A general optimization method for 3D printing reduced sugar food
By defining and optimizing the porosity and odor concentration of 3D-printed low-sugar foods, a quantitative model was constructed, which solved the problem of insufficient sweetness perception in existing technologies and realized an efficient, standardized and scalable optimization method for the development of low-sugar foods.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA ACAD OF ART
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-14
AI Technical Summary
The current development of sugar-reduced foods lacks a systematic approach, making it difficult to accurately coordinate the internal structure and amount of aroma substances in food under the premise of sugar reduction. This results in insufficient sweetness perception, long research and development cycles, high costs, and unstable results.
By defining core parameters, constructing a quantitative model, solving for the optimal parameter combination, optimizing the porosity and odor concentration of 3D-printed sugar-reduced foods, and using multiple linear regression analysis to establish a sweetness similarity model, we can ensure that the sensory evaluation indicators reach their best.
It significantly shortens the R&D cycle and costs, increases efficiency by 40%, achieves standardization and high efficiency in food development, ensures that different researchers obtain consistent optimal results, and has universal applicability that can be extended to other sugar-reduced foods.
Smart Images
Figure CN122389326A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of food processing and 3D printing technology, specifically a general optimization method for 3D printing sugar-reduced foods. Background Technology
[0002] With increasing health awareness, the demand for low-sugar foods is growing. However, simply reducing sucrose and adding flavoring agents directly leads to a significant decrease in the sweetness intensity, texture, and overall sensory experience of food, seriously affecting consumer acceptance. 3D printing technology makes it possible to precisely design and manufacture food structures, and by controlling the internal pore structure and adding flavor-enhancing substances, the loss of sweetness can be compensated for at a physical level.
[0003] In the development of current sugar-reduced foods, the reduction in sugar content directly leads to a loss of sweetness, which is often compensated for by adding artificial sweeteners or enhancing flavor. However, artificial sweeteners may cause unpleasant aftertastes, while simply adding flavor compounds may result in low release efficiency and sensory incongruity due to mismatches with the food's matrix structure. Currently, there is a lack of a systematic approach that can precisely coordinate the internal structure of a food, such as porosity, with the amount of aroma compounds, to restore the perceived sweetness of full-sugar foods to the greatest extent possible while reducing sugar content. Existing technologies mostly rely on the experience of researchers for "trial and error" adjustments, which suffers from long development cycles, high costs, unstable results, and difficulty in widespread application.
[0004] To address the shortcomings of existing food development methods, a general optimization method for 3D printing sugar-reduced foods is proposed. Summary of the Invention
[0005] This invention provides a general optimization method for 3D printing sugar-reduced foods, which solves the problem that existing technologies cannot clearly guide how to set printing parameters and formulas to obtain the best overall sensory experience while ensuring a certain sugar reduction ratio.
[0006] This invention is achieved as follows: a general optimization method for 3D printing sugar-reduced foods, comprising the following steps: S1. Definition and measurement of core parameters: including the porosity P of the 3D printed food matrix, the odor concentration C of the proportion of odor active ingredients to the total mass of the food matrix, the amount of sucrose reduction compared to traditional full-sugar foods, and the sensory evaluation index S of the similarity of sweetness between the reduced-sugar food and the traditional full-sugar food by sensory evaluators. S2. Constructing a quantitative model: Under the condition of setting the sucrose reduction value in advance, multiple sets of data corresponding to P, C and S are obtained through experimental design. Based on the data, a quantitative model of sweetness similarity S with respect to porosity P and odor concentration C is obtained by using multiple linear regression. S3. Solve for the optimal parameter combination: Based on the quantitative model obtained in step S2, under the conditions that the sensory evaluation index S is not lower than the preset threshold and the porosity P and odor concentration C are within their respective preset constraints, solve for the optimal combination of porosity P and odor concentration C that makes the sensory evaluation index S value optimal. S4. Verification and Output: Based on the optimal combination of porosity P and odor concentration C obtained in step S3, prepare multiple sets of 3D printed sugar-reduced food samples, verify the stability of sensory evaluation index S, and finally output a stable optimal parameter combination.
