High-precision fruit and vegetable cell wall simulation system construction method based on 3D printing technology

Abstract

The invention relates to a high-precision fruit and vegetable cell wall simulation system construction method based on a 3D printing technology, and belongs to the technical field of food processing and bionic manufacturing. The method aims at solving the problems that a traditional cell wall simulation method is too simplified in structure, and complex gradient and heterogeneous characteristics of the cell wall are difficult to reproduce truly. According to the technical scheme, the method comprises the following steps: preparing bio-ink containing cellulose, hemicellulose and pectin according to the proportion of real components of target fruits and vegetables; obtaining cell wall layer thickness and fiber arrangement angle parameters through micro-CT scanning; and controlling the 3D printer to deposit ink layer by layer according to the parameters to construct a bionic structure. The simulation system constructed by the method is mainly used for accurately researching the structure evolution and texture change mechanism of the cell wall in the food processing process.

Description

Technical Field

[0001] This invention relates to the fields of food processing and biomimetic manufacturing technology. More specifically, this invention relates to a method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology. Background Technology

[0002] Cell walls are a unique and essential component of higher plants, consisting of a complex layered structure primarily composed of cellulose, hemicellulose, pectin, and lignin. They play a crucial role in maintaining cell structure and mechanical strength, and regulating the firmness and toughness of plant tissues. During food processing, processes such as heat treatment (drying, cooking, sterilization, etc.), mechanical processing (pulverizing, homogenization, etc.), and enzymatic treatment (pectinase, cellulase, etc.) significantly alter the structure and composition of cell walls, thereby affecting the texture, nutrient release, and retention of functional components in food. Therefore, understanding the compositional and structural changes of cell walls during processing is essential for precise control of product quality. Currently, techniques such as confocal laser scanning microscopy (CLSM) and atomic force microscopy (AFM) are widely used for dynamic observation of cell walls; however, the results are somewhat subjective and difficult to quantify. Some researchers have also constructed simulated systems such as pectin / cellulose / hemicellulose to explore their structural evolution during processing or their interaction mechanisms with other components. However, the structure of cell walls is quite complex, and traditional simulation methods (such as solution blending) may be overly simplified, making it difficult to achieve the gradient distribution of chemical components and controllable microstructure. This results in an inability to realistically reflect the dynamic changes of cell walls, thus necessitating the construction of a high-precision plant cell wall simulation system. Currently, cell wall simulation research suffers from two disconnects: firstly, traditional methods, such as solution blending, while allowing for component control, result in overly homogeneous structures that fail to reproduce the spatial gradient of cell walls; secondly, while conventional 3D printing technology can create complex structures, its general-purpose bio-inks and printing strategies are not specifically optimized for the complexity of chemical components and structural anisotropy of fruit and vegetable cell walls, leading to deficiencies in both component realism and structural biomimicry in the constructed models. Based on this research background, a method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology is proposed. Summary of the Invention

[0003] One object of the present invention is to solve at least the above-mentioned problems and to provide at least the advantages that will be described later.

[0004] Another objective of this invention is to provide a method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology. By adjusting the types and contents of cellulose, pectin, and hemicellulose, a cell wall simulation system for different fruit and vegetable raw materials is constructed. This system can simulate the influence of different processing methods on the cell wall structure during fruit and vegetable processing, thereby revealing the mechanism of changes in macroscopic quality such as texture. It has the characteristics of being convenient and objective, and provides a theoretical basis for analyzing the quality changes of fruits and vegetables during thermal processing.

[0005] To achieve these objectives and other advantages according to the present invention, a method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology is provided, comprising the following steps: S1. Ink preparation: Cellulose, hemicellulose and pectin are mixed according to the mass percentage of the actual cell wall components of the target fruits and vegetables, dispersed in a buffer solution with a pH value consistent with the natural pH value of the target fruits and vegetables, and mixed evenly using dynamic high-pressure microfluidic technology to obtain bio-ink. The components, in their mass percentage range, are: cellulose 1.5-15%, pectin 0.1-6%, and hemicellulose 0.5-12%; the buffer solution is a disodium hydrogen phosphate-citric acid buffer solution, an acetate-sodium acetate buffer solution, or a citrate-sodium citrate buffer solution; the cellulose is selected from at least one of nanocellulose crystals, cellulose nanofibers, and amorphous cellulose; the hemicellulose is selected from at least one of xylo-glucan, arabinoxylan, xylan, glucomannan, and arabinogalactan. S2. Obtaining structural parameters: The cell wall thickness and fiber arrangement angle parameters of the target fruit and vegetable are obtained by X-ray computed tomography (CT) technology. S3. Bionic Printing: Based on the layer thickness and fiber arrangement angle parameters, control the coaxial extrusion 3D printer to deposit the bio-ink layer by layer at the set temperature and speed to construct the cell wall simulation system of the fruit and vegetable.

[0006] Preferably, in step S1, the pectin is extracted from the target fruit and vegetable raw materials through the following steps: dissolving the fruit and vegetable powder in a 0.4% HCl solution at a solid-liquid ratio of 1:3 to 1:8, stirring at 28°C for 40 min, collecting the filtrate after centrifugation, adjusting the pH value to 3 to 4 with NaOH solution, precipitating with 95% ethanol for 2 h, and obtaining the final product after filtration, washing, and drying.

[0007] Preferably, when the target fruit or vegetable is hawthorn, the composition of the bio-ink in step S1 is: 1%~4% nanocellulose crystals, 3%~6% hawthorn pectin, 3%~5% xylan, and the balance is disodium hydrogen phosphate-citric acid buffer solution with pH 2.0~6.0; When the target fruit or vegetable is an apple, the composition of the bio-ink in step S1 is: 1.5%~3% amorphous cellulose, 1%~2% apple pectin, 0.2%~0.8% xylan, 0.3%~0.6% arabinoxylan, and the balance is a disodium hydrogen phosphate-citric acid buffer solution with a pH of 2.0~6.0. When the target fruit or vegetable is carrot, the composition of the bio-ink in step S1 is: 2%~3.5% nanocellulose crystals, 1%~3% carrot pectin, 0.5%~1% xylan, 0.5%~1% arabinogalactan, and the balance is an acetate-sodium acetate buffer solution with pH 3.6~5.8; When the target fruit or vegetable is pumpkin, the composition of the bio-ink in step S1 is: 1%~3% amorphous cellulose, 0.5%~2% pumpkin pectin, 2%~5% glucomannan, and the remainder is a citric acid-sodium citrate buffer solution with a pH of 4.0~6.0. When the target fruit or vegetable is lotus root, the composition of the bio-ink in step S1 is: 2%~3% cellulose nanofibers, 0.1%~0.3% lotus root pectin, 2%~4% xyloglucan, 1%~3% arabinoxylan, and the remainder is a citric acid-sodium citrate buffer solution with a pH of 4.0~6.0. When the target fruit or vegetable is sugarcane, the composition of the bio-ink in step S1 is: 8%~15% nanocellulose crystals, 0.1%~0.5% sugarcane pectin, 10%~12% xylan, and the balance is an acetate-sodium acetate buffer solution with a pH of 3.6~5.8.

[0008] Preferably, step S3 employs a gradient construction method to programmatically construct a heterogeneous component distribution within the simulation system, specifically including the following sub-steps: S3.1 Gradient Distribution Analysis: Based on the high-resolution three-dimensional image of the cell wall obtained by the X-computed tomography technology, analyze the spatial distribution law of its microstructure density from the outer primary cell wall to the inner primary cell wall. S3.2 Gradient Ink Formulation: Using the bio-ink prepared in step S1 as the base formula, ink A and ink B are formulated by adjusting the amount of cellulose added; wherein the cellulose concentration of ink A is 5%~40% higher than that of the base formula, and the cellulose concentration of ink B is 5%~40% lower than that of the base formula; S3.3 Gradient Printing Execution: A dual-feed printing unit is adopted, in which two precision injection pumps drive ink A and ink B respectively and are connected to a dynamic online mixer and a printhead. The dual-feed printing unit is connected to a printing control system. During the printing process, the printing control system generates corresponding gradient control commands according to the spatial distribution pattern obtained in step S3.1, and controls the flow rate ratio of the two injection pumps in the dual-feed printing unit, thereby constructing a component gradient simulation system.

[0009] Preferably, the step S3.1 of analyzing the spatial distribution pattern specifically involves: processing the X-ray computed tomography three-dimensional image to extract the gray value change curve along the cell wall thickness direction; normalizing the gray value change curve and mapping it to the corresponding target cellulose concentration change curve, which serves as the basis for the gradient control command.

[0010] Preferably, in step S3.2, a rheology modifier is added to ink A and / or ink B to make both react within 10 s. -1 The viscosity ratio at the shear rate is between 0.8 and 1.2; the rheology modifier is one of xanthan gum, gellan gum or carrageenan.

[0011] Preferably, the amount of rheology modifier added does not exceed 0.5% of the total mass of the corresponding ink.

[0012] Preferably, the method further includes step S4: immersing the printed simulation system in a solution containing divalent metal ions to form an ionic cross-linking network between the rheology modifier and the pectin and / or hemicellulose. The rheology modifier is selected as gellan gum, and the divalent metal ions are Ca2+. 2+ or Zn 2+ This allows for the acquisition of a cell wall mimicry system that is responsive to pectinase.

[0013] Preferably, step S4 specifically includes a phased programmed crosslinking process: S4.1 Pre-crosslinking stage: Place the printed simulation system at a temperature of 4°C to 15°C, a pH of 3.0 to 4.5, and Ca... 2+ The system is treated in a first crosslinking solution with a concentration of 0.01 M to 0.05 M for 5 to 20 minutes to form a preliminary, weak ionic crosslinking network on the surface of the system. S4.2 Gradient Permeation Stage: Subsequently, the simulated system was transferred to an environment with a temperature of 25°C to 37°C, a pH of 5.0 to 6.5, and Ca... 2+In a second crosslinking solution with a concentration of 0.1 M to 0.3 M, the system was treated for 30 to 120 minutes. During this stage, by controlling the heating rate and pH change rate of the system from the first solution to the second solution, a spatial gradient was formed between the diffusion rate of divalent metal ions into the simulated system and the crosslinking reaction rate, thereby constructing a gradient network structure with decreasing crosslinking density from the outside to the inside of the system. S4.3 Equilibrium Stabilization Stage: Finally, the simulated system is placed in a buffer solution with the same ionic strength as the second crosslinking solution but without divalent metal ions, and equilibrated at 20°C to 25°C for 30 to 60 minutes to stabilize the crosslinking structure and remove unbound excess ions.

[0014] The present invention also discloses a method for constructing a high-precision fruit and vegetable cell wall simulation system as described above, and the resulting fruit and vegetable cell wall.

[0015] The present invention has at least the following beneficial effects: This invention constructs a cell wall simulation system for different fruit and vegetable raw materials by adjusting the types and contents of cellulose, pectin, and hemicellulose. This simulation system can simulate the effects of different processing methods on the cell wall structure of fruits and vegetables during processing, thereby revealing the mechanisms of changes in macroscopic qualities such as texture. This invention is convenient and objective, providing a theoretical basis for analyzing the changes in fruit and vegetable quality during thermal processing.

[0016] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to embodiments, so that those skilled in the art can implement it based on the description.

