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Post-processing feasibility of composite-layer 3D printed beef
Meat Science 153 (2019) 9–18
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Meat Science journal homepage: www.elsevier.com/locate/meatsci
Post-processing feasibility of composite-layer 3D printed beef Arianna Dick, Bhesh Bhandari, Sangeeta Prakash
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School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
ARTICLE INFO
ABSTRACT
Keywords: 3D printing Meat Beef Post-processing Texture Meat composite
Post-processing feasibility studies the integrity of the designed internal and external structures of 3D printed products. This study examined the effect of infill density (50%, 75%, 100%) and fat content (0, 1, 2, 3 fat layers within a structure) on the post-processing physical changes and texture of lean meat-lard composite layer 3D printed meat products cooked sous-vide. Data from raw and cooked samples were collected to determine cooking loss, shrinkage, moisture retention, fat retention, hardness, chewiness, and cohesiveness. 3D printed objects were successfully cooked, maintaining the internal and external structures. Infill density contributed proportionally (P < .05) to moisture retention, hardness and chewiness, and inversely (P < .05) to shrinkage and cohesiveness, with no effect on fat retention. Whereas, the fat content influenced proportionally (P < .05) to cooking loss, shrinkage, and cohesiveness, and inversely (P < .05) to fat and moisture retention, hardness, and chewiness. The interaction of both independent variables showed a significant effect (P < .05) on all the responses, except for fat retention.
1. Introduction Three-dimensional (3D) printing positions as a novel technology in the food science field, which has a potential of designing complex internal and external structures that would be rather unfeasible by conventional practices such as moulding, while providing unique eating experiences and an opportunity to address special dietary needs (Kouzani et al., 2017). However, this technology is still under development, and the suitability of new foodstuffs (Hamilton, Alici, & Panhuis, 2018), formulations (Liu, Zhang, Bhandari, & Yang, 2018; Yang, Zhang, Bhandari, & Liu, 2018), methodologies (Derossi, Caporizzi, Azzollini, & Severini, 2018; Severini, Derossi, & Azzollini, 2016), and printers (Liu, Ho, & Wang, 2018), among others, are continually being investigated. Due to printers' limitations, most studies employ a single food material blended mix, such as fruits and vegetable blends, pizza and pasta doughs, seafood products, meat products, cheese, chocolates, and more, which are usually deposited using a single nozzle-extruder type 3D printer. Few studies have reported on 3D printing employing dual-nozzle extrusion, such as strawberry juice gel deposited in alternate layers with- or inside a shell of- mashed potatoes (Liu, Zhang, & Yang, 2018), and celery slurry printed into a turkey puree cube (Lipton et al., 2010). Dual-nozzle extrusion for 3D food printing offers the opportunity of developing countless appealing food products intended for different consumer's dietary needs and convenience. Both, ready-to-eat meals and products requiring post-processing can be 3D printed. However, in
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the later ones, the post-processing feasibility (Godoi, Prakash, & Bhandari, 2016) has to be assessed. Post-processing feasibility refers to the study of the integrity of the desired internal and external designs of 3D printed products. Due to the physical and chemical changes occurring during cooking one needs to account the formulation design, printing settings, and post-processing conditions, among other factors to achieve both the expected structure and textural attributes of the food. Limited information regarding the post-processing feasibility of 3D printed products including meat products is currently available (Lille, Nurmela, Nordlund, Metsä-Kortelainen, & Sozer, 2018; Lipton et al., 2010; Lipton, Cutler, Nigl, Cohen, & Lipson, 2015; Severini et al., 2016). Therefore, this study employed the composite layer 3D printing technology by dual extrusion to investigate the feasibility of fabricating 3D printed objects made of lean beef paste with three infill densities and varying the layers of lard deposited within the structure. Infill density is an important factor in 3D printing processing that contributes to the strength and stability of the structure by varying the void fraction and the mass deposited in the internal structure, suggesting an impact on the textural attributes of the printed food. On the other hand, varying the fat content (lard) can provide information about the textural changes caused by the displacement of fat within the structures at varying infill densities, besides giving an idea for future materials combinations for dual extrusion like the customization of 3D meat products with desired fat content and fat distribution. Further, the study evaluates the effects on the post-processing quality parameters
Corresponding author. E-mail address: [email protected] (S. Prakash).
https://doi.org/10.1016/j.meatsci.2019.02.024 Received 2 November 2018; Received in revised form 30 January 2019; Accepted 28 February 2019 Available online 01 March 2019 0309-1740/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Composite multi-layer models: (a) Transparent view of CAD designs for dual nozzle-3D printing of lean meat paste and lard. (b) Sliced model (3 layers of lard): The yellow and green streams denote the meat paste and lard, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
such as cooking loss, moisture retention, fat retention, and shrinkage, as well as the textural properties of the final products. Results from this research will help to understand the physical changes occurring in 3D printed meat products that require cooking.
2.2. Viscoelastic properties of lard Duplicate temperature ramps for lard were performed in order to examine the viscoelastic properties of lard at varying temperature conditions. This allowed to obtain a suitable printing temperature. The test was performed with an ARG2 Rheometer (TA Instruments Ltd), attached with a sandblasted 2° steel cone (40 mm diameter) using a 1 mm gap. The method described by Adewale, Dumont, and Ngadi (2014) at different temperature conditions was employed to record the storage modulus (G′), loss modulus (G″), and tan δ (G″/G′) over a temperature ramp from 4 to 50 °C (ramp rate of 5 °C/min) at a controlled strain of 0.1% (identified within the linear viscoelastic region) and frequency of 1 Hz.
2. Materials and methods 2.1. Sample preparation Heart smart stir-fry beef (with < 5% total fat and < 4% saturated fat) and lard (Yorkfoods – 100% non‑hydrogenated pork fat (39% saturated fat)) were purchased from a local supermarket. The visible connective tissue (CT) and fat were removed from the meat, which was then spread in trays and stored at −18 °C. After 24 h, the meat was kept at 4 °C for 3 h before mincing to reach a chilled state for ease of comminution (Eilert, Blackmer, Mandigo, & Calkins, 1993). Then, it was ground twice using a meat mincer (TRE SPADE, Tube No. 8) with a rotational speed of 160 rpm and 20 kg/h capacity, equipped with a 4.5 mm diameter disc, and vacuum packed (Food Saver, Sunbeam) and frozen until usage. The comminution of the meat contributed to the extraction of soluble proteins that interact with water, salt, and fat to form an emulsion-like printable paste. The meat paste base consisted of 85% meat and 15% water. 1.5% NaCl and 0.5% guar gum (GG) (Melbourne Food Ingredient Depot, Australia) relative to meat paste base were added. The results obtained during preliminary experiments helped set the formulation. Initially, only 1.5% NaCl was added to the meat paste that was not easily extrudable. Following this, the addition of different hydrocolloids and water were tested for extrudability, where a ratio of 30:1 for water:GG was found to be appropriate for extrusion. NaCl allows the extraction of the myofibrillar proteins, which have an important role in binding and stabilizing the meat paste. According to Allais (2010), a minimum quantity of 1.5% NaCl is required in meat processing when no phosphates are used. Whereas GG increases the viscosity (Feiner, 2006) and elasticity of the gel that will support the printed structure, which was attained at 0.5% GG in this study. GG was mixed with water to make a GG slurry, and stored overnight at 0–4 °C to allow solubilisation. Then, the meat was mixed with NaCl in a food processor with 2 speed settings (Model FDP646SI (1000 W), Kenwood, United Kingdom) attached with a multipurpose knife blade for 10 s at speed 1, with a resting period of 10 s in between. Subsequently, GG slurry was added and mixed twice for 20 s at speed 2 with a resting time of 10 s between runs that produced an emulsion-like paste.
2.3. Modelling of 3D design Four computer-aided design (CAD) composite multi-layer models were designed with TINKERCAD® (AUTODESK®) and sliced with Slic3r software, adapted to Repetier-Host V2.1.2. The designs consisted of a rectangular prism with the following dimensions: length = 40 mm, width = 40 mm, and height = 10 mm. In order to vary the fat content within the structure, 0, 1, 2 and 3 layers of 1 mm height of lard were inserted to samples labelled as 0F, LF, MF, and HF, respectively. The layers of lard were located in the interior part of the structure (2 mm in per side), surrounded by meat paste (perimeters and solid top and bottom layers) to reduce the fat release from the sides of the printed meat products during post-processing cooking. Transparent views of the CAD designs are shown in Fig. 1a. 2.4. 3D Printing process The printing process was carried out at ambient temperature (23 ± 1 °C) using a dual nozzle model 3D printer (Shinnove, Hangzhou Shiyin Technology, China) with Cartesian configuration. The printing settings were defined based on preliminary trials, as following: 1.95 mm layer height and 1.5 mm first layer height for extruder 1 (meat paste), 1 mm layer height for extruder 2 (lard), 2 vertical shell perimeters, 2 solid layers on top and bottom, 20 mm/s speed, and 100% flow rate. The first layer was set to 1.5 mm to provide the structure with a stable base to enhance the support of the following layers. In addition, three different infill densities were tested (50%, 75%, and 100%) using rectilinear infill pattern. In order to slice the multi-material design, the templates for both materials (meat paste and lard) composing the structure were designed 10
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independently, exported as .stl files and merged with Slic3r, assigning each material to one extruder, as described by Liu, Zhang, and Yang (2018). Extruder 2 offsets were set at 65.7 mm for the X-axis, and 0.5 mm for the Y-axis, as recommended by the printer manufacturer. Fig. 1b displays a sliced HF model with 100% infill and rectilinear pattern. Two skirt loops at 6 mm distance from the object were added to allow the release of any air entrapped during the cartridge filling that would, otherwise, break the filament during printing. This ensures a continuous filament as of the bottom layer. The treatments were labelled as following: 0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100. The two first digits correspond to the fat content, as described in Section 2.3, and the subsequent numbers denote the infill density (%). Nine replicates per treatment were 3D printed.
Cooking loss (%) =
Raw weight Cooked Weight × 100 Raw weight
Shrinkage (%) (Raw L Cooked L) + (Raw W Cooked W) + (Raw H = Raw L + Raw W + Raw H
Cooked H)
× 100
where L refers to length (mm), W refers to width (mm) and H to height (mm).
