Method and device for evaluating sensory attributes of a yoghurt

Abstract

The application provides a yogurt sensory attribute evaluation method and device. The method comprises the following steps: determining a yogurt product to be evaluated; obtaining sensory melting degree data of the yogurt product; detecting and analyzing the sensory melting degree data based on a preset yogurt sample sensory melting degree detection model to obtain corresponding yogurt melting degree sensory actual evaluation data; wherein the yogurt sample sensory melting degree detection model is a correlation coefficient fitting curve model obtained by pre-training based on yogurt samples and corresponding yogurt sample sensory melting degree evaluation data. The yogurt sensory attribute evaluation method disclosed by the application has low detection cost, high speed and high repeatability, can quickly predict the yogurt sensory melting degree, effectively improves the detection efficiency and accuracy of the yogurt product sensory melting degree, and thus helps to shorten the product development cycle.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, specifically to a method and apparatus for evaluating the sensory attributes of yogurt. Additionally, it relates to an electronic device and a processor-readable storage medium. Background Technology

[0002] Yogurt, with its rich nutritional value, is gradually becoming a staple snack for consumers of all ages. With rising living standards and a growing awareness of health, the per capita demand for yogurt among urban residents is experiencing rapid growth. Sensory experience is one of the most important factors influencing consumer purchases, second only to health considerations. However, due to processing techniques and product formulations, yogurt products generally suffer from a sticky, gelatinous texture and difficulty in dispersing, resulting in a poor sensory experience that can negatively impact sales in severe cases.

[0003] Currently, the main method for evaluating the meltability of yogurt products during development relies on descriptive sensory tests conducted by consumers (i.e., the degree to which yogurt disperses during oral chewing). This method often suffers from individual differences and subjective bias with small sample sizes (≤50 people), failing to accurately and objectively evaluate product attributes. With large sample sizes (≥100 people), while data trends show consistency, it is time-consuming and labor-intensive, extending product development cycles and increasing costs. Current instrumental testing methods related to yogurt sensory properties primarily include rheometer single-point viscosity testing and texture analyzer consistency indices. However, these two parameters have low correlation with sensory meltability and can only serve as one means of evaluating system stability. Therefore, designing a more stable and efficient yogurt sensory attribute evaluation scheme has become a crucial issue that urgently needs to be addressed by those skilled in the art. Summary of the Invention

[0004] Therefore, this invention provides a method for evaluating the sensory attributes of yogurt, in order to solve the problems of high limitations, poor detection efficiency and accuracy of existing yogurt sensory attribute evaluation schemes.

[0005] In a first aspect, the present invention provides a method for evaluating the sensory attributes of yogurt, comprising:

[0006] Identify the yogurt products to be evaluated;

[0007] Obtain sensory melting data of the yogurt product;

[0008] The sensory melting rate data is detected and analyzed based on a preset sensory melting rate detection model for yogurt samples to obtain corresponding actual sensory evaluation data of yogurt melting rate; wherein, the sensory melting rate detection model for yogurt samples is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting rate evaluation data.

[0009] Furthermore, before analyzing the sensory melting rate data based on a preset sensory melting rate detection model for yogurt samples, the following steps are also included:

[0010] Identify the yogurt sample to be tested;

[0011] Quantitative descriptive analysis was used to characterize the product characteristics and differences among the yogurt samples, so as to quantitatively evaluate the product characteristics of the yogurt samples and obtain sensory melting data of the yogurt samples.

[0012] Using a yogurt melting instrument, the yogurt sample was tested under preset constant-rate temperature change and constant-rate temperature change conditions. Based on the measured data, a viscosity curve was determined, and the instrument characterization parameter index of the viscosity curve was determined; wherein, the instrument characterization parameter index is the slope value of the viscosity curve.

[0013] Based on the sensory melting rate data of the yogurt sample and the instrument characterization parameter index, a correlation fitting is performed to determine the sensory melting rate detection model of the yogurt sample; wherein, the sensory melting rate detection model of the yogurt sample is a correlation coefficient fitting curve model between the instrument characterization parameter index and the sensory melting rate data of the yogurt sample.

[0014] Furthermore, the yogurt sample is tested using a yogurt melting instrument under preset constant-rate temperature change and constant-rate temperature change conditions, and the viscosity curve is determined based on the measured data. Specifically, this includes:

[0015] For room-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, a constant-rate temperature change test was conducted on the yogurt samples within a temperature range of 25℃-28℃ to obtain a preset number of first data points corresponding to the yogurt samples; and a shear rate of 70s was applied. -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 28°C to obtain a preset number of second data points corresponding to the yogurt sample.

[0016] The viscosity curve corresponding to the room temperature yogurt sample is determined based on the first data point and the second data point.

[0017] Furthermore, the yogurt sample is tested using a yogurt melting instrument under preset constant-rate temperature change and constant-rate temperature change conditions, and the viscosity curve is determined based on the measured data. Specifically, this includes:

[0018] For low-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1Furthermore, under a temperature change range of 9℃-20℃, a constant-rate temperature variation test was conducted on the yogurt samples to obtain a preset number of third data points corresponding to the yogurt samples; and at a shear rate of 70s... -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 20°C to obtain a preset number of fourth data points corresponding to the yogurt sample.

