Chicken soup quality evaluation method based on electronic tongue and fuzzy mathematics

Abstract

The application discloses a chicken soup quality evaluation method based on an electronic tongue and fuzzy mathematics, relates to the field of fuzzy mathematics sensory evaluation, and comprises the following steps: S1, professional sensory evaluation personnel evaluate model chicken soup, and indexes are color: U1, taste: U2, and aroma: U3; S2, a fuzzy mathematics model is established; S3, model chicken soup samples are acquired, and each taste parameter value of the model chicken soup is collected by using the electronic tongue; S4, a matrix is established and converted; S5, grey correlation degree analysis is performed; S5.1, a grey correlation coefficient is calculated; S5.2, the grey correlation degree is calculated; S6, statistical calculation is performed; and S7, each taste parameter value in the electronic tongue is collected and substituted into each taste parameter sensory evaluation model, so that sensory evaluation scores are obtained. The scheme establishes the correlation between the electronic tongue and the fuzzy mathematics sensory evaluation and a quality evaluation equation, provides technical support for the feasibility and accuracy of optimizing the chicken soup process by using technologies such as the electronic tongue, and provides a reference for replacing sensory analysis and evaluation of chicken soup quality by using the electronic tongue.

Description

Technical Field

[0001] This invention relates to the field of fuzzy mathematics sensory rating, and in particular to a method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics. Background Technology

[0002] Chicken is an important component and source of nutrition in the human diet. As people's living standards continue to improve, their demand for chicken has shifted from quantity to quality. Consumers are now more concerned about factors such as flavor, texture, nutrition, and safety. Fast-growing, low-quality broiler breeds are gradually losing popularity and their market share is shrinking year by year, while broiler breeds with excellent muscle quality are increasingly favored by consumers.

[0003] Studies have found that raw chicken has a slightly fishy taste, a metallic flavor, and a slight saltiness, with virtually no aroma. However, simmering it to make broth releases high-quality protein, flavor-enhancing peptides, creatinine, and free amino acids, resulting in a delicious and easily digestible broth. Chicken broth is also a traditional Chinese cooking method. Currently, there is increasing research on the flavor of chicken broth. However, research on chicken broth flavor is still limited to studying one or a few components, and a comprehensive evaluation system and standards for chicken broth flavor have not yet been established. The most common evaluation method for chicken broth is direct sensory evaluation. However, in practice, many laboratories lack professional evaluation teams, standardized evaluation procedures, and professional sensory evaluation laboratories, making sensory evaluation highly susceptible to human factors. Fuzzy mathematics can quantify the attributes of sensory evaluation, reducing the differences caused by subjective factors to some extent. Some studies have reported that fuzzy mathematical sensory analysis has been used for food quality evaluation and optimization. Furthermore, the composition of chicken broth is very complex, including fatty acids, nucleotides, amino acids, and peptides; therefore, chicken broth cannot be evaluated using a single or simple indicator. However, measuring all the components in chicken soup requires a lot of equipment, complicated steps, and a long testing cycle. Moreover, quantitative indicators cannot intuitively reflect changes in flavor, nor can they reflect the overall effect of flavor. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a chicken soup quality evaluation method based on electronic tongue and fuzzy mathematics. It employs grey relational analysis and multiple regression equations to establish the correlation and quality evaluation equation between electronic tongue and fuzzy mathematical sensory evaluation. This provides technical support for optimizing the chicken soup process using technologies such as electronic tongue, and offers a reference for using electronic tongue to equivalently replace sensory analysis in evaluating chicken soup quality.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics, comprising the following steps:

[0006] S1. Professional sensory evaluators evaluated the model chicken soup using a 10-point scale, with the indicators being color: U1, taste: U2, and aroma: U3.

[0007] S2. Establish a fuzzy mathematical model, including a factor set, a comment set, and a weighted set;

[0008] S3. Obtain a sample of model chicken soup and collect the taste parameter values ​​of the model chicken soup using an electronic tongue;

[0009] S4. Matrix Establishment and Transformation;

[0010] The fuzzy relation matrix R is obtained by dividing the number of votes for different indicators of chicken soup by the number of evaluators;

[0011] S5. Grey Relational Analysis: Through processes such as data dimensionlessization, the correlation between the electronic tongue sensor and sensory evaluation is calculated.