[0018] This invention discloses a method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology, comprising the following steps: S1. Ink preparation: Cellulose, hemicellulose and pectin are mixed according to the mass percentage of the actual cell wall components of the target fruits and vegetables, dispersed in a buffer solution with a pH value consistent with the natural pH value of the target fruits and vegetables, and mixed evenly using dynamic high-pressure microfluidic technology to obtain bio-ink. The components, in their mass percentage range, are: cellulose 1.5-15%, pectin 0.1-6%, and hemicellulose 0.5-12%; the buffer solution is a disodium hydrogen phosphate-citric acid buffer solution, an acetate-sodium acetate buffer solution, or a citrate-sodium citrate buffer solution; the cellulose is selected from at least one of nanocellulose crystals, cellulose nanofibers, and amorphous cellulose; the hemicellulose is selected from at least one of xylo-glucan, arabinoxylan, xylan, glucomannan, and arabinogalactan. S2. Obtaining structural parameters: The cell wall thickness and fiber arrangement angle parameters of the target fruit and vegetable are obtained by X-ray computed tomography (CT) technology. S3. Bionic Printing: Based on the layer thickness and fiber arrangement angle parameters, control the coaxial extrusion 3D printer to deposit the bio-ink layer by layer at the set temperature and speed to construct the cell wall simulation system of the fruit and vegetable.

[0019] The specific implementation method of the high-precision fruit and vegetable cell wall simulation system construction method based on 3D printing technology in the above technical solution is as follows: Step 1: Ink Preparation. Select cellulose raw materials, such as nano-cellulose crystals or microcrystalline cellulose, at an addition amount of 2%, 5%, or 10% of the total mass of the bio-ink. Pectin raw materials can be pectin extracted from the target fruit / vegetable (e.g., apple) or commercially available citrus pectin, at an addition amount of 1%, 3%, or 5%. Hemicellulose raw materials can be xylan or arabinoxylan, at an addition amount of 2%, 6%, or 10%. Prepare a buffer solution, using disodium hydrogen phosphate-citric acid buffer, adjusting its pH to match the pH of the target fruit / vegetable tissue, for example, pH 3.5, 4.0, or 5.5. Add the weighed cellulose, pectin, and hemicellulose powders to the buffer solution and premix for 30 minutes at room temperature using a magnetic stirrer to form a preliminary dispersion slurry. Then, transfer the slurry to the feed tank of a dynamic high-pressure microfluidic homogenizer. Set the homogenization pressure to 200 MPa and circulate the material three times at a flow rate of 25 mL / min. During the processing, the material temperature is maintained below 25°C using a built-in cooling system. After the above treatment, a rheometer is used to measure the material temperature at a shear rate of 10 s⁻¹. -1Measure the ink viscosity. If the measured viscosity does not fall within the target range of 1 kPa·s to 100 kPa·s, the process can be adjusted. Specifically: if the viscosity is too low, the homogenization pressure can be increased or the number of cycles increased to enhance the dispersion and entanglement of the cellulose network; if the viscosity is too high, the homogenization pressure can be decreased or the number of cycles reduced. Additionally, a trace amount of rheology modifier (such as xanthan gum or gellan gum) can be selectively added for fine-tuning, typically not exceeding 0.1% of the total ink mass. After adjustment, measure the viscosity again using a rheometer until it falls within the target range, ultimately obtaining a uniform, stable, and suitable printable bio-ink with appropriate rheological properties.

[0020] Step Two: Obtaining Structural Parameters. Obtain the actual microstructural parameters of the target fruit or vegetable. Taking apple pulp as an example, first use a blade to cut off approximately 1 cm of pulp. 3 Apple tissue samples were then fixed in a 50% ethanol solution for 4 hours to preserve their structure. After fixation, the samples were removed, blotted dry with absorbent paper, and placed on the sample stage of a microfocus X-ray computed tomography (CT) system. Scanning parameters were set as follows: X-ray voltage 50 kV, current 200 µA, 0.5 mm thick aluminum filter, and detector pixel size 5 µm. The sample stage rotated 360° during the scan, acquiring 1000 projection images. After scanning, the two-dimensional projection images were reconstructed into 3D volumetric data images with a resolution of 5 µm using either the system's proprietary or general-purpose 3D reconstruction software (e.g., commercial software based on the Feldkamp-Davis-Kress algorithm). To analyze cell wall thickness, a complete cell wall region was selected in the 3D image, and a virtual slice was made along its normal direction. The total thickness of the primary and secondary cells was directly measured using the software's measurement tools; for example, a thickness of 8 µm was measured. To analyze the alignment angle of cellulose microfibrils, a clearly defined fiber bundle region was selected on a virtual cross-section perpendicular to the cell's long axis. Fourier transform analysis was performed on this region using image analysis software (e.g., ImageJ software with the "Directionality" plugin). By calculating the ellipse fitting of the intensity distribution along its principal axis, the average alignment angle of the fiber bundle relative to the cell's long axis was obtained; for example, a measured angle of 25°. The obtained layer thickness (8 µm) and fiber alignment angle (25°) data were recorded and used to guide subsequent printing procedures.

[0021] Step 3: Bionic Printing. Printing is performed using a three-axis coaxial extrusion bioprinter. First, the bio-ink prepared in Step 1 is loaded into a 30 mL medical syringe (as a feeder) and mounted on the printer's extrusion platform. A coaxial nozzle is selected for printing, with its inner diameter chosen based on ink characteristics, for example, 200 µm. The printing program is set in the printing control software according to the parameters obtained in Step 2. Based on the measured cell wall layer thickness of 8 µm, the single-layer thickness is set to 8 µm. To simulate the 25° alignment angle of cellulose microfibrils, the printing infill path is oriented in the software: linear infill mode is selected, and the direction of the infill line is set to a 25° angle with the printing forward direction. The printing temperature is set to room temperature (25°C), the printhead movement speed is set to 3 mm / s, and the extrusion pressure is adjusted via the pneumatic control system, initially set to 0.2 bar, and fine-tuned according to the continuity and uniformity of the extruded filaments. At the start of printing, the extrusion system propels bio-ink steadily from the nozzle inner tube, while a three-axis motion platform moves the print head along a pre-set path at a specific angle (25°), depositing ink line by line and layer by layer onto a glass substrate coated with an anti-stick coating. After each layer is printed, the printing platform descends 8 µm along the Z-axis to deposit the next layer. By repeating this process, a two-dimensional or three-dimensional simulation system simulating the cell wall structure of the target fruit / vegetable (apple) in terms of macroscopic layer thickness and microscopic fiber orientation is finally constructed on the substrate. The printed simulation system can be allowed to equilibrate at room temperature for 1 hour before being used for further performance testing or processing studies. Through microscopic observation and mechanical testing, the simulation system constructed by this method shows structural morphology similarity to the target cell wall.

[0022] In another technical solution, in step S1, the pectin is extracted from the target fruit and vegetable raw material through the following steps: dissolving the fruit and vegetable powder with a volume fraction of 0.4% HCl solution at a solid-liquid ratio of 1:3 to 1:8, stirring at 28°C for 40 min, collecting the filtrate after centrifugation, adjusting the pH value to 3 to 4 with NaOH solution, precipitating with 95% ethanol for 2 h, and obtaining the pectin after filtration, washing, and drying.

[0023] In the above technical solution, the first step in the extraction of pectin from the target fruits and vegetables is acid dissolution. Weigh out dried, pulverized, and sieved target fruit and vegetable powder, such as apple powder or hawthorn powder. Mix the fruit and vegetable powder with a 0.4% HCl solution at a solid-liquid ratio of 1:5 (g / mL), which can be adjusted within the range of 1:3 to 1:8. Place the mixture in a constant temperature water bath with a magnetic stirrer and stir at 300 rpm for 40 minutes at 28°C. During this process, the acid solution hydrolyzes some of the covalent bonds in the cell walls of the fruits and vegetables, promoting pectin dissolution. After stirring, transfer the mixture to centrifuge tubes and centrifuge using a benchtop high-speed centrifuge at 4°C and 8000×g for 15 minutes. After centrifugation, carefully collect the supernatant, which contains dissolved pectin and other water-soluble components.

[0024] The second step is precipitation and purification. Add 2M NaOH solution dropwise to the filtrate obtained in the previous step, while simultaneously monitoring the pH with a pH meter to adjust the pH to 3.5. Then, perform alcohol precipitation. Slowly add pre-cooled 95% ethanol solution (volume ratio 3:1) to the pH-adjusted filtrate. Incubate this mixture at 4°C for 2 hours to allow pectin to fully precipitate. After settling, use a Buchner funnel with medium-speed qualitative filter paper for vacuum filtration, collecting the precipitate on the filter paper. To remove residual acid, salts, and small molecule impurities, wash the precipitate with 95% ethanol solution (volume volume approximately 3 times the precipitate volume) twice. After each wash, perform vacuum filtration to remove the washings.

[0025] The third step is drying and obtaining the finished product. The washed, moistened pectin precipitate is removed from the filter paper and spread evenly in a petri dish. The petri dish is placed in a forced-air drying oven and dried at 35°C for 6 hours until constant weight is achieved. After drying, the lumpy pectin is ground into powder using a mortar and pestle, and then passed through an 80-mesh sieve to obtain a light-colored, fluffy pectin powder sample. The sample is then subjected to quality assessment, for example, by determining its galacturonic acid content using the carbazole-sulfuric acid method to confirm the extraction efficiency. The final pectin sample can be sealed and stored in a desiccator for subsequent use in the preparation of bio-inks. The pectin extracted using this method has a structure and properties closer to the natural pectin of the target fruits and vegetables, which helps to improve the biomimetic realism of the simulation system.

[0026] In another technical solution, when the target fruit or vegetable is hawthorn, the composition of the bio-ink in step S1 is: 1%~4% nanocellulose crystals, 3%~6% hawthorn pectin, 3%~5% xylan, and the balance is a disodium hydrogen phosphate-citric acid buffer solution with pH 2.0~6.0. When the target fruit or vegetable is an apple, the composition of the bio-ink in step S1 is: 1.5%~3% amorphous cellulose, 1%~2% apple pectin, 0.2%~0.8% xylan, 0.3%~0.6% arabinoxylan, and the balance is a disodium hydrogen phosphate-citric acid buffer solution with a pH of 2.0~6.0. When the target fruit or vegetable is carrot, the composition of the bio-ink in step S1 is: 2%~3.5% nanocellulose crystals, 1%~3% carrot pectin, 0.5%~1% xylan, 0.5%~1% arabinogalactan, and the balance is an acetate-sodium acetate buffer solution with pH 3.6~5.8; When the target fruit or vegetable is pumpkin, the composition of the bio-ink in step S1 is: 1%~3% amorphous cellulose, 0.5%~2% pumpkin pectin, 2%~5% glucomannan, and the remainder is a citric acid-sodium citrate buffer solution with a pH of 4.0~6.0. When the target fruit or vegetable is lotus root, the composition of the bio-ink in step S1 is: 2%~3% cellulose nanofibers, 0.1%~0.3% lotus root pectin, 2%~4% xyloglucan, 1%~3% arabinoxylan, and the remainder is a citric acid-sodium citrate buffer solution with a pH of 4.0~6.0. When the target fruit or vegetable is sugarcane, the composition of the bio-ink in step S1 is: 8%~15% nanocellulose crystals, 0.1%~0.5% sugarcane pectin, 10%~12% xylan, and the balance is an acetate-sodium acetate buffer solution with a pH of 3.6~5.8.

[0027] In the above technical solution, taking the hawthorn cell wall simulation system as an example, the construction method of the cell wall simulation system is as follows: First, extract hawthorn pectin from hawthorn powder using the aforementioned pectin extraction method, or use commercially available food-grade pectin. Prepare other raw materials: nanocellulose crystals, xylan, and disodium hydrogen phosphate and citric acid. Prepare a disodium hydrogen phosphate-citric acid buffer solution, adjusting the pH of the buffer solution precisely to 3.0 by adjusting the ratio of the two substances.

[0028] Next, accurately weigh each component. Weigh 3.0 g of nanocellulose crystals (3% of the total formulation mass), 4.0 g of hawthorn pectin (4%), and 4.0 g of xylan (4%). Add the weighed solid powders together to 89.0 g of prepared pH 3.0 disodium hydrogen phosphate-citric acid buffer solution (the buffer solution is used as a solvent and accounts for 89% of the total mass). Place the mixture on a magnetic stirrer and stir at 500 rpm for 1 hour at room temperature to initially wet and disperse the solid powders, forming a premixed slurry.