[0019] The viscosity curve corresponding to the low-temperature yogurt sample is determined based on the third and fourth data points.

[0020] Furthermore, obtaining the sensory melting data of the yogurt product specifically includes:

[0021] The product characteristics of the yogurt product are quantitatively evaluated based on quantitative descriptive analysis to obtain the corresponding sensory melting data.

[0022] Furthermore, the sensory melting test data of the actual yogurt samples are oral melting characteristic data of the yogurt products.

[0023] Secondly, the present invention also provides a sensory attribute evaluation device for yogurt, comprising:

[0024] Yogurt product identification unit, used to identify yogurt products to be evaluated;

[0025] Sensory melting data acquisition unit, used to acquire sensory melting data of the yogurt product;

[0026] The sensory melting degree detection unit is used to detect and analyze the sensory melting degree data based on a preset sensory melting degree detection model for yogurt samples, and obtain the corresponding actual sensory evaluation data of yogurt melting degree; wherein, the sensory melting degree detection model for yogurt samples is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting degree evaluation data.

[0027] Furthermore, before analyzing the sensory melting rate data based on a preset sensory melting rate detection model for yogurt samples, the following steps are also included:

[0028] Yogurt sample determination unit, used to determine the yogurt sample to be tested;

[0029] The sensory melting data collection unit is used to characterize the product characteristics and differences between the yogurt samples using quantitative descriptive analysis methods, so as to quantitatively evaluate the product characteristics of the yogurt samples and obtain the sensory melting data of the yogurt samples.

[0030] The sample instrument characterization parameter index determination unit is used to test the yogurt sample using a yogurt melting degree instrument under preset constant rate temperature change and constant rate temperature change conditions, determine the viscosity curve based on the measured data, and determine the instrument characterization parameter index of the viscosity curve; wherein, the instrument characterization parameter index is the slope value of the viscosity curve.

[0031] The correlation coefficient fitting curve model construction unit is used to perform correlation fitting based on the sensory melting degree data of the yogurt sample and the instrument characterization parameter index to determine the sensory melting degree detection model of the yogurt sample; wherein, the sensory melting degree detection model of the yogurt sample is the correlation coefficient fitting curve model between the instrument characterization parameter index and the sensory melting degree data of the yogurt sample.

[0032] Furthermore, the sample instrument characterization parameter index determination unit is specifically used for:

[0033] For room-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, a constant-rate temperature change test was conducted on the yogurt samples within a temperature range of 25℃-28℃ to obtain a preset number of first data points corresponding to the yogurt samples; and a shear rate of 70s was applied. -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 28°C to obtain a preset number of second data points corresponding to the yogurt sample.

[0034] The viscosity curve corresponding to the room temperature yogurt sample is determined based on the first data point and the second data point.

[0035] Furthermore, the sample instrument characterization parameter index determination unit is specifically used for:

[0036] For low-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, under a temperature change range of 9℃-20℃, a constant-rate temperature variation test was conducted on the yogurt samples to obtain a preset number of third data points corresponding to the yogurt samples; and at a shear rate of 70s... -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 20°C to obtain a preset number of fourth data points corresponding to the yogurt sample.

[0037] The viscosity curve corresponding to the low-temperature yogurt sample is determined based on the third and fourth data points.

[0038] Furthermore, the sensory melting degree data acquisition unit is specifically used for:

[0039] The product characteristics of the yogurt product are quantitatively evaluated based on quantitative descriptive analysis to obtain the corresponding sensory melting data.

[0040] Furthermore, the sensory melting test data of the actual yogurt samples are oral melting characteristic data of the yogurt products.

[0041] Thirdly, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the yogurt sensory attribute evaluation method as described in any of the above claims.

[0042] Fourthly, the present invention also provides a processor-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the yogurt sensory attribute evaluation method as described above.

[0043] The sensory attribute evaluation method for yogurt provided by this invention acquires sensory melting degree data of the yogurt product to be evaluated, and analyzes the sensory melting degree data based on a preset yogurt sample sensory melting degree detection model to obtain corresponding actual sensory evaluation data of yogurt melting degree. The yogurt sample sensory melting degree detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and their corresponding yogurt sensory melting degree evaluation data. This method has low detection cost, high speed, and high repeatability, and can quickly predict the sensory melting degree of yogurt, effectively improving the detection efficiency and accuracy of yogurt product sensory melting degree, thereby helping to shorten the product development cycle. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A schematic flowchart of the yogurt sensory attribute evaluation method provided in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the complete process of the yogurt sensory attribute evaluation method provided in the embodiments of the present invention;

[0047] Figure 3 This is a schematic diagram of the yogurt viscosity curve in the yogurt sensory attribute evaluation method provided in this embodiment of the invention;

[0048] Figure 4 This is a schematic diagram of the structure of the yogurt sensory attribute evaluation device provided in an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The following is a detailed description of embodiments of the yogurt sensory attribute evaluation method described in this invention. Figure 1 The diagram shown is a flowchart of the yogurt sensory attribute evaluation method provided in an embodiment of the present invention. The specific implementation process includes the following steps:

[0052] Step 101: Identify the yogurt products to be evaluated.