Fat retention (%) =
Cooked weight × percentfat in cooked samples × 100 Raw weight × percent fat in raw samples
Moisture retention (%) Cooked weight × percent moisture in cooked samples = × 100 Raw weight × percent moisture in raw samples
2.5. Dimensional printing deviation
2.7. Textural properties of cooked printed samples
In order to determine the printing accuracy, the dimensional printing deviation was measured as per the method described by Liu, Zhang, and Bhandari (2018). Five replicates of each sample were evaluated by measuring three different positions (bottom, middle and top) for each dimension (length, width, height) using a digital calliper (CraftRight). The printing deviation (mm) was calculated by the difference between the designed and experimental dimensions. Averaged values per dimension were used. Values for dimensions and weight were recorded immediately after printing. Then, the samples were stored at −18 °C for 24 h in an airtight container.
2.7.1. Instrumental puncture test The puncture test was performed on cooked samples (average of 8 different positions), using a texture analyser (TA.XT Plus, Stable Micro system, Arrow Scientific Pty., Ltd) attached with a 4 mm diameter cylinder aluminum probe. Pre-test speed of 1 mm/s, test and post-test speed of 1.5 mm/s were applied to compress the samples to a 4.0 mm distance (36%–53% compression) before returning to the start position. Hardness was identified from the test curves to estimate the firmness of the sample, on two replicates. 2.7.2. Instrumental texture profile analysis (TPA) For TPA, the cooked samples were subjected to a double-cycle compression using a texture analyser (TA.XT Plus, Stable Micro system, Arrow Scientific Pty., Ltd) attached with a 50.8 mm diameter and 20 mm height acrylic cylinder probe. Pre-test speed of 1 mm/s, test and post-test speed of 1.5 mm/s, and a 4.0 mm distance were used. The holding time between compressions was set to 5 s. Hardness, cohesiveness, and chewiness were recorded from the test curves, on three replicates.
2.6. Post-processing conditions and characterisation 2.6.1. Moisture content The moisture content of the 3D printed samples (raw and cooked) was determined on two replicates by the standard vacuum drying method at 95°–100 °C and < 1.33 × 104 Pa for 6 h (AOAC, 1991). 2.6.2. Fat (crude) extract Extractable fat content in 3D printed samples (raw and cooked) was determined on two replicates by the AOAC Official Method 960.39(b) for fat (crude) or ether extract in meat (AOAC, 2005). Petroleum ether was employed for an extraction period of 6 h at a condensation rate of 4–5 drops/s in a Soxhlet extractor. Furthermore, in order to analyse the post-processing feasibility of the 3D printed samples, as affected by the infill percentage and fat content, the cooking loss, shrinkage, moisture retention, and fat retention were determined. After 24 h of storage, 3D printed samples were vacuum-packed (Food Saver, Sunbeam) in the frozen state to avoid damaging the structures during packaging and heat-treatment. Subsequently, the samples were cooked sous-vide with precisely controlled heating in a digital uncirculated water bath (Thermoline Scientific, AU) at 75 °C for 30 min by submerging the vacuum-packed samples in randomized locations without overlapping under a wire rack to keep the samples at the same height level within the water bath. After cooking, weight and dimensions (averaged values of three different positions per dimension) were recorded in five replicates to calculate cooking loss (%) and shrinkage (%); while fat content and moisture were determined in two replicates to calculate fat retention (%) and moisture retention (%), respectively. Shrinkage values were calculated based on the equation proposed by El-Magoli, Laroia, and Hansen (1996) for cylinder shapes, and thus modified for a rectangular prism in this study. The moisture retention and fat retention values were determined according to the equations described by Murphy, Criner, and Gray (1975).
2.8. Data analysis All the data are presented as mean values with their standard errors, and were analysed using Minitab® 17.1.0. The effect of fat content (4 levels: 0, 1, 2, and 3 layers of lard within a structure), infill density (3 levels: 50%, 75%, and 100%), and their interaction (Fat content*Infill density) on each response was estimated by two-way analysis of variance (ANOVA) using a confidence level of 95%. Pairwise comparisons were determined by Tukey's test with 5% level of significance. One replicate per treatment was 3D printed per day, and the printing process lasted 9 days, so in total there were 9 replicates for each treatment. The beef meat was procured from the market prior to each printing session and the meat paste was prepared on the day of printing. The following responses were measured on individual replicates: the same 5 replicates were used for dimensional printing deviation, cooking loss, shrinkage, TPA (3), and puncture test (2), while the remaining 4 replicates were used for moisture retention and fat retention (2 for raw samples and 2 for cooked samples). 3. Results and discussion 3.1. Viscoelastic behaviour of lard A temperature ramp for lard was performed in order to examine its viscoelastic properties at varying temperature conditions and identify a suitable printing temperature. As shown in Fig. 2, a decline in G′ was 11
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Fig. 2. Viscoelastic properties of lard (G′: Storage modulus, G″: Loss modulus, tan δ (G″/G′)) at a temperature ramp (4–50 °C, rate of 5 °C/min) in a rheometer at a controlled strain of 0.1% and frequency of 1 Hz.
observed as the temperature increased. This is attributed to the melting of crystalline fats (Adewale et al., 2014). G′ (elastic or solid-like behaviour of the material) was higher than G″ (viscous or fluid-like behaviour) until 27.9 °C was reached. Beyond this temperature, the material behaved more fluid-like (tanδ = G″/G′ > 1). Therefore, considering that the 3D printer cannot work at controlled-temperatures below ambient temperature (23 ± 1 °C), the lard was left at ambient temperature overnight before printing, and the printing process was carried out without temperature control. At such conditions, the lard presented solid-like behaviour with suitable tan δ (0.73), being printable and behaved as a self-supporting material during printing.
3.3. Dimensional printing deviation The dimensional printing deviation for each raw sample was calculated in order to determine the printing accuracy, by comparing the dimensions (length, width, and height) of the printed object in relation to the CAD design, as affected by the infill density, fat content and their interaction. The deviation values ranged from −0.005% to 1.423%, −0.053% to 1.667%, and 1.43% to 11.04% for the length, width and height, respectively. Positive values indicate expansion of dimension, and negative values denote reduced dimension of objects (Liu, Zhang, & Bhandari, 2018). Overall, no effect was observed by the interaction of infill densities and fat content on each dimensional deviation (data not shown). The length and width of samples showed lower deviation from the designed structure, with maximum values of 0.6 mm and 0.7 mm, respectively. The fat content did not affect the deviation of length and width of samples (Fig. 4b,d), which may be attributed to the lard being printed inside the samples perimeter, minimising the fat loss. On the other hand, significant larger deviation for both dimensions was observed when increasing the infill density to 100% (Fig. 4a,c), which is attributed to a dense structure of deposited material, which can be related to an increased weight, as shown in Table 1. Also, increased amount of meat paste deposited within a structure (e.g., 0F vs. HF) may contribute to overall morphological deviation due to the ‘extrude swell phenomenon’, which may be affected by the springiness of the material, as described by Kim, Bae, and Park (2017). Similar results were observed for the morphological accuracy of 3D printed cereal-based snacks by Severini et al. (2016). In case of height, opposite results were observed. The infill density showed no effect on height deviation (Fig. 4e), whereas an inverse relationship was observed with fat content (Fig. 4f). This may be
3.2. 3D Printed samples Meat paste and lard were successfully 3D printed as composite multi-layer models. Depending on the designs, the estimated printing times (EPT) ranged from 6 min 35 s to 10 min 21 s. EPT, weight, moisture and fat contents of raw and cooked samples are presented in Table 1. A 3D printed sample (LF100) before and after sous-vide cooking is presented in Fig. 3a. The layers of lard were printed inside a shell of meat paste and thus, cannot be observed in the final product. As mentioned earlier, this was designed with the purpose of reducing fat loss during cooking. The internal structure is shown in Fig. 3b. The layers of meat paste and lard were inserted according to the fat content and infill density. Differences between the stream diameters for meat paste and lard at the three levels of infill densities (50%, 75%, and 100%) used in this study can be distinguished (Fig. 3b). As expected, due to the different nozzle diameters used for meat (2 mm) and lard (1 mm), more filament was needed to complete a layer of lard than a layer of meat paste.
Table 1 Experimental data for estimated printing time (EPT), weight, and moisture and fat content of raw and cooked 3D printed samples with different internal infill structure and composition (RS = raw sample, CS = cooked sample). (0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100: The two first digits correspond to the fat content (0F, LF, MF, HF refer to 0, 1, 2 and 3 layers of lards, respectively), and the subsequent numbers denote the infill density (%)). 3D printed sample
EPT (min)
RS weight (g)
CS weight (g)
RS moisture (%)
CS moisture (%)
RS fat content (%)
CS fat content (%)
0F50 0F75 0F100 LF50 LF75 LF100 MF50 MF75 MF100 HF50 HF75 HF100
6.58 7.08 7.60 7.13 7.80 8.47 7.73 8.57 9.38 8.22 9.30 10.35
14.60 ± 0.04d 15.98 ± 0.05bc 17.30 ± 0.02a 13.61 ± 0.06ef 14.94 ± 0.10d 16.15 ± 0.05b 13.45 ± 0.10 fg 14.68 ± 0.13d 15.62 ± 0.10c 12.98 ± 0. 11 g 14.01 ± 0.16e 15.6 ± 0.18c
12.80 ± 0.06 cd 13.22 ± 0.10b 15.30 ± 0.03a 11.21 ± 0.14 fg 11.86 ± 0.10de 12.80 ± 0.09bc 10.26 ± 0.11hi 10.98 ± 0. 16 g 11.62 ± 0.12ef 9.59 ± 0.10j 10.01 ± 0.14ij 10.74 ± 0. 05 gh
74.13 74.35 75.46 73.08 72.30 72.53 68.94 67.62 67.76 65.70 64.37 62.03
70.40 69.15 72.40 69.41 69.20 70.02 65.56 68.04 68.53 66.45 67.86 67.19
0.49 ± 0. 05 g 0.53 ± 0. 05 g 0.45 ± 0. 02 g 4.03 ± 0.17f 4.61 ± 0.39f 4.84 ± 0.68f 8.21 ± 0.28e 11.12 ± 0.92d 12.65 ± 0.28 cd 14.76 ± 0.37bc 16.80 ± 0.001ab 18.97 ± 0.43a
0.44 0.56 0.48 2.28 2.47 2.15 4.32 3.18 3.28 5.15 5.13 4.25
Values that do not share a letter within a column are significantly different. 12
± ± ± ± ± ± ± ± ± ± ± ±
0.03abc 0.11ab 0.41a 0.41bc 0.07c 0.53bc 0.53d 0.47de 0.43d 0.46ef 0.15f 0. 03 g
± ± ± ± ± ± ± ± ± ± ± ±
0.11ab 0.08abcd 0.23a 0.07abc 0.14abcd 0.09abc 0.24d 0.46bcd 0.46bcd 0.13 cd 0.08bcd 0.12bcd
± ± ± ± ± ± ± ± ± ± ± ±
0.11c 0.02c 0.06c 0.24bc 0.29bc 0.03bc 1.00ab 0.29ab 0.33ab 0.38a 0.67a 0.21ab
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Fig. 3. 3D Printed composite multi-layer meat models: (a) Raw and cooked samples (LF100: 1 layer of lard, 100% infill density), (b) filament stream at different infill densities (50%, 75%, and 100%) for meat paste (2 mm nozzle diameter) and lard (1 mm nozzle diameter).
