A method for evaluating the texture of cauliflower florets based on a texture analyzer
By using a texture analyzer (TPA) and establishing a mathematical model, the problem of strong subjectivity in the evaluation of cauliflower head texture was solved, achieving rapid and accurate texture evaluation and establishing a unified evaluation system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CROP RES INST OF FUJIAN ACAD OF AGRI SCI
- Filing Date
- 2026-02-28
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, the evaluation of cauliflower head texture mainly relies on sensory evaluations such as taste and touch, lacking a unified and objective evaluation method. This results in highly subjective and varied evaluation results, making it difficult to accurately reflect the texture condition.
A texture analyzer was used for TPA texture testing. Combined with principal component analysis, membership function analysis and cluster analysis, a mathematical model for evaluating the texture of cauliflower heads was established, texture groups were classified, and a texture analyzer-based method for evaluating the texture of cauliflower heads was provided.
This technology enables rapid and accurate evaluation of the texture of cauliflower heads, improves detection efficiency, establishes a unified and objective evaluation system, and solves the problem of inaccurate evaluation results in existing technologies.
Smart Images
Figure CN122109459A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of fruit and vegetable quality testing technology, specifically relating to a method for evaluating the texture of cauliflower heads based on a texture analyzer. Background Technology
[0002] Cauliflower (Brassica oleracea L. var. botrytis L.) is widely favored by consumers for its unique taste and potential anti-cancer effects. According to statistics from the Food and Agriculture Organization of the United Nations (FAO), my country has become the world's largest producer and consumer of cauliflower. Cauliflower varieties are mainly divided into two types: loose-flowered and compact-flowered. In the past decade, the cauliflower variety structure has rapidly shifted from "compact" to "loose" and "high-quality." Currently, loose-flowered cauliflower accounts for more than 90% of the total cauliflower cultivation area in my country. Different loose-flowered cauliflower varieties exhibit differences in head texture and quality, directly affecting consumer preferences. For a long time, the evaluation of cauliflower head texture has mainly relied on sensory evaluation methods such as taste and touch. These methods are highly subjective, highly variable, and difficult to accurately reflect the texture. Therefore, there is an urgent need to establish a more scientific and comprehensive evaluation system to accurately assess the head texture and quality of loose-flowered cauliflower.
[0003] A texture analyzer is an instrument used for precise quantitative sensory evaluation, primarily reflecting texture characteristics related to mechanical properties. It has been widely applied in the texture quality evaluation of food and fruits and vegetables. Currently, various detection modes, such as TPA, shear, and puncture, have been developed, capable of measuring multiple texture parameters of fruits and vegetables, including maturity, hardness, crispness, elasticity, breaking strength, and toughness. In the texture determination of various fruits and vegetables such as bell peppers, Chinese cabbage, cucumbers, and melons, the consistency between texture data and sensory evaluation is high, indicating that texture analyzers can quantify sensory quality evaluation. However, current research on the texture of cauliflower heads lacks systematicity, and a unified and objective evaluation method has not yet been established. Summary of the Invention
[0004] The purpose of this invention is to provide a unified and objective method for evaluating the texture quality of cauliflower heads. Therefore, it provides a texture evaluation method for cauliflower heads based on a texture analyzer. Five texture indicators are analyzed using variance analysis, Spearman correlation analysis, linear regression analysis, principal component analysis, membership function analysis, and cluster analysis to establish a mathematical model for evaluating the texture of cauliflower heads, classify the texture groups of cauliflower germplasm resources, and provide theoretical support for the subsequent utilization of cauliflower germplasm resources and the breeding of high-quality varieties.
[0005] To achieve the above objectives, the technical solution of the present invention is: a method for evaluating the texture of cauliflower heads based on a texture analyzer, comprising:
[0006] (1) Preparation of cauliflower test samples;
[0007] (2) TPA texture was tested using a texture analyzer to obtain data on five texture indicators: hardness, cohesiveness, elasticity, chewiness, and resilience.
[0008] (3) Extraction of principal components
[0009] Principal component analysis was performed on the five texture indicators measured in step (2). Principal components were extracted according to the criteria that the extracted eigenvalues were greater than 1 or the cumulative contribution rate was greater than 80%. The rotational loading matrix, eigenvalues, contribution rates, cumulative contribution rates and score coefficients of each principal component were obtained.