[0053] Specifically, the yogurt products mentioned are various types of yogurt products, such as drinking yogurt, stirred yogurt, and set yogurt, including drinking room-temperature yogurt, stirred room-temperature yogurt, set room-temperature yogurt, drinking chilled yogurt, stirred chilled yogurt, and set chilled yogurt, etc., without being specifically limited here.

[0054] In the specific application and testing process of this invention, the yogurt product to be evaluated needs to be determined in advance. For example, the room-temperature yogurt product to be evaluated is poured into a product pool at 25±2℃ and left to stand for 15 minutes until the product structure is fully restored before starting subsequent testing; or, the low-temperature yogurt product to be evaluated is poured into a sample pool at 4±2℃ and left to stand for 15 minutes until the product structure is fully restored before starting subsequent testing. The yogurt product includes various types of dairy products made through milk fermentation.

[0055] Step 102: Obtain sensory melting data of the yogurt product.

[0056] Specifically, the product characteristics of the yogurt product can be quantitatively evaluated using Quantitative Descriptive Analysis (QDA) to obtain the corresponding sensory melting rate data. That is, the sensory evaluation of the yogurt product is conducted according to the sensory melting rate definition. The sensory melting rate of a yogurt product is defined as the degree to which, after chewing, the yogurt product can be fully spread and rapidly dispersed in the oral cavity.

[0057] Step 103: Analyze the sensory melting rate data based on a pre-set sensory melting rate detection model for yogurt samples to obtain corresponding actual sensory evaluation data for yogurt melting rate. The sensory melting rate detection model for yogurt samples is a correlation coefficient fitting curve model trained on yogurt samples and their corresponding sensory melting rate evaluation data. The actual sensory melting rate detection data for yogurt samples is the oral melting characteristic data of the yogurt product.

[0058] In this invention, before analyzing the sensory melting rate data based on a preset sensory melting rate detection model for yogurt samples, it is necessary to pre-calculate the correlation coefficient and construct a correlation coefficient fitting curve model by fitting the correlation between the instrument characterization parameters and the sensory melting rate data of the yogurt samples. Specifically, the process of constructing the correlation coefficient fitting curve model includes: determining the yogurt samples to be tested; using quantitative descriptive analysis to characterize the product characteristics and differences among the yogurt samples to quantitatively evaluate the product characteristics of the yogurt samples and obtain the sensory melting rate data of the yogurt samples; using a yogurt melting rate instrument to test the yogurt samples under preset constant-rate temperature change and constant-rate temperature change conditions, determining the viscosity curve based on the measured data, and determining the instrument characterization parameters of the viscosity curve; wherein, the instrument characterization parameters are the slope values ​​of the viscosity curve; and performing correlation fitting between the sensory melting rate data of the yogurt samples and the instrument characterization parameters to determine the sensory melting rate detection model for the yogurt samples; wherein, the sensory melting rate detection model for the yogurt samples is a correlation coefficient fitting curve model between the instrument characterization parameters and the sensory melting rate data of the yogurt samples.

[0059] Among them, such as Figure 3 As shown, the yogurt melting instrument is used to test the yogurt sample under preset constant-rate temperature change and constant-rate temperature change conditions. Based on the measured data, the viscosity curve is determined. The specific implementation process includes: for room temperature yogurt samples, using the yogurt melting instrument, at a shear rate of 70s... -1 Furthermore, a constant-rate temperature change test was conducted on the yogurt samples within a temperature range of 25℃-28℃ to obtain a preset number of first data points corresponding to the yogurt samples; and a shear rate of 70s was applied.-1 Furthermore, a constant-rate, constant-temperature test was performed on the yogurt sample at 28°C to obtain a preset number of second data points corresponding to the yogurt sample; based on the first and second data points, the viscosity curve corresponding to the room-temperature yogurt sample was determined (e.g., ...). Figure 3 The viscosity curve is located in the lower part of the range of approximately 5℃-20℃. In addition, for low-temperature yogurt samples, a yogurt melting instrument was used at a shear rate of 70s. -1 Furthermore, under a temperature change range of 9℃-20℃, a constant-rate temperature variation test was conducted on the yogurt samples to obtain a preset number of third data points corresponding to the yogurt samples; and at a shear rate of 70s... -1 Furthermore, a constant-rate, constant-temperature test was performed on the yogurt sample at a temperature of 20℃ to obtain a preset number of fourth data points corresponding to the yogurt sample; based on the third and fourth data points, the viscosity curve corresponding to the low-temperature yogurt sample was determined (e.g., Figure 3 The viscosity curve is located in the upper part of the temperature range of approximately 5℃-20℃.