[0012] S5.1 Calculation of Grey Relational Coefficient:

[0013] S5.2 Calculate the grey relational degree;

[0014] S6. Statistical Calculation: Establish an evaluation equation for the quality of chicken soup using multiple regression equations and fuzzy mathematics;

[0015] S7. Detection of chicken soup to be evaluated: For the chicken soup sample to be evaluated, the values ​​of each taste parameter are collected by an electronic tongue; then, each taste parameter value is substituted into the sensory evaluation model of each taste parameter of the chicken soup to obtain the sensory score of each taste parameter.

[0016] Preferably, the factor set includes evaluation indicators, and U1, U2, and U3 constitute the factor set U = {U1, U2, U3};

[0017] The comment set includes feedback information on evaluation indicators, including evaluation level information, which consists of very good: V1, good: V2, average: V) and poor: V4, forming the comment set V = {V1, V2, V3, V4};

[0018] The weight set includes the proportion of each evaluation indicator in the overall evaluation, and the weights form the weight domain, denoted by X.

[0019] Preferably, step S4,

[0020] The fuzzy relation evaluation set Y includes the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. The evaluation result of the i-th sample can be represented as Y. i =X×R i ;

[0021] Based on the special characteristics of sensory evaluation, the evaluation level set K = {9, 7, 4, 1} is identified, and the total fuzzy comprehensive evaluation score T is obtained. The calculation formula is T = Y × K.

[0022] Preferably, step S5.1,

[0023] All samples are treated as a gray system, and different sensors of the electronic tongue are treated as subsystems of the gray system. The response values ​​of the sensors are used as factors in the system for calculation.

[0024] Sensory ratings are denoted as the reference series Y0 and the comparison series Y0. i (x), Y i The electronic tongue sensor response value of the sample;

[0025] The grey relational coefficient is calculated using the following formula:

[0026]

[0027] In the formula:

[0028] x represents different groups;

[0029] Y i The response values ​​of the electronic tongue sensors for different groups;

[0030] ε i (x) is the subsequence Y of the x-th sample group. i The correlation coefficient with the parent sequence Y0; min i min k |Y0(x)-Y i (x)| represents the minimum difference between the two levels;

[0031] max i max k |Y0(x)-Y i (x)| represents the maximum difference between the two levels;

[0032] |Y0(x)-Y i (x)| represents the absolute difference between the parent sequence and the child sequence;

[0033] ρ is the resolution coefficient, and the value of ρ ranges from 0 to 1, with ρ set to 0.5;

[0034] When ρ = 0.5, the grey relational degree is greater than 0.6, and the result is good.

[0035] Preferably, step S5.2,

[0036] The arithmetic mean of the correlation coefficients is the correlation degree, which is calculated according to the following formula:

[0037]

[0038] In the formula:

[0039] r i The parent sequence Y0 and the child sequence Y i The degree of correlation between them;

[0040] N is the number of subsequence data;

[0041] PCA analysis was performed on the chicken soup samples, followed by fuzzy mathematical sensory evaluation and grey relational analysis.

[0042] Preferably, step S6,

[0043] The sensory quality model obtained from multiple regression is as follows: y =

[0044] k n +a n x1+b n x2+c n x3+d n x4+e n x5+f n x6+g n x7+h n x8;

[0045] n = 1, 2, ..., 8;

[0046] Where y represents the sensory evaluation model of taste parameter n; taste parameters n = 1, 2, ..., 8 represent the taste parameters sourness, bitterness, bitter aftertaste, astringency, astringency aftertaste, umami, richness and saltiness respectively; x1 to x8 correspond to the taste parameter values ​​of sourness, bitterness, bitter aftertaste, astringency, astringency aftertaste, umami, richness and saltiness respectively.

