Watermelon flavor evaluation method

Abstract

The invention discloses a watermelon flavor evaluation method, which comprises four embodiments: a first flavor evaluation system performs evaluation through sensory testing and chemical indexes, and establishes preliminary evaluation of flavor grades by using data processing methods such as PCA, UV standardization and the like; in the second flavor evaluation system, the determination of volatile aroma substances is added on the basis of the first system, so that the flavor evaluation is further refined; the third flavor evaluation system analyzes the influence of temperature on flavor by simulating storage under different temperature conditions; and a fourth flavor evaluation system adopts non-destructive external skin color phase analysis to establish association with flavor grades so as to realize rapid screening. The method can be used for watermelon variety improvement, storage management and market screening, and the scientificity and efficiency of evaluation are improved.

Description

Technical Field

[0001] The invention relates to the field of fruit quality evaluation, and in particular to a method for evaluating watermelon flavor. Background Art

[0002] Watermelon is a popular fruit in summer, and its flavor is an important basis for consumers to choose. The flavor of watermelon is affected by many factors, including the sugar-acid ratio, volatile aroma substances, taste and tissue structure. At present, the evaluation methods of watermelon flavor in the market mostly rely on subjective sensory testing, which is affected by the experience and preferences of the evaluators and is difficult to quantify and standardize. In addition, the existing scientific evaluation methods mostly focus on a single indicator and fail to comprehensively reflect the overall characteristics of watermelon flavor.

[0003] The formation of watermelon flavor involves complex biochemical processes, such as the accumulation of sugars and organic acids, the synthesis of aromatic substances, and changes in texture. Factors that affect these processes include planting varieties, soil conditions, climate environment, irrigation methods, and picking time. For example, a high day-night temperature difference usually helps to increase the sugar content of watermelon, while moderate water stress may increase the accumulation of volatile aroma substances. However, the specific mechanisms of these factors are not fully understood, so scientific research on the flavor characteristics of watermelon is still of great significance.

[0004] As consumers' requirements for fruit quality continue to increase, it is particularly important to develop a scientific, systematic and efficient watermelon flavor evaluation method. This can not only provide growers with guidance on improving varieties and optimizing planting management, but also provide a scientific basis for market promotion and brand building. Therefore, the present invention aims to establish a watermelon flavor evaluation system that combines multiple measurement techniques and data analysis methods to provide technical support for all links in the watermelon industry chain. Summary of the invention

[0005] The technical problem to be solved by the present invention is to propose a set of objective quantitative evaluation methods for flavor that is complete in process, unique in insight, economical and efficient, and suitable for different application scenarios in response to the current status of the above-mentioned prior art.

[0006] The technical solution adopted by the present invention to solve the above technical problem is: a method for evaluating watermelon flavor, comprising the following steps S01 to S06: S01 prepare W kinds of watermelon materials, select M watermelons for each watermelon material, the number of M is greater than 2, half of each watermelon is used to determine the content of each soluble solid and the content of titratable acid, and the other half is divided into 16 test blocks; S02: providing the test blocks in step S01 to four evaluators A, B, C, and D respectively, and tasting the watermelon material at a complete combination of temperatures of 9°C, 16°C, 23°C, and 30°C; After the S03 tasting, each evaluator scored the four tastings; S04 presents the data of the content of each soluble solid and the content of titratable acid in a multidimensional coordinate system; S05 uses the Scale mathematical method to UV standardize the data so that all variables have the same dimension; uses the PCA principal component analysis method to process the data of S04, reduces the dimension of these high-dimensional data, and finds the most variable principal component; uses OPLS-DA to classify different watermelon materials, distinguish between good flavor groups and poor flavor groups, and find out the key features that affect the flavor; S06 establishes a first flavor evaluation system according to the key features related to flavor influence found in S05.

[0007] In a preferred embodiment, the soluble solids measured in step S01 include fructose and sucrose, and the titratable acids include malic acid, citric acid and tartaric acid.