[0060] Specifically, in the testing of the room-temperature yogurt system: a CC27 fixture was used, and a constant-rate temperature variation experiment was conducted in rotational viscosity mode. The test was conducted in two stages, the first being a constant-rate temperature variation with a shear rate of 60 s. -1 -90s -1 (Optimal shear rate: 70s) -1 Temperature variation: 25℃-28℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 60s. -1 -90s -1 The number of data points was 10, and the data collection time was 10 seconds. Further, a yogurt melting instrument (such as an Anton Paar Rheolab QC rheometer) was used, and a viscosity curve was plotted based on the measured data (i.e., the 33 and 10 data points mentioned above), and the curve slope value K was calculated. The yogurt melting instrument contains a sensory melting detection model for the yogurt sample. Further, SPSS 17.0 statistical software was used to perform a fitting analysis on the curve slope value K and the sensory melting data, and the correlation coefficient R was calculated. 2 Similarly, in the low-temperature yogurt system testing process: a CC27 fixture was used, and a constant-rate temperature variation experiment was conducted in rotational viscosity mode. The test was conducted in two stages, the first being a constant-rate temperature variation with a shear rate of 60 s. -1 -90s -1 (Optimal shear rate: 70s) -1 Temperature variation: 9℃-20℃, number of data points: 33, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 60s. -1 -90s -1The number of data points was 10, and the data collection time was 10 seconds. Further, a yogurt melting instrument (such as an Anton Paar Rheolab QC rheometer) was used, and a viscosity curve was plotted based on the measured data (i.e., the 33 and 10 data points mentioned above), and the curve slope value K was calculated. The yogurt melting instrument contains a sensory melting detection model for the yogurt sample. Further, SPSS 17.0 statistical software was used to perform a fitting analysis on the curve slope value K and the sensory melting data, and the correlation coefficient R was calculated. 2 .

[0061] It should be noted that before performing the above steps, it is necessary to conduct comparative analysis using multiple examples and comparative cases to determine the optimal detection conditions based on the magnitude of the correlation coefficient. For example, in the detection of room temperature yogurt systems, the shear rate is 70s. -1 Temperature variation: 25℃-28℃ is the optimal detection condition; in the detection of the low-temperature yogurt system, the shear rate is 70s. -1 Temperature variation: 9℃-20℃ is the optimal testing condition.

[0062] Real-time Example 1 (Room temperature yogurt system, shear rate: 60s) -1 Temperature variation: 25℃-28℃.

[0063] 1. The sensory melting degree testing stage of yogurt samples includes: (1) screening of consumers. (2) Sample loading stage: a complete block balance design is adopted (each consumer tastes all samples), and the samples are randomly loaded into two groups (executed twice, 5 samples each time); the room temperature samples are stored in advance at 25±2℃. (3) Sensory characteristic description stage: QDA quantitative descriptive test is used to characterize the characteristics and differences between products. Sensory definition of yogurt melting degree: the degree to which the yogurt sample can be fully spread and quickly dispersed in the oral cavity after chewing after entering the mouth. (4) Consumers evaluate the sensory quality of the yogurt samples according to the sensory definition of yogurt melting degree, score the test samples and record them, thus obtaining the sensory melting degree data of the yogurt samples.

[0064] 2. Yogurt melting test stage (Anton Paar Rheolab QC rheometer) includes: (1) Sample preparation stage: The room temperature yogurt samples to be tested are poured into the sample pool and left to stand for 15 minutes at 25±2℃ until the sample structure is fully restored before the test begins. (2) Program setting stage: The CC27 fixture is used to conduct the constant-rate temperature change test in the rotational viscosity mode. The test is divided into two stages, the first stage is constant-rate temperature change: the shear rate is 60s. -1 Temperature variation: 25℃-28℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 60s. -1The number of data points is 10, and the data point time is 10s. (3) Data processing stage: Based on the data measured in (2) (i.e., 10 and 33 data points), draw the viscosity curve and calculate the slope value K, which is the slope value of the viscosity curve.

[0065] 3. The correlation fitting stage between sensory scores and viscosity curves includes: (1) using SPSS 17.0 statistical software to perform fitting analysis on the curve slope value K and sensory melting degree data, and calculating the correlation coefficient R. 2 Thus, the sensory melting degree detection model of the yogurt sample was obtained.

[0066] Example 2 (Room temperature yogurt system, shear rate: 70s) -1 Temperature variation: 25℃-28℃.

[0067] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two phases, the first phase being a constant-rate temperature variation with a shear rate of 70 s. -1 Temperature variation: 25℃-28℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 70s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 1, and will not be repeated here.

[0068] Example 3 (Room temperature yogurt system, shear rate: 90s) -1 Temperature variation: 25℃-28℃.

[0069] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two phases, the first phase being a constant-rate temperature variation with a shear rate of 90 s. -1 Temperature variation: 25℃-28℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 90s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 1, and will not be repeated here.

[0070] Example 4 (Low-temperature yogurt system, shear rate: 60s) -1 Temperature variation: 9℃-20℃.