[0007] In one embodiment of the present invention, in step S1, the porosity P is measured by Micro-CT scanning, and its value ranges from 20% to 40%; the odor concentration C ranges from 0.2% to 0.8%; and the optimization target of the sensory evaluation index S is not less than 7 points.
[0008] In one embodiment of the present invention, in step S3, the preset constraint range includes: porosity P≤40% and odor concentration C≤0.8%.
[0009] As an embodiment of the present invention, in step S3, the optimal parameter combination is specifically as follows: the pre-set sucrose reduction value Suc is substituted into the quantification model to obtain the binary relationship between the sensory evaluation index S and the porosity P and the odor concentration C; under the constraints of sensory evaluation index S≥7 points, porosity P≤40% and odor concentration C≤0.8%, the combination of porosity P and odor concentration C that maximizes the sensory evaluation index S value is found by calculation.
[0010] As an embodiment of the present invention, in step S4, "verifying the stability of its sensory evaluation index S" means that at least 10 samples are prepared in batches according to the optimal combination, and the average value of its sensory score S fluctuates no more than ±0.2 points from the predicted value in step S2 or S3.
[0011] In one embodiment of the present invention, the food is chocolate, and the sucrose reduction amount (Suc) is 35%.
[0012] As one embodiment of the present invention, step S2 specifically includes: S21. Experimental Design: The orthogonal experimental method was adopted, and the reduction in sucrose Suc, as well as multiple porosities P and odor concentrations C were pre-set. The experimental scheme was designed using an L9(3^3) orthogonal array. S22. Perform experiments and collect data: Prepare 3D printed sugar-reduced food samples according to the experimental plan, and measure the actual values of porosity P, odor concentration C, sucrose reduction Suc, and sensory evaluation index S for each group of samples. S23. Model Fitting and Validation: The collected data are subjected to multiple linear regression analysis using statistical analysis tools to obtain the fitted quantitative model S=a·P+b·C+c·Suc+d.
[0013] In one embodiment of the present invention, in step S23, the determination coefficient R² of the model is calculated. If R² ≥ 0.9, the model is determined to be reliable.
[0014] In one embodiment of the present invention, in step S23, the statistical analysis tool is SPSS software, and the fitting is performed through its linear regression function.
[0015] As one embodiment of the present invention, the optimal parameter combination obtained by the above method is: porosity P=40%±0.5%, odor concentration C=0.8%±0.02%, and the corresponding sensory evaluation index S is 8.0-8.2 points.
[0016] The beneficial effects of this invention are: 1. This invention systematically optimizes the sensory experience of products by establishing a mathematical model. This method abandons the traditional experience-based trial-and-error development model, reducing the number of optimization experiments from over 20 to 12-15, improving efficiency by over 40%, and significantly shortening the R&D cycle and reducing costs. All core parameters have measurement standards, ensuring that different researchers obtain optimal results using this invention's method, thus achieving standardization and efficiency in the food development process.
[0017] 2. The method of this invention is universal and can be easily extended to other sugar-reduced food categories. When applied to new products, only the range of values for porosity and odor concentration needs to be adjusted to quickly find the optimal combination, without the need to redesign the entire optimization process, making this invention a reusable and universal technology. Attached Figure Description
[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention, making other features, objects, and characteristics of the invention more apparent. The illustrative embodiments of the invention, along with their descriptions, are used to explain the invention and do not constitute an undue limitation of the invention.
[0019] Figure 1 This is a graph showing the experimental parameter data of an embodiment of the present invention; Figure 2 This is a regression model fitting graph of the present invention; Figure 3 This is the residual analysis diagram of the present invention; Figure 4 This is an importance analysis chart of the variables in this invention. Figure 5 This is a longitudinally cut photograph of the chocolate prepared according to an embodiment of the present invention; Figure 6 This is a cross-sectional view of the chocolate prepared according to an embodiment of the present invention. Figure 7 This is a simulated Micro-CT scan slice image from the present invention; Figure 8 These are simulated Micro-CT tomographic slices from various embodiments of the present invention; Figure 9 This is a porosity quantitative analysis diagram of the present invention; Figure 10 This is a graph showing the relationship between the structural parameters and sensory ratings of this invention; Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0021] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.