[0008] In the preferred scheme, in step S05, since the units of the data measured in step S01 are different, there are great differences in their dimensions and orders of magnitude. Therefore, the present invention adopts a special data processing method, that is, the data with different dimensions and orders of magnitude of each portion of watermelon are converted with 0 as the average value and 1 as the standard deviation, so that the data with different dimensions and orders of magnitude of each portion of watermelon are normalized into a uniformly processable data set; secondly, the first n principal components that can explain more than 75% of the information of the 10 measured indicators are obtained through PCA, and the load sizes of the 10 measured indicators are obtained from the load matrix under each principal component. The indicator (Zi) with a larger load (Si,i=1,2,n) under each principal component is used as an alternative indicator; at the same time, the first n principal components are analyzed by regression analysis method, and saved as variables Xi,u (i=1,2...n;u=1,2,3...w, where w is the parameter watermelon material quantity), and then enter the next step of screening. Again, using the saved variables Xi,U, use [Xi,u-Min(Xi,u)] / [Max(Xi,u)-Min(Xi,u)] to get the D value, take the D value as the independent variable and Si as the dependent variable, construct Y=a1+a2*S1+a3S2...+ai*Si, if 0<Y≤0.2, the flavor is excellent; 0.2<Y≤0.4, the flavor is excellent; 0.4<Y≤0.6, the flavor is average; 0.6<Y≤0.8, the flavor is poor; 0.8<Y≤1.0, the flavor is extremely poor, which is used as the first flavor evaluation system for watermelon materials.

[0009] In a preferred embodiment, a second flavor evaluation system is established, that is, step S07 is added after step S06, including using the first flavor evaluation system to screen out 16 watermelon materials with excellent flavors, dividing them into 4 parts on average, and tasting the watermelon materials after the temperatures of 9°C, 16°C, 23°C, and 30°C in S08 to determine the content of each aroma compound; and performing steps S03, S08, and S05 to obtain a second flavor evaluation system.

[0010] In a preferred embodiment, a third flavor evaluation system is established, that is, S10 uses the second flavor evaluation system to screen out 16 watermelon materials with excellent flavors, divides them into 4 groups on average, and places them in a temperature-controlled room with initial ambient temperatures of 9°C, 16°C, 23°C, and 30°C, respectively. The temperature is periodically raised and lowered by 5°C every 6 hours for 48 hours.

[0011] S12: Repeat steps S03 to S05 to obtain a third flavor evaluation system.

[0012] In a preferred embodiment, in order to screen without destroying the watermelon, a fourth flavor evaluation system is set, that is, S13 uses the first flavor evaluation system to screen out watermelon materials with excellent flavor, excellent flavor, average flavor, poor flavor, and extremely poor flavor; S14 uses high-resolution optical equipment to collect images of the outer skin within the orthographic projection range of the watermelon navel under standardized light source conditions; S15 uses image processing technology to extract the hue information of the watermelon's outer skin. The hue data is represented by the H value in the HSV color space. S16 performs correlation analysis on the extracted hue data and the watermelon flavor grade data obtained in step S13, and uses statistical methods or machine learning models to establish a mapping relationship between hue and flavor grade to obtain a fourth flavor evaluation system.

[0013] In a preferred solution, the association analysis specifically includes: presetting hue ranges to correspond to different flavor characteristics, green with a hue value of 120-150 degrees indicates fresh or unripe watermelon; yellow with a hue value of 40-70 degrees indicates a more mature watermelon; other hue ranges correspond to different flavor characteristics. Regression analysis is applied to predict the flavor level corresponding to the hue data.

[0014] Compared with the prior art, the advantages of the present invention are: Through mathematical methods such as UV standardization and PCA principal component analysis, different data indicators are standardized, eliminating the deviation caused by different measurement units and dimensions, making data processing more scientific and accurate. Using advanced data analysis techniques such as OPLS-DA, it is possible to accurately distinguish between good and bad flavors and help identify key features that affect flavor.