[0071] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two stages, the first being a constant-rate temperature variation with a shear rate of 60 s. -1 Temperature variation: 9℃-20℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 60s. -1The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 1, and will not be repeated here.

[0072] Example 5 (Low-temperature yogurt system, shear rate: 70s) -1 Temperature variation: 9℃-20℃.

[0073] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two phases, the first phase being a constant-rate temperature variation with a shear rate of 70 s. -1 Temperature variation: 9℃-20℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 70s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 2, and will not be repeated here.

[0074] Example 6 (Low-temperature yogurt system, shear rate: 90s) -1 Temperature variation: 9℃-20℃.

[0075] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two phases, the first phase being a constant-rate temperature variation with a shear rate of 90 s. -1 Temperature variation: 9℃-20℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 90s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 3, and will not be repeated here.

[0076] Comparative Example 1 (Room temperature yogurt system, shear rate: 60s) -1 Temperature variation: 4℃-37℃.

[0077] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two stages, the first being a constant-rate temperature variation with a shear rate of 60 s. -1 Temperature variation: 4℃-37℃, 33 data points, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 60s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 1, and will not be repeated here.

[0078] Comparative Example 2 (Room temperature yogurt system, shear rate: 70s) -1 Temperature variation: 4℃-37℃.

[0079] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two phases, the first phase being a constant-rate temperature variation with a shear rate of 70 s. -1 Temperature variation: 4℃-37℃, number of data points: 33, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 70s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 1, and will not be repeated here.

[0080] Comparative Example 3 (Low-temperature yogurt system, shear rate: 90s) -1 Temperature variation: 4℃-37℃.

[0081] During the program setup phase: a CC27 fixture was used to conduct a constant-rate temperature variation experiment in rotational viscosity mode. The test was conducted in two phases, the first phase being a constant-rate temperature variation with a shear rate of 90 s. -1 Temperature variation: 4℃-37℃, number of data points: 33, data point duration: 10s; Two-stage constant rate and temperature control: shear rate: 90s. -1 The number of data points is 10, and the data point duration is 10 seconds. The remaining stages are the same as in Example 1, and will not be repeated here.

[0082] Comparative Example 5 (fitting of conventional instrument test parameters for room temperature yogurt system).

[0083] 1. The sensory melting degree testing stage of yogurt samples includes: (1) screening of consumers. (2) Sample loading: a complete block balance design is adopted (each consumer tastes all samples), and the samples are randomly loaded into two groups (executed twice, 5 samples each time); the room temperature samples are stored in advance at 25±2℃. (3) Sensory characteristic description: QDA quantitative descriptive test is used to characterize the characteristics and differences between products. Sensory definition of yogurt melting degree: the degree to which the yogurt sample can be fully spread and quickly dispersed in the oral cavity after chewing after entering the mouth. (4) Consumers evaluate the sensory quality according to the sensory definition of yogurt melting degree and score the test samples.

[0084] 2. The routine instrument testing stage includes: (1) Single-point viscosity test: Anton Paar Rheolab QC rheometer is used for testing. The low-temperature sample stored at 4±2℃ is poured into the sample pool and left to stand for 15 minutes until the sample structure is fully restored before starting the test. Program setting: The CC27 fixture is used to perform constant temperature variable speed test in rotational viscosity mode. Shear rate: 0-150s -1 Temperature: 25℃, number of data points: 50, data point duration: 30 seconds. Recording time: 75 seconds. -1(2) The yogurt consistency test stage includes: using a TAXT plus physical property analyzer to test the viscoelasticity of yogurt. At 25℃, yogurt is poured into a cylindrical sample cup with a diameter of 50mm and left to stand for 15min. Each time, the sample volume is 100-110g (about 3 / 4 of the sample cup). The probe is AB / E (35mm diameter disc shape). The yogurt consistency test module has a pre-test speed and test speed of 1.00mm / s, a return speed of 10mm / s, a test pressure distance of 30.00mm, and a preset homogeneity threshold of 0.52g. Record the yogurt consistency.

[0085] 3. The correlation fitting stage between sensory scores and viscosity curves includes: (1) using SPSS 17.0 statistical software to perform correlation fitting on the 75s viscosity curve. -1 The viscosity, yogurt consistency, and sensory melting rate data were fitted and analyzed to calculate the correlation coefficient R. 2 .

[0086] Comparative Example 6 (fitting of conventional instrument test parameters for low-temperature yogurt system).

[0087] 1. Sensory melting test of yogurt samples

[0088] (1) Screening of consumers: 100 consumers; aged 20-40 (20-30:31-40 = 45%:55%); male:female = 3:7; had consumed low-temperature yogurt in the past month.

[0089] (2) Sample loading for testing: A complete block balance design was adopted (each consumer tasted all samples), and the samples were randomly loaded into two groups (two executions, five samples each time); the low-temperature samples were stored at 4±2℃ for at least 12 hours in advance.