[0047] k n This represents a constant in the sensory evaluation model for the taste parameter n;

[0048] a n This represents the weight of the sour taste parameter value in the sensory evaluation model for taste parameter n;

[0049] b n This represents the weight of the bitter taste parameter value in the sensory evaluation model for taste parameter n;

[0050] c n This represents the weight of the bitter aftertaste taste parameter value in the sensory evaluation model for taste parameter n;

[0051] d n This represents the weight corresponding to the astringent taste parameter value in the sensory evaluation model for taste parameter n;

[0052] e nThis represents the weight of the astringent and aftertaste taste parameter values ​​in the sensory evaluation model for taste parameter n;

[0053] f n This represents the weight corresponding to the umami taste parameter value in the sensory evaluation model for taste parameter n;

[0054] g n This represents the weight corresponding to the richness taste parameter value in the sensory evaluation model for taste parameter n;

[0055] h n This represents the weight corresponding to the salty taste parameter value in the sensory evaluation model for taste parameter n.

[0056] The beneficial effects of this invention are:

[0057] This scheme utilizes analysis of variance, principal component analysis, and grey relational analysis to establish the correlation between the electronic tongue for chicken soup and fuzzy mathematical sensory ratings. It establishes a direct link between the electronic tongue and fuzzy mathematical sensory evaluation methods, providing a simple, rapid, and accurate method for optimizing chicken soup production processes and evaluating quality using detection methods such as the electronic tongue. Attached Figure Description

[0058] 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 will be briefly introduced below. Obviously, the drawings described below are only two of the drawings in this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 PCA analysis of electronic tongues in chicken soup from different breeds and with different feeding methods;

[0060] Figure 2 Radar graphs showing the eight taste values ​​of chicken soup from different breeds and feeding methods measured by an electronic tongue. Detailed Implementation

[0061] To enhance understanding of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. These embodiments are only used to explain the invention and do not limit the scope of protection of the invention.

[0062] Example

[0063] 1. Materials and Methods

[0064] 1.1 Materials

[0065] The experimental materials for this study were Daninghe Chicken (D), a local breed from Chongqing, and Qingjiao Ma Chicken (Q), a fast-growing broiler breed. Daninghe Chicken is a dual-purpose (meat and egg) local superior chicken breed in Chongqing and is a national-level livestock and poultry genetic resource. Qingjiao Ma Chicken is a fast-growing meat breed with fast growth rate and strong adaptability.

[0066] The experimental group consisted of three batches of chickens raised every month, with detailed grouping information for each batch shown in Table 1. The experimental period lasted 120 days. All experimental chickens were raised at the Wulong Native Chicken Breeding Center of the Chongqing Academy of Animal Sciences. At 60 days of age, the chickens were divided into two groups: those kept in cages indoors and those free-range outdoors. Each group consisted of 60 hens (3 replicates of 20 each). The cage-rearing group was housed in individual cages, while the free-range group was grazed in woodland at a stocking density of 0.15 chickens / m². 2 To facilitate feeding and management, the grazing area was divided into several zones using fencing, with a 20m fence erected in each zone. 2 The sheds were designed to provide shelter for the chickens at night and were equipped with feed troughs and waterers. The experimental chickens were raised at the same nutritional level until they reached 120 days of age.

[0067] Table 14: Hybridization combinations and feeding methods for the 14 treatment groups

[0068]

[0069] 1.2 Instruments and Equipment

[0070] SA-402B taste analysis system (Beijing Yingsheng Hengtai Technology Co., Ltd.); electric slow cooker (Joyoung electric slow cooker); disposable plastic cups; electronic scale.

[0071] 1.3 Methods

[0072] 1.3.1 Preparation of Chicken Broth

[0073] The experimental chickens were slaughtered by cutting their throats, plucked with hot water, and gutted at the tail. After cleaning the whole chicken, the net weight was 1.00-1.25 kg. Chicken broth was prepared with a chicken meat to water ratio of 1:1.5. The broth was then cooked in an electric slow cooker for 2.5 hours. After cooking for another 2 hours, salt was added at 0.2% of the total weight of the meat and broth. After cooking, the broth was kept warm at 80℃ for subsequent experiments.

