A yeast protein-based enhanced 3D printing ink and a quantitative evaluation method for its 3D printing performance

Abstract

The present invention discloses an enhanced 3D printing ink based on yeast protein and a quantitative evaluation method for its 3D printing performance. Yeast protein, starch, carrageenan and water are mixed and stirred uniformly, the mixture is heated, and cooled to room temperature to obtain a 3D printing ink. The ink is used for 3D printing, the effects of yeast protein and its molecular weight on printing performance are studied, and the printing accuracy of the ink is evaluated; rheology and texture tests are performed on the ink, and the contribution rate of each test index is evaluated by principal component analysis to determine the principal component; based on actual 3D printing results and PCA analysis, a linear discriminant analysis is used to establish a printing performance (extrudability and supportability) prediction model, and the extrudability and supportability of the ink are determined. The yeast protein added in the present invention can significantly improve the 3D printing performance of starch-based ink; the extrudability and supportability of the ink are reflected by four main indicators, K, G′, G′′ and hardness, and a prediction model is established, and the model prediction accuracy is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of food 3D printing processing, and specifically relates to an enhanced 3D printing ink based on yeast protein and a quantitative evaluation method for its 3D printing performance. Background Art

[0002] 3D printing technology is a technique for producing three-dimensional products through layer-by-layer deposition. In recent years, 3D printing has been widely applied in fields such as automotive manufacturing, aerospace, food processing, and healthcare. Food 3D printing technology can personalize the nutritional and appearance of food products to meet diverse consumer needs. Currently, most 3D printing in the food industry uses direct-write printing materials and extrusion-based 3D printing devices. However, the lack of food inks with suitable rheological properties, high fidelity, and deformation resistance is a major constraint to its development. Furthermore, current research in the field of 3D printing primarily focuses on the development and application of different types of food inks, with little research on the correlation between the extrudability and support properties of 3D printing inks. Extrudability and support properties are two crucial factors affecting the 3D printing performance of inks, directly impacting both the ink's printability and the sample's print accuracy. Therefore, in-depth research on indicators reflecting ink extrudability and support properties is crucial for the future development of 3D printing inks.

[0003] Yeast protein is a microbial protein produced from yeast, fulfilling the strategic requirement of "demanding protein from microbes." Yeast protein contains eight essential amino acids and has benefits such as muscle building, preventing muscle loss, and regulating the intestines. Furthermore, its factory-based production method offers short production cycles, unrestricted by natural conditions, high yields, and stable quality. Currently, there is no research on the use of yeast protein in food 3D printing. Most protein 3D printing inks utilize plant and animal proteins. However, land shortages, long production cycles, and the risk of genetically modified organisms (GMOs) associated with plant protein production, along with antibiotic residues in animal protein production, have become significant obstacles to the development of both. As a microbial protein, yeast protein offers significant potential for development as an alternative to plant and animal proteins. Summary of the Invention

[0004] The present invention overcomes the shortcomings of the prior art and provides an enhanced 3D printing ink based on yeast protein and a quantitative evaluation method for its 3D printing performance.

[0005] The technical solution of the present invention is achieved as follows:

[0006] A yeast protein-based enhanced 3D printing ink, the method comprising the following steps:

[0007] (1) Weighing yeast protein, starch, carrageenan, and water respectively, mixing the four substances and stirring them evenly to obtain a yeast protein-starch-carrageenan complex;

[0008] (2) heating the yeast protein-starch-carrageenan complex at 70-100° C. for 15-120 minutes, stirring continuously during the heating process to ensure that the starch is fully gelatinized;

[0009] (3) The mixture is cooled to room temperature to obtain ink for 3D printing.

[0010] Preferably, in step (1), the following ingredients are used in parts by weight: 0.25-2 parts of yeast protein, 5-15 parts of starch, 0.05-0.12 parts of carrageenan, and 50-150 parts of water.

[0011] Preferably, in step (1), the protein content of the yeast protein used is 60%-95%, and the proportion of yeast protein with a molecular weight ≥2000 (%) is 35%-78%.