[0010] (4) Calculation of the overall score of texture quality
[0011] The quality index data obtained in step (2) were standardized using SPSS 18.0 data processing software. Then, the standardized data and the principal component score coefficients obtained in step (3) were multiplied accordingly to calculate the comprehensive index value F of each principal component for each variety.
[0012] The membership function is used to standardize the comprehensive index values of each variety in each principal component. The membership function value μ(F) of each variety in each principal component is then calculated using the following formula. i ),
[0013] μ(F i )=(F i -F min ) / (F max -F min );
[0014] In the formula, i = 1, 2, 3, ..., n; F i F represents the comprehensive index value of the i-th variety; min and F max These represent the minimum and maximum values of the comprehensive index for each variety of each principal component, respectively.
[0015] The weights of the composite indices for each principal component are calculated using the following formula:
[0016]
[0017] In the formula, W p λ represents the weight of the p-th principal component extracted; p This represents the contribution rate corresponding to the p-th extracted principal component, and N represents the number of principal components;
[0018] Calculate the overall quality score C for each variety using the following formula:
[0019]
[0020] (5) Establishment of a mathematical model for evaluating the texture of cauliflower heads
[0021] Using the comprehensive quality score C of each variety in step (4) as the dependent variable and the five quality indicators measured in step (2) as the independent variables, a mathematical model for evaluating the quality of cauliflower head was established.
[0022] (6) Classification of floret texture groups of cauliflower germplasm resources
[0023] Based on the comprehensive texture score C of each variety in step (4), the Euclidean distance and group average method were used to perform flower head texture cluster analysis on the cauliflower germplasm resources and divide them into texture groups.
[0024] Furthermore, in step (1), different genotypes and phenotypic differences of cauliflower germplasm are selected. Based on the maturity period of the germplasm, flower heads without disease or insect damage and with good marketability are harvested in batches, and one flower head is selected from each germplasm.
[0025] Further, in step (1), the flower branches of the cauliflower head test sample used for texture analysis are decomposed, the flower buds are removed, and the remaining flower branches are cut into 1 cm thick slices. One round slice is taken from each thick slice using a 1 cm diameter punch for texture analysis. Four round slices are taken from each flower head.
[0026] Furthermore, in step (2), a 75 mm diameter disc extrusion probe is used to detect the texture parameters. The test parameters are set as follows: force sensor range 250 N, probe height above the sample surface 30 mm, deformation percentage 25%, and detection speed 80 mm / min. -1 The initial force is 0.38 N.
[0027] Furthermore, in step (2), after the texture instrument is used to test the texture, the average value of the three test results is taken as the final result, and five texture index data of hardness, cohesion, elasticity, chewiness and resilience are obtained.
[0028] Furthermore, in step (3), the extracted principal components are respectively denoted as the first principal component and the second principal component;
[0029] The primary components include hardness, chewiness, and elasticity;
[0030] The second principal component includes cohesion.
[0031] Furthermore, in step (5), the mathematical model for evaluating the texture of cauliflower heads is expressed as follows:
[0032] C = -2.045 + 0.017 × chewiness + 3.108 × resilience + 0.555 × elasticity + 0.010 × hardness + 1.950 × cohesion.
[0033] Furthermore, in step (5), the determination coefficient R of the mathematical model for evaluating the texture of cauliflower heads is... 2 =0.963, reaching the highly significant level, i.e., P<0.001.
[0034] Furthermore, in step (6), the texture groups include three categories: Group I has the lowest C value, exhibiting low hardness and poor chewiness, and is classified as soft; Group II has a medium C value, with balanced texture and good palatability, and is classified as crisp and tender; Group III has the highest C value, exhibiting high hardness, elasticity and chewiness, and is classified as crisp and elastic.
[0035] Furthermore, in step (6), the texture groups are classified as follows:
[0036] Class I: 0.01≤C≤0.35; Class II: 0.37≤C≤0.62; Class III: 0.65≤C≤1.00.