[0029] Next, the premixed slurry undergoes fine homogenization. The slurry is transferred to the feed tank of a dynamic high-pressure microjets homogenizer. The homogenization pressure is set to 180 MPa, and the slurry is circulated through the homogenization chamber three times. During the process, the cooling system maintains the material temperature below 30°C. After homogenization, the rheological properties of the ink are measured using a rotational rheometer. The apparent viscosity is measured at a shear rate of 10 s⁻¹. If the viscosity does not fall within the preset suitable printing range (e.g., 1–100 kPa·s), the process can be fine-tuned: if the viscosity is too low, the homogenization pressure can be increased to 200 MPa and the process repeated; if the viscosity is too high, a small amount of deionized water can be added for dilution, and the mixture can be briefly stirred before re-measuring. Finally, a uniform, stable bio-ink for hawthorn cell wall simulation systems with shear-thinning properties is obtained.

[0030] For the other five systems—apple, carrot, pumpkin, lotus root, and sugarcane—the specific implementation process is exactly the same as that of the hawthorn system described above. The only differences are in the components and proportions, buffer type and pH, and fine-tuning of homogenization parameters. Due to the different rheological behaviors of the different components, the homogenization pressure or number of cycles required to achieve printable viscosity may need to be slightly optimized within the range described above (100~300 MPa, 1~5 cycles). For example, for the sugarcane system with a high cellulose content, the initial homogenization pressure may be selected as 250 MPa.

[0031] Through the standardized yet finely tuned process described above, high-precision biomimetic printing inks suitable for different fruits and vegetables can be repeatedly prepared, laying the material foundation for subsequent 3D printing construction based on structural parameters. This method allows the chemical composition of the simulated system to more realistically reflect the inherent characteristics of the cell walls of specific fruits and vegetables.

[0032] <Example 1> This embodiment constructs a hawthorn cell wall simulation system. The specific preparation method is as follows: Step 1: Obtaining hawthorn pectin samples. First, dissolve hawthorn powder in 0.4% HCl solution (solid-liquid ratio 1:5), stir at 300 rpm for 40 min at 28℃, then centrifuge at 8000×g for 15 min at 4℃, and collect the supernatant. Adjust the pH to 3.5 with 2 M NaOH solution, then add 3 volumes of pre-cooled 95% ethanol solution, and precipitate at 4℃ for 2 h. Collect the precipitate by vacuum filtration, and wash the precipitate twice with 95% ethanol, filtering after each wash. Finally, dry the precipitate in a 35℃ oven for 6 h, grind it through an 80-mesh sieve to obtain hawthorn pectin powder.

[0033] Step 2: Prepare the buffer solution. Weigh out disodium hydrogen phosphate and citric acid, and prepare a disodium hydrogen phosphate-citric acid buffer solution with a pH of 3.0 using ultrapure water.

[0034] Step 3: Prepare the printing ink. Accurately weigh 3.0 g of nanocellulose crystals (3.0% of the total formula mass), 4.0 g of hawthorn pectin (4.0%), and 4.0 g of xylan (4.0%), and add them together to 89.0 g of disodium hydrogen phosphate-citric acid buffer solution at pH 3.0.

[0035] Step 4: Obtaining cell wall thickness and arrangement parameters. Fresh hawthorn pulp tissue was fixed in 50% ethanol solution for 4 h and then scanned using a microfocus X-ray computed tomography system (the model used in this embodiment is Bruker Skyscan 1272). Scanning parameters: voltage 50 kV, current 200µA, 0.5 mm aluminum filter, pixel size 5 µm, 1000 projections were acquired by rotating 360°. After 3D reconstruction and analysis, the cell wall thickness of the thin-walled tissue was measured to be approximately 1 µm, and the thickness of the thick-walled tissue (stone cells) layer was approximately 6 µm. The cellulose microfibrils were arranged relatively randomly, and the microfibrils of the thick-walled cells were arranged in a spiral shape, with a spiral angle of approximately 40° relative to the long axis of the cell.

[0036] Step 5: Ink Homogenization. The mixed solution prepared in Step 3 is transferred to the feed tank of a dynamic high-pressure micro-jet homogenizer. The pressure is set to 110 MPa, and the homogenization is repeated 3 times. The material temperature is maintained below 25°C by a cooling system. The treated ink is then subjected to a shear rate of 10 s⁻¹. -1 The measured apparent viscosity was 28.3 kPa·s, which meets the printing requirements.

[0037] Step 6: Inject printing ink. Load the printing ink obtained in Step 5 into a 30 mL medical syringe and attach it to the feed platform of the coaxial extrusion bioprinter (the model used in this embodiment is Cellink BIO X). A coaxial nozzle with an inner / outer diameter of 200 / 400 µm is selected as the printhead.

[0038] Step 7: 3D Printing. In the printing control software, set the printing program according to the parameters obtained in Step 4: set the outer layer (simulating the primary cell wall) thickness to 1 µm, the middle layer (simulating the secondary cell wall) thickness to 6 µm, and the infill path angle to a 40° spiral in the thick-walled region. Printing was performed at room temperature (25°C), with a printhead movement speed of 1 mm / s and an initial extrusion pressure of 0.25 bar, fine-tuned according to the filament output. After printing, the sample was allowed to stand at room temperature for 1 h to equilibrate, resulting in a high-precision hawthorn cell wall simulation film.

[0039] The cell wall simulation architecture obtained in Example 1 is stable.

[0040] <Example 2> This embodiment constructs a high-precision apple cell wall simulation system. Details are as follows: Step 1: Obtaining apple pectin samples. Using the same extraction method as in Example 1, apple powder was used as the raw material to finally obtain apple pectin powder.

[0041] Step 2: Prepare the buffer solution. Prepare a disodium hydrogen phosphate-citric acid buffer solution with a pH of 3.6.

[0042] Step 3: Prepare the printing ink. Accurately weigh 2.0 g (2.0%) of amorphous cellulose, 1.5 g (1.5%) of apple pectin, 0.6 g (0.6%) of xylan, and 0.45 g (0.45%) of arabinoxylan, and add them to 95.45 g of disodium hydrogen phosphate-citrate buffer solution at pH 3.6.

[0043] Step 4: Obtain cell wall thickness and arrangement parameters. Fresh apple pulp was subjected to X-CT scanning (parameters same as in Example 1). After three-dimensional reconstruction, the cell wall thickness of the thin-walled tissue was measured to be approximately 0.5 µm, and the thickness of the thick-walled tissue (vascular fibers) layer was approximately 5 µm. The fibers were arranged relatively randomly in the thin-walled tissue, while in the thick-walled tissue they showed a cross-layered arrangement, with an average angle of approximately 20° relative to the long axis of the cell.

[0044] Step 5: Ink homogenization. The mixed solution was circulated three times in a dynamic high-pressure microfluidic homogenizer at 95 MPa. The ink's viscosity was measured at 10 s. -1 The viscosity at the shear rate is 9.6 kPa·s.

[0045] Steps 6 and 7: Printing Execution. Printing parameter settings: Simulated primary cell wall single-layer thickness 0.5 µm, simulated secondary cell wall single-layer thickness 5 µm, fill path angle set to 20° cross-layer in thick-walled areas. Printing temperature 25℃, speed 0.5 mm / s. After printing, an apple cell wall simulation system is obtained.

[0046] The cell wall simulation architecture obtained in Example 2 is stable.

[0047] <Example 3> This embodiment constructs a high-precision carrot cell wall simulation system. Details are as follows: Step 1: Obtain carrot pectin samples. Carrot pectin was obtained by extracting it from carrot powder using the same method.

[0048] Step 2: Prepare the buffer solution. Prepare an acetate-sodium acetate buffer solution with a pH of 5.0.

[0049] Step 3: Prepare the printing ink. Accurately weigh 2.8 g (2.8%) of nanocellulose crystals, 2.0 g (2.0%) of carrot pectin, 0.8 g (0.8%) of xylan, and 0.7 g (0.7%) of arabinogalactan, and add them to 93.7 g of acetate-sodium acetate buffer solution at pH 5.0.

[0050] Step 4: Obtain cell wall thickness and arrangement parameters. X-CT scan of carrot root tissue revealed that the cell wall thickness of the vascular bundle fiber cells was approximately 1.5 µm, and the cellulose microfibrils were arranged in parallel longitudinally (0° angle).

[0051] Step 5: Ink homogenization treatment. Dynamic high-pressure microfluidic treatment was performed three times at 180 MPa, and the ink viscosity was measured to be 46.5 kPa·s.

[0052] Steps 6 and 7: Printing execution. Set the single-layer thickness to 1.5 µm and the infill path to 0° parallel lines. Printing temperature: 25℃, printing speed: 3 mm / s. A carrot cell wall simulation system is obtained after printing.

[0053] The cell wall simulation architecture obtained in Example 3 is stable.

[0054] <Example 4> This embodiment constructs a high-precision pumpkin cell wall simulation system. Details are as follows: Step 1: Obtain pumpkin pectin samples. Pectin is extracted from pumpkin powder.

[0055] Step 2: Prepare the buffer solution. Prepare a citric acid-sodium citrate buffer solution with a pH of 5.2.

[0056] Step 3: Prepare the printing ink. Accurately weigh 2.0 g (2.0%) of amorphous cellulose, 1.2 g (1.2%) of pumpkin pectin, and 3.5 g (3.5%) of glucomannan, and add them to 93.3 g of citrate-sodium citrate buffer solution at pH 5.2.

[0057] Step 4: Obtain cell wall thickness and arrangement parameters. X-CT scan of pumpkin pulp tissue showed that the thickness of the thin-walled tissue cell wall layer was about 0.3 µm, and the thickness of the thick-walled tissue (fiber bundle) layer was about 5 µm. In the thick-walled tissue, the cellulose microfibrils were arranged in a cross-layered manner at an angle of about 45°.

[0058] Step 5: Ink homogenization treatment. The ink was treated three times under a pressure of 220 MPa, and the viscosity was measured to be 65.8 kPa·s.

[0059] Steps 6 and 7: Printing execution. Set the single-layer thickness of the thin-walled area to 0.3 µm and the single-layer thickness of the thick-walled area to 5 µm. Set a 45° cross-fill path in the thick-walled area. Printing temperature: 25°C, printing speed: 5 mm / s.

[0060] The cell wall simulation architecture obtained in Example 4 is stable.

[0061] <Example 5> This embodiment constructs a high-precision lotus root cell wall simulation system. Details are as follows: Step 1: Obtain lotus root pectin samples. Pectin is extracted from lotus root powder.

[0062] Step 2: Prepare the buffer solution. Prepare a citric acid-sodium citrate buffer solution with a pH of 6.0.

[0063] Step 3: Prepare the printing ink. Accurately weigh 2.5 g (2.5%) of cellulose nanofibers, 0.2 g (0.2%) of lotus root pectin, 3.0 g (3.0%) of xyloglucan, and 2.0 g (2.0%) of arabinoxylan, and add them to 92.3 g of citrate-sodium citrate buffer solution at pH 6.0.

[0064] Step 4: Obtain cell wall thickness and arrangement parameters. X-CT scan of lotus root internode tissue showed that the thin-walled cell wall thickness was approximately 0.8 µm, and the vascular bundle thick-walled cell wall thickness was approximately 8 µm. The microfibrils of the thin-walled cells were loosely arranged, while the microfibrils of the vascular bundle fiber cells were arranged in a spiral shape with a spiral angle of approximately 40°.

[0065] Step 5: Ink homogenization treatment. The ink was treated three times under a pressure of 245 MPa, and the viscosity was measured to be 75.2 kPa·s.