[0015] Through several clear steps (such as S01 to S06), this method reduces the time and labor costs of traditional sensory testing and improves evaluation efficiency.

[0016] The present invention not only uses sensory testing, but also integrates multiple chemical indicators such as soluble solids (such as fructose, sucrose), titratable acids (such as malic acid, citric acid, tartaric acid), etc., while considering factors such as volatile aroma substances, taste and tissue structure, providing a comprehensive evaluation of watermelon flavor.

[0017] Multiple flavor evaluation systems have been designed, which can be applied to different scenarios and needs, such as market sales, planting management, scientific research, etc.

[0018] The fourth flavor evaluation system can predict the flavor grade of watermelon by correlating the hue analysis of watermelon appearance with the flavor grade, providing the possibility of non-destructive evaluation and further improving efficiency and economy.

[0019] The invention takes into account the complex biochemical process of watermelon flavor formation, such as the accumulation of sugars and organic acids and the synthesis of aromatic substances, and provides a strong scientific basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of establishing a first flavor evaluation system of the present invention; Figure 2 is a flow chart of establishing a second flavor evaluation system according to the present invention; Figure 3 is a flow chart of establishing a third flavor evaluation system of the present invention; Figure 4 It is a flow chart of establishing the fourth flavor evaluation system of the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0022] Example 1, first flavor evaluation system: A method for evaluating watermelon flavor, comprising the following steps S01 to S06:

[0023] S01 prepare W kinds of watermelon materials, select M watermelons for each watermelon material, the number of M is greater than 2, half of each watermelon is used to determine the content of each soluble solid and the content of titratable acid, and the other half is divided into 16 test blocks; S02: providing the test blocks in step S01 to four evaluators A, B, C, and D respectively, and tasting the watermelon material at a complete combination of temperatures of 9°C, 16°C, 23°C, and 30°C; After the S03 tasting, each evaluator scored the four tastings; S04 presents the data of the content of each soluble solid and the content of titratable acid in a multidimensional coordinate system; S05 uses the Scale mathematical method to UV standardize the data so that all variables have the same dimension; uses the PCA principal component analysis method to process the data of S04, reduces the dimension of these high-dimensional data, and finds the most variable principal component; uses OPLS-DA to classify different watermelon materials, distinguish between good flavor groups and poor flavor groups, and find out the key features that affect the flavor; S06 establishes a first flavor evaluation system according to the key features related to flavor influence found in S05.

[0024] In the embodiment, the soluble solids measured in step S01 include: fructose, sucrose, and the titratable acids include: malic acid, citric acid, and tartaric acid.

[0025] In the embodiment, in step S05, since the units of the data measured in step S01 are different, there are great differences in their dimensions and orders of magnitude. Therefore, the present invention adopts a special data processing method, that is, the data with different dimensions and orders of magnitude of each portion of watermelon are converted with 0 as the average value and 1 as the standard deviation, so that the data with different dimensions and orders of magnitude of each portion of watermelon are normalized into a uniformly processable data set; secondly, the first n principal components that can explain more than 75% of the information of the 10 measured indicators are obtained by PCA, and the load sizes of the 10 measured indicators are obtained from the load matrix under each principal component. The indicator (Zi) with a larger load (Si,i=1,2,n) under each principal component is used as an alternative indicator; at the same time, the first n principal components are analyzed by regression analysis method, and saved as variables Xi,u (i=1,2...n;u=1,2,3...w, where w is the number of parameter watermelon materials), and enter the next step of screening. Again, using the saved variables Xi,U, use [Xi,u-Min(Xi,u)] / [Max(Xi,u)-Min(Xi,u)] to get the D value, take the D value as the independent variable and Si as the dependent variable, construct Y=a1+a2*S1+a3S2...+ai*Si, if 0<Y≤0.2, the flavor is excellent; 0.2<Y≤0.4, the flavor is excellent; 0.4<Y≤0.6, the flavor is average; 0.6<Y≤0.8, the flavor is poor; 0.8<Y≤1.0, the flavor is extremely poor, which is used as the first flavor evaluation system for watermelon materials.