[0012] The quantitative evaluation method of the 3D printing performance of the 3D printing ink obtained by the preparation method comprises the following steps:

[0013] (1′) Conducting 3D printing experiments on 3D printing inks and evaluating the printing performance (printing accuracy) of the inks;

[0014] (2′) Testing the rheological and textural properties of the ink, and evaluating the contribution rates of the rheological and textural test indicators through PCA analysis to determine the principal components;

[0015] (3′) Combining actual 3D printing experiments with PCA analysis results, determine the indicators that affect the ink printing performance, namely, extrudability and supportability;

[0016] (4′) A printing performance (extrudability and supportability) prediction model was established through linear discriminant analysis (LDA), and the ink extrudability and supportability were judged, and the evaluation method of ink extrudability and supportability was quantified.

[0017] Preferably, in step (1′), the specific parameters of the 3D printing experiment are: the printing model is a cuboid of 22 mm × 22 mm × 15 mm, the printing temperature is 20-35° C., the nozzle diameter is 0.8 mm, and the printing speed is 20 mm / s;

[0018] Preferably, in step (1′), the evaluation of the 3D printing performance of the ink is achieved by printing accuracy, and the specific calculation formula for printing accuracy is:

[0019]

[0020] Among them, H e、H c and L b are the edge height, center height and length of the printed sample, H s and L s The height and length are set for the model respectively. A printing accuracy less than 90% is considered as low printing accuracy, and a printing accuracy greater than 90% is considered as high printing accuracy.

[0021] Preferably, in step (2′), the specific steps of the rheological test are:

[0022] (1″) Apparent viscosity test: Obtain the apparent viscosity of the ink as a function of shear rate, and fit it using a power law model to obtain the consistency coefficient (K) and flow index (n);

[0023] (2″) Frequency sweep test: Obtain the change curves of the ink's storage modulus (G′), loss modulus (G″) and complex modulus (G*) with angular frequency.

[0024] Preferably, in step (2′), the specific parameters of the texture test are: selecting the full texture analysis (TPA) mode, P36 / R probe, trigger force of 5 g, compression deformation of 50%; speed before test of 5 mm / s; speed after test of 1 mm / s; time interval between two compressions of 5 s.

[0025] Preferably, in step (3'), the determined indices affecting the ink printing performance, i.e., extrudability and supportability, are K, G', G" and hardness.

[0026] Preferably, in step (4′), the steps of establishing the prediction model are: evaluating the printing performance of the ink through 3D printing experiments; obtaining the values ​​corresponding to the four indicators K, G′, G″ and hardness through rheological and texture tests, and then using linear discriminant analysis (LDA) to obtain different classification parameters in the model.

[0027] Preferably, in step (4′), the prediction model of ink extrudability and supportability established by LDA is:

[0028] Y1=0.190×G′+1.892×G″+1.799×K+1.753×hardness-1367.382;

[0029] Y2=0.225×G′+2.103×G″+2.011×K+1.951×hardness-1719.633;

[0030] Y3=0.275×G′+2.364×G″+2.248×K+2.183×hardness-2222.247;

[0031] Y4=0.315×G′+2.879×G″+2.722×K+2.666×hardness-3226.843;

[0032] Note: Y1, Y2, Y3, and Y4 respectively represent extrudable but unsupportable with low printing precision, extrudable and supportable with low printing precision, extrudable and supportable with high printing precision, and unextrudable but supportable.

[0033] Preferably, in step (4′), the specific process of determining the extrudability and supportability of the ink by using the established prediction model is as follows: the parameter values ​​of the ink (K, G′, G″ and hardness) are respectively brought into four prediction models (Y1, Y2, Y3, Y4), the values ​​(Y) of the ink on the four models are respectively calculated, and the ink is classified according to the largest model value.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The yeast protein used in the present invention is a microbial protein derived from yeast, which meets the strategic requirement of "demanding protein from microorganisms". Adding it to starch-based ink can significantly improve the 3D printing performance of the ink, and the ink preparation process is simple. Due to the stable quality, high yield and short production cycle of yeast protein, it is suitable for industrial large-scale production. At the same time, it can avoid the shortcomings of land resource shortage and long production cycle in plant protein production and antibiotic residues in animal protein production, and is an ideal protein source.