layers increased from 1 to 3. These observations were corroborated by both the cooking loss and shrinkage values, which were similarly affected by fat content (Fig. 6b,d). The higher the fat content, the higher the cooking loss and shrinkage was observed (no significant difference beyond LF). These results were expected as fat was deposited in layers within the structure instead of initially being emulsified in the meat paste. Therefore, the instability of fat droplets may have resulted in coalescence during cooking (Tornberg, 2013), increasing the probability of melted fat displacement from the inner to the outer part of the structure and facilitating its partial release through the porous shell (mainly top and bottom layers, Fig. 8b) during cooking, leaving room for contraction. In addition, it has been reported that drip loss starts earlier in samples with higher fat content due to an improved heat transfer, affecting the total drip mass and the ratio of fat to water in the drip (Sheridan & Shilton, 2002). As shown in Fig. 8a, the fat layers were imperceptible after cooking, explaining the contraction of the structures with increased fat content as not enough support remained inside for the shell. Ulu (2006) reported similar cooking loss results for meatballs containing 0.5% guar gum and fat contents ranging from 10% to 25%. While, Sheridan and Shilton (2002) reported that above 30% added fat, the amount lost as drip was nearly equivalent to the added amount. Regarding infill density, shrinkage values were significantly lower at 100% only (Fig. 6c). However, this result may be affected by the interactive effect of infill density with fat content, since for 0F samples a significant reduction (P < .5) from 8.4% to 4.3% was determined for 50% and 100% infill density, respectively, whilst no significant differences were found at varying infill densities of each fat-containing sample (explaining the larger error bar in Fig. 6c). This suggests a packed structure in 0F100 samples (containing only meat paste with < 0.5% fat) that after the aggregation of proteins during heat treatment, formed a network where water was trapped, as denoted by
explained by the smaller nozzle diameter used for lard (1 mm) and a lower density of fat (0 .9g/mL (FAO/WHO Food Standards Programme, 2001)) compared to that of the meat paste (1.106 g/mL), resulting in a lower mass of material deposited per layer of lard as compared to meat paste. 3.4. Post-processing conditions and product characterisation Composite multi-layer 3D printed samples were cooked sous-vide. Both, the shell and the interior design were well maintained during post-processing. In order to analyse the effect of the intricate designs of the 3D printed structures on the post-processing conditions, cooking loss, shrinkage, moisture and fat retention after sous-vide cooking were determined for better understanding of the changes in the textural properties, as a function of infill density and fat content. 3.4.1. Cooking loss and shrinkage Cooking loss and shrinkage are common changes during post-processing of meat products, due to the thermal denaturation and consequent contraction of meat proteins (Oroszvári, Rocha, Sjöholm, & Tornberg, 2006). The main cooking losses of meat products are attributed to water and fat. Bulk water, which is held by capillary forces in the meat matrix, is mostly expulsed from the meat network when a contraction pressure is generated by heat, causing protein shrinkage and water release. Whereas, fat is displaced by drip and depends on both, the fat content and the stability of the fat droplets within the meat network (Tornberg, 2013). As seen in Fig. 5, visual shrinkage of the samples was observed with increasing fat content. In 0F samples (Fig. 5a), the structure looked more packed and tighter after cooking with a slight upward bow, compared to the samples containing lard (Fig. 5b-d), for which increased contraction was observed in the topcenter and the central part of the perimeter, as the number of lard 13
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Fig. 4. Dimensional printing deviation of 3DP meat products: (a) length vs infill density, (b) length vs fat content, (c) width vs infill density, (d) width vs fat content, (e) height vs infill density, (f) height vs fat content (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
Fig. 5. 3D printed cooked samples at varying fat contents: (a) 0F (no added lard), (b) LF (1 lard layer), (c) MF (2 lard layers), (d) HF (3 lard layers).
Deng, Toledo, and Lillard (1981) for emulsion-like stabilizations. Thus, better maintaining the shape of the 3D printed constructs. Similar situation was observed for cooking loss as affected by infill density. Probably due to the interactive effect of the independent variables, 0F100 presented the minimum cooking loss (11.5%) of all samples, which explains the shrinkage results previously discussed. On the other hand, higher cooking loss was observed when infill density increased from 50% to 75%. This advises greater retention of moisture and fat within the structure at 50% infill density, which may be because of the less packed and more porous structure that -under the same cooking conditions- may had had lower heat transfer as compared to samples with 75% infill density. It is suggested that in samples with lower
heating rates, the water released from the myofibrils due to contraction fills the air pores within the meat matrix, whereas higher heating rates drive the water out of the pores due to shrinkage of connective tissue (Oroszvári, Rocha, et al., 2006; Poon, Durance, & Kitts, 2001). 3.4.2. Moisture and fat retention As a result of shrinkage due to the contraction of meat proteins during heat treatment, moisture and fat losses are triggered (Oroszvári, Bayod, Sjöholm, & Tornberg, 2006). Moisture retention was found to be affected by both independent variables (infill density and fat content) and their interaction (Fig. 7a). A significant decrease in moisture retention was obtained when increasing fat content, with no difference 14
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Fig. 6. Cooking loss (%) vs (a) infill density, (b) fat content. Shrinkage (%) vs. (c) infill density, (d) fat content of 3D printed meat samples (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
cooked samples, which could likely influence tenderness, as suggested by Rohlik et al. (2017). In addition, the authors reported that when cooking beef muscles (raw eye and striploin) at temperatures between 60 and 75 °C for 20–60 min, the total drip and water retention were not influenced by intramuscular fat content, but by the cooking time and temperature. Taking into account the temperature employed in our study (75 °C), besides the transversal (at around 50 °C) and longitudinal (above 70 °C) shrinkage due to the denaturation of myosin and actin, respectively, the contraction pressure on heating which mostly triggers water loss is primarily originated by the shrinkage of connective tissue at 60–70 °C (Tornberg, 2013). This helps to explain the meaningful water losses (15–26%) observed in all the samples. In addition, one possible way to reduce dripping is to minimize the porosity of top and bottom layers by using concentric pattern for 3D printing instead of a rectilinear pattern, since fewer spaces (porous) would be left due to a parallel deposition of the infill pattern to that of the perimeters. Further, higher moisture retention was only observed at 100% infill density (data not shown), and sample 0F100 presented the highest moisture retention (84.9%) thus corroborating the cooking loss and shrinkage results discussed in Section 3.4.1. On the other hand, fat retention was inversely affected by fat content (Fig. 7b). As the number of lard layers increased and the layers of meat paste decreased in order to complete 10 layers per object, less amount of meat was available to strengthen the shell and thus the ease of fat to drop out of the structure may have improved. In addition, this result is in agreement with the findings reported by Sheridan and Shilton (2002) for fat drip production. By increasing the fat content of beefburgers subjected to infrared cooking, more fat was lost by dripping, and beyond 30% added fat, the drippings were nearly equal to the added amount. Similar fat loss outcomes were identified for pork-fat injected beef steaks (Reed, Walter, Schmitz, Guadián-García, & Lawrence, 2017). As previously mentioned, this may be explained by the coalescence of unstable fat deposited within the structure, which is not stabilized in the protein network but ready for displacement upon melting.
Fig. 7. Fat (FR) and moisture retention (MR): (a) vs. the interactive effect of fat content and infill density, (b) vs. the independent effect of fat content. (0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100: The two first digits correspond to the fat content (0F, LF, MF, HF refer to 0, 1, 2 and 3 layers of lards, respectively), and the subsequent numbers denote the infill density (%)).
observed beyond 2 layers (Fig. 7b). This may be attributed to (i) lower initial bulk water, and (ii) shorter cooking times required for samples containing lard in order to obtain equivalent moisture content in the 15
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Fig. 8. 3D printed-cooked samples: (a) imperceptible fat layers after cooking, (b) fluids release observed during TPA test, (c) puncture test at different locations.
Fig. 9. Hardness by puncture test of 3D printed samples vs. (a) infill density and (b) fat content (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
Regarding infill density and their interaction within fat contents, no significant differences were detected (Fig. 7a). However, a different trend of numerical values was noted for samples with no added fat (0F). As shown in Fig. 7a, for samples with added fat, although no significant differences were found, the trend showed lower values for fat retention as the infill density increased, whereas opposite trend was seen for 0F. Contrary to samples with added fat, the fat content determined in 0F samples corresponds entirely to the fat contained in the meat paste, which is retrieved from the meat used (mostly intramuscular fat, as visible fat was removed) and stabilized in the system. Hence, as moisture was lost during cooking, the fat seemed to increase, on an average. In addition, since fat is suspended within the meat paste by a protein film, during heat treatment, myofibrillar proteins produce a gel that stabilizes the emulsion (Ugalde-Benitez, 2012). Similar findings were reported by Hughes, Mullen, and Troy (1998). 3.4.3. Hardness measurement by puncture test 3D printed samples presented a non-uniform structure with alternate pattern direction of layers throughout (e.g., perimeters and infill). Hence, wide variability in hardness was expected for the outer layers of the structure. Therefore, average points from different locations were considered for puncture. One-cycle puncture test with 4 mm cylinder probe (Fig. 8c) was performed in order to calculate the force (hardness) required to penetrate at various points of the structure. As described by Braeckman, Ronsse, Hidalgo, and Pieters (2009), a puncture test provides the hardness of the desired crust in grilled-meat products to avoid moisture and fat release. Although, no such “crust” was formed on the sous-vide cooked 3D printed samples, the mechanical properties of the outer layer was expected to proportionally increase with infill density, due to a packed inner structure acting as support. The two independent variables and their interaction showed a
Fig. 10. TPA results (hardness, chewiness, and cohesiveness) of 3D printed samples vs. (a) infill density and (b) fat content (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
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Table 2 Experimental results for TPA and puncture test as affected by the interaction of fat content and infill density. (0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100: The two first digits correspond to the fat content (0F, LF, MF, HF refer to 0, 1, 2 and 3 layers of lards, respectively), and the subsequent numbers denote the infill density (%)). Sample
0F50 0F75 0F100 LF50 LF75 LF100 MF50 MF75 MF100 HF50 HF75 HF100
TPA
Puncture
Hardness (N)
Chewiness (N)
Cohesiveness
Hardness (N)
58.36 93.58 93.60 54.11 73.00 98.65 58.04 49.83 60.80 30.42 36.33 53.35
81.14 ± 12.14d 137.01 ± 14.56abc 144.96 ± 9.50ab 95.39 ± 1.58bcd 103.22 ± 12.42abcd 154.29 ± 5.39a 99.67 ± 12.27bcd 80.12 ± 8.20d 86.66 ± 9.33 cd 60.21 ± 11.56d 68.20 ± 4.96d 94.57 ± 10.55bcd
1.38 1.46 1.55 1.76 1.41 1.56 1.75 1.60 1.42 1.99 1.88 1.78
3.14 ± 0.12ab 2.96 ± 0.25abc 2.56 ± 0.32bc 2.50 ± 0.14bcd 3.65 ± 0.05a 2.69 ± 0.20abc 2.25 ± 0.21bcd 2.5 ± 0.0003bcd 2.48 ± 0.04bcd 1.48 ± 0.25d 2.01 ± 0.27 cd 2.32 ± 0.04bcd
± ± ± ± ± ± ± ± ± ± ± ±
8.80bc 4.67a 2.86a 1.06bcd 6.51ab 2.53a 9.82bcd 4.28bcd 5.21bc 5.81d 2.41 cd 6.46bcd
± ± ± ± ± ± ± ± ± ± ± ±
0.06e 0.09cde 0.09bcde 0.02abc 0.08e 0.04bcde 0.11abcd 0.03bcde 0.04de 0.06a 0.03ab 0.08abc
Values that do not share a letter within a column are significantly different.