[0026] The first flavor evaluation system is an evaluation system based on sensory evaluation and basic chemical indicators (soluble solids, titratable acid). It covers the main factors affecting the flavor of watermelon and lays the foundation for further research. This evaluation method is relatively simple and easy to use, suitable for rapid screening of large-scale samples, and also provides basic data for subsequent research, such as the flavor characteristics of watermelons of different varieties and origins. Example 2, second flavor evaluation system: Adding step S07 after step S06 includes using the first flavor evaluation system to screen out 16 watermelon materials with excellent flavors, dividing them into 4 parts on average, and tasting the watermelon materials after determining the content of each aroma compound in S08 when the temperature of the watermelon materials is 9°C, 16°C, 23°C, and 30°C; and performing steps S03, S08, and S05 to obtain a second flavor evaluation system.

[0027] The second flavor evaluation system is based on the first system, adding the determination of volatile aroma substances to more comprehensively reflect the flavor characteristics of watermelon. By analyzing volatile aroma substances, we can have a deeper understanding of the formation mechanism of watermelon flavor and explore the relationship between watermelon flavor and aroma substances. Example 3, the third flavor evaluation system: That is, S10 used the second flavor evaluation system to screen out 16 watermelon materials with excellent flavors, divided them into 4 groups on average, and placed them in temperature-controlled rooms with initial ambient temperatures of 9°C, 16°C, 23°C, and 30°C, respectively. The temperature was periodically raised and lowered by 5°C every 6 hours for 48 hours.

[0028] S12: Repeat steps S03 to S05 to obtain a third flavor evaluation system.

[0029] The third flavor evaluation system takes into account the effect of temperature on watermelon flavor and simulates the flavor changes under different storage conditions. It can evaluate the stability of watermelon flavor under different storage conditions. It provides a reference for the preservation and transportation of watermelons and predicts the best sales period of watermelons. Example 4, the fourth flavor evaluation system: In order to screen without damaging the watermelons, a fourth flavor evaluation system is set, that is, S13 uses the first flavor evaluation system to screen out watermelon materials with excellent flavor, excellent flavor, average flavor, poor flavor, and extremely poor flavor;

[0030] S14 uses high-resolution optical equipment to collect images of the outer skin within the orthographic projection range of the watermelon navel under standardized light source conditions; S15 uses image processing technology to extract the hue information of the watermelon's outer skin. The hue data is represented by the H value in the HSV color space. S16 performs correlation analysis on the extracted hue data and the watermelon flavor grade data obtained in step S13, and uses statistical methods or machine learning models to establish a mapping relationship between hue and flavor grade to obtain a fourth flavor evaluation system.

[0031] In the embodiment, the association analysis specifically includes: presetting hue ranges to correspond to different flavor characteristics, green with a hue value of 120-150 degrees indicates fresh or unripe watermelon; yellow with a hue value of 40-70 degrees indicates a more mature watermelon; other hue ranges correspond to different flavor characteristics. Regression analysis is applied to predict the flavor level corresponding to the hue data.

[0032] The fourth flavor evaluation system is a non-destructive evaluation method based on the correlation between the hue of the watermelon's outer skin and its flavor. By measuring the hue of the watermelon's outer skin, the flavor of the watermelon can be quickly evaluated. There is no need to destroy the watermelon, so more watermelon samples can be evaluated. Grading watermelons based on hue characteristics can quickly screen high-quality watermelons in the market.

[0033] The best embodiment of the present invention has been described, and various changes or modifications made by those skilled in the art will not depart from the scope of the present invention.