[0036] 2. The present invention evaluates the printing performance of inks through actual 3D printing experiments, studies the rheological and textural properties of inks, and performs PCA analysis on the rheological and texture test indicators of the inks to obtain indicators reflecting the extrudability and support properties of the inks (K, G′, G″, and hardness). Based on these four indicators, the 3D printing performance of the inks is classified. A quantitative evaluation method for the 3D printing performance of inks based on rheological and texture tests is developed. Conventional rheological and texture tests are used instead of 3D printing to achieve quantitative evaluation of the 3D printing performance of inks.

[0037] 3. The present invention establishes a prediction model based on K, G′, G″ and hardness, which can determine the extrudability and supportability of the ink with high prediction accuracy and can be used as an accurate prediction method.

[0038] 4. The present invention reveals another key factor affecting the 3D printing performance of materials - molecular weight and its distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 These are 3D printed images of Example 1, Example 4, Example 7, Example 8, and Comparative Example 1.

[0040] Figure 2 Graph showing the change in apparent viscosity versus shear rate for Examples 1-8.

[0041] Figure 3 Graph showing the changes in storage modulus, loss modulus, and composite modulus with angular frequency for Examples 1-8.

[0042] Figure 4 Graph showing the texture characteristics of Examples 1-8.

[0043] Figure 5 PCA analysis chart of rheological, texture properties and 3D printability of Examples 1-8. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to examples, comparative examples and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0045] Example 1

[0046] Preparation of 3D printing ink:

[0047] (1) Weigh 1.50 g yeast protein, 10.00 g starch, 0.08 g carrageenan, and 100.00 g water; mix the four substances and stir them evenly to obtain a yeast protein-starch-carrageenan complex;

[0048] (2) heating the yeast protein-starch-carrageenan complex at 95°C for 15 minutes, stirring continuously during the heating process to ensure that the starch is fully gelatinized;

[0049] (3) The mixture is cooled to room temperature to obtain ink for 3D printing.

[0050] Examples 2-8

[0051] Based on Example 1, the molecular weight of the yeast protein was changed, and other conditions were the same as in Example 1. The specific molecular weight distribution conditions and results are shown in Table 1.

[0052] Comparative Example 1

[0053] Based on Example 1, yeast protein was not added and other conditions were the same as in Example 1.

[0054] The present invention conducted 3D printing experiments on the above-mentioned embodiments and comparative examples, and conducted rheological and texture tests on the embodiments. PCA analysis was performed on the rheological and texture test indicators to determine indicators reflecting the extrudability and support properties of the ink and to classify the ink printing performance. The obtained indicators were analyzed by LDA to establish a prediction model, as follows:

[0055] 3D printing process:

[0056] The prepared composite ink was loaded into the printer's barrel, and the printing parameters were as follows: the printing model was a 22 mm × 22 mm × 15 mm cuboid, the printing temperature was 25 °C, the nozzle diameter was 0.8 mm, and the printing speed was 20 mm / s;

[0057] Printing accuracy calculation formula:

[0058]

[0059] Rheological testing:

[0060] (1) Apparent viscosity test: Place the prepared composite ink on the rheometer test plate and balance for 5 minutes. -1 The variation curve of ink's apparent viscosity with shear rate was obtained in the shear rate range and fitted using a power law model;

[0061] (2) Frequency sweep test: The prepared composite ink was placed on the rheometer test plate and equilibrated for 5 minutes. Under the conditions of frequency of 1-100 rad / s and stress of 0.1% (within the linear viscoelastic region), the curves of G′, G″, and G* of the ink as a function of angular frequency were obtained.

[0062] Texture test:

[0063] The composite ink was cut into a 2 cm × 2 cm × 2 cm cube, and the total texture analysis (TPA) mode was selected, with a P36 / R probe, a trigger force of 5 g, and a compression deformation of 50%; the speed before the test was 5 mm / s; the speed during and after the test was 1 mm / s; and the time interval between two compressions was 5 s.