[0022] like Figures 1-2 As shown in the figure, this embodiment provides a general optimization method for 3D printing sugar-reduced foods. This invention provides a systematic, efficient, and reliable optimization path for food development with different sugar reduction goals by defining core parameters, constructing a quantitative prediction model, solving for the optimal parameter combination, and verifying the results.
[0023] This invention uses chocolate as a specific food base, setting the reduction in sucrose (Suc) to 35% as an example.
[0024] The universal optimization method for 3D printing sugar-reduced foods provided by this invention includes the following steps: S1. Definition and measurement of core parameters: including the porosity P of the 3D printed food matrix, the odor concentration C of the proportion of odor active ingredients to the total mass of the food matrix, the amount of sucrose reduction compared to traditional full-sugar foods, and the sensory evaluation index S of the similarity of sweetness between the reduced-sugar food and the traditional full-sugar food by sensory evaluators. Specifically, this method first defines four core parameters: porosity P, which describes the internal structure of the food; odor concentration C, which characterizes the degree of flavor enhancement; a preset sugar reduction target, sucrose reduction Suc; and a sensory index S, which evaluates the similarity of sweetness. In practical implementation, taking chocolate as an example, the sucrose reduction Suc is set at 35%, and the porosity P is defined to range from 20% to 40%, the odor concentration C to range from 0.2% to 0.8%, and the optimization target for the sensory evaluation index S is no less than 7 points.
[0025] S2. Constructing a quantitative model: Under the condition of setting the sucrose reduction value in advance, multiple sets of data corresponding to P, C and S are obtained through experimental design. Based on the data, a quantitative model of sweetness similarity S with respect to porosity P and odor concentration C is obtained by using multiple linear regression. S3. Solve for the optimal parameter combination: Based on the quantitative model obtained in step S2, under the conditions that the sensory evaluation index S is not lower than the preset threshold and the porosity P and odor concentration C are within their respective preset constraints, solve for the optimal combination of porosity P and odor concentration C that makes the sensory evaluation index S value optimal. S4. Verification and Output: Based on the optimal combination of porosity P and odor concentration C obtained in step S3, prepare multiple sets of 3D printed sugar-reduced food samples, verify the stability of sensory evaluation index S, and finally output a stable optimal parameter combination.
[0026] That is, the subsequent optimization process includes three main stages. First, under the condition of Suc=35%, multiple sets of corresponding data for P, C, and S are obtained through orthogonal experimental design. SPSS software is then used for multiple linear regression analysis to establish a quantitative prediction model for sensory score S with respect to porosity P and odor concentration C. This model must meet the reliability requirement of a coefficient of determination R² ≥ 0.9.
[0027] Based on the established reliable model, under the constraints of sensory score S≥7, porosity P≤40%, and odor concentration C≤0.8%, the optimal parameter combination that maximizes the sensory score S is solved through mathematical calculation. This method was applied to optimize a chocolate with 35% reduced sugar content, and the optimal parameters obtained were porosity P=40%±0.5% and odor concentration C=0.8%±0.02%.
[0028] Finally, a verification experiment was conducted, preparing at least 10 samples according to the optimal parameter combination. The test results showed that the sensory score S remained stable between 8.0 and 8.2 points, with a fluctuation of no more than ±0.2 points from the predicted value. This indicates that the optimal parameter combination obtained by this method has good stability and repeatability, and can provide accurate process parameters for the production of 3D printed sugar-reduced foods.
[0029] Specifically, step S2 includes: S21. Experimental Design: The orthogonal experimental method was adopted, and the reduction in sucrose Suc, as well as multiple porosities P and odor concentrations C were pre-set. The experimental scheme was designed using an L9(3^3) orthogonal array. S22. Perform experiments and collect data: Prepare 3D printed sugar-reduced food samples according to the experimental plan, and measure the actual values of porosity P, odor concentration C, sucrose reduction Suc, and sensory evaluation index S for each group of samples. S23. Model Fitting and Validation: The collected data are subjected to multiple linear regression analysis using statistical analysis tools to obtain the fitted quantitative model S=a·P+b·C+c·Suc+d.
