and their interaction in the network, which affect the printing performance. Hence, it is reasonable to state that the success of the printing process is based on computation between the material structural build-up rate and the deposition rate (Perrot et al., 2016).

Tailoring the formula of food materials for the 3D printing process will encourage the food industry to adopt 3D food printing for broader applications, which requires understanding multi-component food systems. Most food materials used for 3D printing applications are modified using hydrocolloids and flow enhancers for extrusion-based 3D printing; however, those additives have ‘safety and limits of use’ issues when incorporated in the food formula (FDA, 2019). Therefore, developing simple processes and formulations for printable food materials is of the utmost importance. The cookie dough was a model system consisting of multiple ingredients, including fat, sugar, flour, and milk, which, when heated, can affect the conformation and structure of proteins and starch in cookie dough. Thus, the present study aimed to investigate the effects of pre-heating on the internal structure of cookie dough to establish correlations between its structure, printability, and dimensional stability. Mechanical, thermal, macrostructural, and microstructural properties of cookie dough were measured by rheological analysis, differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FTIR), and confocal laser scanning microscopy (CLSM). Lastly, dimensional changes and moisture loss due to the baking of selected 3D printed structures were assessed. This study elucidated the mechanisms of 3D cookie dough printing, together with the implications of processing and component effect on dough properties.

Section snippets

Materials

Wheat (9.68% protein, 74.19% carbohydrate, 9.50% moisture), rice (7.50% protein, 83.20% carbohydrate, 12.67% moisture), and tapioca flours (0% protein, 88.60% carbohydrate, 12.40% moisture), fat (butter; 7.14% protein and shortening), powdered sugar and non-fat milk powder (34.7% protein) were purchased locally (Columbia, MO, USA).

Preparation of cookie dough

A previously optimized formulation and method (Pulatsu et al., 2020) was used to prepare the cookie dough with slight modifications. In brief, 100 g of fat (butter or

Rheological analysis results

The yield stress values ranged between 4.48 ± 0.00 and 315.70 ± 1.70 Pa (Table 2). The yield stress of the printing medium is an important parameter in determining the ability to produce self-supporting layers (Liu, Z. et al., 2018; Zhong et al., 2017). Pre-heating significantly increased the yield stress values of cookie doughs as shown in Table 2. For instance, the yield stress value of BTWF (51.36 ± 2.20 Pa) was significantly lower than that of pre-heated BTWF-T (126.00 ± 0.50 Pa). The

Conclusions

The effects of ingredients and pre-heating on the printability of cookie dough were elucidated by defining the network attributes and building rheological models. In the light of the experiments, pre-heating greatly improved the printability and shape stability of the cookie dough systems by yielding dense network and affecting conformational changes. It was found that solid-like behavior and Burgers’ material behavior are essential for the printability of the cookie dough. Moreover, the

Credit author statement

Ezgi Pulatsu, Methodology, Formal analysis, Writing. Jheng-Wun Su, Methodology. Stuart Kenderes, Methodology. Jian Lin, Writing, Supervision. Bongkosh Vardhanabhuti, Methodology, Writing. Mengshi Lin, Writing, Editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research was partially supported by the Robert T. Marshall Scholarship, USDA National Institute of Food and Agriculture (2020-67030-31336). We would like to acknowledge Dr. Alan Whittington (Geological Sciences, University of Texas – San Antonio) and the University of Missouri calorimetry lab established by the National Science Foundation (Award number: EAR 1220051) and NASA (Award number: NNX12AO44G) for helping with the measurements of cookie dough samples using differential scanning

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