[0037] Compared with existing technologies, this invention has the following advantages: This invention utilizes a texture analyzer (TPA) to quickly and accurately obtain texture parameters from a large number of cauliflower samples, improving detection efficiency. Furthermore, it classifies the texture groups of cauliflower heads and establishes a mathematical model for evaluating cauliflower head texture, effectively solving the problem that current cauliflower texture evaluation systems mainly rely on sensory evaluations such as taste and touch, and lack comprehensive, accurate, and reliable evaluation results. Attached Figure Description
[0038] Figure 1 This is a schematic diagram of the method flow of the present invention.
[0039] Figure 2 Cluster analysis of the head texture of 166 cauliflower germplasm accessions. Detailed Implementation
[0040] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.
[0041] like Figure 1 As shown, this invention provides a method for evaluating the texture of cauliflower heads based on a texture analyzer, comprising:
[0042] (1) Preparation of cauliflower test samples;
[0043] (2) TPA texture was tested using a texture analyzer to obtain data on five texture indicators: hardness, cohesiveness, elasticity, chewiness, and resilience.
[0044] (3) Extraction of principal components
[0045] Principal component analysis was performed on the five texture indicators measured in step (2). Principal components were extracted according to the criteria that the extracted eigenvalues were greater than 1 or the cumulative contribution rate was greater than 80%. The rotational loading matrix, eigenvalues, contribution rates, cumulative contribution rates and score coefficients of each principal component were obtained.
[0046] (4) Calculation of the overall score of texture quality
[0047] The quality index data obtained in step (2) were standardized using SPSS 18.0 data processing software. Then, the standardized data and the principal component score coefficients obtained in step (3) were multiplied accordingly to calculate the comprehensive index value F of each principal component for each variety.
[0048] The membership function is used to standardize the comprehensive index values of each variety in each principal component. The membership function value μ(F) of each variety in each principal component is then calculated using the following formula. i ),
[0049] μ(F i )=(F i -F min ) / (F max -F min );
[0050] In the formula, i = 1, 2, 3, ..., n; F i F represents the comprehensive index value of the i-th variety; min and F max These represent the minimum and maximum values of the comprehensive index for each variety of each principal component, respectively.
[0051] The weights of the composite indices for each principal component are calculated using the following formula:
[0052]
[0053] In the formula, W p λ represents the weight of the p-th principal component extracted; p This represents the contribution rate corresponding to the p-th extracted principal component, and N represents the number of principal components;
[0054] Calculate the overall quality score C for each variety using the following formula:
[0055]
[0056] (5) Establishment of a mathematical model for evaluating the texture of cauliflower heads
[0057] Using the comprehensive quality score C of each variety in step (4) as the dependent variable and the five quality indicators measured in step (2) as the independent variables, a mathematical model for evaluating the quality of cauliflower head is established, as follows:
[0058] C = -2.045 + 0.017 × chewiness + 3.108 × resilience + 0.555 × elasticity + 0.010 × hardness + 1.950 × cohesion
[0059] (6) Classification of floret texture groups of cauliflower germplasm resources
[0060] Based on the comprehensive texture score C of each variety in step (4), the Euclidean distance and group average method were used to perform flower head texture cluster analysis on the cauliflower germplasm resources and divide them into texture groups.
[0061] The following is a detailed implementation process of the present invention.
[0062] 1. Materials and Methods
[0063] 1.1 Test Materials
[0064] The test materials were 166 cauliflower germplasms preserved by our research center (Table 1). Sowing was carried out in the autumn of 2024 at the center's experimental base (sowing on September 10th for seedlings less than 90 days mature, and on September 30th for seedlings older than 90 days mature). Seedlings were transplanted when they reached approximately 30 days old and had 4 or 5 leaves and 1 heart. Four seedlings were planted per germplasm, in double rows, with a spacing of 55 cm × 60 cm. Flower heads free from disease and pest damage and of good marketability were harvested in batches according to the maturity of the germplasm, with one flower head selected from each germplasm.