[0074] 1.3.2 Electronic Tongue Measurement Method

[0075] The insulated chicken broth sample was poured directly into a beaker specifically designed for the electronic tongue. After equilibration to room temperature, the sample was measured. Each beaker contained 30 mL of sample, and four replicates were performed for each sample. The electronic tongue sensor collected data for 120 seconds per sample, acquiring one data point per second. The horizontal axis represents the measurement time, and the vertical axis represents the collected sensor intensity value. The stable data obtained at the 120-second mark was used as the output value. Initially, the sensor intensity fluctuated. After 2-3 measurements, the sensor response stabilized. In this study, each sample was measured seven times, and the data from the last three measurements were used as the raw data for principal component analysis and cluster analysis.

[0076] 1.3.3 Sensory Evaluation Methods

[0077] The Chongqing Academy of Animal Science's Food Research Institute established an artificial sensory evaluation laboratory, and ten professional sensory evaluators (five men and five women) conducted a sensory evaluation of the experimental chicken soup. The evaluation used a 10-point scale, with indicators including color, taste, and aroma. All samples were placed in standardized containers and randomly numbered. Evaluators were required to be in good health, without sensory impairments; abstain from smoking and alcohol, and not eat within one hour before the evaluation; discussion among themselves was prohibited; they were required to smell the food before tasting, and after evaluating each sample, evaluators were required to rinse their mouths with water three times and rest for 30 seconds before proceeding to the next evaluation. Specific scoring criteria are shown in Table 2.

[0078] Table 2 Sensory Evaluation Table for Chicken Soup Flavor

[0079]

[0080]

[0081] 1.3.4 Theoretical Basis of Fuzzy Mathematical Model

[0082] 1.3.4.1 Establishment of Factor Set, Comment Set, and Weighted Set

[0083] The factor set consists of evaluation indicators. Color (U1), taste (U2), and aroma (U3) constitute the factor set U = {U1, U2, U3}.

[0084] The comment set consists of feedback information from evaluation indicators, including evaluation level information. Excellent (V1), Good (V2), Average (V3), Poor (V4) constitute the comment set V = {V1, V2, V3, V4}.

[0085] The weight set is represented by the proportion of each evaluation index in the overall evaluation. The weights form a weight domain called a fuzzy vector, denoted by X.

[0086] 1.3.4.2 Matrix Establishment and Transformation

[0087] Dividing the number of votes for different indicators of "chicken soup for the soul" (or similar motivational quotes) by the number of evaluators yields the fuzzy relation matrix R. The fuzzy relation evaluation set is composed of the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. Therefore, the evaluation result of the i-th sample can be represented as Y. i =X×R i (i is 1-12).

[0088] Based on the special characteristics of sensory evaluation, the evaluation level set K = {9, 7, 4, 1} is identified, and the total score T of the fuzzy comprehensive evaluation is obtained by calculation. The calculation formula is T = Y × K.

[0089] 1.3.5 Grey Relational Analysis

[0090] Based on grey mathematical system theory and the application requirements in food, grey relational analysis is conducted to calculate the correlation between electronic tongue sensor and sensory evaluation through processes such as data dimensionlessization.

[0091] 1.3.5.1 Calculation of Grey Relational Coefficient

[0092] All samples are treated as a gray system, and different sensors of the electronic tongue are treated as subsystems of the gray system. The sensor response values ​​are used as factors in the system calculation. The sensory score is denoted as Y0 (reference series) and Y0 (comparison series). i (x), Y i Let be the response value of the electronic tongue sensor for the sample. The grey relational coefficient is calculated according to formula (1):

[0093]

[0094] In the formula: x represents different groups; Y i For different groups of electronic tongue sensor response values; ε i (x) is the subsequence Y of the x-th sample group. i The correlation coefficient with the parent sequence Y0; min i min k |Y0(x)-Y i (x)| represents the minimum difference between the two levels; max i max k |Y0(x)-Y i (x)| represents the maximum difference between the two levels; |Y0(x)-Y i (x)| represents the absolute difference between the parent sequence and the child sequence; ρ is the resolution coefficient, which ranges from 0 to 1, and is set to 0.5. When ρ = 0.5, the grey relational degree is greater than 0.6, indicating a better result.