[0066] Steps 6 and 7: Printing execution. Set the single-layer thickness to 0.8 µm in the thin-walled region and 8 µm in the thick-walled region, and set a 40° spiral infill path in the vascular bundle region. Printing temperature: 35℃, printing speed: 7 mm / s.

[0067] The cell wall simulation architecture obtained in Example 5 is stable.

[0068] <Example 6> This embodiment constructs a high-precision sugarcane cell wall simulation system. Details are as follows: Step 1: Obtain sugarcane pectin samples. Pectin is extracted from sugarcane bagasse powder.

[0069] Step 2: Prepare the buffer solution. Prepare an acetate-sodium acetate buffer solution with a pH of 5.8.

[0070] Step 3: Prepare the printing ink. Accurately weigh 12.0 g (12.0%) of nanocellulose crystals, 0.3 g (0.3%) of sugarcane pectin, and 11.0 g (11.0%) of xylan, and add them to 76.7 g of acetate-sodium acetate buffer solution at pH 5.8.

[0071] Step 4: Obtain cell wall thickness and arrangement parameters. X-CT scan of sugarcane stalk fibers showed that the fiber cell wall thickness was approximately 15 µm, and the cellulose microfibrils in the secondary wall were arranged in a highly ordered parallel pattern (0° angle).

[0072] Step 5: Ink homogenization treatment. The ink was treated four times under a pressure of 295 MPa, and the viscosity was measured to be 98.5 kPa·s.

[0073] Steps 6 and 7: Printing execution. Set the single-layer thickness to 15 µm and the infill path to 0° parallel lines. Printing temperature: 40℃, printing speed: 10 mm / s.

[0074] The cell wall simulation system obtained in Example 6 is structurally stable.

[0075] <Comparative Example 1> This comparative example aims to verify the necessity of utilizing the technical feature of uniform mixing through dynamic high-pressure microjets.

[0076] Preparation method: Except for step 5, "ink homogenization treatment," which is modified as follows, all other steps (steps 1 to 4, and steps 6 to 7) are exactly the same as in Example 1: The mixed solution prepared in step 3 (3.0% nanocellulose crystals, 4.0% hawthorn pectin, 4.0% xylan, and 89.0% pH 3.0 buffer solution) is placed in a beaker and stirred continuously at 1000 rpm for 2 hours using only a regular magnetic stirrer until the system is visually homogeneous. After stirring, the subsequent printing steps are performed directly without dynamic high-pressure microfluidic treatment.

[0077] <Comparative Example 2> This comparative example aims to verify the necessity of the technical feature of "obtaining the cell wall thickness and fiber arrangement angle parameters of target fruits and vegetables through X-ray computed tomography".

[0078] Preparation method: Except for the fourth step, "obtaining cell wall thickness and arrangement parameters", which was completely omitted, and the seventh step, printing, which was not based on any real scanning parameters but was arbitrarily set based on experience, all other steps were exactly the same as in Example 1.

[0079] Arbitrarily set printing parameters: The thickness of a single layer is uniformly set to 10 µm (instead of the actual 1 µm and 6 µm), and the fill path uses a simple 90° orthogonal grid (instead of a simulated 40° spiral arrangement).

[0080] <Comparative Example 3> This comparative example aims to demonstrate the limitations of traditional simulation methods, thereby highlighting the innovation of the 3D printing construction method of this invention.

[0081] Preparation method: No 3D printing technology was used. Only the exact same components and proportions as in Example 1 were used: 3.0 g of nanocellulose crystals, 4.0 g of hawthorn pectin, 4.0 g of xylan, and 89.0 g of disodium hydrogen phosphate-citric acid buffer solution (pH 3.0). All solid powders were added to the buffer solution and stirred with a magnetic stirrer (1000 rpm) for 2 hours until homogeneous. The mixture was then poured into 60 mm diameter culture dishes and placed in a 4°C refrigerator to allow gelation for 12 hours, forming a homogeneous hydrogel, which served as a control sample for the traditional cell wall simulation system.

[0082] <Swelling rate> 1. Test Samples: The cell wall simulation systems (usually thin films or small block structures) prepared in Examples 1-6 and Comparative Examples 1-3 were cut into square samples with a size of 10 mm × 10 mm using a special mold or blade. At least 5 parallel samples were prepared for each example or comparative example.

[0083] Pretreatment: The cut sample was placed in the same buffer solution as during preparation (such as pH 3.0 disodium hydrogen phosphate-citric acid buffer solution from Example 1) and equilibrated at 4°C for 24 hours to eliminate stress from the preparation process and reach initial swelling equilibrium. After removal, the surface liquid was gently blotted off with filter paper, and the sample was weighed immediately. This weight was recorded as the initial dry weight (W0). Subsequently, the sample was placed in a 40°C forced-air drying oven to dry to constant weight (approximately 6-8 hours), and the absolute dry weight (Wd) was obtained.

[0084] 2. Detection method: a. Immersion: Completely immerse the pretreated sample (initially in equilibrium wet state) in a sealed container containing 50 mL of the corresponding pH buffer solution (temperature pre-stabilized at 37°C). b. Constant Temperature Treatment: Place the container in a 37°C constant temperature water bath shaker and gently oscillate at 60 rpm for 2 hours to ensure uniform solution contact and prevent sample adhesion. c. Weighing: After the specified time, quickly remove the sample with tweezers and lay it flat on a surface lined with multiple layers of absorbent filter paper. Gently press the sample surface with another sheet of filter paper for 10 seconds to remove surface free water. Immediately weigh the sample using an analytical balance with an accuracy of 0.1 mg. Record this weight as the swollen weight (Wt). d. Calculation: The swelling ratio (SR) is calculated using the following formula: SR(%) = 100% × (Wt − Wd) / Wd.

[0085] <Mechanical Property Testing> 1. Test sample Simulated system samples: Similar to the swelling rate test, the simulated system was cut into 15 mm × 15 mm cubes, equilibrated in the corresponding buffer at 4°C for 24 hours, and then used for testing.

[0086] Real fruit and vegetable tissue control samples: Fresh, undamaged fruits and vegetables corresponding to the simulation system (such as fresh hawthorn pulp corresponding to Example 1) were taken and cylindrical tissue blocks with a diameter of 15 mm were obtained using a punch. The tissue blocks were immersed in the same buffer solution as the simulation system and equilibrated at 4°C for 24 hours to ensure that their pH and ionic environment were consistent with the simulation system for fair comparison.

[0087] 2. Detection method (puncture test): a. Instruments and parameters: A texture analyzer equipped with a P / 2 type cylindrical stainless steel probe (2 mm in diameter) was used. The instrument was calibrated for force and height before testing.

[0088] b. Test Setup: Place the balanced sample (simulated system or real tissue) flat in the center of the stage. Test parameters are set as follows: test mode: puncture; trigger force: 5 g; test speed: 1 mm / s; compression distance: 50% of the original sample thickness (sample thickness must be measured with a digital caliper and entered into the software before testing). Data acquisition rate: 200 points / second.

[0089] c. Test execution: The probe is pressed down at a set speed, penetrating the sample to a preset depth and then returning. The software automatically records the force-time or force-displacement curve.

[0090] d. Data analysis: Extract the maximum puncture force (Fmax), i.e. the peak value of the curve, from the curve as a characterization of sample hardness.

[0091] e. Calculate the consistency deviation: For each embodiment, calculate the average hardness (Hsim) of its simulated system and compare it with the average hardness (Hreal) of the corresponding real fruit and vegetable tissue. The formula for calculating the relative hardness deviation (HD) is: HD(%) = |(Hsim - Hreal) / Hreal| × 100%; at least 5 parallel samples should be tested for each group, and the average value should be taken.

[0092] <Hardness retention rate after hot working> 1. Test Samples: Use the same simulated system samples (15 mm × 15 mm cubes, equilibrated in buffer for 24 hours) as the “Mechanical Property Consistency” test. Prepare at least 10 parallel samples for each example or comparative example, and randomly divide them into two groups: a heat-treated group (5 samples) and an untreated control group (5 samples).

[0093] 2. Detection method: a. Heat treatment: Each sample in the heat treatment group was individually sealed in a heat-resistant, breathable Teflon (PTFE) film bag, and 1 mL of the corresponding buffer solution was added to create a humid heat environment. The sealed samples were completely immersed in a constant temperature water bath and precisely treated at 80.0°C for 10 minutes. After treatment, the sample bags were immediately removed and immersed in an ice-water mixture to rapidly cool to room temperature (approximately 5 minutes).

[0094] b. Equilibration recovery: The cooled heat-treated samples and the untreated control samples were placed together in freshly prepared corresponding buffer solutions and reequilibrated at 4°C for 2 hours to eliminate the instantaneous stress caused by the sudden temperature change.

[0095] c. Hardness test: Following the puncture test steps in the above "Mechanical property consistency" test method, test the hardness (Hheated and Hcontrol) of the heat-treated group and the control group samples respectively.

[0096] d. Calculate the hardness retention rate: The hardness retention rate (HRR) is calculated using the formula: HRR(%) = Hheated / Hcontrol × 100%, and the result is the average of the calculated values ​​of 5 parallel samples.

[0097] <Test Results> The above-mentioned indicators of Examples 1-6 and Comparative Examples 1-3 were tested, and the results are shown in Table 1.

[0098] Table 1 As shown in Table 1, the present invention, through the construction and systematic testing of cell wall simulation systems for six fruits and vegetables (hawthorn, apple, carrot, pumpkin, lotus root, and sugarcane) (Examples 1-6), demonstrates the universality of this technical solution. The obtained simulation systems all exhibit excellent structural stability, high mechanical biomimicry, and reasonable processing response. Compared with the comparative examples, Comparative Example 1 (without dynamic high-pressure microjets) has a loose structure, Comparative Example 2 (without using real structural parameters) has low simulation accuracy, and Comparative Example 3 (traditional blending and casting method) completely loses its structural gradient. These examples collectively demonstrate that the three technical features of "ink formulation according to real proportions, dynamic high-pressure microjets for homogenization, and 3D printing based on real X-CT structural parameters" are indispensable. The present invention effectively overcomes the limitations of traditional simulation methods, such as simplified structures and the inability to reproduce the chemical composition gradient and microstructure of cell walls. It provides a biomimetic construction method with good repeatability, high precision, and the ability to accurately simulate structural evolution behavior during processing.

[0099] In another technical solution, step S3 employs a gradient construction method to programmatically construct a heterogeneous component distribution within the simulation system, specifically including the following sub-steps: S3.1 Gradient Distribution Analysis: Based on the high-resolution three-dimensional image of the cell wall obtained by the X-computed tomography technology, analyze the spatial distribution law of its microstructure density from the outer primary cell wall to the inner primary cell wall. S3.2 Gradient Ink Formulation: Using the bio-ink prepared in step S1 as the base formula, ink A and ink B are formulated by adjusting the amount of cellulose added; wherein the cellulose concentration of ink A is 5%~40% higher than that of the base formula, and the cellulose concentration of ink B is 5%~40% lower than that of the base formula; S3.3 Gradient Printing Execution: A dual-feed printing unit is adopted, in which two precision injection pumps drive ink A and ink B respectively and are connected to a dynamic online mixer and a printhead. The dual-feed printing unit is connected to a printing control system. During the printing process, the printing control system generates corresponding gradient control commands according to the spatial distribution pattern obtained in step S3.1, and controls the flow rate ratio of the two injection pumps in the dual-feed printing unit, thereby constructing a component gradient simulation system.

[0100] The aforementioned technical solution aims to simulate the chemical composition and density gradient of a real cell wall, from the primary wall to the secondary wall. Its implementation mainly includes three sequentially connected sub-steps.