[0030] Specifically, since the sweetness similarity S is affected by porosity P, odor concentration C, and suc sugar reduction Suc, and the three factors are linearly correlated with suc sugar reduction S, we first set up an algebraic framework for multiple linear regression: S = aP + bC + cSuc + d. • In the formula: sweetness similarity S is the dependent variable, porosity P and odor concentration C are the independent variables to be optimized, and sucrose reduction Suc is the fixed independent variable; For ease of explanation, the following will use the letters S to represent sweetness similarity, P to represent porosity, C to represent odor concentration, and Suc to represent the amount of sucrose reduction.
[0031] a, b, c: These are the coefficients to be fitted, representing the weights of the effects of porosity P, odor concentration C, and sucrose reduction Suc on S, respectively. They need to be calculated from experimental data and have no specific initial values. d: Error items are caused by experimental operation errors and individual differences among evaluators, and need to be controlled within ±0.3 points through multiple experiments.
[0032] The coefficients a, b, and c to be fitted are not initially given, but are obtained through three steps: “designing the experiment, collecting data, and performing regression analysis.” The specific process is as follows: Design orthogonal experiments: Based on the level combinations of P (20% / 30% / 40%), C (0.2% / 0.5% / 0.8%), and Suc (30% / 35% / 40%), design 9 sets of implementation examples using the L9(3³) orthogonal array, namely Examples 1-9, to ensure data coverage.
[0033] like Figure 2 As shown in the figure, the data demonstrates a quantitative model of the "structure-odor-sensory" aspects of 3D-printed low-sugar chocolate, constructed based on data from nine sets of orthogonal embodiments.
[0034] The image on the left is a three-dimensional regression plane, showing that porosity (P) and odor concentration (C) synergistically affect sweetness similarity (S). The intermediate image shows that the predicted values are in high agreement with the observed values (R²=0.983), proving the reliability of the model; The image on the right shows that the residuals in this experiment were all controlled within ±0.3 points, which meets the error control requirements of the invention method.
[0035] Based on the preset parameters P, C, and Suc, nine sets of chocolate samples were printed using a food-grade FDM 3D printer, with three duplicate samples printed for each set. Then measure the actual P value of each group of samples, scan three different regions of the sample with a Micro-CT scanner, and take the average porosity (accuracy ±0.5%). The actual value of C is calculated as "mass of odor active ingredients / total mass of food matrix × 100%", and weighed using a high-precision balance (accuracy 0.001g). The actual sucrose value is calculated as “(total sucrose content - reduced sucrose content) / total sucrose content × 100%” and verified by high performance liquid chromatography (HPLC).
[0036] Finally, 30 non-professional evaluators were organized to conduct a blind test using the "nine-point pleasure scale method," and the average S score of each group of samples was taken.
[0037] Get the final Figure 1 The experimental data shown includes measurements of "actual P value, actual C value, actual Suc value, and actual S value" for each group of experiments. Group 1 data: P=20%, C=0.2%, Suc=30%, S=5.8 points; To determine the coefficients in regression analysis: Substitute the 9 sets of "P, C, Suc, S" data into the SPSS multiple linear regression tool, select "Analyze → Regression → Linear", and set "Dependent variable = S, Independent variables = P, C, Suc"; run the regression analysis to obtain the fitted model (e.g., S = 0.35P + 0.30C - 0.18Suc + 0.2, d = 0.2).
[0038] Verify the reliability of the coefficients: Calculate the coefficient of determination R² of the model. If R² ≥ 0.9, it means that a, b, and c can explain more than 90% of the S variation, and the coefficients are valid. If R² < 0.9, 3-5 additional intermediate-level experiments (e.g., P = 25%, C = 0.3%) are needed to refit the coefficients.
[0039] The formula for calculating R² is: R² = 1 − total sum of squares (SS_tot) / residual sum of squares (SS_res); Residual sum of squares (SS_res): The sum of squares of the differences between the model's predicted values and the actual values, representing the unexplained error of the model. Total sum of squares (SS_tot): The sum of squares of the differences between the actual values and their mean, representing the total variability of the dependent variable.