by El-Magoli et al. (1996) when increasing the fat content of beef patties from 11% to 22%. For cohesiveness, the independent effect from both factors showed significantly higher values at 50% infill density and HF only. The interactive effects are shown in Table 2. It should be noted that higher values were recorded in hardness for TPA compared to puncture test, due to the different probes and test parameters used. TPA was performed over the whole sample in order to estimate the eating experience of biting the intact shape (Fig. 8b), whilst puncture test was performed at averaged specific points penetrating through the sample at independent locations (Fig. 8c), such as the center, the perimeter, and the corners. This wide variation is in agreement with compared results of TPA and puncture tests performed to beef patties, by Braeckman et al. (2009). As presented in Table 2, 0F75, 0F100, and LF100 samples exhibited significantly higher hardness (TPA) than the rest of samples (except for LF75), whereas HF50 presented the lowest numerical value for the same attribute. In fact, the texture results for HF50 and HF75 (~5% fat content when cooked) samples provide a potential option for people with chewing and swallowing difficulties, although further texture tests for soft foods, such as the fork and spoon test (International Dysphagia Diet Standardization Initiative, 2017), as well as the adaptation of the sample to a bite-size should be considered. In order to achieve tender products, different cooking conditions should be applied, such as lowering the cooking temperature and increasing holding times during sous-vide cooking, which results in minor cooking losses, as reported by Zielbauer, Franz, Viezens, and Vilgis (2016). Additionally, the assessment of the suitability of 3D printed meat products to different cooking methods would provide valuable information for the applicability of 3D printing technology to a broader range of products.
significant effect (P < .5) on the hardness. An inverse relationship was evidenced by increasing fat content beyond LF, which may be explained by the higher cooking loss and shrinkage observed under the same conditions. In terms of infill density, a similar trend as the one detected for the cooking loss was observed for hardness (Figs. 6a, 9a), being higher at 75% compared to 50% infill. Thus, it is expected that a higher cooking loss resulted in tougher products, caused primarily by moisture loss. In fact, although no significant difference was observed, numerical values for moisture retention at 75% infill were lower than those at 50% infill (data not shown). 3.4.4. TPA of cooked 3D printed meat samples Texture profile analysis of cooked samples was performed to obtain the hardness, chewiness and cohesiveness values of whole samples. Hardness, which denotes the firmness of the samples, was taken from the positive peak of the first compression cycle. As shown in Fig. 10a, a proportional effect on hardness was observed by increasing infill density, whilst inverse relationship was obtained with increasing fat content past LF (Fig. 10b). These results were expected as the fat content in cooked samples increased from 0.44%–2.44% for 0F and LF to 3.18%–5.15% for MF and HF. Thus, relating the results to the bite theory explained by Smith and Carpenter (1974), since fat has a lower density compared to that of lean tissue, a higher content of deposited fat within the matrix results in a lower bulk density of the meat product and ease of bite (tenderness). In addition, due to shrinkage and separation of muscle fibres during cooking (Rohlik et al., 2017), melted fat is liquefied and dispersed through the meat matrix (Sheridan & Shilton, 2002), supposedly spreading apart the strands of connective tissue (Cover & Hostetler, 1960), and breaking the continuous network of protein where fat is partly retained (Sheridan & Shilton, 2002). So, as more fat is added to the matrix, the tenderness is most likely increased since less force is required to split fat tissue than muscle fibres (Rohlik et al., 2017). Furthermore, more room is left in samples with higher fat content after cooking due to fat loss, compared to a tighter structure in samples with lower fat contents and high infill density. Similar results were reported by Reed et al. (2017), who found more tenderness in pork-fat injected beef steaks as compared to control samples (no fat injected). These results suggest that fat can be added to a blended mix to improve its viscosity and printability and then be released during heat treatment. However, some remain trapped within the structure to contribute to cohesiveness and tenderness. A similar trend was observed for chewiness values, but significant difference was only evident at 100% infill density and beyond LF with no difference between MF and HF. Comparable results were described
4. Conclusions Composite multi-layer 3D printed meat products were successfully fabricated and subjected to post-processing (sous-vide cooking). All the samples maintained their internal and external structure after cooking, although partial inwards contraction was observed in MF (2 layers of lard) and HF (3 layers of lard) samples, with an initial fat content ranging from 8.21%–12.65% and 14.76%–18.97%, respectively. Overall, increasing the fat content (or lard layers) resulted in higher cooking loss, shrinkage, and cohesiveness, and lower fat retention, moisture retention, hardness, and chewiness. Whereas, an increase in infill density led to higher moisture retention with lower shrinkage and cohesiveness, resulting in higher hardness and chewiness. In addition, the interaction of both independent variables showed a significant 17
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effect on all the responses, except for fat retention and dimensional printing deviation. Thus, the interactive effect may help to explain better the post-processing textural changes observed. Further work will be pursued to study the feasibility of 3D printed composite multi-layer meat products for post-processing at different cooking methods and conditions. It would be of interest to examine the microstructural changes occurring during cooking to explain the sensorial and textural impact on the final product. Furthermore, this study proposes a meat product design that can be modified by adding other food materials, such as cheese or vegetables to create tailored composite multi-layer 3D printed products.
whey protein on frankfurters formulated with 5% and 12% fat. Meat Science, 48(1), 169–180. International Dysphagia Diet Standardization Initiative (2017). IDDSI framework. Testing methods. Retrieved from www.iddsi.org. Kim, H. W., Bae, H., & Park, H. J. (2017). Classification of the printability of selected food for 3D printing: Development of an assessment method using hydrocolloids as reference material. Journal of Food Engineering, 215, 23–32. Kouzani, A. Z., Adams, S., Whyte, D. J., Oliver, R., Hemsley, B., Palmer, S., & Balandin, S. (2017). 3D printing of food for people with swallowing difficulties. KnE Engineering, 2(2), 23–29. 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. Lipton, J., Arnold, D., Nigl, F., Lopez, N., Cohen, D., Norén, N., & Lipson, H. (2010). Multimaterial food printing with complex internal structure suitable for conventional postprocessing. Paper presented at the annual international solid freeform fabrication symposium, Austin, Texas. Lipton, J. I., Cutler, M., Nigl, F., Cohen, D., & Lipson, H. (2015). Additive manufacturing for the food industry. Trends in Food Science & Technology, 43(1), 114–123. Liu, C., Ho, C., & Wang, J. (2018). The development of 3D food printer for printing fibrous meat materials. IOP Conference Series: Materials Science and Engineering, 284(1), 012019. Liu, Z., Zhang, M., & Bhandari, B. (2018). Effect of gums on the rheological, microstructural and extrusion printing characteristics of mashed potatoes. International Journal of Biological Macromolecules, 117, 1179–1187. Liu, Z., Zhang, M., Bhandari, B., & Yang, C. (2018). Impact of rheological properties of mashed potatoes on 3D printing. Journal of Food Engineering, 220, 76–82. Liu, Z., Zhang, M., & Yang, C. (2018). Dual extrusion 3D printing of mashed potatoes/ strawberry juice gel. LWT - Food Science and Technology, 96, 589–596. Murphy, E. W., Criner, P. E., & Gray, B. C. (1975). Comparisons of methods for calculating retentions of nutrients in cooked foods. Journal of Agricultural and Food Chemistry, 23(6), 1153–1157. Oroszvári, B. K., Bayod, E., Sjöholm, I., & Tornberg, E. (2006). The mechanisms controlling heat and mass transfer on frying of beefburgers. III. Mass transfer evolution during frying. Journal of Food Engineering, 76(2), 169–178. Oroszvári, B. K., Rocha, C. S., Sjöholm, I., & Tornberg, E. (2006). Permeability and mass transfer as a function of the cooking temperature during the frying of beefburgers. Journal of Food Engineering, 74(1), 1–12. Poon, P., Durance, T., & Kitts, D. (2001). Composition and retention of lipid nutrients in cooked ground beef relative to heat-transfer rates. Food Chemistry, 74(4), 485–491. Reed, D. D., Walter, L.-A. J., Schmitz, A. N., Guadián-García, D. E., & Lawrence, T. E. (2017). Post-mortem mechanical injection of low quality beef loins with pork back fat improves palatability and sensory attributes. Meat Science, 123, 205–210. Rohlik, B., Øiseth, S., Garcia, T., Sabarez, H., Watkins, P., Oytam, Y., & Buckow, R. (2017). Muscle structure and water retention in fresh and cooked meat products. Retrieved from Australian Meat Processor Corporation https://www.ampc.com.au. Severini, C., Derossi, A., & Azzollini, D. (2016). Variables affecting the printability of foods: Preliminary tests on cereal-based products. Innovative Food Science & Emerging Technologies, 38, 281–291. Sheridan, P. S., & Shilton, N. C. (2002). Analysis of yield while cooking beefburger patties using far infrared radiation. Journal of Food Engineering, 51(1), 3–11. Smith, G., & Carpenter, Z. (1974). Eating quality of animal products and their fat content. Paper presented at the proceedings of the symposium on changing the fat content and composition of animal products. Tornberg, E. (2013). Engineering processes in meat products and how they influence their biophysical properties. Meat Science, 95(4), 871–878. Ugalde-Benitez, V. (2012). Meat emulsions. In Y. H. Hui (Ed.). Handbook of meat and meat processing(2nd ed.). Chapman and Hall/CRC. Ulu, H. (2006). Effects of carrageenam and guar gum on the cooking and textual properties of low fat meatballs. Food Chemistry, 95(4), 600–605. Yang, F., Zhang, M., Bhandari, B., & Liu, Y. (2018). Investigation on lemon juice gel as food material for 3D printing and optimization of printing parameters. LWT - Food Science and Technology, 87, 67–76. Zielbauer, B. I., Franz, J., Viezens, B., & Vilgis, T. A. (2016). Physical aspects of meat cooking: Time dependent thermal protein denaturation and water loss. Food Biophysics, 11(1), 34–42.