[0065] Table 1. Germplasm resource numbers and names of tested cauliflower germplasm resources
[0066]
[0067] Table 1 (continued)
[0068]
[0069] 1.2 Sample Preparation
[0070] The florets of the cauliflower head test samples used for texture analysis were decomposed, the buds were removed, and the remaining florets were cut into 1 cm thick slices. One round slice was taken from each slice using a 1 cm diameter punch for texture analysis. Four round slices were taken from each floret.
[0071] 1.3 Texture analyzer testing method
[0072] The experiment was conducted using the TPA testing mode of the ENS-iPro texture analyzer. A 75 mm diameter disc extrusion probe was used to detect texture parameters, with the following settings: force sensor range 250 N, probe rise height above the sample surface 30 mm, deformation percentage 25%, and detection speed 80 mm / min. -1 The initial force was 0.38 N. The average of the three test results was taken as the final result. Five TPA indicators were obtained: hardness, cohesion, elasticity, chewiness, and resilience.
[0073] 1.4 Data Statistics and Analysis
[0074] Data was organized using Excel software, and SPSS 18.0 software was used for analysis of variance, Spearman correlation analysis, linear regression analysis, and principal component analysis. Origin 2022 software was used for cluster analysis, employing the group mean method and Euclidean distance method for sample clustering. The membership function was used to standardize the comprehensive index value of each principal component, and the membership function value μ(F) for each variety of each principal component was calculated using the following formula. i ),
[0075] μ(F i )=(F i -F min ) / (F max -F min );
[0076] In the formula, i = 1, 2, 3, ..., n; F i F represents the comprehensive index value of the i-th variety; min and F max These represent the minimum and maximum values of the comprehensive index for each variety of each principal component, respectively.
[0077] The weights of the composite indices for each principal component are calculated using the following formula:
[0078]
[0079] In the formula, W p λ represents the weight of the p-th principal component extracted; p This represents the contribution rate corresponding to the p-th extracted principal component, and N represents the number of principal components;
[0080] Calculate the overall quality score C for each variety using the following formula:
[0081]
[0082] 2 Results and Analysis
[0083] 2.1 Analysis of Variation and Variance of TPA Index in Cauliflower Heads
[0084] Table 2 shows that, based on TPA data, the coefficients of variation for the five texture indicators—hardness, cohesion, elasticity, chewiness, and resilience—ranged from 8.60% to 31.80%. Among them, chewiness (31.80%) showed the greatest dispersion, followed by hardness and elasticity, while resilience and cohesion showed the least. Analysis of variance revealed significant differences in all five TPA indicators (P < 0.01). This indicates that the 166 tested cauliflower germplasm head textures possess a rich genetic variation base, which is beneficial for subsequent model construction.
[0085] Table 2. Analysis of variation and variance of TPA index in cauliflower head.
[0086]
[0087] * and ** indicate significant correlation at the P < 0.05 and P < 0.01 levels, respectively. The same applies below.
[0088] 2.2 Correlation Analysis Among Texture Indicators
[0089] Table 3 shows that the five TPA indicators were positively correlated to varying degrees, with statistical significance at the P < 0.01 level. Chewing property was strongly positively correlated with hardness, elasticity, and resilience, with Spearman correlation coefficients (ρ values) of 0.868, 0.807, and 0.803, respectively. Resilience was relatively strongly positively correlated with hardness and elasticity, with ρ values of 0.685 and 0.673, respectively. Cohesion was moderately correlated with elasticity, chewing property with resilience, and hardness with elasticity, with ρ values of 0.564, 0.536, 0.500, and 0.536, respectively. Hardness and cohesion showed the weakest correlation, with a ρ value of 0.225.
[0090] Table 3. Correlation analysis among the five texture indicators
[0091]
[0092] 2.3 Principal component analysis of cauliflower texture indicators
[0093] As shown in Table 4, two principal components were extracted based on the criteria of eigenvalues greater than 1 or cumulative contribution rates greater than 80%. Their eigenvalues were 3.456 and 1.055, with contribution rates of 69.12% and 21.10%, respectively, and a cumulative contribution rate of 90.22%. These components already encompass most of the information about the texture of cauliflower heads, indicating that these two comprehensive principal components have strong information representativeness. Furthermore, the loading values of each principal component show that the first principal component mainly includes hardness, chewiness, and elasticity, which are palatability factors and key factors influencing consumer preference; the second principal component mainly includes cohesion, which is a texture structure factor reflecting the internal tissue structure characteristics of cauliflower heads. These two principal components can comprehensively summarize the main characteristics of cauliflower head texture and provide a scientific basis for subsequent texture evaluation and germplasm resource screening.