[0101] Gradient distribution analysis is the first step. Based on the high-resolution 3D image of the target fruit and vegetable cell wall obtained by X-ray computed tomography (XCT) in step S2 (e.g., voxel resolution of 1 μm), data processing is performed to analyze its spatial distribution patterns. Specifically, 3D image processing software (such as Avizo or ImageJ's 3D plugin) is used. In the software, an analysis line segment is set along the direction perpendicular to the cell wall surface, penetrating the cell wall thickness. The software will automatically extract the gray values ​​of all voxels on this line segment and generate a gray value variation curve along the thickness direction. Since the density of a material is usually positively correlated with gray value in micro-CT imaging, and cellulose is the main supporting framework of the cell wall, its local concentration is closely related to density. Therefore, this gray value variation curve can be normalized (i.e., all gray values ​​are linearly converted to the range of 0-1). Subsequently, this normalized gray value curve is mapped to a curve showing the change of target cellulose concentration with thickness position based on a pre-established calibration relationship (e.g., a gray-cellulose concentration correspondence established through simulated samples with known components). This curve quantitatively describes the preset gradient distribution of cellulose concentration from the outer layer to the inner layer of the cell wall, and serves as the core basis for subsequent gradient printing control instructions.

[0102] The gradient mapping algorithm is established and implemented as follows: Step 1: Establishing the calibration curve (preliminary preparation). To establish a quantitative relationship between gray value and cellulose concentration, a set of calibration samples with known components needs to be prepared in advance.

[0103] 1. Prepare cellulose concentrations (denoted as C) separately. cal Standardized bio-inks are available in concentrations of 0%, 2%, 4%, 6%, 8%, and 10% (by mass / volume). To simplify variables, concentrations in subsequent formulas refer to mass / volume ratios.

[0104] 2. Keep other components (such as the type and proportion of pectin and hemicellulose) unchanged, and use the same curing method as described in this article.

[0105] 3. Scan the cured sample using the same micro-CT scanning parameters as this method (e.g., voltage 50 kV, current 200 µA, pixel size 1 µm).

[0106] 4. For each sample's scanned image, select a homogeneous region and measure its average gray value (denoted as G). cal ).

[0107] 5. Using cellulose concentration C cal The horizontal axis represents the average grayscale value G. cal Using the ordinate as the vertical axis, a linear or polynomial fit is performed using the least squares method to obtain the gray-scale-density calibration curve. This functional relationship can be expressed as: G cal = f(C cal ).

[0108] Step 2: Extracting grayscale information from the target sample 1. For the 3D CT image of the cell wall of the target fruit and vegetable (taking apple as an example), set an analysis path that runs through the cell wall thickness in a direction perpendicular to the cell wall (from the outer side of the middle layer / primary wall to the inner side of the secondary wall).

[0109] 2. Using the "line profile" function of image processing software (such as ImageJ or Avizo), continuously collect N data points along the path at fixed intervals (e.g., one point every 1 micrometer) to obtain a continuous grayscale value sequence on the path, denoted as G1, G2, ..., GN.

[0110] Step 3: Normalization and Concentration Mapping of Grayscale Data 1. Normalization: The acquired original grayscale sequence Gi is normalized and converted into a relative density value Ri between 0 and 1. Ri = (Gi - Gmin) / (Gmax - Gmin), where Gmax and Gmin are the maximum and minimum grayscale values ​​on the path, respectively.

[0111] 2. Concentration Mapping: Based on biochemical analysis data of the cell walls of target fruits and vegetables, the target cellulose concentration Chigh for the outer layer (corresponding to Ri = 1) and the target concentration Clow for the inner layer (corresponding to Ri = 0) are set. For example, for the primary cell wall of apples, Chigh is set to 2.4%; for the inner side of the secondary cell wall, Clow is set to 1.6%. Then, the target cellulose concentration Ctarget for each point i on the path is... i Ctarget is calculated via a linear mapping. i = Clow + Ri × (Chigh - Clow); The determination of the aforementioned outer target concentration Chigh and inner target concentration Clow is something that can be obtained by those skilled in the art based on publicly available plant physiological data or conventional chemical composition analysis of the target fruit and vegetable varieties. Specifically: Basic range acquisition: By consulting authoritative literature (such as textbooks and research papers on plant physiology and food chemistry) or using standard chemical composition detection methods (such as the Updegraff cellulose assay), the typical range of cell wall cellulose content of this type of fruit and vegetable can be obtained (for example, the cell wall cellulose content of apples is usually between 1% and 4% of dry weight).

[0112] Gradient endpoint assignment: Within the typical range, relatively high values ​​are assigned to the outer layer target concentration Chigh to simulate the dense cellulose network characteristic of the primary wall; relatively low values ​​are assigned to the inner layer target concentration Clow to simulate the structure inside the secondary wall. The specific values ​​of Chigh and Clow can be determined through limited, conventional experimental optimization within the typical range, for example, by comparing the mechanical gradient of the printed structure under different assignments with that of the real tissue to select a set of optimal values.

[0113] Universality of the mapping relationship: The core of this invention is the method of "linearly mapping the normalized gray value Ri to between Chigh and Clow," where Chigh and Clow are input parameters that can be flexibly adjusted according to the target fruits and vegetables. This demonstrates the flexibility of this invention's method to be universally applicable to different fruits and vegetables.

[0114] Step 4: Generating print control instructions. The continuous "position-target density" curves described above are combined with the print path planning to generate executable machine instructions.

[0115] 1. Data Correlation: Based on the set thickness of the printed layer (e.g., 5 µm) and the printhead's movement speed, the position coordinates are converted into time coordinates. Thus, the target concentration sequence Ctarget is obtained. i It is transformed into a function Ctarget(t) that varies with time.

[0116] 2. Command Calculation: The control system knows the fixed concentration CA of ink A (e.g., 2.4%) and the fixed concentration CB of ink B (e.g., 1.6%), as well as the set total extrusion flow rate Ftotal. For any time t, in order to achieve the target concentration Ctarget(t) at that time, it is necessary to adjust the flow rate FA(t) of ink A and the flow rate FB(t) of ink B in real time. According to the law of conservation of mass, the following equations are established: (1) CA × FA(t) + CB × FB(t) = Ctarget(t) × Ftotal; (2) FA(t) + FB(t) = Ftotal.

[0117] Solving equations (1) and (2) simultaneously, we can obtain the real-time control command: FA(t) = [(Ctarget(t) - CB) / (CA - CB)] × Ftotal FB(t) = Ftotal - FA(t).

[0118] 3. Instruction execution: The printing control system (such as control software based on G-code or integrated custom algorithms) will convert the FA(t) and FB(t) sequences calculated in real time according to this formula into pulse signals to control the stepper motors of two precision injection pumps, thereby realizing the dynamic, continuous and stepless adjustment of the ratio of the two inks, and finally depositing a simulated structure with a preset cellulose concentration gradient.

[0119] The second step is gradient ink formulation. The bio-ink formulated in step S1, with component ratios consistent with the overall average composition of the target fruits and vegetables, serves as the base formula. Based on this base formula, two derivative inks are formulated by adjusting the amount of cellulose added. When formulating ink A, the amount of cellulose (e.g., nanocellulose crystals) in the base formula is increased by 5%–40%. When formulating ink B, the amount of cellulose in the base formula is decreased by 5%–40%. The adjustment range of cellulose concentration can be selected according to the actual gradient design requirements.

[0120] To ensure stable flow and prevent cross-contamination of the two inks during dynamic mixing in subsequent gradient printing, their rheological properties need to be matched. A rotational rheometer was used to measure the rheological properties of ink A and ink B at a shear rate of 10 s⁻¹. -1 The apparent viscosity of the two components is as follows. If the viscosity difference between the two components is large (for example, the ratio exceeds the range of 0.8-1.2), a small amount of rheology modifier xanthan gum can be added to the component with lower viscosity to adjust it. The amount added each time is about 0.05% of the total mass of ink. After thorough mixing, the viscosity ratio is measured again until the ratio of the two components reaches the required value.

[0121] Gradient printing execution is the third step and is crucial for achieving gradient construction. This step employs a specialized dual-feed printing unit, consisting of two independent precision injection pumps, one for ink A and the other for ink B. The outlets of the two pumps are connected via piping to a dynamic online mixer (e.g., a static spiral mixer). The mixer's outlet is then connected to the printhead of a coaxial extruder. This dual-feed printing unit is connected to a computer-controlled printing system. During the printing program development phase, the "target cellulose concentration variation curve" obtained in the first step is input into the control system. The control system discretizes this curve into a sequence of instructions corresponding to the number of printing layers, each instruction containing the theoretical cellulose concentration required for that layer. Based on the cellulose concentration of the base formula and the cellulose concentrations of inks A and B, the system calculates in real-time the flow rate ratio of ink A to ink B required to achieve the target concentration for each printing layer. During printing, the control system synchronously and precisely adjusts the motor speeds driving the two injection pumps according to this ratio instruction, thereby controlling the two inks to be input into the online mixer in a dynamically changing ratio. After the two inks are rapidly and uniformly mixed in the mixer, a composite ink with an instantaneous cellulose concentration conforming to the preset gradient is formed and extruded and deposited from the printhead. By adjusting the ratio of the two feeds layer by layer in real time, a cell wall simulation system with a gradient distribution of components (mainly cellulose concentration) is finally constructed in three-dimensional space.

[0122] Printing using the aforementioned gradient construction method yields a cell wall simulation system with programmed control over spatial heterogeneity in component distribution. Compared to a homogeneous simulation system, this method more realistically reflects the natural density and component gradient of the target fruit and vegetable cell walls from primary to secondary walls in terms of microstructure. This internal gradient structure helps to more accurately simulate mass transfer, stress distribution, and the initiation and propagation of structural failure during processing (such as heat treatment and enzymatic hydrolysis), thus providing a more realistic structural model system for a deeper understanding of the dynamic evolution mechanism of cell walls during processing.

[0123] In another technical solution, the specific steps of analyzing the spatial distribution pattern in step S3.1 are as follows: data processing is performed on the X-ray computed tomography three-dimensional image to extract the gray value change curve along the cell wall thickness direction; after normalizing the gray value change curve, it is mapped to the corresponding target cellulose concentration change curve, which serves as the basis for the gradient control command.

[0124] The core of the above technical solution lies in quantitatively converting the grayscale information of CT images into concentration gradient commands that can be used for printing control. Its implementation is as follows.

[0125] First, image data extraction and processing are performed. The acquired X-ray computed tomography (CT) 3D image data of the target fruit and vegetable cell walls is imported into image processing software, such as the open-source Fiji / ImageJ or the commercial Avizo software. In the software, one or more straight lines or curves traversing the entire cell wall thickness are set as analysis paths along the thickness direction of the region to be simulated (e.g., from the outer edge of the cell cavity towards the middle layer). Using the software's plot profile function, the grayscale values ​​of all pixels along this path are extracted. The software will automatically generate a grayscale value variation curve with the path distance as the x-axis and the grayscale value as the y-axis. Since the original image may contain noise, the curve can be smoothed, for example using the Savitzky-Golay filtering method, with the window size set to 5 pixels, to preserve trends and reduce random fluctuations.

[0126] Next, data normalization and mapping transformation are performed. The original grayscale value change curve obtained in the previous step is normalized. Specifically, the maximum grayscale value (Gmax) and minimum grayscale value (Gmin) in the curve are identified. For any point i on the curve with a grayscale value Gi, its normalized value Ni is calculated using the formula Ni = (Gi - Gmin) / (Gmax - Gmin). This results in a normalized grayscale curve with a value range between 0 and 1, whose shape reflects the trend of the relative density of the cell wall along the thickness direction. Subsequently, this normalized curve is mapped to a target cellulose concentration curve. This requires establishing a simple linear mapping relationship: the target cellulose concentration of the outermost layer of the cell wall (usually corresponding to the highest density) is set as Chigh, and the target cellulose concentration of the innermost layer (usually corresponding to the lowest density) is set as Clow. Then, for any point on the curve with a normalized value Ni, the corresponding target cellulose concentration Ci = Clow + Ni × (Chigh - Clow). The settings for Chigh and Clow need to be determined based on analytical data of real fruit and vegetable cell wall components or basic formula concentrations, and must fall within a printable concentration range.