Declarations of interest statement None to declare. Funding source declaration This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Adewale, P., Dumont, M. J., & Ngadi, M. (2014). Rheological, thermal, and physicochemical characterization of animal fat wastes for use in biodiesel production. Energy Technology, 2(7), 634–642. Allais, I. (2010). Emulsification. In F. Toldrá (Ed.). Handbook of meat processing. Blackwell Publishing. AOAC (1991). Loss on drying (moisture) in meat. Official method 950.46B. Gaithersburg, MD: AOAC International. AOAC (2005). Fat (crude) or ether extract in meat. Official method 960. Vol. 39. AOAC International. Braeckman, L., Ronsse, F., Hidalgo, P. C., & Pieters, J. (2009). Influence of combined IRgrilling and hot air cooking conditions on moisture and fat content, texture and colour attributes of meat patties. Journal of Food Engineering, 93(4), 437–443. Cover, S., & Hostetler, R. L. (1960). An examination of some theories about beef tenderness by using new methods. Texas FARMER Collection. Deng, J. C., Toledo, R. T., & Lillard, D. A. (1981). Protein-protein interaction and fat and water binding in comminuted flesh products. Journal of Food Science, 46(4), 1117–1121. Derossi, A., Caporizzi, R., Azzollini, D., & Severini, C. (2018). Application of 3D printing for customized food. A case on the development of a fruit-based snack for children. Journal of Food Engineering, 220, 65–75. Eilert, S. J., Blackmer, D. S., Mandigo, R. W., & Calkins, C. R. (1993). Meat batters manufactured with modified beef connective tissue. Journal of Food Science, 58(4), 691–696. El-Magoli, S. B., Laroia, S., & Hansen, P. M. T. (1996). Flavor and texture characteristics of low fat ground beef patties formulated with whey protein concentrate. Meat Science, 42(2), 179–193. FAO/WHO Food Standards Programme (2001). The codex alimentarius. Fats, oils and related products. Vol. 8. Rome: FAO/WHO. Feiner, G. (2006). Additives: Phosphates, salts (sodium chloride and potassium chloride, citrate, lactate) and hydrocolloids. In G. Feiner (Ed.). Meat products handbook Practical science and technology. Woodhead Publishing. Godoi, F., Prakash, S., & Bhandari, B. (2016). 3D printing technologies applied for food design: Status and prospects. Journal of Food Engineering, 179, 44–54. Hamilton, C. A., Alici, G., & Panhuis, M. (2018). 3D printing vegemite and marmite: Redefining “breadboards”. Journal of Food Engineering, 220, 83–88. Hughes, E., Mullen, A. M., & Troy, D. J. (1998). Effects of fat level, tapioca starch and
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Contents lists available at ScienceDirect
Meat Science journal homepage: www.elsevier.com/locate/meatsci
Post-processing feasibility of composite-layer 3D printed beef Arianna Dick, Bhesh Bhandari, Sangeeta Prakash
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T
School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
ARTICLE INFO
ABSTRACT
Keywords: 3D printing Meat Beef Post-processing Texture Meat composite
Post-processing feasibility studies the integrity of the designed internal and external structures of 3D printed products. This study examined the effect of infill density (50%, 75%, 100%) and fat content (0, 1, 2, 3 fat layers within a structure) on the post-processing physical changes and texture of lean meat-lard composite layer 3D printed meat products cooked sous-vide. Data from raw and cooked samples were collected to determine cooking loss, shrinkage, moisture retention, fat retention, hardness, chewiness, and cohesiveness. 3D printed objects were successfully cooked, maintaining the internal and external structures. Infill density contributed proportionally (P < .05) to moisture retention, hardness and chewiness, and inversely (P < .05) to shrinkage and cohesiveness, with no effect on fat retention. Whereas, the fat content influenced proportionally (P < .05) to cooking loss, shrinkage, and cohesiveness, and inversely (P < .05) to fat and moisture retention, hardness, and chewiness. The interaction of both independent variables showed a significant effect (P < .05) on all the responses, except for fat retention.
1. Introduction Three-dimensional (3D) printing positions as a novel technology in the food science field, which has a potential of designing complex internal and external structures that would be rather unfeasible by conventional practices such as moulding, while providing unique eating experiences and an opportunity to address special dietary needs (Kouzani et al., 2017). However, this technology is still under development, and the suitability of new foodstuffs (Hamilton, Alici, & Panhuis, 2018), formulations (Liu, Zhang, Bhandari, & Yang, 2018; Yang, Zhang, Bhandari, & Liu, 2018), methodologies (Derossi, Caporizzi, Azzollini, & Severini, 2018; Severini, Derossi, & Azzollini, 2016), and printers (Liu, Ho, & Wang, 2018), among others, are continually being investigated. Due to printers' limitations, most studies employ a single food material blended mix, such as fruits and vegetable blends, pizza and pasta doughs, seafood products, meat products, cheese, chocolates, and more, which are usually deposited using a single nozzle-extruder type 3D printer. Few studies have reported on 3D printing employing dual-nozzle extrusion, such as strawberry juice gel deposited in alternate layers with- or inside a shell of- mashed potatoes (Liu, Zhang, & Yang, 2018), and celery slurry printed into a turkey puree cube (Lipton et al., 2010). Dual-nozzle extrusion for 3D food printing offers the opportunity of developing countless appealing food products intended for different consumer's dietary needs and convenience. Both, ready-to-eat meals and products requiring post-processing can be 3D printed. However, in
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the later ones, the post-processing feasibility (Godoi, Prakash, & Bhandari, 2016) has to be assessed. Post-processing feasibility refers to the study of the integrity of the desired internal and external designs of 3D printed products. Due to the physical and chemical changes occurring during cooking one needs to account the formulation design, printing settings, and post-processing conditions, among other factors to achieve both the expected structure and textural attributes of the food. Limited information regarding the post-processing feasibility of 3D printed products including meat products is currently available (Lille, Nurmela, Nordlund, Metsä-Kortelainen, & Sozer, 2018; Lipton et al., 2010; Lipton, Cutler, Nigl, Cohen, & Lipson, 2015; Severini et al., 2016). Therefore, this study employed the composite layer 3D printing technology by dual extrusion to investigate the feasibility of fabricating 3D printed objects made of lean beef paste with three infill densities and varying the layers of lard deposited within the structure. Infill density is an important factor in 3D printing processing that contributes to the strength and stability of the structure by varying the void fraction and the mass deposited in the internal structure, suggesting an impact on the textural attributes of the printed food. On the other hand, varying the fat content (lard) can provide information about the textural changes caused by the displacement of fat within the structures at varying infill densities, besides giving an idea for future materials combinations for dual extrusion like the customization of 3D meat products with desired fat content and fat distribution. Further, the study evaluates the effects on the post-processing quality parameters
Corresponding author. E-mail address: [email protected] (S. Prakash).
https://doi.org/10.1016/j.meatsci.2019.02.024 Received 2 November 2018; Received in revised form 30 January 2019; Accepted 28 February 2019 Available online 01 March 2019 0309-1740/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Composite multi-layer models: (a) Transparent view of CAD designs for dual nozzle-3D printing of lean meat paste and lard. (b) Sliced model (3 layers of lard): The yellow and green streams denote the meat paste and lard, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
such as cooking loss, moisture retention, fat retention, and shrinkage, as well as the textural properties of the final products. Results from this research will help to understand the physical changes occurring in 3D printed meat products that require cooking.
2.2. Viscoelastic properties of lard Duplicate temperature ramps for lard were performed in order to examine the viscoelastic properties of lard at varying temperature conditions. This allowed to obtain a suitable printing temperature. The test was performed with an ARG2 Rheometer (TA Instruments Ltd), attached with a sandblasted 2° steel cone (40 mm diameter) using a 1 mm gap. The method described by Adewale, Dumont, and Ngadi (2014) at different temperature conditions was employed to record the storage modulus (G′), loss modulus (G″), and tan δ (G″/G′) over a temperature ramp from 4 to 50 °C (ramp rate of 5 °C/min) at a controlled strain of 0.1% (identified within the linear viscoelastic region) and frequency of 1 Hz.
2. Materials and methods 2.1. Sample preparation Heart smart stir-fry beef (with < 5% total fat and < 4% saturated fat) and lard (Yorkfoods – 100% non‑hydrogenated pork fat (39% saturated fat)) were purchased from a local supermarket. The visible connective tissue (CT) and fat were removed from the meat, which was then spread in trays and stored at −18 °C. After 24 h, the meat was kept at 4 °C for 3 h before mincing to reach a chilled state for ease of comminution (Eilert, Blackmer, Mandigo, & Calkins, 1993). Then, it was ground twice using a meat mincer (TRE SPADE, Tube No. 8) with a rotational speed of 160 rpm and 20 kg/h capacity, equipped with a 4.5 mm diameter disc, and vacuum packed (Food Saver, Sunbeam) and frozen until usage. The comminution of the meat contributed to the extraction of soluble proteins that interact with water, salt, and fat to form an emulsion-like printable paste. The meat paste base consisted of 85% meat and 15% water. 1.5% NaCl and 0.5% guar gum (GG) (Melbourne Food Ingredient Depot, Australia) relative to meat paste base were added. The results obtained during preliminary experiments helped set the formulation. Initially, only 1.5% NaCl was added to the meat paste that was not easily extrudable. Following this, the addition of different hydrocolloids and water were tested for extrudability, where a ratio of 30:1 for water:GG was found to be appropriate for extrusion. NaCl allows the extraction of the myofibrillar proteins, which have an important role in binding and stabilizing the meat paste. According to Allais (2010), a minimum quantity of 1.5% NaCl is required in meat processing when no phosphates are used. Whereas GG increases the viscosity (Feiner, 2006) and elasticity of the gel that will support the printed structure, which was attained at 0.5% GG in this study. GG was mixed with water to make a GG slurry, and stored overnight at 0–4 °C to allow solubilisation. Then, the meat was mixed with NaCl in a food processor with 2 speed settings (Model FDP646SI (1000 W), Kenwood, United Kingdom) attached with a multipurpose knife blade for 10 s at speed 1, with a resting period of 10 s in between. Subsequently, GG slurry was added and mixed twice for 20 s at speed 2 with a resting time of 10 s between runs that produced an emulsion-like paste.