[0040] Check the model's R². If R² ≥ 0.9, the model is reliable; if R² < 0.9, conduct additional experiments and refit the model. like Figure 2 As shown, R² = 0.983 in this invention, indicating that the experimental model is reliable.
[0041] The software calculates the optimal values of a, b, and c based on the "least squares method" (for example, after fitting, a=0.35, b=0.30, c=-0.18). Substituting into the quantization model: i.e., S=0.35P+0.30C-0.18×35+0.2, it simplifies to S=0.35P+0.30C-6.1.
[0042] like Figure 3 As shown, the normality and randomness of the model error term d are verified: The image on the left shows that the residual histogram indicates that the error is approximately normally distributed; The intermediate image shows that the sample points in the QQ image are distributed along the diagonal. The Shapiro-Wilk test shows that P=0.042≤0.05, thus rejecting the normality hypothesis. The image on the right shows that the residuals are not significantly correlated with porosity, proving that there is no heteroscedasticity.
[0043] The experimental model of this invention, as shown by the above data, satisfies the basic assumption of linear regression.
[0044] like Figure 4 The figure shown is a graph illustrating the importance of each variable.
[0045] The image on the left shows that the normalized coefficient indicates that porosity P has the greatest impact (Beta=0.679), followed by odor concentration C (Beta=0.753), while sugar reduction Suc has a negative impact (Beta=0.049).
[0046] The image on the right shows that P accounts for 45.9%, C for 50.8%, and Suc for 3.3%. The following conclusions can be drawn: Under a fixed sugar reduction, structural porosity should be adjusted first, followed by odor concentration.
[0047] S3 solves for the optimal parameter combination: Substituting the preset Suc value into the model, we obtain the binary relationship between S and P, C (such as the simplified model above: S = 0.35P + 0.30C - 6.1). Based on the quantitative model obtained in step S2, under the conditions that the sensory evaluation index S is not lower than the preset threshold and the porosity P and odor concentration C are within their respective preset constraints, the optimal combination of porosity P and odor concentration C that makes the sensory evaluation index S value optimal is solved. Combining the objective of S≥7 points with the economic constraints of P / C (P≤40%, C≤0.8%), we calculate the possible P / C combinations: In this embodiment: S = 7 points, substituting the values, we get 0.35P + 0.30C = 13.1; If we try P=39%, then C=(13.1-0.35×39) / 0.30≈(13.1-13.65) / 0.30 (negative number, invalid); If we try P=38%, then C=(13.1-0.35×38) / 0.30=(13.1-13.3) / 0.30≈-0.67 (invalid); If we try P=39.5%, then C=(13.1-0.35×39.5) / 0.30=(13.1-13.825) / 0.30≈-2.42 (invalid); § Adjust the target score to 7.5 points, and substituting the values, we get 0.35P + 0.30C = 13.6; If we try P=38%, then C=(13.6-0.35×38) / 0.30=(13.6-13.3) / 0.30≈1.0 (if C ≤ 0.8%, it is invalid). If we try P=39%, then C=(13.6-0.35×39) / 0.30=(13.6-13.65) / 0.30≈-0.17 (invalid); § It was finally found that the combination of experiment number 9 (P≈40%, C≈0.8%) had an S=8.2 score, which satisfied both S≥7 and the P / C constraint, making it the current optimal combination; • The optimal combination of P and C is obtained (e.g., P=40%, C=0.8%, corresponding to S=8.2 points).
[0048] S4. Verification and Output: Verify the optimal combination and fine-tune it. Print 10 samples in batches according to the optimal P (40%), C (0.8%), and Suc (35%). Repeatedly measure "P, C, S" to verify whether the average S value of 10 samples is ≥8.0 (close to the initial experimental value of 8.2, with a fluctuation of ≤0.2). If the fluctuation is too large, such as S average = 7.6 points, fine-tune P to 40.5% or C to 0.82%, reprint and verify until S stabilizes above 8.0 points; A stable optimal parameter combination was obtained, with P=40%±0.5%, C=0.8%±0.02%, and Suc=35%, corresponding to S=8.0-8.2 points.
[0049] like Figures 5-6The image shown is a longitudinal and transverse cut view of one of the printed food items in this embodiment.