[0127] Finally, gradient control instructions are generated. The series of data points (position, target cellulose concentration Ci) calculated above are organized. Based on the 3D printing layer thickness (as described in step S3), the continuous position information along the thickness direction is discretized into corresponding printing layer numbers. Each printing layer is assigned an average target cellulose concentration value calculated based on its position. This series of "layer number - target concentration" correspondence tables, which is the direct digital basis for the "gradient control instructions" described in step S3.3, is input to the printing control system for real-time control of the mixing ratio of the two inks.

[0128] Through the above implementation methods, grayscale information reflecting microstructural differences obtained by non-destructive imaging is transformed into digital instructions that can accurately guide component gradient printing, realizing a direct connection from biomimetic structure analysis to digital manufacturing, and improving the accuracy and repeatability of gradient construction.

[0129] <Example 7> This example uses the apple cell wall as an example.

[0130] Step 1: Ink Preparation (Gradient Ink Formulation) 1. Basic ink formulation: Same as in Example 2, prepare apple cell wall basic bio-ink: 2.0% amorphous cellulose, 1.5% apple pectin, 0.6% xylan, 0.45% arabinoxylan, and the balance of pH 3.6 disodium hydrogen phosphate-citric acid buffer.

[0131] 2. Gradient ink formulation: Ink A (High Cellulose Concentration): Based on the basic formula, only the amount of amorphous cellulose added is increased to 2.4% (i.e., the amount of amorphous cellulose added is increased by 20%), while the amounts of apple pectin, xylan, and arabinoxylan remain the same as in the basic formula. Subsequently, the amount of buffer solution is reduced accordingly so that the total mass fraction of the entire system remains 100%.

[0132] Ink B (Low Cellulose Concentration): Based on the basic formula, only the amount of amorphous cellulose added is reduced to 1.6% (i.e., the amount of amorphous cellulose added is reduced by 20%), while the amounts of apple pectin, xylan, and arabinoxylan remain the same as in the basic formula. Subsequently, the amount of buffer solution is increased accordingly to ensure that the total mass fraction of the entire system remains 100%.

[0133] 3. Rheological property matching: The rheological properties of ink A and ink B at a shear rate of 10 s⁻¹ were measured using a rotational rheometer. -1 The apparent viscosity was measured. Ink A had a viscosity of 11.2 kPa·s, and ink B had a viscosity of 8.9 kPa·s, with a viscosity ratio of 1.26, which is outside the range of 0.8-1.2. Xanthan gum was slowly added to ink B as a rheology modifier, and after thorough mixing for 30 minutes, the viscosity was measured again, and it rose to 10.5 kPa·s. At this point, the viscosity ratio was 1.07, which meets the requirement (viscosity ratio between 0.8 and 1.2).

[0134] Step 2: Obtaining structural parameters and analyzing gradient distribution X-CT scanning and 3D reconstruction: Same as in Example 2, high-resolution 3D images of apple cell walls were obtained (voxel resolution 1 µm).

[0135] Gray-scale value variation curve extraction: Using 3D image processing software (Avizo 9.0), an analysis line segment is defined along a direction perpendicular to the cell wall surface (from the outside of the cell lumen to the inside), running through the entire cell wall thickness. The software automatically extracts the gray-scale values ​​of all voxels on this line segment, generating a curve showing how the gray-scale values ​​change with the thickness.

[0136] Concentration curve mapping: The acquired original grayscale curve was normalized to obtain a normalized grayscale curve (value range 0-1). Based on the pre-established calibration relationship between apple cell wall grayscale values ​​and cellulose concentration (obtained by preparing calibration samples with known components), the normalized grayscale curve was linearly mapped to a target cellulose concentration change curve. The target concentration for the outermost layer (highest grayscale value) was set at 2.4%, and the target concentration for the innermost layer (lowest grayscale value) was set at 1.6%. The mapped curves show that the cellulose concentration decreases continuously from the outside to the inside.

[0137] Step 3: Bionic Printing (Gradient Printing Execution) Printing system configuration: A dual-feed bioprinting system is employed. This system comprises two independent precision injection pumps, one for ink A and the other for ink B. The outlets of the two pumps are connected via tubing to a dynamic online static helical mixer, the outlet of which is connected to a coaxial extrusion printhead (200 µm inner diameter). This system is integrated with a computer-controlled printing system.

[0138] Gradient control command generation: The target cellulose concentration change curve obtained in the second step is input into the printing control system. The control system discretizes the curve into a sequence of commands corresponding to the number of printing layers (based on a total wall thickness of 5.5 µm and a single layer thickness of 5.5 µm; in this example, it is a single-layer model, but to demonstrate the gradient, continuous gradient changes are performed within a single layer). Each command contains the target cellulose concentration required at that position.

[0139] Gradient printing: The control system calculates and dynamically adjusts the flow rate ratio of the two injection pumps in real time based on the target concentration, ink A concentration (2.4%), and ink B concentration (1.6%). During printing, the two inks are input into the online mixer in varying ratios, mixing to form a "composite ink" with a continuously varying cellulose concentration along the printing path (simulating the cell wall thickness direction), which is then extruded and deposited from the printhead. The printing temperature is 25°C, and the printhead movement speed is 0.5 mm / s.

[0140] <Mechanical Property Gradient> Detection method: Multi-layer puncture test was performed using a texture analyzer.

[0141] Sample preparation: Cut each system sample into 15 mm × 15 mm squares and equilibrate them in buffer solution.

[0142] Test setup: Use a P / 2 probe (2 mm in diameter). Select two fixed locations on the sample surface along the thickness direction (from one edge to the other) for puncture testing: an outer layer point (0.1 mm from the edge) and an inner layer point (0.9 mm from the edge, assuming a sample thickness of approximately 1 mm).

[0143] Test parameters: trigger force 5 g, test speed 1 mm / s, and pressure depth 30% of the sample thickness.

[0144] Data analysis: Record the maximum puncture force (hardness) at the outer and inner layer points, calculate the difference between them (Δhardness) and the rate of change of hardness ((inner layer hardness - outer layer hardness) / outer layer hardness × 100%). <Overall weight loss rate> Enzymatic digestion: Immerse each system sample (initial dry weight W0 known) in a solution containing 0.1% (w / v) pectinase (prepared with pH 4.0 buffer) and incubate for 60 minutes in a 40°C water bath shaker (60 rpm).

[0145] Post-treatment and weighing: After incubation, remove the sample, rinse it quickly with deionized water to terminate the reaction, then blot the surface moisture with filter paper and weigh it immediately (W1).

[0146] Calculate: Weight loss rate = (W0 - W1) / W0 × 100%. Five parallel samples are tested in each group.

[0147] Results and Analysis The above-mentioned performance indicators of Example 7 and Example 2 were measured, and the results are shown in Table 2.

[0148] Table 2 As shown in Table 2, the simulation system constructed using precise mapping of X-CT image grayscale values ​​and programmed gradient printing most effectively simulates the heterogeneous characteristics of real cell walls from its initial structure. In terms of mechanical properties, this system exhibits the most significant gradient change from the outer layer to the inner layer, far superior to the homogeneous system of Example 2. Regarding functional response, Example 7 shows the highest sensitivity to pectinase, indicating that its structure more realistically simulates the initial degradation behavior of the outer cell wall, which is more susceptible to enzymatic attack.

[0149] In another technical solution, in step S3.2, a rheology modifier is added to ink A and / or ink B to make both react within 10 seconds. -1 The viscosity ratio at the shear rate is between 0.8 and 1.2; the rheology modifier is one of xanthan gum, gellan gum or carrageenan.

[0150] The above technical solution, by adding a rheology modifier, controls the viscosity ratio of ink A and ink B within a specific range at a given shear rate, thereby ensuring the stability of material delivery and mixing during gradient printing. Its implementation is as follows.

[0151] First, the selection and preparation of rheology modifiers are crucial. Xanthan gum, gellan gum, or carrageenan are candidate rheology modifiers. In practice, food-grade or reagent-grade xanthan gum powder can be used. Before use, the xanthan gum powder should be placed in a dry environment to equilibrate its temperature. Second, viscosity measurement and initial evaluation are necessary. After preparing base ink A (high cellulose concentration) and ink B (low cellulose concentration), the rheological properties of both are immediately measured using a rotational rheometer. A cone-plate measurement system is used, and at a constant temperature of 25°C, the shear rate is set from 0.1 s⁻¹. -1 Scan to 100 s -1 Focus on 10 seconds -1 The apparent viscosity at the shear rate is recorded. The viscosity ηA of ink A and the viscosity ηB of ink B are recorded at this point, and their ratio ηA / ηB is calculated. Finally, viscosity matching and adjustment are performed. If the initially measured viscosity ratio ηA / ηB exceeds the range of 0.8 to 1.2, adjustment is required. Taking a ratio greater than 1.2 (i.e., ink A is too viscous) as an example: Take a certain amount (e.g., 10 grams) of ink B and place it in a small beaker. Using a precision analytical balance, weigh a small amount of xanthan gum powder. The amount of xanthan gum added must be carefully controlled; the initial addition can be 0.05% of the total mass of ink B. Slowly sprinkle the weighed xanthan gum powder onto the surface of ink B while continuously stirring with a magnetic stirrer at 400 rpm. Continue stirring for 30 minutes to ensure complete hydration and dispersion of the xanthan gum. Then, use a rheometer again to measure the viscosity of the adjusted ink B at 10 s. -1 The new viscosity ηB'.

[0152] Then, recalculate ηA / ηB'. If the ratio is still greater than 1.2, add 0.03% xanthan gum by mass to ink B and repeat the stirring and measurement steps. If the ratio falls within the range of 0.8-1.2, the adjustment is complete. If the addition of xanthan gum causes ηB' to be too high, making the ratio less than 0.8, add a trace amount of deionized water (e.g., 0.5% by mass) to ink A for slight dilution and remeasure ηA until the ratio meets the standard.

[0153] Through this iterative fine-tuning process, ink A and ink B were finally synchronized within 10 seconds. -1 The apparent viscosity ratio at the shear rate is stable between 0.9 and 1.1, thus meeting the requirements of gradient co-extrusion printing for ink rheological consistency and effectively avoiding extrusion instability or uneven mixing caused by viscosity differences.

[0154] In another technical solution, step S4 is further included: immersing the printed simulation system in a solution containing divalent metal ions to form an ionic cross-linking network between the rheology modifier and the pectin and / or hemicellulose. The rheology modifier is selected as gellan gum, and the divalent metal ions are Ca2+. 2+ or Zn 2+ This allows for the acquisition of a cell wall mimicry system that is responsive to pectinase.

[0155] The above technical solution, based on the aforementioned technical solution, adds a step of ion cross-linking treatment on the printed simulation system, aiming to construct a network structure with enzyme-responsive characteristics. Its specific implementation is as follows.

[0156] First, prepare the crosslinking solution and the sample to be treated. Following the aforementioned method, no more than 0.5% of the rheology modifier gellan gum has been added to the printing ink by mass. The cell wall simulation system sample, after printing and initial equilibration (e.g., standing at room temperature for 1 hour), is used as the object to be crosslinked. Preparation of the crosslinking solution: Divalent metal ions Ca2+ are selected. 2+ Using its soluble salt calcium chloride (CaCl2·2H2O) as a raw material, a 0.15 M CaCl2 solution was prepared by dissolving it in deionized water. The pH of this CaCl2 solution was adjusted to 5.5 by adding dilute hydrochloric acid or sodium hydroxide solution using a pH meter. This pH value is conducive to the ionization of carboxyl groups in pectin and gellan gum, thereby promoting the reaction with CaCl2. 2+ Crosslinking.