[0064] PCA analysis and prediction model establishment:

[0065] PCA analysis was performed on the rheological and texture data to identify indicators that reflect the extrudability and support of the ink (K, G′, G″ and hardness). LDA analysis was then performed based on these four indicators to establish a prediction model:

[0066] Y1=0.190×G′+1.892×G″+1.799×K+1.753×hardness-1367.382;

[0067] Y2=0.225×G′+2.103×G″+2.011×K+1.951×hardness-1719.633;

[0068] Y3=0.275×G′+2.364×G″+2.248×K+2.183×hardness-2222.247;

[0069] Y4=0.315×G′+2.879×G″+2.722×K+2.666×hardness-3226.843;

[0070] Figure 1 The 3D print images of Examples 1, 4, 7, and 8, as well as Comparative Example 1, are shown. It can be seen that the addition of yeast protein and its increased molecular weight can improve the printing accuracy of the composite ink. Example 1 exhibits deformation due to a lack of support; Example 4 exhibits improved printing accuracy compared to Example 1; Example 7 achieves the highest printing accuracy, while Example 8 exhibits discontinuity. Comparative Example 1, which does not contain yeast protein, exhibits the lowest printing accuracy.

[0071] Table 1 Effect of yeast protein molecular weight distribution on printing accuracy

[0072]

[0073] Table 1 shows the effect of the molecular weight distribution of yeast protein on printing accuracy. It can be seen that the proportion of molecular weight ≥2000(%) is 50%-78%, and the proportion of ≤400(%) is 14%-20%, with higher printing accuracy. If the proportion of molecular weight ≥2000(%) is less than 50% and the proportion of lower molecular weight ≤400(%) is ≥20%, or the proportion of molecular weight ≥2000(%) is greater than 60% and the proportion of lower molecular weight ≤400(%) is ≥24%, the printing accuracy is low. This may be because different molecular weight distributions lead to different viscoelasticity and water holding properties of the gel, which in turn affects its printing accuracy.

[0074] Figure 2 This study shows the effect of yeast protein molecular weight on the apparent viscosity of the ink. Apparent viscosity reflects the ink's fluidity and extrudability. Excessively high viscosity can cause the sample to adhere to the barrel and make extrusion difficult. All samples exhibited shear-thinning properties, and the viscosity increased with increasing yeast protein molecular weight. At high shear rates, Example 7 had the lowest viscosity, which facilitates extrusion of the ink from the barrel. However, Example 8 maintained a high viscosity even at high shear rates, making it difficult to extrude smoothly from the nozzle.

[0075] Table 2 Power law model fitting parameters of different yeast protein molecular weight distribution inks

[0076] sample <![CDATA[K(Pa.s n )]]> n <![CDATA[R 2 ]]> Example 1 491.37±4.12 0.21±0.01 0.985 Example 2 416.00±15.61 0.14±0.02 0.973 Example 3 545.45±10.98 0.16±0.03 0.986 Example 4 618.43±32.17 0.16±0.03 0.996 Example 5 677.91±27.26 0.14±0.04 0.964 Example 6 836.16±16.60 0.15±0.06 0.975 Example 7 943.49±8.90 0.13±0.08 0.992 Example 8 988.45±17.01 0.21±0.02 0.981

[0077] Table 2 shows the fitting data of the apparent viscosity of inks with different yeast protein molecular weights using the power law model. 2Greater than 0.96, indicating that the power law model is applicable to the evaluation of all samples. The size of the K value will affect the fluidity of the ink, thereby affecting the extrudability of the ink. A smaller K value allows the ink to flow out smoothly, but lacks a certain supporting capacity, while a larger K value will make it difficult to extrude the ink. The K value increases with the increase of the molecular weight of the yeast protein. The K value of Example 1 is the smallest, and the ink can be extruded smoothly but lacks a certain supporting capacity. For Example 8, the K value is the largest. The 3D printing experiment found that the material at this time is difficult to extrude smoothly. The n value can reflect the shear thinning ability of the ink. The smaller the n value, the stronger the shear thinning ability. The n values ​​of all samples are less than 1, but the difference in n values ​​between different samples is not significant.