[0095] 1.3.5.2 Calculating Grey Relational Degree

[0096] The arithmetic mean of the correlation coefficients is the correlation degree, which is calculated according to formula (2):

[0097] In the formula: r i The parent sequence Y0 and the child sequence Y i The degree of correlation between them; N is the number of subsequence data.

[0098] 1.4 Statistical Analysis

[0099] A multivariate regression equation was used to establish an evaluation equation for the quality of chicken soup using electronic tongue and fuzzy mathematics. The software built into the electronic tongue was used to perform PCA analysis on the chicken soup samples, and then fuzzy mathematical sensory evaluation and grey relational analysis were performed using EXCEL2010 and SPSS19 software.

[0100] The sensory quality model obtained from multiple regression is as follows:

[0101] k n +a n x1+b n x2+c n x3+d n x4+e n x5+f n x6+g n x7+h n x8;

[0102] n = 1, 2, ..., 8;

[0103] Where y represents the sensory evaluation model of taste parameter n; taste parameters n = 1, 2, ..., 8 represent the taste parameters sourness, bitterness, bitter aftertaste, astringency, astringency aftertaste, umami, richness and saltiness respectively; x1 to x8 correspond to the taste parameter values ​​of sourness, bitterness, bitter aftertaste, astringency, astringency aftertaste, umami, richness and saltiness respectively.

[0104] k n This represents a constant in the sensory evaluation model for the taste parameter n;

[0105] a n This represents the weight of the sour taste parameter value in the sensory evaluation model for taste parameter n;

[0106] b n This represents the weight of the bitter taste parameter value in the sensory evaluation model for taste parameter n;

[0107] c n This represents the weight of the bitter aftertaste taste parameter value in the sensory evaluation model for taste parameter n; d n This represents the weight corresponding to the astringent taste parameter value in the sensory evaluation model for taste parameter n;

[0108] e nThis represents the weight of the astringent and aftertaste taste parameter values ​​in the sensory evaluation model for taste parameter n; f n This represents the weight corresponding to the umami taste parameter value in the sensory evaluation model for taste parameter n;

[0109] g n In the sensory evaluation model for taste parameter n, h represents the weight corresponding to the umami and aftertaste taste parameter values; n This represents the weight corresponding to the salty taste parameter value in the sensory evaluation model for taste parameter n.

[0110] 2. Results and Analysis

[0111] 2.1 Results of electronic tongue analysis on different samples

[0112] Table 3. Electronic tongue data for different samples

[0113]

[0114]

[0115] Principal component analysis (PCA) is an analytical method that linearly transforms the original data vector of a sample, thereby differentiating samples by changing the coordinate axes. Table 3 shows the electronic tongue data measured for different chicken soup samples. PCA analysis was performed on the data using the software provided with the electronic tongue, and a two-dimensional graph was constructed with principal components 1 and 2 as the x and y axes, respectively. Figure 1 .Depend on Figure 1 It can be seen that the contribution rate of principal component 1 is 93.81%, the contribution rate of principal component 2 is 3.69%, and the cumulative contribution rate of principal components 1 and 2 is 97.5%, indicating that principal components 1 and 2 contain a large amount of information and can reflect the overall information of the sample. Figure 1 It can be seen that the distribution area of ​​sample B overlaps with that of sample D, indicating that the taste difference between samples B and D is small. The distribution areas of samples A and C are relatively close, indicating that samples A and C are quite similar in taste. The distribution areas of samples B and D differ significantly from those of samples A and C, especially samples B and C, indicating that the taste differences between different varieties of chicken soup are relatively large. Figure 1 It can be seen that the electronic tongue can distinguish different chicken soup samples well, which is consistent with the results of Wang Xia et al.

[0116] 2.2 Fuzzy sensory evaluation results of different samples

[0117] Sensory evaluations are inherently subjective and subject to variability due to individual differences. Fuzzy mathematical analysis can quantify and mathematically analyze these concepts. Furthermore, expert panel sensory evaluations can be limited by factors such as training requirements, staff turnover, and individual differences. Fuzzy mathematical sensory evaluation can reduce the demands on sensory personnel, using big data processing to evaluate and screen products, minimizing the impact of individual subjective factors on the overall evaluation. Numerous studies have demonstrated that fuzzy mathematical sensory evaluation is a feasible method for food processing.