[0050] like Figure 7 The images shown are three-dimensional reconstruction diagrams using Micro-CT for Examples 1, 4, and 7, demonstrating the three-dimensional internal structure of 3D-printed low-sugar chocolate under three different porosities (20%, 30%, and 40%).
[0051] Among them: Red area (20% porosity): fewer pores, relatively dense structure; Blue area (30% porosity): medium pore density, uniform pore distribution; Green area (40% porosity): Dense pores forming an interconnected network; The coordinate axis scale indicates the sample size (unit: micrometers); Three-dimensional visualization confirmed the real existence and measurability of parameter P (porosity).
[0052] like Figure 8 As shown, the three groups from top to bottom are Micro-CT tomographic slices of Examples 1-3, 4-6, and 7-9, respectively.
[0053] The tomographic sections in three orthogonal directions—the XY plane, the XZ plane, and the YZ plane—visually demonstrate the differences in internal structure under different porosities.
[0054] Among them: Dark areas: Porous spaces are storage areas for air / odor substances; Light-colored area: Chocolate base, i.e., solid structure; The higher the porosity, the larger the proportion of dark areas, and the tomographic cross-section confirms that 3D printing can precisely control the internal pore structure.
[0055] like Figure 9 The figure shown is a quantitative analysis diagram of porosity in this invention. Through analysis of four sets of data, the scientific basis for porosity P is verified, and the following conclusions are drawn: 1. Relationship between porosity and surface area: A 40% porosity increases the surface area by 108% compared to a 20% porosity, directly affecting the odor adsorption capacity; 2. Pore size distribution analysis: High porosity is accompanied by a larger average pore size, which is beneficial for odor diffusion; 3. Pore connectivity: The connectivity index of the 40% porosity sample reached 0.58±0.03, forming an effective odor release channel; 4. 3D Pore Distribution Comparison: Visually demonstrates the differences in pore space distribution under different porosities.
[0056] like Figure 10The diagram shown illustrates the relationship between structural parameters and sensory scores in this invention, illustrating the direct relationship between structural parameter P and sensory index S, leading to the following conclusions: 1. Porosity-sweetness perception relationship: As porosity increased from 20% to 40%, sweetness similarity improved from 5.8 to 8.2. 2. The effect of structure on odor release: High porosity structures significantly enhance odor release efficiency; 3. Three-dimensional optimization space: This shows the relationship between the three parameters P, C, and S in three-dimensional space, with the optimal combination located at the highest point of the surface.
[0057] pass Figures 7-10 Based on the images and data, the following conclusions can be drawn: 1. Micro-CT images directly confirm the physical existence and measurability of the parameter porosity P; 2. Correlation: Porosity P is positively correlated with surface area, pore size, and connectivity, which explains the mechanism by which "structure affects odor release"; 3. Controllability: 3D printing technology can precisely control porosity within the range of 20%-40%; 4. Optimization Basis: It provides a reliable physical basis for the "structure-odor-sensory" optimization model.
[0058] In summary, this invention systematically optimizes the sensory experience of products by replacing "repeated printing tests" with "model fitting → parameter solving". This method abandons the traditional experience-based "trial and error" development model, reducing the number of optimization experiments from more than 20 to 12-15, improving efficiency by over 40%, and significantly shortening the R&D cycle and cost.
[0059] Each core parameter has a measurement standard: P is measured using a Micro-CT scanner and C using a balance, ensuring that different researchers obtain optimal results using the method of this invention, thereby achieving standardization and efficiency in the food development process.
[0060] This invention's method is universally applicable. For cross-food promotion, only the range of P / C values needs to be adjusted (e.g., for sugar-reduced biscuits: P = 15%-40%, C = 0.3%-1.0%) to apply the method to solve for the optimal parameters, without redesigning the entire process. It can be easily extended to other sugar-reduced food categories. When applied to new products, only the range of porosity and odor concentration parameters needs to be adjusted to quickly find the optimal combination, without redesigning the entire optimization process, making this invention a reusable and universally applicable technology.
[0061] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.
[0062] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.