[0078] Figure 3 The effect of yeast protein molecular weight on G', G" and G* of ink is shown. G' represents the amount of energy stored due to elastic deformation, reflecting the elasticity of the material; G" represents the amount of energy lost due to viscous deformation; G* represents the amount of anti-deformation energy, and the larger the G*, the stronger the anti-deformation ability. With the increase of yeast protein molecular weight, the G', G" and G* of the ink all increase, and G' is higher than G", indicating that the ink exhibits viscoelastic behavior. G' can reflect the support capacity of the sample to a certain extent. Inks with higher G' have better support properties, but too high a modulus will make it difficult to extrude the ink. In Example 1, the three modulus values ​​are all the smallest, and it is difficult to resist deformation caused by gravity; in Example 7, the three moduli are all within the optimal range, and the ink can be extruded smoothly while maintaining good support capacity and having the highest printing accuracy; in Example 8, since the three moduli all reach the maximum value, the ink is difficult to extrude evenly.

[0079] Figure 4 The results show that the molecular weight of yeast protein has a significant impact on the texture of ink. Increasing the molecular weight of yeast protein can effectively improve the texture of ink. Figure 5The PCA analysis diagram of the rheological and textural properties of the ink and its 3D printing performance is shown. As can be seen from the loading diagram, the selected factors can explain most of the information on the 3D printing performance of the ink (extrudability and supportability), accounting for 85.20% of the cumulative contribution rate, of which 67.47% comes from PC1 and 17.73% comes from PC2. The variables in PC1 are G′, G″, G*, hardness, chewiness, and adhesiveness; the variables in PC2 are K value and n value. Since G* is calculated from G′ and G″, and chewiness and adhesiveness are both related to hardness, the variables in PC1 can actually be regarded as G′, G″, and hardness. For PC2, the n value is the main component variable of PC2. Although the K value accounts for a larger proportion in PC2 than in PC1, it can still explain most of the information in PC1 from a numerical point of view. Therefore, G′, G″, K, and hardness are taken as the main variables. It can be seen from the scatter plot that the samples can be divided into four categories based on the PC1 principal component (from left to right: extrudable but unsupported with low printing accuracy, extrudable and supported with low printing accuracy, extrudable and supported with high printing accuracy, and unextrudable and supported), and the classification results are consistent with the actual 3D printing experimental results.

[0080] Table 3 Sample prediction results

[0081] sample <![CDATA[Y1]]> <![CDATA[Y2]]> <![CDATA[Y3]]> <![CDATA[Y4]]> Measurement group Forecast Group 1 1384.69 1366.38 1280.20 998.62 1 1 2 1831.41 1870.03 1855.99 1690.55 2 2 3 2039.54 2107.35 2131.25 2019.71 3 3 4 2792.88 2946.73 3074.60 3164.23 4 4

[0082] Table 3 shows the prediction results of the prediction model for the samples. The prediction model can be used to determine the printing performance of the ink. Specifically, the parameter values ​​of the ink (K, G′, G″ and hardness) are respectively substituted into four prediction models (Y1, Y2, Y3, Y4), the values ​​(Y) of the ink on the four models are calculated respectively, and the ink is classified according to the largest model value. As shown in the table, the calculation results of samples 1, 2, 3, and 4 on Y1, Y2, Y3, and Y4 are the largest, so the four samples belong to Y1, Y2, Y3, and Y4 respectively. It can be seen that the prediction model is accurate in classifying the printing performance of the samples, with an accuracy rate of up to 100%. Therefore, based on the K value reflecting the extrudability of the ink, G′, G″ and hardness reflecting the supportability of the ink, the quantification of the 3D printing performance of the ink (extrudability and supportability) is achieved.