[0094] Table 4. Rotation loading matrix, eigenvalues, contribution rate, and cumulative contribution rate of each principal component.
[0095]
[0096] 2.4 Comprehensive Evaluation of the Quality of Cauliflower Germplasm Resources
[0097] Z-score standardization was performed on the five TPA index data using SPSS 18.0 software, and the comprehensive index value F of the two principal components was calculated as follows: F1 = 0.488X1 + 0.347X2 + 0.464X3 + 0.485X4 + 0.437X5; F2 = -0.529X1 + 0.694X2 + 0.408X3 - 0.224X4 - 0.146X5; X1-X5 represent the standardized results of the five index data of hardness, cohesion, elasticity, chewiness, and resilience, respectively. The membership function values μ(F1) and μ(F2) of the comprehensive index of the two principal components of each germplasm were calculated using the membership function method, and the comprehensive texture score C of each quality was calculated using the texture comprehensive score expression C = 0.766μ(F1) + 0.234μ(F2) (Table 5).
[0098] Table 5. Overall scores of floret texture from 166 cauliflower germplasm samples.
[0099]
[0100] Using stepwise regression with the overall texture score (C) as the dependent variable and five TPA indicators as independent variables, an optimal mathematical model for evaluating the texture of cauliflower heads was established: C = -2.045 + 0.017 × chewiness + 3.108 × resilience + 0.555 × elasticity + 0.010 × hardness + 1.950 × cohesion. The equation had a coefficient of determination R² = 0.963, reaching a highly significant level (P < 0.001), indicating that the model has strong explanatory power.
[0101] 2.5 Texture Cluster Analysis of Different Cauliflower Germplasm Resources
[0102] Further, the flower head texture of different cauliflower germplasm resources was classified into groups. Based on the C-value, Euclidean distance and unweighted pair group mean average (UPGMA) were used to perform cluster analysis on 166 cauliflower germplasm resources. Figure 2 The results showed that at a Euclidean distance of 0.3, the 166 cauliflower germplasm resources could be divided into three categories: Category I had the lowest total score for head texture (0.01≤C≤0.35), mainly including 54 varieties; Category II had a middle total score for head texture (0.37≤C≤0.62), including 62 varieties; and Category III had the highest total score for head texture (0.65≤C≤1.00), including 50 varieties.
[0103] 3. Conclusion
[0104] 3.1 In this study, texture analyzer was used to perform TPA texture testing on 166 cauliflower germplasm resources, and data on five texture indices were obtained: hardness, cohesion, elasticity, chewiness, and resilience. Principal component analysis and membership function method were used to transform the five indices into two independent comprehensive indices, and the comprehensive texture score (C) of each material was calculated.
[0105] 3.2 The C-values of 166 cauliflower germplasm resources were analyzed by Euclidean distance and group average method to form three groups. Group I had the lowest C-value (0.01≤C≤0.35), exhibiting low firmness and poor chewiness, and was classified as soft. Group II had a medium C-value (0.37≤C≤0.62), with a balanced texture and good palatability, and was classified as crisp. Group III had the highest C-value (0.65≤C≤1.00), exhibiting high firmness, elasticity and chewiness, and was classified as crisp and elastic.
[0106] 3.3 A reliable mathematical model for evaluating the texture of cauliflower was established using linear regression: C = -2.045 + 0.017 × chewiness + 3.108 × resilience + 0.555 × elasticity + 0.010 × firmness + 1.950 × cohesion. These five texture indicators are closely related to the texture of cauliflower heads and can be used as indicators for identifying the texture of cauliflower heads.
[0107] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.