[0157] Next, an immersion crosslinking treatment was performed. The prepared simulation system sample was completely immersed in the crosslinking solution. To ensure uniform reaction and controlled, gentle stirring, the container holding the sample and solution was placed in a constant-temperature water bath shaker. The shaker temperature was set to 30°C, and the shaking speed to 50 rpm to ensure solution flow without causing mechanical damage to the structurally fragile sample. The immersion treatment lasted for 90 minutes. During this process, the Ca in the solution... 2+ It will gradually diffuse into the interior of the simulated system, reacting with the carboxyl groups (-COO) on the gellan gum and pectin molecular chains. - Ionic bonding occurs, forming an "egg-box" shaped cross-linked structure, thereby constructing an ionic cross-linked network on the basis of the original physical entanglement network.

[0158] Finally, post-processing and effect verification were performed. After soaking, the sample was carefully removed with tweezers and rinsed for 10 seconds in a beaker containing a large amount of deionized water to remove excess calcium ions adsorbed on the surface. Subsequently, the sample was transferred to another buffer solution containing pH 5.5 and ionic strength (adjusted by adding NaCl) the same as the crosslinking solution, and allowed to stand at 25°C for 45 minutes to stabilize the internal crosslinking structure and remove loosely bound ions.

[0159] To verify the cross-linking effect and enzyme responsiveness, a functional test can be performed: The cross-linked sample and the uncross-linked control sample are immersed separately in a pH 4.0 acetate buffer containing 0.1% (w / v) pectinase (commercial pectinase from Aspergillus niger can be used) and incubated at 40°C for 60 minutes. After incubation, the samples are removed, blotted dry with filter paper, and weighed. Typically, it can be observed that after Ca... 2+ The cross-linked sample showed a significantly lower mass loss rate than the uncross-linked control due to network enhancement, confirming its structural strengthening. Furthermore, when the enzymatic hydrolysis time was sufficiently long, the cross-linked sample was eventually degraded, indicating that this ionic cross-linked network exhibits a specific response to pectinase. Adjusting the Ca content in the cross-linking solution... 2+ Concentration (e.g., selected in the range of 0.05 M to 0.3 M), pH value, and treatment time can be used to regulate the cross-linking density and enzyme response rate.

[0160] In another technical solution, step S4 specifically includes a phased programmed crosslinking process: S4.1 Pre-crosslinking stage: Place the printed simulation system at a temperature of 4°C to 15°C, a pH of 3.0 to 4.5, and Ca... 2+ The system is treated in a first crosslinking solution with a concentration of 0.01 M to 0.05 M for 5 to 20 minutes to form a preliminary, weak ionic crosslinking network on the surface of the system. S4.2 Gradient Permeation Stage: Subsequently, the simulated system was transferred to an environment with a temperature of 25°C to 37°C, a pH of 5.0 to 6.5, and Ca... 2+ In a second crosslinking solution with a concentration of 0.1 M to 0.3 M, the system was treated for 30 to 120 minutes. During this stage, by controlling the heating rate and pH change rate of the system from the first solution to the second solution, a spatial gradient was formed between the diffusion rate of divalent metal ions into the simulated system and the crosslinking reaction rate, thereby constructing a gradient network structure with decreasing crosslinking density from the outside to the inside of the system. S4.3 Equilibrium Stabilization Stage: Finally, the simulated system is placed in a buffer solution with the same ionic strength as the second crosslinking solution but without divalent metal ions, and equilibrated at 20°C to 25°C for 30 to 60 minutes to stabilize the crosslinking structure and remove unbound excess ions.

[0161] The above technical solution further refines the ionic crosslinking process, defining it as a programmed process comprising three stages (pre-crosslinking, gradient penetration, and equilibrium stabilization), aiming to construct a gradient network structure with decreasing crosslinking density from the outside to the inside. The specific implementation method is as follows.

[0162] The first stage is pre-crosslinking, the purpose of which is to form a thin and loose initial crosslinking network on the surface of the simulated system, providing a structural basis for subsequent gradient diffusion. First, the first crosslinking solution is prepared: calcium chloride dihydrate is weighed and dissolved in deionized water to prepare a 0.03 M CaCl2 solution. The pH of the solution is precisely adjusted to 3.8 using dilute hydrochloric acid and a pH meter. The solution is placed in a water bath, cooled, and maintained at 10°C. After the printed simulated system sample is carefully removed from the receiving platform, it is directly immersed in this pre-cooled solution. The processing container can be placed in a 4°C freezer or a low-temperature water bath to maintain the low temperature of the system. The immersion treatment time is 10 minutes. The low temperature, low calcium concentration, and acidic environment in this stage allow CaCl2 to... 2+ The initial cross-linking reaction with pectin / gellan gum is relatively slow, mainly occurring on the outer surface of the sample in contact with the solution, forming a preliminary, unstable gel layer to prevent the sample from swelling or collapsing too quickly during subsequent processing.

[0163] The second stage is gradient permeation, the core of which is to guide the formation of a spatial gradient of crosslinking density within the system by controlling the rate of change of environmental conditions. After pre-crosslinking, the sample is removed from the first crosslinking solution using plastic tweezers and quickly transferred to a second crosslinking solution that has been preheated and kept at a constant temperature of 30°C. The second crosslinking solution is prepared by using deionized water to prepare a 0.2 M CaCl2 solution and adjusting its pH to 6.0 using dilute NaOH solution. The sample transfer process itself constitutes a sudden change in conditions: the temperature rises from 10°C to 30°C, the pH rises from 3.8 to 6.0, and the CaCl2 concentration increases dramatically. 2+ The concentration was increased from 0.03 M to 0.2 M. To achieve "programmed" control, the transfer operation was performed using a simple automated device that moved the sample from one temperature-controlled bath to another within 10 seconds, defining the initial rate of temperature and pH change. The sample was continuously immersed in the second crosslinking solution for 90 minutes. During this stage, the crosslinking reaction rate significantly accelerated due to the increase in ambient temperature and pH. Simultaneously, the high concentration of Ca... 2+ The cross-linked network diffuses from the surface layer inwards. Due to the fast reaction rate and abundant ion supply, the cross-linking density in the surface region increases rapidly and tends to become denser. As ions diffuse inwards, the diffusion path becomes longer and the concentration gradient decreases, resulting in a decrease in the reaction rate and the final cross-linking density in the interior. This spontaneously forms a gradient network structure with decreasing cross-linking density from the outside to the inside.

[0164] The third stage is stabilization, designed to fix the formed gradient structure and remove residual ions. After the gradient permeation stage, the sample is removed from the second cross-linking solution. Prepare the equilibration buffer: To match the ionic strength and avoid excessive sample swelling, prepare a pH 6.0 phosphate buffer containing 0.2 M NaCl (to simulate the ionic strength of the second cross-linking solution) but without CaCl2. Immerse the sample in this buffer and incubate at 23°C for 45 minutes. This process terminates the cross-linking reaction, allowing the incompletely reacted non-equilibrium network to relax and stabilize, while simultaneously removing excess CaCl2 that was physically adsorbed and did not participate in strong cross-linking through ion exchange and diffusion. 2+ The sample is displaced and removed with the buffer solution. After processing, the sample is removed, and excess liquid on the surface is gently blotted away with filter paper to obtain the final simulation system with an internal gradient cross-linked network and a stable structure.

[0165] Through the aforementioned phased, programmed processing, not only was the overall mechanical performance of the simulation system enhanced, but more importantly, a heterogeneous network structure was recreated within it. This structural gradient simulates the local modifications that may occur in certain natural cell walls during maturation or in response to environmental stimuli, enabling the simulation system to exhibit spatially differentiated response behaviors more closely resembling those of real biological tissues when used to study processing processes such as enzymatic hydrolysis and mechanical stress.

[0166] More specifically, traditional single-solution crosslinking can only form homogeneous networks. This invention, by designing a three-stage programmed crosslinking process, actively controls the crosslinking of divalent metal ions (such as Ca²⁺). 2+ The competition between the diffusion rate and the cross-linking reaction rate is used to construct a gradient cross-linking network in space. The core control logic is as follows: The first stage is pre-crosslinking (initial surface fixation), the purpose of which is to form a loose, porous initial gel layer on the sample surface, serving as a control layer for subsequent ion permeation. This is achieved through: low-temperature control to significantly reduce ion diffusion and reaction rates, and low ion concentration and an acidic environment to inhibit the crosslinking reaction (H+). + With Ca 2+ Competitive binding sites). Effect: Ca 2+ It can only penetrate to the shallowest layer (about a few micrometers) to form a preliminary network, preventing the sample from swelling excessively or the surface from becoming too dense in subsequent steps.

[0167] The second stage is gradient infiltration (building an internal gradient), the purpose of which is to form a gradient network with decreasing cross-linking density from the outside to the inside of the sample. Implementation methods include: 1) Rapid transfer: Rapidly transferring the sample from the first-stage solution (e.g., within 30 seconds) to the second-stage solution with drastically different conditions. 2) Sudden environmental change: The second stage uses higher temperatures and higher Ca2+ conditions. 2+Concentration and near-neutral pH. Mechanism of gradient formation: In the outer layer, abrupt changes in conditions cause the cross-linking reaction rate to exceed the ion diffusion rate. Ca 2+ It rapidly combines with the surface carboxyl groups to form a dense, highly cross-linked outer shell. Inward, Ca... 2+ Diffusion must proceed inward through the already formed dense layer. As depth increases, ion concentration decreases, and changes in internal temperature and pH lag behind, causing diffusion to become the controlling step. This leads to a decrease in the cross-linking reaction rate, resulting in a gradient where the cross-linking density gradually decreases from the outside to the inside. Control methods: The steepness of the gradient distribution can be precisely controlled by adjusting the transfer rate and the initial conditions of the second-stage solution.

[0168] The third stage is equilibrium stabilization (structure fixation and purification), the purpose of which is to terminate the reaction, remove excess ions, and stabilize the final structure. This is achieved by transferring the sample to an environment with the same ionic strength as the second stage but without Ca. 2+ In a buffer solution (e.g., using NaCl to maintain ionic strength). Function: Through ion exchange (NaCl... + Substitution of weakly bonded Ca 2+ Remove unstable ions from the network. Allow the cross-linked network to relax and fix in a stable environment to obtain a structurally stable final simulation system.

[0169] <Example 8> This embodiment performs programmed gradient crosslinking on a pre-constructed apple cell wall simulation system (either a uniform component or a gradient component can be used; this example uses a uniform component as the basis).

[0170] Step 1: Sample Preparation. The apple cell wall simulation system sample (15 mm × 15 mm cube) obtained in Example 2 was printed. After printing and equilibration at room temperature for 1 hour, it was used as the initial sample for crosslinking treatment.

[0171] Step 2: Staged programmed crosslinking treatment.

[0172] 1. S4.1 Pre-crosslinking stage. Prepare the first crosslinking solution: 0.03 M CaCl2 solution, adjust the pH to 3.8 with dilute HCl. Place the sample in this solution at 10°C and immerse it for 10 minutes.

[0173] 2. S4.2 Gradient Permeation Stage. Prepare the second crosslinking solution: 0.2 M CaCl2 solution, adjusted to pH 6.0 with dilute NaOH. Remove the sample from the first solution and transfer it within 30 seconds, immersing it completely in the second solution preheated to 30°C. Continue treatment at 30°C for 90 minutes.

[0174] 3. S4.3 Equilibrium and Stabilization Stage. Prepare the equilibrium buffer: a phosphate buffer containing 0.2 M NaCl (simulating ionic strength), pH 6.0. Remove the sample from the second crosslinking solution and immerse it in the equilibrium buffer at 23°C for 45 minutes. After processing, remove the sample and blot the surface liquid with filter paper to obtain the final programmed gradient crosslinked sample.