2.3. Modelling of 3D design Four computer-aided design (CAD) composite multi-layer models were designed with TINKERCAD® (AUTODESK®) and sliced with Slic3r software, adapted to Repetier-Host V2.1.2. The designs consisted of a rectangular prism with the following dimensions: length = 40 mm, width = 40 mm, and height = 10 mm. In order to vary the fat content within the structure, 0, 1, 2 and 3 layers of 1 mm height of lard were inserted to samples labelled as 0F, LF, MF, and HF, respectively. The layers of lard were located in the interior part of the structure (2 mm in per side), surrounded by meat paste (perimeters and solid top and bottom layers) to reduce the fat release from the sides of the printed meat products during post-processing cooking. Transparent views of the CAD designs are shown in Fig. 1a. 2.4. 3D Printing process The printing process was carried out at ambient temperature (23 ± 1 °C) using a dual nozzle model 3D printer (Shinnove, Hangzhou Shiyin Technology, China) with Cartesian configuration. The printing settings were defined based on preliminary trials, as following: 1.95 mm layer height and 1.5 mm first layer height for extruder 1 (meat paste), 1 mm layer height for extruder 2 (lard), 2 vertical shell perimeters, 2 solid layers on top and bottom, 20 mm/s speed, and 100% flow rate. The first layer was set to 1.5 mm to provide the structure with a stable base to enhance the support of the following layers. In addition, three different infill densities were tested (50%, 75%, and 100%) using rectilinear infill pattern. In order to slice the multi-material design, the templates for both materials (meat paste and lard) composing the structure were designed 10
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independently, exported as .stl files and merged with Slic3r, assigning each material to one extruder, as described by Liu, Zhang, and Yang (2018). Extruder 2 offsets were set at 65.7 mm for the X-axis, and 0.5 mm for the Y-axis, as recommended by the printer manufacturer. Fig. 1b displays a sliced HF model with 100% infill and rectilinear pattern. Two skirt loops at 6 mm distance from the object were added to allow the release of any air entrapped during the cartridge filling that would, otherwise, break the filament during printing. This ensures a continuous filament as of the bottom layer. The treatments were labelled as following: 0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100. The two first digits correspond to the fat content, as described in Section 2.3, and the subsequent numbers denote the infill density (%). Nine replicates per treatment were 3D printed.
Cooking loss (%) =
Raw weight Cooked Weight × 100 Raw weight
Shrinkage (%) (Raw L Cooked L) + (Raw W Cooked W) + (Raw H = Raw L + Raw W + Raw H
Cooked H)
× 100
where L refers to length (mm), W refers to width (mm) and H to height (mm).
Fat retention (%) =
Cooked weight × percentfat in cooked samples × 100 Raw weight × percent fat in raw samples
Moisture retention (%) Cooked weight × percent moisture in cooked samples = × 100 Raw weight × percent moisture in raw samples
2.5. Dimensional printing deviation
2.7. Textural properties of cooked printed samples
In order to determine the printing accuracy, the dimensional printing deviation was measured as per the method described by Liu, Zhang, and Bhandari (2018). Five replicates of each sample were evaluated by measuring three different positions (bottom, middle and top) for each dimension (length, width, height) using a digital calliper (CraftRight). The printing deviation (mm) was calculated by the difference between the designed and experimental dimensions. Averaged values per dimension were used. Values for dimensions and weight were recorded immediately after printing. Then, the samples were stored at −18 °C for 24 h in an airtight container.
2.7.1. Instrumental puncture test The puncture test was performed on cooked samples (average of 8 different positions), using a texture analyser (TA.XT Plus, Stable Micro system, Arrow Scientific Pty., Ltd) attached with a 4 mm diameter cylinder aluminum probe. Pre-test speed of 1 mm/s, test and post-test speed of 1.5 mm/s were applied to compress the samples to a 4.0 mm distance (36%–53% compression) before returning to the start position. Hardness was identified from the test curves to estimate the firmness of the sample, on two replicates. 2.7.2. Instrumental texture profile analysis (TPA) For TPA, the cooked samples were subjected to a double-cycle compression using a texture analyser (TA.XT Plus, Stable Micro system, Arrow Scientific Pty., Ltd) attached with a 50.8 mm diameter and 20 mm height acrylic cylinder probe. Pre-test speed of 1 mm/s, test and post-test speed of 1.5 mm/s, and a 4.0 mm distance were used. The holding time between compressions was set to 5 s. Hardness, cohesiveness, and chewiness were recorded from the test curves, on three replicates.
2.6. Post-processing conditions and characterisation 2.6.1. Moisture content The moisture content of the 3D printed samples (raw and cooked) was determined on two replicates by the standard vacuum drying method at 95°–100 °C and < 1.33 × 104 Pa for 6 h (AOAC, 1991). 2.6.2. Fat (crude) extract Extractable fat content in 3D printed samples (raw and cooked) was determined on two replicates by the AOAC Official Method 960.39(b) for fat (crude) or ether extract in meat (AOAC, 2005). Petroleum ether was employed for an extraction period of 6 h at a condensation rate of 4–5 drops/s in a Soxhlet extractor. Furthermore, in order to analyse the post-processing feasibility of the 3D printed samples, as affected by the infill percentage and fat content, the cooking loss, shrinkage, moisture retention, and fat retention were determined. After 24 h of storage, 3D printed samples were vacuum-packed (Food Saver, Sunbeam) in the frozen state to avoid damaging the structures during packaging and heat-treatment. Subsequently, the samples were cooked sous-vide with precisely controlled heating in a digital uncirculated water bath (Thermoline Scientific, AU) at 75 °C for 30 min by submerging the vacuum-packed samples in randomized locations without overlapping under a wire rack to keep the samples at the same height level within the water bath. After cooking, weight and dimensions (averaged values of three different positions per dimension) were recorded in five replicates to calculate cooking loss (%) and shrinkage (%); while fat content and moisture were determined in two replicates to calculate fat retention (%) and moisture retention (%), respectively. Shrinkage values were calculated based on the equation proposed by El-Magoli, Laroia, and Hansen (1996) for cylinder shapes, and thus modified for a rectangular prism in this study. The moisture retention and fat retention values were determined according to the equations described by Murphy, Criner, and Gray (1975).
2.8. Data analysis All the data are presented as mean values with their standard errors, and were analysed using Minitab® 17.1.0. The effect of fat content (4 levels: 0, 1, 2, and 3 layers of lard within a structure), infill density (3 levels: 50%, 75%, and 100%), and their interaction (Fat content*Infill density) on each response was estimated by two-way analysis of variance (ANOVA) using a confidence level of 95%. Pairwise comparisons were determined by Tukey's test with 5% level of significance. One replicate per treatment was 3D printed per day, and the printing process lasted 9 days, so in total there were 9 replicates for each treatment. The beef meat was procured from the market prior to each printing session and the meat paste was prepared on the day of printing. The following responses were measured on individual replicates: the same 5 replicates were used for dimensional printing deviation, cooking loss, shrinkage, TPA (3), and puncture test (2), while the remaining 4 replicates were used for moisture retention and fat retention (2 for raw samples and 2 for cooked samples). 3. Results and discussion 3.1. Viscoelastic behaviour of lard A temperature ramp for lard was performed in order to examine its viscoelastic properties at varying temperature conditions and identify a suitable printing temperature. As shown in Fig. 2, a decline in G′ was 11
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Fig. 2. Viscoelastic properties of lard (G′: Storage modulus, G″: Loss modulus, tan δ (G″/G′)) at a temperature ramp (4–50 °C, rate of 5 °C/min) in a rheometer at a controlled strain of 0.1% and frequency of 1 Hz.
observed as the temperature increased. This is attributed to the melting of crystalline fats (Adewale et al., 2014). G′ (elastic or solid-like behaviour of the material) was higher than G″ (viscous or fluid-like behaviour) until 27.9 °C was reached. Beyond this temperature, the material behaved more fluid-like (tanδ = G″/G′ > 1). Therefore, considering that the 3D printer cannot work at controlled-temperatures below ambient temperature (23 ± 1 °C), the lard was left at ambient temperature overnight before printing, and the printing process was carried out without temperature control. At such conditions, the lard presented solid-like behaviour with suitable tan δ (0.73), being printable and behaved as a self-supporting material during printing.
3.3. Dimensional printing deviation The dimensional printing deviation for each raw sample was calculated in order to determine the printing accuracy, by comparing the dimensions (length, width, and height) of the printed object in relation to the CAD design, as affected by the infill density, fat content and their interaction. The deviation values ranged from −0.005% to 1.423%, −0.053% to 1.667%, and 1.43% to 11.04% for the length, width and height, respectively. Positive values indicate expansion of dimension, and negative values denote reduced dimension of objects (Liu, Zhang, & Bhandari, 2018). Overall, no effect was observed by the interaction of infill densities and fat content on each dimensional deviation (data not shown). The length and width of samples showed lower deviation from the designed structure, with maximum values of 0.6 mm and 0.7 mm, respectively. The fat content did not affect the deviation of length and width of samples (Fig. 4b,d), which may be attributed to the lard being printed inside the samples perimeter, minimising the fat loss. On the other hand, significant larger deviation for both dimensions was observed when increasing the infill density to 100% (Fig. 4a,c), which is attributed to a dense structure of deposited material, which can be related to an increased weight, as shown in Table 1. Also, increased amount of meat paste deposited within a structure (e.g., 0F vs. HF) may contribute to overall morphological deviation due to the ‘extrude swell phenomenon’, which may be affected by the springiness of the material, as described by Kim, Bae, and Park (2017). Similar results were observed for the morphological accuracy of 3D printed cereal-based snacks by Severini et al. (2016). In case of height, opposite results were observed. The infill density showed no effect on height deviation (Fig. 4e), whereas an inverse relationship was observed with fat content (Fig. 4f). This may be
3.2. 3D Printed samples Meat paste and lard were successfully 3D printed as composite multi-layer models. Depending on the designs, the estimated printing times (EPT) ranged from 6 min 35 s to 10 min 21 s. EPT, weight, moisture and fat contents of raw and cooked samples are presented in Table 1. A 3D printed sample (LF100) before and after sous-vide cooking is presented in Fig. 3a. The layers of lard were printed inside a shell of meat paste and thus, cannot be observed in the final product. As mentioned earlier, this was designed with the purpose of reducing fat loss during cooking. The internal structure is shown in Fig. 3b. The layers of meat paste and lard were inserted according to the fat content and infill density. Differences between the stream diameters for meat paste and lard at the three levels of infill densities (50%, 75%, and 100%) used in this study can be distinguished (Fig. 3b). As expected, due to the different nozzle diameters used for meat (2 mm) and lard (1 mm), more filament was needed to complete a layer of lard than a layer of meat paste.