Claims
1. A method for evaluating the texture of cauliflower heads based on a texture analyzer, characterized in that, include: (1) Preparation of cauliflower test samples; (2) TPA texture was tested using a texture analyzer to obtain data on five texture indicators: hardness, cohesiveness, elasticity, chewiness, and resilience. (3) Extraction of principal components Principal component analysis was performed on the five texture indicators measured in step (2). Principal components were extracted according to the criteria that the extracted eigenvalues were greater than 1 or the cumulative contribution rate was greater than 80%. The rotational loading matrix, eigenvalues, contribution rates, cumulative contribution rates and score coefficients of each principal component were obtained. (4) Calculation of the overall score of texture quality The quality index data obtained in step (2) were standardized using SPSS 18.0 data processing software. Then, the standardized data and the principal component score coefficients obtained in step (3) were multiplied accordingly to calculate the comprehensive index value F of each principal component for each variety. The membership function is used to standardize the comprehensive index values of each variety in each principal component. The membership function value μ(F) of each variety in each principal component is then calculated using the following formula. i ), μ(F i )=(F i -F min ) / (F max -F min ); In the formula, i = 1, 2, 3, ..., n; F i F represents the comprehensive index value of the i-th variety; min and F max These represent the minimum and maximum values of the comprehensive index for each variety of each principal component, respectively. The weights of the composite indices for each principal component are calculated using the following formula: In the formula, W p This represents the weight of the p-th principal component extracted; λ p This represents the contribution rate corresponding to the p-th extracted principal component, and N represents the number of principal components; Calculate the overall quality score C for each variety using the following formula: (5) Establishment of a mathematical model for evaluating the texture of cauliflower heads Using the comprehensive quality score C of each variety in step (4) as the dependent variable and the five quality indicators measured in step (2) as the independent variables, a mathematical model for evaluating the quality of cauliflower head was established. (6) Classification of floret texture groups of cauliflower germplasm resources Based on the comprehensive texture score C of each variety in step (4), the Euclidean distance and group average method were used to perform flower head texture cluster analysis on the cauliflower germplasm resources and divide them into texture groups.
2. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (1), different genotypes and phenotypic traits of cauliflower germplasm are selected. Based on the maturity period of the germplasm, flower heads without disease or insect damage and with good marketability are harvested in batches, and one flower head is selected from each germplasm.
3. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (1), the flower branches of the cauliflower head test sample used for texture analysis are decomposed, the flower buds are removed, and the remaining flower branches are cut into 1 cm thick slices. One round slice is taken from each thick slice using a 1 cm diameter punch for texture analysis. Four round slices are taken from each flower head.
4. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (2), a 75 mm diameter disc extrusion probe was used to detect the texture parameters. The test parameters were set as follows: force sensor range 250 N, probe height above the sample surface 30 mm, deformation percentage 25%, and detection speed 80 mm / min. -1 The initial force is 0.38 N.
5. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (2), after the texture instrument is used to test the texture, the average value of the three test results is taken as the final result, and five texture index data of hardness, cohesion, elasticity, chewiness and resilience are obtained.
6. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (3), the extracted principal components are denoted as the first principal component and the second principal component, respectively; The primary components include hardness, chewiness, and elasticity; The second principal component includes cohesion.
7. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (5), the mathematical model for evaluating the texture of cauliflower heads is expressed as follows: C = -2.045 + 0.017 × chewiness + 3.108 × resilience + 0.555 × elasticity + 0.010 × hardness + 1.950 × cohesion.
8. A method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1 or 7, characterized in that, In step (5), the determination coefficient R of the mathematical model for evaluating the texture of cauliflower heads is... 2 =0.963, reaching the highly significant level, i.e., P<0.
001.
9. The method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1, characterized in that, In step (6), the texture groups include three categories: Group I has the lowest C value, exhibiting low hardness and poor chewiness, and is classified as soft; Group II has a medium C value, with balanced texture and good palatability, and is classified as crisp and tender; Group III has the highest C value, exhibiting high hardness, elasticity and chewiness, and is classified as crisp and elastic.
10. A method for evaluating the texture of cauliflower heads based on a texture analyzer according to claim 1 or 9, characterized in that, In step (6), the texture groups are divided as follows: Class I: 0.01≤C≤0.35; Class II: 0.37≤C≤0.62; Class III: 0.65≤C≤1.00.