[0118] As shown in Table 4, the weights of the different sensory quality indicators (color, smell, and taste) of smoked beef for 18 respondents were calculated using a survey method. The weights of the three sensory indicators were 0.6, 0.3, and 0.1, which can be represented as X = {0.6, 0.3, 0.1}.

[0119] Table 3 shows the results of the importance analysis of each factor.

[0120]

[0121] Table 5. Results of Vague Sensory Evaluation of Chicken Soup

[0122]

[0123]

[0124] Table 5 shows that, taking the taste of sample 1 as an example, the votes for "very good," "good," "average," and "poor" are 5, 5, 0, and 0 respectively, resulting in the set U1 = {0.5, 0.5, 0, 0}. Similarly, the fuzzy evaluation rectangles for other indicators can be obtained. The fuzzy mathematical comprehensive evaluation is Y = R × y, where R = {0.6, 0.3, 0.1} is the weight set of taste, color, and aroma indicators, and y is the fuzzy evaluation matrix for color, aroma, taste, and meat quality. The obtained Y is then multiplied by the comment set V = {9, 7, 4, 1} to obtain the comprehensive sensory score. For example, the comprehensive sensory score for sample 1 is:

[0125]

[0126] Similarly, the overall scores for the other 11 types of chicken soup can be calculated. (See Table 6.)

[0127] Table 5: Overall Sensory Evaluation Scores of Chicken Soup

[0128]

[0129] A higher overall score indicates better product quality. Table 6 shows that the overall sensory scores for all four types of chicken soup were above 6 but below 8, indicating that the sensory quality of all four soups fell between "good" and "very good." Group C's score was closer to "very good," indicating that the chicken soup made from Group C had better sensory quality; while Group B's chicken soup had the lowest fuzzy sensory score, indicating that the sensory quality of the chicken soup made from Group B was relatively poor. 2.3 Grey Relational Analysis of Chicken Soup Sensory Evaluation and Electronic Tongue Data

[0130] 2.3.1 Dimensionless treatment

[0131] The sensory evaluation indicators of 12 samples were set as the reference series, and the comparison series consisted of the response values ​​of 8 sensors of the electronic tongue. Grey relational analysis was performed on the parent and child series. First, the mean transformation method was used to perform dimensionless processing on each series, and the results are shown in Table 6.

[0132] Table 6. Fuzzy sensory scores and electronic tongue data underwent infinity rigidification processing.

[0133]

[0134] 2.3.2 Calculation of the correlation coefficient and correlation degree between the electronic tongue sensor response value and sensory indicators

[0135] The maximum difference between the two levels of the fuzzy total score of 12 chicken soup samples, Δmax, was 0.67; the minimum difference between the two levels, Δmin, was 0. Substituting these values ​​into formula (1) yields the correlation coefficient between the electronic tongue sensor response value and the fuzzy mathematical sensory score. Substituting the grey correlation coefficient into formula (2) yields the correlation degree between the sensor response value and the sensory index, allowing for their ranking. The results are shown in Table 7. Table 7 shows that the electronic tongue sensor response values ​​have a high correlation with the flavor of the samples. In particular, the richness, bitterness, and saltiness have a high correlation with the sensory evaluation scores obtained by fuzzy mathematics, with correlations all above 0.8, especially the richness, which is as high as 9.1. However, the other sensor response values ​​also have a high correlation with the sensory score, indicating that the sensory score is a comprehensive indicator and cannot be represented by a single one or a few factors. It should be represented comprehensively. From the high grey correlation between the electronic tongue and the fuzzy mathematical sensory evaluation, it can be seen that the electronic tongue can represent the quality of chicken soup to a certain extent, providing a reference for further optimizing the chicken soup process and evaluating the quality of chicken soup. Existing research has shown that electronic tongues can distinguish the quality of olive oil, Pu-erh tea, melon, spring water, fish, and meat.