Table 1 Experimental data for estimated printing time (EPT), weight, and moisture and fat content of raw and cooked 3D printed samples with different internal infill structure and composition (RS = raw sample, CS = cooked sample). (0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100: The two first digits correspond to the fat content (0F, LF, MF, HF refer to 0, 1, 2 and 3 layers of lards, respectively), and the subsequent numbers denote the infill density (%)). 3D printed sample
EPT (min)
RS weight (g)
CS weight (g)
RS moisture (%)
CS moisture (%)
RS fat content (%)
CS fat content (%)
0F50 0F75 0F100 LF50 LF75 LF100 MF50 MF75 MF100 HF50 HF75 HF100
6.58 7.08 7.60 7.13 7.80 8.47 7.73 8.57 9.38 8.22 9.30 10.35
14.60 ± 0.04d 15.98 ± 0.05bc 17.30 ± 0.02a 13.61 ± 0.06ef 14.94 ± 0.10d 16.15 ± 0.05b 13.45 ± 0.10 fg 14.68 ± 0.13d 15.62 ± 0.10c 12.98 ± 0. 11 g 14.01 ± 0.16e 15.6 ± 0.18c
12.80 ± 0.06 cd 13.22 ± 0.10b 15.30 ± 0.03a 11.21 ± 0.14 fg 11.86 ± 0.10de 12.80 ± 0.09bc 10.26 ± 0.11hi 10.98 ± 0. 16 g 11.62 ± 0.12ef 9.59 ± 0.10j 10.01 ± 0.14ij 10.74 ± 0. 05 gh
74.13 74.35 75.46 73.08 72.30 72.53 68.94 67.62 67.76 65.70 64.37 62.03
70.40 69.15 72.40 69.41 69.20 70.02 65.56 68.04 68.53 66.45 67.86 67.19
0.49 ± 0. 05 g 0.53 ± 0. 05 g 0.45 ± 0. 02 g 4.03 ± 0.17f 4.61 ± 0.39f 4.84 ± 0.68f 8.21 ± 0.28e 11.12 ± 0.92d 12.65 ± 0.28 cd 14.76 ± 0.37bc 16.80 ± 0.001ab 18.97 ± 0.43a
0.44 0.56 0.48 2.28 2.47 2.15 4.32 3.18 3.28 5.15 5.13 4.25
Values that do not share a letter within a column are significantly different. 12
± ± ± ± ± ± ± ± ± ± ± ±
0.03abc 0.11ab 0.41a 0.41bc 0.07c 0.53bc 0.53d 0.47de 0.43d 0.46ef 0.15f 0. 03 g
± ± ± ± ± ± ± ± ± ± ± ±
0.11ab 0.08abcd 0.23a 0.07abc 0.14abcd 0.09abc 0.24d 0.46bcd 0.46bcd 0.13 cd 0.08bcd 0.12bcd
± ± ± ± ± ± ± ± ± ± ± ±
0.11c 0.02c 0.06c 0.24bc 0.29bc 0.03bc 1.00ab 0.29ab 0.33ab 0.38a 0.67a 0.21ab
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Fig. 3. 3D Printed composite multi-layer meat models: (a) Raw and cooked samples (LF100: 1 layer of lard, 100% infill density), (b) filament stream at different infill densities (50%, 75%, and 100%) for meat paste (2 mm nozzle diameter) and lard (1 mm nozzle diameter).
layers increased from 1 to 3. These observations were corroborated by both the cooking loss and shrinkage values, which were similarly affected by fat content (Fig. 6b,d). The higher the fat content, the higher the cooking loss and shrinkage was observed (no significant difference beyond LF). These results were expected as fat was deposited in layers within the structure instead of initially being emulsified in the meat paste. Therefore, the instability of fat droplets may have resulted in coalescence during cooking (Tornberg, 2013), increasing the probability of melted fat displacement from the inner to the outer part of the structure and facilitating its partial release through the porous shell (mainly top and bottom layers, Fig. 8b) during cooking, leaving room for contraction. In addition, it has been reported that drip loss starts earlier in samples with higher fat content due to an improved heat transfer, affecting the total drip mass and the ratio of fat to water in the drip (Sheridan & Shilton, 2002). As shown in Fig. 8a, the fat layers were imperceptible after cooking, explaining the contraction of the structures with increased fat content as not enough support remained inside for the shell. Ulu (2006) reported similar cooking loss results for meatballs containing 0.5% guar gum and fat contents ranging from 10% to 25%. While, Sheridan and Shilton (2002) reported that above 30% added fat, the amount lost as drip was nearly equivalent to the added amount. Regarding infill density, shrinkage values were significantly lower at 100% only (Fig. 6c). However, this result may be affected by the interactive effect of infill density with fat content, since for 0F samples a significant reduction (P < .5) from 8.4% to 4.3% was determined for 50% and 100% infill density, respectively, whilst no significant differences were found at varying infill densities of each fat-containing sample (explaining the larger error bar in Fig. 6c). This suggests a packed structure in 0F100 samples (containing only meat paste with < 0.5% fat) that after the aggregation of proteins during heat treatment, formed a network where water was trapped, as denoted by
explained by the smaller nozzle diameter used for lard (1 mm) and a lower density of fat (0 .9g/mL (FAO/WHO Food Standards Programme, 2001)) compared to that of the meat paste (1.106 g/mL), resulting in a lower mass of material deposited per layer of lard as compared to meat paste. 3.4. Post-processing conditions and product characterisation Composite multi-layer 3D printed samples were cooked sous-vide. Both, the shell and the interior design were well maintained during post-processing. In order to analyse the effect of the intricate designs of the 3D printed structures on the post-processing conditions, cooking loss, shrinkage, moisture and fat retention after sous-vide cooking were determined for better understanding of the changes in the textural properties, as a function of infill density and fat content. 3.4.1. Cooking loss and shrinkage Cooking loss and shrinkage are common changes during post-processing of meat products, due to the thermal denaturation and consequent contraction of meat proteins (Oroszvári, Rocha, Sjöholm, & Tornberg, 2006). The main cooking losses of meat products are attributed to water and fat. Bulk water, which is held by capillary forces in the meat matrix, is mostly expulsed from the meat network when a contraction pressure is generated by heat, causing protein shrinkage and water release. Whereas, fat is displaced by drip and depends on both, the fat content and the stability of the fat droplets within the meat network (Tornberg, 2013). As seen in Fig. 5, visual shrinkage of the samples was observed with increasing fat content. In 0F samples (Fig. 5a), the structure looked more packed and tighter after cooking with a slight upward bow, compared to the samples containing lard (Fig. 5b-d), for which increased contraction was observed in the topcenter and the central part of the perimeter, as the number of lard 13
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Fig. 4. Dimensional printing deviation of 3DP meat products: (a) length vs infill density, (b) length vs fat content, (c) width vs infill density, (d) width vs fat content, (e) height vs infill density, (f) height vs fat content (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
Fig. 5. 3D printed cooked samples at varying fat contents: (a) 0F (no added lard), (b) LF (1 lard layer), (c) MF (2 lard layers), (d) HF (3 lard layers).
Deng, Toledo, and Lillard (1981) for emulsion-like stabilizations. Thus, better maintaining the shape of the 3D printed constructs. Similar situation was observed for cooking loss as affected by infill density. Probably due to the interactive effect of the independent variables, 0F100 presented the minimum cooking loss (11.5%) of all samples, which explains the shrinkage results previously discussed. On the other hand, higher cooking loss was observed when infill density increased from 50% to 75%. This advises greater retention of moisture and fat within the structure at 50% infill density, which may be because of the less packed and more porous structure that -under the same cooking conditions- may had had lower heat transfer as compared to samples with 75% infill density. It is suggested that in samples with lower
heating rates, the water released from the myofibrils due to contraction fills the air pores within the meat matrix, whereas higher heating rates drive the water out of the pores due to shrinkage of connective tissue (Oroszvári, Rocha, et al., 2006; Poon, Durance, & Kitts, 2001). 3.4.2. Moisture and fat retention As a result of shrinkage due to the contraction of meat proteins during heat treatment, moisture and fat losses are triggered (Oroszvári, Bayod, Sjöholm, & Tornberg, 2006). Moisture retention was found to be affected by both independent variables (infill density and fat content) and their interaction (Fig. 7a). A significant decrease in moisture retention was obtained when increasing fat content, with no difference 14
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Fig. 6. Cooking loss (%) vs (a) infill density, (b) fat content. Shrinkage (%) vs. (c) infill density, (d) fat content of 3D printed meat samples (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
cooked samples, which could likely influence tenderness, as suggested by Rohlik et al. (2017). In addition, the authors reported that when cooking beef muscles (raw eye and striploin) at temperatures between 60 and 75 °C for 20–60 min, the total drip and water retention were not influenced by intramuscular fat content, but by the cooking time and temperature. Taking into account the temperature employed in our study (75 °C), besides the transversal (at around 50 °C) and longitudinal (above 70 °C) shrinkage due to the denaturation of myosin and actin, respectively, the contraction pressure on heating which mostly triggers water loss is primarily originated by the shrinkage of connective tissue at 60–70 °C (Tornberg, 2013). This helps to explain the meaningful water losses (15–26%) observed in all the samples. In addition, one possible way to reduce dripping is to minimize the porosity of top and bottom layers by using concentric pattern for 3D printing instead of a rectilinear pattern, since fewer spaces (porous) would be left due to a parallel deposition of the infill pattern to that of the perimeters. Further, higher moisture retention was only observed at 100% infill density (data not shown), and sample 0F100 presented the highest moisture retention (84.9%) thus corroborating the cooking loss and shrinkage results discussed in Section 3.4.1. On the other hand, fat retention was inversely affected by fat content (Fig. 7b). As the number of lard layers increased and the layers of meat paste decreased in order to complete 10 layers per object, less amount of meat was available to strengthen the shell and thus the ease of fat to drop out of the structure may have improved. In addition, this result is in agreement with the findings reported by Sheridan and Shilton (2002) for fat drip production. By increasing the fat content of beefburgers subjected to infrared cooking, more fat was lost by dripping, and beyond 30% added fat, the drippings were nearly equal to the added amount. Similar fat loss outcomes were identified for pork-fat injected beef steaks (Reed, Walter, Schmitz, Guadián-García, & Lawrence, 2017). As previously mentioned, this may be explained by the coalescence of unstable fat deposited within the structure, which is not stabilized in the protein network but ready for displacement upon melting.