[0090] (3) Sensory characteristics description: QDA quantitative descriptive testing was used to characterize the characteristics and differences between products. Yogurt meltability sensory definition: the degree to which the yogurt sample can be fully spread and quickly dispersed in the oral cavity after chewing after entering the mouth.

[0091] (4) Consumers evaluate the sensory quality of yogurt based on the sensory definition of melting degree, and score and record the test samples.

[0092] 2. The routine instrument testing phase includes:

[0093] (1) The single-point viscosity test stage includes: testing using an Anton Paar Rheolab QC rheometer. Low-temperature samples stored at 4±2℃ are poured into the sample pool and allowed to stand for 15 minutes to allow the sample structure to fully recover before starting the test. Program settings: A CC27 fixture is used for isothermal variable-speed testing in rotational viscosity mode, with a shear rate of 0-150 s. -1Temperature: 25℃, number of data points: 50, data point duration: 30 seconds. Recording time: 75 seconds. -1 Viscosity value at the specified location.

[0094] (2) The yogurt consistency test stage included: using a TAXT plus physical property analyzer to test the viscoelasticity of the yogurt. At 25℃, yogurt was poured into a cylindrical sample cup with a diameter of 50mm and allowed to stand for 15 minutes. Each sample addition was 100-110g (approximately 3 / 4 full). The probe was AB / E (35mm diameter disc-shaped). The yogurt consistency test module had a pre-test speed and test speed of 1.00mm / s, a return speed of 10mm / s, a test pressure distance of 30.00mm, and a preset homogeneity threshold of 0.52g. The yogurt consistency was recorded.

[0095] 3. The correlation fitting stage between sensory scores and viscosity curves includes: (1) using SPSS 17.0 statistical software to perform correlation fitting on the 75s viscosity curve. -1 The viscosity, yogurt consistency, and sensory melting rate data were fitted and analyzed to calculate the correlation coefficient R. 2 .

[0096] Table 2. Correlation coefficients between instrument characterization parameters and sensory data

[0097]

[0098]

[0099] As shown in Table 2, in the test of the room temperature yogurt system, the shear rate in Example 2 was 70 s. -1 Temperature variation: 25℃-28℃ is the optimal testing condition. Under this condition, the correlation coefficient between the test index and the sensory melting degree of yogurt reaches 0.906, indicating a significant correlation. In the low-temperature yogurt system test, the shear rate in Example 5 is 70s. -1 Temperature variation: 9℃-20℃ is the optimal testing condition. Under this condition, the correlation coefficient between the test index and the sensory melting degree of yogurt reaches 0.894, and the correlation is significant. Furthermore, it can be seen that compared with traditional instrumental methods, the method adopted in this embodiment has a high correlation and can replace sensory methods to evaluate the oral melting characteristics of yogurt products.

[0100] This invention, based on large-sample descriptive testing of a population, uses instrumental measurements to convert the sensory language of product melting degree into instrumental parameters, forming a stable measurement system. This achieves the goal of online, real-time, rapid, and accurate prediction of the melting degree of yogurt. It solves the problems of individual subjective differences, time-consuming processes, and high development costs associated with relying solely on descriptive sensory testing, and overcomes the low accuracy of single testing methods.

[0101] The sensory attribute evaluation method for yogurt provided by this invention acquires sensory melting rate data of the yogurt product to be evaluated, and analyzes this data based on a preset yogurt sample sensory melting rate detection model to obtain corresponding actual sensory evaluation data for yogurt melting rate. The yogurt sample sensory melting rate detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and their corresponding sensory melting rate evaluation data. This method is low-cost, fast, and highly repeatable, enabling rapid prediction of yogurt sensory melting rate, effectively improving the detection efficiency and accuracy of yogurt product sensory melting rate, thereby helping to shorten the product development cycle.

[0102] Corresponding to the above-described method for evaluating the sensory attributes of yogurt, this invention also provides a device for evaluating the sensory attributes of yogurt. Since the embodiments of this device are similar to the above-described method embodiments, the description is relatively simple. For relevant details, please refer to the description in the above-described method embodiment section. The embodiments of the yogurt sensory attribute evaluation device described below are merely illustrative. Please refer to... Figure 4 As shown, it is a structural schematic diagram of a yogurt sensory attribute evaluation device provided in an embodiment of the present invention.

[0103] The yogurt sensory attribute evaluation device of the present invention specifically includes the following parts:

[0104] Yogurt product determination unit 401 is used to determine the yogurt product to be evaluated;

[0105] Sensory melting data acquisition unit 402 is used to acquire sensory melting data of the yogurt product;

[0106] The sensory melting degree detection unit 403 is used to detect and analyze the sensory melting degree data based on a preset yogurt sample sensory melting degree detection model to obtain the corresponding actual sensory evaluation data of yogurt melting degree; wherein, the yogurt sample sensory melting degree detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting degree evaluation data.

[0107] Furthermore, before analyzing the sensory melting rate data based on a preset sensory melting rate detection model for yogurt samples, the following steps are also included:

[0108] Yogurt sample determination unit, used to determine the yogurt sample to be tested;

[0109] The sensory melting data collection unit is used to characterize the product characteristics and differences between the yogurt samples using quantitative descriptive analysis methods, so as to quantitatively evaluate the product characteristics of the yogurt samples and obtain the sensory melting data of the yogurt samples.