[0083] In summary, the technical solutions of the present invention have been described above in conjunction with the embodiments and comparative examples. It is clear that the specific implementation of the present invention is not limited to the above-described methods, and many modifications and variations can be made based on the contents of the specification. Any non-substantial improvements made using the method concepts and technical solutions of the present invention, or any direct application of the inventive concepts and technical solutions to other situations without modification, are within the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating the 3D printing performance of a 3D printing ink, characterized in that: The following steps are involved: (1′) A 3D printing experiment was conducted on the 3D printing ink to evaluate the printing performance, i.e., the printing accuracy, of the ink. The specific calculation formula for the printing accuracy is: Among them, H e 、H c and L b are the edge height, center height and length of the printed sample, H s and L s The height and length are set for the model respectively. A printing accuracy less than 90% is considered as low printing accuracy, and a printing accuracy greater than 90% is considered as high printing accuracy. (2′) Testing the rheological and textural properties of the ink, and evaluating the contribution rates of the rheological and textural test indicators through PCA analysis to determine the principal components; (3′) Combining actual 3D printing experiments with PCA analysis results, determine the indices that affect the ink printing performance, namely, extrudability and supportability; (4′) A prediction model for printing performance, namely extrudability and supportability, was established through linear discriminant analysis. The extrudability and supportability of ink were determined, and the evaluation method for ink extrudability and supportability was quantified. In step (2′), the graphical parameters for rheological and texture testing are: (1′′) Apparent viscosity test: Obtain the apparent viscosity of the ink as a function of shear rate, and fit it using a power law model to obtain the consistency coefficient K and flow index n; (2′′) Frequency sweep test: Obtain the curves of the storage modulus G′, loss modulus G′′ and complex modulus G* of the ink as a function of angular frequency; (3′′) Texture test: Select the full texture analysis mode, P36 / R probe, trigger force of 5 g, compression deformation of 50%; speed before test is 5 mm / s; speed after test is 1 mm / s; the time interval between two compressions is 5 s; In step (3′), the determined indices affecting the ink printing performance, i.e., extrudability and supportability, are K, G′, G′′ and hardness; In step (4′), (1) The steps for establishing the prediction model are as follows: obtain the ink printing accuracy through 3D printing experiments; obtain the values ​​corresponding to the four indicators K, G′, G′′ and hardness through rheological and texture tests, and then use linear discriminant analysis (LDA) to obtain different classification parameters in the model; (2) The prediction model of ink extrudability and supportability was established by LDA: Y1=0.190×G′+1.892×G′′+1.799×K+1.753×hardness-1367.382; Y2=0.225×G′+2.103×G′′+2.011×K+1.951×hardness-1719.633; Y3=0.275×G′+2.364×G′′+2.248×K+2.183×hardness-2222.247; Y4=0.315×G′+2.879×G′′+2.722×K+2.666×hardness-3226.843; Note: Y1, Y2, Y3, and Y4 are respectively extrudable but not supported with low printing precision, extrudable and supported with low printing precision, extrudable and supported with high printing precision, and non-extrudable and supported; In step (4′), the specific process of determining the extrudability and supportability of the ink using the established prediction model is as follows: the ink parameter values ​​K, G′, G′′ and hardness are respectively introduced into the four prediction models Y1, Y2, Y3, and Y4, the ink values ​​Y on the four models are respectively calculated, and the ink is classified according to the largest model value; The 3D printing ink preparation method in the quantitative evaluation method comprises the following steps: (a) Yeast protein, starch, carrageenan, and water were weighed separately, mixed, and stirred to obtain a yeast protein-starch-carrageenan complex; (b) heating the yeast protein-starch-carrageenan complex at 70-100° C. for 15-120 minutes, stirring continuously during the heating process to ensure that the starch is fully gelatinized; (c) The mixture was cooled to room temperature to obtain ink for 3D printing.

2. The quantitative evaluation method for ink 3D printing performance according to claim 1, characterized in that: In step (1′), The specific parameters of the 3D printing experiment are as follows: the printing model is a cuboid of 22 mm × 22 mm × 15 mm, the printing temperature is 20-35 °C, the nozzle diameter is 0.8 mm, and the printing speed is 20 mm / s.

3. The quantitative evaluation method for ink 3D printing performance according to claim 1, characterized in that: In the step (a), the following ingredients are prepared by weight: 0.25-2 parts of yeast protein, 5-15 parts of starch, 0.05-0.12 parts of carrageenan, and 50-150 parts of water.

4. The quantitative evaluation method for ink 3D printing performance according to claim 1, characterized in that: In the step (a), the protein content of the yeast protein used is 60%-95%, and the proportion of yeast protein with a molecular weight of ≥2000 (%) is 35%-78%.

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