[0136] Table 7. Correlation coefficients and correlation degrees between electronic tongue and sensory evaluation

[0137]

[0138] 2.4 Establishing a method for evaluating the flavor quality of chicken soup using multiple linear regression

[0139] Electronic tongues can simulate the overall flavor profile of food. Fuzzy mathematical sensory evaluation methods consider the contribution of multiple factors to the overall effect, enabling more objective, accurate, and scientific quality evaluation, reducing errors caused by evaluation standards, personal preferences, etc. A chicken soup quality evaluation method is established through multiple linear regression, linking the flavor-reflecting electronic tongue data with a comprehensive sensory scoring system established using fuzzy mathematics. The chicken soup quality evaluation score is calculated using the electronic tongue detection data. Grey relational analysis between the electronic tongue and sensory quality shows a high correlation between different sensors and sensory quality; therefore, a relationship between the electronic tongue and fuzzy sensory scores can be established. The chicken soup flavor quality evaluation equation obtained using multiple linear regression is as follows:

[0140] y=36.205+0.803X1+7.603X2-0.106X3-12.973X4+2.1260X5-0.505X6+0.461X7-2.194X8

[0141] In the formula: y represents the fuzzy sensory score, and X1, X2, X3, X4, X5, X6, X7, and X8 are the eight sensors for the electronic tongue's sour, bitter, astringent, bitter aftertaste, astringent aftertaste, umami, richness, and saltiness, respectively. This multiple regression model passed the F-test and t-test, with a correlation coefficient of R = 0.98 and a determination coefficient R0.98. 2 =0.959, indicating that the regression equation between the fuzzy sensory rating and the electronic tongue has a high degree of fit. This demonstrates that it is feasible to use the measurement data of the electronic tongue to simulate the comprehensive human sensory rating.

[0142] 3. Conclusion

[0143] This paper uses two local chicken breeds (Qingjiao Ma Chicken and Daninghe Chicken) raised at 120 days old under two different rearing methods (cage-rearing and free-range) as research subjects. Qualitative and quantitative analyses of chicken soup samples were conducted using fuzzy sensory evaluation and electronic tongue. Furthermore, analysis of variance, principal component analysis, grey relational analysis, and multiple linear regression were employed to establish a chicken soup flavor evaluation method based on electronic tongue and fuzzy mathematical sensory evaluation. Experimental results show that the electronic tongue can effectively distinguish different chicken soup samples; there is a high correlation (all greater than 0.6) between the electronic tongue data and the fuzzy mathematical sensory comprehensive scoring system, especially for richness, saltiness, and bitterness, all exceeding 0.80; and a chicken soup quality evaluation method was constructed using the electronic tongue flavor detection method. This provides a simple and rapid method for optimizing chicken soup production processes and evaluating quality using electronic tongue and other detection methods.

[0144] This study analyzes and evaluates the flavor characteristics of chicken soup from different rearing methods (cage-raised and free-range) using electronic tongue technology and fuzzy mathematical sensory scoring. Based on this, grey relational analysis and multiple regression equations are used to establish the correlation and quality evaluation equation between electronic tongue and fuzzy mathematical sensory evaluation. This provides technical support for optimizing the chicken soup process using technologies such as electronic tongue, and provides a reference for using electronic tongue as an equivalent substitute for sensory analysis in evaluating chicken soup quality.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics, characterized in that, Includes the following steps: S1. Professional sensory evaluators evaluated the model chicken soup using a 10-point scale, with the indicators being color: U1, taste: U2, and aroma: U3. S2. Establish a fuzzy mathematical model, including a factor set, a comment set, and a weighted set; S3. Obtain a chicken soup sample and collect the values ​​of various taste parameters of the chicken soup using an electronic tongue; S4. Matrix Establishment and Transformation; The fuzzy relation matrix R is obtained by dividing the number of votes for different indicators of chicken soup by the number of evaluators; S5. Grey Relational Analysis: Through processes such as data dimensionlessization, the correlation between the electronic tongue sensor and sensory evaluation is calculated. S5.1 Calculation of Grey Relational Coefficient: S5.2 Calculate the grey relational degree; S6. Statistical Calculation: Establish an evaluation equation for the quality of chicken soup using multiple regression equations and fuzzy mathematics; S7. Detection of chicken soup to be evaluated: For the chicken soup sample to be evaluated, the values ​​of each taste parameter are collected by an electronic tongue; then, each taste parameter value is substituted into the sensory evaluation model of each taste parameter of the chicken soup to obtain the sensory score of each taste parameter.