[0110] The sample instrument characterization parameter index determination unit is used to test the yogurt sample using a yogurt melting degree instrument under preset constant rate temperature change and constant rate temperature change conditions, determine the viscosity curve based on the measured data, and determine the instrument characterization parameter index of the viscosity curve; wherein, the instrument characterization parameter index is the slope value of the viscosity curve.

[0111] The correlation coefficient fitting curve model construction unit is used to perform correlation fitting based on the sensory melting degree data of the yogurt sample and the instrument characterization parameter index to determine the sensory melting degree detection model of the yogurt sample; wherein, the sensory melting degree detection model of the yogurt sample is the correlation coefficient fitting curve model between the instrument characterization parameter index and the sensory melting degree data of the yogurt sample.

[0112] Furthermore, the sample instrument characterization parameter index determination unit is specifically used for:

[0113] For room-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, a constant-rate temperature change test was conducted on the yogurt samples within a temperature range of 25℃-28℃ to obtain a preset number of first data points corresponding to the yogurt samples; and a shear rate of 70s was applied. -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 28°C to obtain a preset number of second data points corresponding to the yogurt sample.

[0114] The viscosity curve corresponding to the room temperature yogurt sample is determined based on the first data point and the second data point.

[0115] Furthermore, the sample instrument characterization parameter index determination unit is specifically used for:

[0116] For low-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, under a temperature change range of 9℃-20℃, a constant-rate temperature variation test was conducted on the yogurt samples to obtain a preset number of third data points corresponding to the yogurt samples; and at a shear rate of 70s... -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 20°C to obtain a preset number of fourth data points corresponding to the yogurt sample.

[0117] The viscosity curve corresponding to the low-temperature yogurt sample is determined based on the third and fourth data points.

[0118] Furthermore, the sensory melting degree data acquisition unit is specifically used for:

[0119] The product characteristics of the yogurt product are quantitatively evaluated based on quantitative descriptive analysis to obtain the corresponding sensory melting data.

[0120] Furthermore, the sensory melting test data of the actual yogurt samples are oral melting characteristic data of the yogurt products.

[0121] The yogurt sensory attribute evaluation device provided by this invention acquires sensory melting degree data of the yogurt product to be evaluated, and analyzes the sensory melting degree data based on a preset yogurt sample sensory melting degree detection model to obtain corresponding actual sensory evaluation data of yogurt melting degree. The yogurt sample sensory melting degree detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and their corresponding yogurt sensory melting degree evaluation data. This method has low detection cost, high speed, and high repeatability, and can quickly predict the sensory melting degree of yogurt, effectively improving the detection efficiency and accuracy of yogurt product sensory melting degree, thereby helping to shorten the product development cycle.

[0122] Corresponding to the above-described method for evaluating the sensory attributes of yogurt, this invention also provides an electronic device. Since the embodiments of this electronic device are similar to the above-described method embodiments, the description is relatively simple. For relevant details, please refer to the description in the above-described method embodiment section. The electronic device described below is merely illustrative. Figure 5 The diagram shows a physical structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may include a processor 501, a memory 502, and a communication bus 503. The processor 501 and the memory 502 communicate with each other via the communication bus 503 and communicate with external systems via a communication interface 504. The processor 501 can call logical instructions in the memory 502 to execute a yogurt sensory attribute evaluation method. This method includes: determining the yogurt product to be evaluated; acquiring sensory melting data of the yogurt product; and detecting and analyzing the sensory melting data based on a preset yogurt sample sensory melting detection model to obtain corresponding actual sensory evaluation data of yogurt melting. The yogurt sample sensory melting detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting evaluation data.

[0123] Furthermore, the logical instructions in the aforementioned memory 502 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program stored on a processor-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer is able to execute the yogurt sensory attribute evaluation method provided in the above-described method embodiments, the method including: determining the yogurt product to be evaluated; acquiring sensory melting data of the yogurt product; detecting and analyzing the sensory melting data based on a preset yogurt sample sensory melting detection model to obtain corresponding actual sensory evaluation data of yogurt melting; wherein, the yogurt sample sensory melting detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting evaluation data.

[0125] In another aspect, embodiments of the present invention also provide a processor-readable storage medium storing a computer program. When executed by a processor, the computer program implements the yogurt sensory attribute evaluation method provided in the above embodiments. The method includes: determining a yogurt product to be evaluated; acquiring sensory melting data of the yogurt product; and detecting and analyzing the sensory melting data based on a preset yogurt sample sensory melting detection model to obtain corresponding actual sensory evaluation data of yogurt melting. The yogurt sample sensory melting detection model is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting evaluation data.