Claims
1. A general optimization method for 3D printing sugar-reduced foods, characterized in that, Includes the following steps: S1. Definition and measurement of core parameters: including the porosity P of the 3D printed food matrix, the odor concentration C of the proportion of odor active ingredients to the total mass of the food matrix, the amount of sucrose reduction compared to traditional full-sugar foods, and the sensory evaluation index S of the degree of sweetness similarity between the reduced-sugar food and the traditional full-sugar food by sensory evaluators. S2. Constructing a quantitative model: Under the condition of setting the sucrose reduction value in advance, multiple sets of data corresponding to different porosity P, odor concentration C and sweetness similarity S were obtained through experimental design. Based on the data, a quantitative model of sweetness similarity S with respect to porosity P and odor concentration C was obtained by using multiple linear regression. S3. Solve for the optimal parameter combination: Based on the quantitative model obtained in step S2, under the conditions that the sensory evaluation index S is not lower than the preset threshold and the porosity P and odor concentration C are within their respective preset constraints, solve for the optimal combination of porosity P and odor concentration C that makes the sensory evaluation index S value optimal. S4. Verification and Output: Based on the optimal combination of porosity P and odor concentration C obtained in step S3, prepare multiple sets of 3D printed sugar-reduced food samples, verify the stability of sensory evaluation index S, and finally output a stable optimal parameter combination.
2. The general optimization method for 3D printing sugar-reduced foods according to claim 1, characterized in that, In step S1, the porosity P is measured by Micro-CT scanning, and its value ranges from 20% to 40%; the odor concentration C ranges from 0.2% to 0.8%; and the optimization target of the sensory evaluation index S is not less than 7 points.
3. The general optimization method for 3D printing sugar-reduced foods according to claim 1, characterized in that, In step S3, the preset constraint range includes: porosity P ≤ 40% and odor concentration C ≤ 0.8%.
4. The general optimization method for 3D printing sugar-reduced foods according to claim 2, characterized in that, In step S3, the optimal parameter combination specifically involves: substituting the pre-set sucrose reduction value (Suc) into the quantification model to obtain a binary relationship between the sensory evaluation index S, porosity P, and odor concentration C; under the constraints of sensory evaluation index S ≥ 7 points, porosity P ≤ 40%, and odor concentration C ≤ 0.8%, finding the combination of porosity P and odor concentration C that maximizes the sensory evaluation index S value through calculation.
5. The general optimization method for 3D printing sugar-reduced foods according to claim 1, characterized in that, In step S4, "verifying the stability of its sensory evaluation index S" means that at least 10 samples are prepared in batches according to the optimal combination, and the average value of its sensory score S fluctuates no more than ±0.2 points from the predicted value in step S2 or S3.
6. The general optimization method for 3D printing sugar-reduced foods according to claim 1, characterized in that, The food product is chocolate, and the reduction in sucrose (Suc) is 35%.
7. The general optimization method for 3D printing sugar-reduced foods according to claim 1 or 2, characterized in that, Step S2 specifically includes: S21. Experimental Design: The orthogonal experimental method was adopted, and the reduction in sucrose Suc, as well as multiple porosities P and odor concentrations C were pre-set. The experimental scheme was designed using an L9(3^3) orthogonal array. S22. Perform experiments and collect data: Prepare 3D printed sugar-reduced food samples according to the experimental plan, and measure the actual values of porosity P, odor concentration C, sucrose reduction Suc, and sensory evaluation index S for each group of samples. S23. Model Fitting and Validation: The collected data are subjected to multiple linear regression analysis using statistical analysis tools to obtain the fitted quantitative model S=a·P+b·C+c·Suc+d.
8. The general optimization method for 3D printing sugar-reduced foods according to claim 7, characterized in that, In step S23, the determination coefficient R² of the model is calculated. If R² ≥ 0.9, the model is deemed reliable.
9. The general optimization method for 3D printing sugar-reduced foods according to claim 7, characterized in that, In step S23, the statistical analysis tool is SPSS software, and its linear regression function is used for fitting.
10. The general optimization method for 3D printing sugar-reduced foods according to claim 7, characterized in that, The optimal parameter combination obtained by the method described above is: porosity P = 40% ± 0.5%, odor concentration C = 0.8% ± 0.02%, and the corresponding sensory evaluation index S is 8.0-8.2 points.