Fig. 7. Fat (FR) and moisture retention (MR): (a) vs. the interactive effect of fat content and infill density, (b) vs. the independent effect of fat content. (0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100: The two first digits correspond to the fat content (0F, LF, MF, HF refer to 0, 1, 2 and 3 layers of lards, respectively), and the subsequent numbers denote the infill density (%)).
observed beyond 2 layers (Fig. 7b). This may be attributed to (i) lower initial bulk water, and (ii) shorter cooking times required for samples containing lard in order to obtain equivalent moisture content in the 15
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Fig. 8. 3D printed-cooked samples: (a) imperceptible fat layers after cooking, (b) fluids release observed during TPA test, (c) puncture test at different locations.
Fig. 9. Hardness by puncture test of 3D printed samples vs. (a) infill density and (b) fat content (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
Regarding infill density and their interaction within fat contents, no significant differences were detected (Fig. 7a). However, a different trend of numerical values was noted for samples with no added fat (0F). As shown in Fig. 7a, for samples with added fat, although no significant differences were found, the trend showed lower values for fat retention as the infill density increased, whereas opposite trend was seen for 0F. Contrary to samples with added fat, the fat content determined in 0F samples corresponds entirely to the fat contained in the meat paste, which is retrieved from the meat used (mostly intramuscular fat, as visible fat was removed) and stabilized in the system. Hence, as moisture was lost during cooking, the fat seemed to increase, on an average. In addition, since fat is suspended within the meat paste by a protein film, during heat treatment, myofibrillar proteins produce a gel that stabilizes the emulsion (Ugalde-Benitez, 2012). Similar findings were reported by Hughes, Mullen, and Troy (1998). 3.4.3. Hardness measurement by puncture test 3D printed samples presented a non-uniform structure with alternate pattern direction of layers throughout (e.g., perimeters and infill). Hence, wide variability in hardness was expected for the outer layers of the structure. Therefore, average points from different locations were considered for puncture. One-cycle puncture test with 4 mm cylinder probe (Fig. 8c) was performed in order to calculate the force (hardness) required to penetrate at various points of the structure. As described by Braeckman, Ronsse, Hidalgo, and Pieters (2009), a puncture test provides the hardness of the desired crust in grilled-meat products to avoid moisture and fat release. Although, no such “crust” was formed on the sous-vide cooked 3D printed samples, the mechanical properties of the outer layer was expected to proportionally increase with infill density, due to a packed inner structure acting as support. The two independent variables and their interaction showed a
Fig. 10. TPA results (hardness, chewiness, and cohesiveness) of 3D printed samples vs. (a) infill density and (b) fat content (0F, LF, MF, HF correspond to 0, 1, 2 and 3 layers of lards, respectively, deposited within the structure).
16
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Table 2 Experimental results for TPA and puncture test as affected by the interaction of fat content and infill density. (0F50, 0F75, 0F100, LF50, LF75, LF100, MF50, MF75, MF100, HF50, HF75, and HF100: The two first digits correspond to the fat content (0F, LF, MF, HF refer to 0, 1, 2 and 3 layers of lards, respectively), and the subsequent numbers denote the infill density (%)). Sample
0F50 0F75 0F100 LF50 LF75 LF100 MF50 MF75 MF100 HF50 HF75 HF100
TPA
Puncture
Hardness (N)
Chewiness (N)
Cohesiveness
Hardness (N)
58.36 93.58 93.60 54.11 73.00 98.65 58.04 49.83 60.80 30.42 36.33 53.35
81.14 ± 12.14d 137.01 ± 14.56abc 144.96 ± 9.50ab 95.39 ± 1.58bcd 103.22 ± 12.42abcd 154.29 ± 5.39a 99.67 ± 12.27bcd 80.12 ± 8.20d 86.66 ± 9.33 cd 60.21 ± 11.56d 68.20 ± 4.96d 94.57 ± 10.55bcd
1.38 1.46 1.55 1.76 1.41 1.56 1.75 1.60 1.42 1.99 1.88 1.78
3.14 ± 0.12ab 2.96 ± 0.25abc 2.56 ± 0.32bc 2.50 ± 0.14bcd 3.65 ± 0.05a 2.69 ± 0.20abc 2.25 ± 0.21bcd 2.5 ± 0.0003bcd 2.48 ± 0.04bcd 1.48 ± 0.25d 2.01 ± 0.27 cd 2.32 ± 0.04bcd
± ± ± ± ± ± ± ± ± ± ± ±
8.80bc 4.67a 2.86a 1.06bcd 6.51ab 2.53a 9.82bcd 4.28bcd 5.21bc 5.81d 2.41 cd 6.46bcd
± ± ± ± ± ± ± ± ± ± ± ±
0.06e 0.09cde 0.09bcde 0.02abc 0.08e 0.04bcde 0.11abcd 0.03bcde 0.04de 0.06a 0.03ab 0.08abc
Values that do not share a letter within a column are significantly different.
by El-Magoli et al. (1996) when increasing the fat content of beef patties from 11% to 22%. For cohesiveness, the independent effect from both factors showed significantly higher values at 50% infill density and HF only. The interactive effects are shown in Table 2. It should be noted that higher values were recorded in hardness for TPA compared to puncture test, due to the different probes and test parameters used. TPA was performed over the whole sample in order to estimate the eating experience of biting the intact shape (Fig. 8b), whilst puncture test was performed at averaged specific points penetrating through the sample at independent locations (Fig. 8c), such as the center, the perimeter, and the corners. This wide variation is in agreement with compared results of TPA and puncture tests performed to beef patties, by Braeckman et al. (2009). As presented in Table 2, 0F75, 0F100, and LF100 samples exhibited significantly higher hardness (TPA) than the rest of samples (except for LF75), whereas HF50 presented the lowest numerical value for the same attribute. In fact, the texture results for HF50 and HF75 (~5% fat content when cooked) samples provide a potential option for people with chewing and swallowing difficulties, although further texture tests for soft foods, such as the fork and spoon test (International Dysphagia Diet Standardization Initiative, 2017), as well as the adaptation of the sample to a bite-size should be considered. In order to achieve tender products, different cooking conditions should be applied, such as lowering the cooking temperature and increasing holding times during sous-vide cooking, which results in minor cooking losses, as reported by Zielbauer, Franz, Viezens, and Vilgis (2016). Additionally, the assessment of the suitability of 3D printed meat products to different cooking methods would provide valuable information for the applicability of 3D printing technology to a broader range of products.
significant effect (P < .5) on the hardness. An inverse relationship was evidenced by increasing fat content beyond LF, which may be explained by the higher cooking loss and shrinkage observed under the same conditions. In terms of infill density, a similar trend as the one detected for the cooking loss was observed for hardness (Figs. 6a, 9a), being higher at 75% compared to 50% infill. Thus, it is expected that a higher cooking loss resulted in tougher products, caused primarily by moisture loss. In fact, although no significant difference was observed, numerical values for moisture retention at 75% infill were lower than those at 50% infill (data not shown). 3.4.4. TPA of cooked 3D printed meat samples Texture profile analysis of cooked samples was performed to obtain the hardness, chewiness and cohesiveness values of whole samples. Hardness, which denotes the firmness of the samples, was taken from the positive peak of the first compression cycle. As shown in Fig. 10a, a proportional effect on hardness was observed by increasing infill density, whilst inverse relationship was obtained with increasing fat content past LF (Fig. 10b). These results were expected as the fat content in cooked samples increased from 0.44%–2.44% for 0F and LF to 3.18%–5.15% for MF and HF. Thus, relating the results to the bite theory explained by Smith and Carpenter (1974), since fat has a lower density compared to that of lean tissue, a higher content of deposited fat within the matrix results in a lower bulk density of the meat product and ease of bite (tenderness). In addition, due to shrinkage and separation of muscle fibres during cooking (Rohlik et al., 2017), melted fat is liquefied and dispersed through the meat matrix (Sheridan & Shilton, 2002), supposedly spreading apart the strands of connective tissue (Cover & Hostetler, 1960), and breaking the continuous network of protein where fat is partly retained (Sheridan & Shilton, 2002). So, as more fat is added to the matrix, the tenderness is most likely increased since less force is required to split fat tissue than muscle fibres (Rohlik et al., 2017). Furthermore, more room is left in samples with higher fat content after cooking due to fat loss, compared to a tighter structure in samples with lower fat contents and high infill density. Similar results were reported by Reed et al. (2017), who found more tenderness in pork-fat injected beef steaks as compared to control samples (no fat injected). These results suggest that fat can be added to a blended mix to improve its viscosity and printability and then be released during heat treatment. However, some remain trapped within the structure to contribute to cohesiveness and tenderness. A similar trend was observed for chewiness values, but significant difference was only evident at 100% infill density and beyond LF with no difference between MF and HF. Comparable results were described
4. Conclusions Composite multi-layer 3D printed meat products were successfully fabricated and subjected to post-processing (sous-vide cooking). All the samples maintained their internal and external structure after cooking, although partial inwards contraction was observed in MF (2 layers of lard) and HF (3 layers of lard) samples, with an initial fat content ranging from 8.21%–12.65% and 14.76%–18.97%, respectively. Overall, increasing the fat content (or lard layers) resulted in higher cooking loss, shrinkage, and cohesiveness, and lower fat retention, moisture retention, hardness, and chewiness. Whereas, an increase in infill density led to higher moisture retention with lower shrinkage and cohesiveness, resulting in higher hardness and chewiness. In addition, the interaction of both independent variables showed a significant 17
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effect on all the responses, except for fat retention and dimensional printing deviation. Thus, the interactive effect may help to explain better the post-processing textural changes observed. Further work will be pursued to study the feasibility of 3D printed composite multi-layer meat products for post-processing at different cooking methods and conditions. It would be of interest to examine the microstructural changes occurring during cooking to explain the sensorial and textural impact on the final product. Furthermore, this study proposes a meat product design that can be modified by adding other food materials, such as cheese or vegetables to create tailored composite multi-layer 3D printed products.
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