[0126] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the sensory attributes of yogurt, characterized in that, include: Identify the yogurt products to be evaluated; Obtain sensory melting data of the yogurt product; The sensory melting rate data is detected and analyzed based on a pre-set sensory melting rate detection model for yogurt samples to obtain corresponding actual sensory evaluation data of yogurt melting rate; wherein, the sensory melting rate detection model for yogurt samples is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting rate evaluation data. Before analyzing the sensory melting rate data based on a preset sensory melting rate detection model for yogurt samples, the method further includes: Identify the yogurt sample to be tested; Quantitative descriptive analysis was used to characterize the product characteristics and differences among the yogurt samples, so as to quantitatively evaluate the product characteristics of the yogurt samples and obtain sensory melting data of the yogurt samples. Using a yogurt melting instrument, the yogurt sample was tested under preset constant-rate temperature change and constant-rate temperature change conditions. Based on the measured data, a viscosity curve was determined, and the instrument characterization parameter index of the viscosity curve was determined; wherein, the instrument characterization parameter index is the slope value of the viscosity curve. Based on the sensory melting rate data of the yogurt sample and the instrument characterization parameter index, a correlation fitting is performed to determine the sensory melting rate detection model of the yogurt sample; wherein, the sensory melting rate detection model of the yogurt sample is a correlation coefficient fitting curve model between the instrument characterization parameter index and the sensory melting rate data of the yogurt sample.

2. The method for evaluating the sensory attributes of yogurt according to claim 1, characterized in that, The process involves using a yogurt melting instrument to test the yogurt samples under preset constant-rate temperature change and constant-rate temperature change conditions, and determining the viscosity curve based on the measured data. Specifically, this includes: For room-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, a constant-rate temperature change test was conducted on the yogurt samples within a temperature range of 25℃-28℃ to obtain a preset number of first data points corresponding to the yogurt samples; and a shear rate of 70s was applied. -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 28°C to obtain a preset number of second data points corresponding to the yogurt sample. The viscosity curve corresponding to the room temperature yogurt sample is determined based on the first data point and the second data point.

3. The method for evaluating the sensory attributes of yogurt according to claim 1, characterized in that, The process involves using a yogurt melting instrument to test the yogurt samples under preset constant-rate temperature change and constant-rate temperature change conditions, and determining the viscosity curve based on the measured data. Specifically, this includes: For low-temperature yogurt samples, a yogurt melting tester was used at a shear rate of 70 s. -1 Furthermore, under a temperature change range of 9℃-20℃, a constant-rate temperature variation test was conducted on the yogurt samples to obtain a preset number of third data points corresponding to the yogurt samples; and at a shear rate of 70s... -1 Furthermore, the yogurt sample was subjected to a constant-rate, constant-temperature test at a temperature of 20°C to obtain a preset number of fourth data points corresponding to the yogurt sample. The viscosity curve corresponding to the low-temperature yogurt sample is determined based on the third and fourth data points.

4. The method for evaluating the sensory attributes of yogurt according to claim 1, characterized in that, The acquisition of sensory melting data of the yogurt product specifically includes: The product characteristics of the yogurt product are quantitatively evaluated based on quantitative descriptive analysis to obtain the corresponding sensory melting data.

5. The method for evaluating the sensory attributes of yogurt according to claim 1, characterized in that, The sensory melting data refers to the oral melting characteristics of yogurt products.

6. A sensory attribute evaluation device for yogurt, characterized in that, include: Yogurt product identification unit, used to identify yogurt products to be evaluated; Sensory melting data acquisition unit, used to acquire sensory melting data of the yogurt product; The sensory melting degree detection unit is used to detect and analyze the sensory melting degree data based on a preset sensory melting degree detection model for yogurt samples, and obtain the corresponding actual sensory evaluation data of yogurt melting degree; wherein, the sensory melting degree detection model for yogurt samples is a correlation coefficient fitting curve model pre-trained based on yogurt samples and the corresponding yogurt sensory melting degree evaluation data of yogurt samples. The device further includes: Yogurt sample determination unit, used to determine the yogurt sample to be tested; The sensory melting data collection unit is used to characterize the product characteristics and differences between the yogurt samples using quantitative descriptive analysis methods, so as to quantitatively evaluate the product characteristics of the yogurt samples and obtain the sensory melting data of the yogurt samples. The sample instrument characterization parameter index determination unit is used to test the yogurt sample using a yogurt melting degree instrument under preset constant rate temperature change and constant rate temperature change conditions, determine the viscosity curve based on the measured data, and determine the instrument characterization parameter index of the viscosity curve; wherein, the instrument characterization parameter index is the slope value of the viscosity curve. The correlation coefficient fitting curve model construction unit is used to perform correlation fitting based on the sensory melting degree data of the yogurt sample and the instrument characterization parameter index to determine the sensory melting degree detection model of the yogurt sample; wherein, the sensory melting degree detection model of the yogurt sample is the correlation coefficient fitting curve model between the instrument characterization parameter index and the sensory melting degree data of the yogurt sample.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the yogurt sensory attribute evaluation method as described in any one of claims 1 to 5.

8. A processor-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the yogurt sensory attribute evaluation method as described in any one of claims 1 to 5.

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