[0175] Specifically, the aforementioned staged crosslinking can be automatically executed by programming conventional laboratory equipment (such as multi-unit temperature-controlled water baths and sample transfer robotic arms) according to the following procedure: Pre-crosslinking: Soak in 0.03 M CaCl2 solution at 10°C for 10 minutes, pH 3.8.

[0176] Gradient osmosis: Rapidly (within 30 seconds) transfer to 30°C, pH 6.0, 0.2 M CaCl2 solution and soak for 90 minutes.

[0177] Equilibrium stabilized: Transfer to 23°C, pH 6.0, 0.2 M NaCl buffer, and let stand for 45 minutes.

[0178] After processing, remove the sample for later use.

[0179] <Comparative Example 4> This comparative example aims to illustrate the necessity of staged programmed crosslinking. A conventional single-step crosslinking method was used.

[0180] Preparation method: The same initial printed sample as in Example 8 was directly immersed in a single crosslinking solution (0.2 MCaCl2, pH 6.0, 30°C) for 100 minutes (total time similar to Example 8). After treatment, it was treated in the same equilibration buffer (23°C, 45 minutes). This method simulates a traditional, simple ionic crosslinking process.

[0181] <Comparative Example 5> This comparative example serves as a baseline control.

[0182] Preparation method: Take the same initial printed sample as in Example 8, without any ionic crosslinking treatment, and prepare it only in a buffer solution at pH 6.0 (containing no Ca). 2+ Soak at 23°C for the same total time to achieve equilibrium.

[0183] <Controllability of Enzymatic Hydrolysis Response> Detection method: a. The samples of Example 8, Comparative Example 4, and Comparative Example 5 (initial dry weight W0 known) were immersed in a solution containing 0.1% pectinase (pH 4.0 buffer) and incubated in a 40°C water bath shaker (60 rpm).

[0184] b. At two time points, 30 minutes and 120 minutes of incubation, the samples were taken out (n=3 at each time point), quickly rinsed, dried, and weighed (Wt).

[0185] c. Calculate the weight loss rate at each time point: Loss rate t = (W0 - Wt) / W0 × 100%.

[0186] Evaluation index: Compare the differences in the degree of degradation of different samples in the short term (30 min) and long term (120 min).

[0187] <Textural Stability> Test method: Refer to the aforementioned swelling rate test method, but perform comparative soaking.

[0188] a. Immerse the sample in two different solutions: (i) Deionized water (low ionic strength): promotes network swelling and tests the tightness of the cross-linked network.

[0189] (ii) 0.2 M CaCl2 solution (high calcium ion strength): inhibits further swelling of the network and tests the stability of the network under saturated cross-linking environment.

[0190] b. After treating at 37°C for 2 hours, weigh the product and calculate the swelling rate.

[0191] Evaluation metric: The difference in swelling ratio (ΔSR) between the sample in water and CaCl2 solution is calculated. ΔSR can indirectly reflect the heterogeneity or accessibility of network crosslinking. A gradient crosslinked network with a dense outer shell and a loose inner layer is expected to have a different ΔSR than a homogeneous crosslinked network.

[0192] Results and Analysis The above-mentioned performance indicators of Example 8 and Comparative Examples 4-5 were measured, and the results are shown in Table 3.

[0193] Table 3 As shown in Table 3, the phased programmed crosslinking method of this invention, by precisely controlling the temporal and spatial variations of temperature, pH, and calcium ion concentration in the crosslinking environment, successfully constructed an ion crosslinking network with an internal gradient structure in a homogeneous cell wall simulation system. Performance tests show that the system treated by this method (Example 8) exhibits unique controllable enzymatic hydrolysis kinetics: initially (30 minutes), due to the protective effect of the dense surface network, its degradation rate is significantly lower than the uncrosslinked control, but higher than the homogeneous network of single-stage crosslinking (Comparative Example 4); while in the long-term (120 minutes) degradation, its rate is significantly higher than the single-stage crosslinking system, reflecting a two-stage degradation behavior of "slow at first, then fast," from the surface to the interior. Simultaneously, the swelling behavior of this system in deionized water and high-concentration calcium ion solutions shows significant differences, far exceeding that of the single-stage crosslinking system, indirectly confirming the spatial heterogeneity of its internal crosslinking density. The above data collectively demonstrate that traditional single-stage constant-condition crosslinking can only form homogeneous and highly resistant networks, while the "programmed gradient crosslinking" process of this invention is a key and necessary technical means to endow the simulated system with biomimetic, gradient enzyme response characteristics and unique swelling behavior, highlighting its ingenuity.

[0194] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology, characterized in that, Includes the following steps: S1. Ink preparation: Cellulose, hemicellulose and pectin are mixed according to the mass percentage of the actual cell wall components of the target fruits and vegetables, dispersed in a buffer solution with a pH value consistent with the natural pH value of the target fruits and vegetables, and mixed evenly using dynamic high-pressure microfluidic technology to obtain bio-ink. The components, in their mass percentage range, are: cellulose 1.5-15%, pectin 0.1-6%, and hemicellulose 0.5-12%; the buffer solution is a disodium hydrogen phosphate-citric acid buffer solution, an acetate-sodium acetate buffer solution, or a citrate-sodium citrate buffer solution; the cellulose is selected from at least one of nanocellulose crystals, cellulose nanofibers, and amorphous cellulose; the hemicellulose is selected from at least one of xylo-glucan, arabinoxylan, xylan, glucomannan, and arabinogalactan. S2. Obtaining structural parameters: The cell wall thickness and fiber arrangement angle parameters of the target fruit and vegetable are obtained by X-ray computed tomography (CT) technology. S3. Bionic Printing: Based on the layer thickness and fiber arrangement angle parameters, control the coaxial extrusion 3D printer to deposit the bio-ink layer by layer at the set temperature and speed to construct the cell wall simulation system of the fruit and vegetable.

2. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 1, characterized in that, In step S1, the pectin is extracted from the target fruit and vegetable raw materials through the following steps: the fruit and vegetable powder is dissolved in 0.4% HCl solution at a solid-liquid ratio of 1:3 to 1:8, stirred at 28°C for 40 min, centrifuged and the filtrate is collected, the pH is adjusted to 3 to 4 with NaOH solution, precipitated with 95% ethanol for 2 h, and obtained after filtration, washing and drying.

3. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 1, characterized in that, When the target fruit or vegetable is hawthorn, the composition of the bio-ink in step S1 is: 1%~4% nanocellulose crystals, 3%~6% hawthorn pectin, 3%~5% xylan, and the balance is disodium hydrogen phosphate-citric acid buffer solution with pH 2.0~6.0; When the target fruit or vegetable is an apple, the composition of the bio-ink in step S1 is: 1.5%~3% amorphous cellulose, 1%~2% apple pectin, 0.2%~0.8% xylan, 0.3%~0.6% arabinoxylan, and the balance is a disodium hydrogen phosphate-citric acid buffer solution with a pH of 2.0~6.

0. When the target fruit or vegetable is carrot, the composition of the bio-ink in step S1 is: 2%~3.5% nanocellulose crystals, 1%~3% carrot pectin, 0.5%~1% xylan, 0.5%~1% arabinogalactan, and the balance is an acetate-sodium acetate buffer solution with pH 3.6~5.8; When the target fruit or vegetable is pumpkin, the composition of the bio-ink in step S1 is: 1%~3% amorphous cellulose, 0.5%~2% pumpkin pectin, 2%~5% glucomannan, and the remainder is a citric acid-sodium citrate buffer solution with a pH of 4.0~6.

0. When the target fruit or vegetable is lotus root, the composition of the bio-ink in step S1 is: 2%~3% cellulose nanofibers, 0.1%~0.3% lotus root pectin, 2%~4% xyloglucan, 1%~3% arabinoxylan, and the remainder is a citric acid-sodium citrate buffer solution with a pH of 4.0~6.

0. When the target fruit or vegetable is sugarcane, the composition of the bio-ink in step S1 is: 8%~15% nanocellulose crystals, 0.1%~0.5% sugarcane pectin, 10%~12% xylan, and the balance is an acetate-sodium acetate buffer solution with a pH of 3.6~5.

8.

4. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 1, characterized in that, Step S3 employs a gradient construction method to programmatically construct a heterogeneous component distribution within the simulation system, specifically including the following sub-steps: S3.1 Gradient Distribution Analysis: Based on the high-resolution three-dimensional image of the cell wall obtained by the X-computed tomography technology, analyze the spatial distribution law of its microstructure density from the outer primary cell wall to the inner primary cell wall. S3.2 Gradient Ink Formulation: Using the bio-ink prepared in step S1 as the base formula, ink A and ink B are formulated by adjusting the amount of cellulose added; wherein the cellulose concentration of ink A is 5%~40% higher than that of the base formula, and the cellulose concentration of ink B is 5%~40% lower than that of the base formula; S3.3 Gradient Printing Execution: A dual-feed printing unit is adopted, in which two precision injection pumps drive ink A and ink B respectively and are connected to a dynamic online mixer and a printhead. The dual-feed printing unit is connected to a printing control system. During the printing process, the printing control system generates corresponding gradient control commands according to the spatial distribution pattern obtained in step S3.1, and controls the flow rate ratio of the two injection pumps in the dual-feed printing unit, thereby constructing a component gradient simulation system.

5. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 4, characterized in that, The specific steps of analyzing the spatial distribution pattern in step S3.1 are as follows: data processing is performed on the X-ray computed tomography three-dimensional image to extract the gray value change curve along the cell wall thickness direction; after normalizing the gray value change curve, it is mapped to the corresponding target cellulose concentration change curve, which serves as the basis for the gradient control command.

6. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 4, characterized in that, In step S3.2, a rheology modifier is added to ink A and / or ink B to make both react within 10 seconds. -1 The viscosity ratio at the shear rate is between 0.8 and 1.2; the rheology modifier is one of xanthan gum, gellan gum or carrageenan.

7. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 6, characterized in that, The process also includes step S4: immersing the printed simulation system in a solution containing divalent metal ions to form an ionic cross-linking network between the rheology modifier and the pectin and / or hemicellulose. The rheology modifier is selected as gellan gum, and the divalent metal ions are Ca2+. 2+ or Zn 2+ This allows for the acquisition of a cell wall mimicry system that is responsive to pectinase.

8. The method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in claim 7, characterized in that, Step S4 specifically includes a phased programmed crosslinking process: S4.1 Pre-crosslinking stage: Place the printed simulation system at a temperature of 4°C to 15°C, a pH of 3.0 to 4.5, and Ca... 2 + The system is treated in a first crosslinking solution with a concentration of 0.01 M to 0.05 M for 5 to 20 minutes to form a preliminary, weak ionic crosslinking network on the surface of the system. S4.2 Gradient Permeation Stage: Subsequently, the simulated system was transferred to an environment with a temperature of 25°C to 37°C, a pH of 5.0 to 6.5, and Ca... 2+ In a second crosslinking solution with a concentration of 0.1 M to 0.3 M, the system was treated for 30 to 120 minutes. During this stage, by controlling the heating rate and pH change rate of the system from the first solution to the second solution, a spatial gradient was formed between the diffusion rate of divalent metal ions into the simulated system and the crosslinking reaction rate, thereby constructing a gradient network structure with decreasing crosslinking density from the outside to the inside of the system. S4.3 Equilibrium Stabilization Stage: Finally, the simulated system is placed in a buffer solution with the same ionic strength as the second crosslinking solution but without divalent metal ions, and equilibrated at 20°C to 25°C for 30 to 60 minutes to stabilize the crosslinking structure and remove unbound excess ions.

9. The fruit and vegetable cell wall constructed by the method for constructing a high-precision fruit and vegetable cell wall simulation system based on 3D printing technology as described in any one of claims 1-8.

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