CAI Wei, WANG Yu, GAO Yan'e, et al. Optimization of the process parameters for 3D printing plant protein meat[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(12): 254-264. DOI: 10.11975/j.issn.1002-6819.202304141
    Citation: CAI Wei, WANG Yu, GAO Yan'e, et al. Optimization of the process parameters for 3D printing plant protein meat[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(12): 254-264. DOI: 10.11975/j.issn.1002-6819.202304141

    Optimization of the process parameters for 3D printing plant protein meat

    • Meat demand is ever-increasing the globally, as the constant expansion of animal husbandry leads to massive resource consumption and serious environmental pollution. Plant protein meat (PPM), as the meat analogue, is gradually replacing animal meat to alleviate resource consumption and environmental pollution. The current processing technology of SSPM mainly includes extrusion, spinning, shearing, and 3D printing. As an emerging production technology in the food industry, 3D printing has been successfully applied to the PPM production with personalized nutritional content, shape, texture and taste. The current research focuses mainly on the optimization of ink materials and hydrocolloid components for the PPM using 3D printing. It is lacking in the relationship between process parameters and the quality of PPM, in order to obtain the optimal process parameters. There is still a great quality gap between 3D-printed PPM and animal meat. In this study, the process parameters were optimized for the PPM using 3D printing. A single-factor test was carried out to explore the effects of process parameters, including the print speed, nozzle height and extrusion rate on the hardness, springiness and chewiness of PPM. The multiple regression fitting models of hardness, springiness and chewiness were established by Box-Behnken Design (BBD) response surface test design. As such, an optimal combination was obtained for the hardness, springiness and chewiness using multi-objective fireworks. The results showed that hardness, springiness, and chewiness were achieved in similar chicken quality with the increase in print speed. The quality parameters of PPM deviated from the chicken parameters with the low quality, when the print speed was too high. Furthermore, the PPM shared higher quality characteristics at the print speed of 40-80 mm/s. The hardness, springiness and chewiness tended to be stable with the increase in nozzle height. The 3D printing quality gradually approached the chicken quality. The PPM presented the higher quality characteristics, when the nozzle height was 1.0-1.4 mm. The hardness, springiness and chewiness also tended to be stable with the increase of extrusion rate. Once the extrusion rate was too high, the 3D printing quality of PPM deviated from the chicken quality. The higher quality characteristics of PPM were achieved at the extrusion rate of 57.6-86.4 mm3/s. Design-Expert 13 software was combined to evaluate the single factor and response surface test. The results showed that the nozzle height posed the most significant effect on the hardness of PPM. The extrusion rate had the most significant effect on the springiness of PPM, while the print speed had the most significant effect on the chewiness of PPM. The BBD analysis was used to fit the multiple regression model with the variance analysis, according to the range of optimized process parameters. The optimal 3D printing process parameters were obtained using multi-objective fireworks. Therefore, when the print speed was 49.06 mm/s, the nozzle height was 1.21 mm, and the extrusion rate was 80.75 mm3/s, the hardness of PPM was 43.14 N, the springiness was 2.78 mm, and the chewiness was 75.92 mJ, indicating the highest similarity with the chicken quality. The errors of the hardness, springiness and chewiness were 2.21 %, 5.48 % and 2.52 %, respectively, compared with the confirmatory test. The quality of PPM was improved using the optimal 3D printing process parameters. The finding can provide a strong reference for the high-quality PPM production.
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