2. The method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics according to claim 1, characterized in that: The factor set includes evaluation indicators. U1, U2, and U3 constitute the factor set U = {U1, U2, U3}. The comment set includes feedback information on evaluation indicators, including evaluation level information, which consists of very good: V1, good: V2, average: V) and poor: V4, forming the comment set V = {V1, V2, V3, V4}; The weight set includes the proportion of each evaluation indicator in the overall evaluation, and the weights form the weight field, denoted by X.

3. The method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics according to claim 1, characterized in that: Step S4, The fuzzy relation evaluation set Y includes the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. The evaluation result of the i-th sample can be represented as Y. i =X×R i ; Based on the special characteristics of sensory evaluation, the evaluation level set K = {9, 7, 4, 1} is identified, and the total fuzzy comprehensive evaluation score T is obtained. The calculation formula is T = Y × K.

4. The method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics according to claim 1, characterized in that: Step S5.1, All samples are treated as a gray system, and different sensors of the electronic tongue are treated as subsystems of the gray system. The response values ​​of the sensors are used as factors in the system for calculation. Sensory ratings are denoted as the reference series Y0 and the comparison series Y0. i (x), Y i The electronic tongue sensor response value of the sample; The grey relational coefficient is calculated using the following formula: In the formula: x represents different groups; Y i The response values ​​of the electronic tongue sensors for different groups; ε i (x) is the subsequence Y of the x-th sample group. i The correlation coefficient with the parent sequence Y0; min i min k |Y0(x)-Y i (x)| represents the minimum difference between the two levels; max i max k |Y0(x)-Y i (x)| represents the maximum difference between the two levels; |Y0(x)-Y i (x)| represents the absolute difference between the parent sequence and the child sequence; ρ is the resolution coefficient, and the value of ρ ranges from 0 to 1, with ρ set to 0.5; When ρ = 0.5, the grey relational degree is greater than 0.6, and the result is good.

5. The method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics according to claim 1, characterized in that: Step S5.2, The arithmetic mean of the correlation coefficients is the correlation degree, which is calculated according to the following formula: In the formula: r i The parent sequence Y0 and the child sequence Y i The degree of correlation between them; N is the number of subsequence data; PCA analysis was performed on the chicken soup samples, followed by fuzzy mathematical sensory evaluation and grey relational analysis.

6. The method for evaluating the quality of inspirational quotes based on electronic tongue and fuzzy mathematics according to claim 1, characterized in that: Step S6, the sensory quality model obtained from multiple regression is as follows: y = k n +a n x1+b n x2+c n x3+d n x4+e n x5+f n x6+g n x7+h n x8; n＝1，2，...，8； Where y represents the sensory evaluation model of taste parameter n; taste parameters n = 1, 2, ..., 8 represent the taste parameters sourness, bitterness, bitter aftertaste, astringency, astringency aftertaste, umami, richness and saltiness, respectively. x1 to x8 represent the taste parameter values ​​for sourness, bitterness, bitter aftertaste, astringency, astringency aftertaste, umami, richness, and saltiness, respectively. k n This represents a constant in the sensory evaluation model for the taste parameter n; a n This represents the weight of the sour taste parameter value in the sensory evaluation model for taste parameter n; b n This represents the weight of the bitter taste parameter value in the sensory evaluation model for taste parameter n; c n This represents the weight of the bitter aftertaste taste parameter value in the sensory evaluation model for taste parameter n; d n This represents the weight corresponding to the astringent taste parameter value in the sensory evaluation model for taste parameter n; e n This represents the weight of the astringent and aftertaste taste parameter values ​​in the sensory evaluation model for taste parameter n; f n This represents the weight corresponding to the umami taste parameter value in the sensory evaluation model for taste parameter n; g n This represents the weight corresponding to the richness taste parameter value in the sensory evaluation model for taste parameter n; h n This represents the weight corresponding to the salty taste parameter value in the sensory evaluation model for taste parameter n.

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