Claims

1. A method for evaluating watermelon flavor, characterized in that: The method comprises the following steps S01 to S06: S01 prepare W kinds of watermelon materials, select M watermelons for each watermelon material, the number of M is greater than 2, half of each watermelon is used to determine the content of each soluble solid and the content of titratable acid, and the other half is divided into 16 test blocks; S02: providing the test blocks in step S01 to four evaluators A, B, C, and D respectively, and tasting the watermelon material at a complete combination of temperatures of 9°C, 16°C, 23°C, and 30°C; After the S03 tasting, each evaluator scored the four tastings; S04 presents the data of the content of each soluble solid and the content of titratable acid in a multidimensional coordinate system; S05 uses the Scale mathematical method to UV standardize the data so that all variables have the same dimension; uses the PCA principal component analysis method to process the data of S04, reduces the dimension of these high-dimensional data, and finds the most variable principal component; uses OPLS-DA to classify different watermelon materials, distinguish between good flavor groups and poor flavor groups, and find out the key features that affect the flavor; S06 establishes a first flavor evaluation system according to the key features related to flavor influence found in S05.

2. The method for evaluating watermelon flavor according to claim 1, characterized in that: The soluble solids measured in step S01 include fructose and sucrose, and the titratable acids include malic acid, citric acid, and tartaric acid.

3. A method for evaluating watermelon flavor according to claim 2, characterized in that: In step S05, since the units of the data measured in step S01 are different, there are great differences in their dimensions and orders of magnitude. Therefore, the present invention adopts a special data processing method, that is, the data with different dimensions and orders of magnitude of each portion of watermelon are converted with 0 as the average value and 1 as the standard deviation, so that the data with different dimensions and orders of magnitude of each portion of watermelon are normalized into a uniformly processable data set; secondly, the first n principal components that can explain more than 75% of the information of the 10 measured indicators are obtained through PCA, and the load sizes of the 10 measured indicators are obtained from the load matrix under each principal component. The indicator (Zi) with a larger load (Si,i=1,2,n) under each principal component is used as an alternative indicator; at the same time, the first n principal components are analyzed by regression analysis method, and saved as variables Xi,u (i=1,2...n;u=1,2,3...w, where w is the parameter watermelon material quantity), and enter the next step of screening. Again, using the saved variables Xi,U, use [Xi,u-Min(Xi,u)] / [Max(Xi,u)-Min(Xi,u)] to get the D value, with D value as the independent variable and Si as the dependent variable, construct Y=a1+a2*S1+a3S2...+ai*Si, if 0<Y≤0.2, the flavor is excellent; 0.2<Y≤0.4, the flavor is excellent; 0.4<Y≤0.6, average flavor; 0.6<Y≤0.8, poor flavor; 0.8<Y≤1.0 The flavor is extremely poor, which is the first flavor evaluation system for watermelon materials.

4. The method for evaluating watermelon flavor according to claim 3, characterized in that: Adding step S07 after step S06 includes using the first flavor evaluation system to screen out 16 watermelon materials with excellent flavors, dividing them into 4 parts on average, and tasting the watermelon materials after the temperatures of 9°C, 16°C, 23°C, and 30°C in S08; and performing steps S03, S08, and S05 in S09 to obtain a second flavor evaluation system.

5. The method for evaluating watermelon flavor according to claim 4, characterized in that: S10 used the second flavor evaluation system to select 16 watermelon materials with excellent flavor, and divided them into 4 groups. S11 placed them in a temperature control room with initial ambient temperatures of 9°C, 16°C, 23°C, and 30°C, and the temperature was periodically raised and lowered by 5°C every 6 hours for 48 hours. S12: Repeat steps S03 to S05 to obtain a third flavor evaluation system.

6. A method for evaluating watermelon flavor according to any one of claims 1 to 5, characterized in that: S13 uses the first flavor evaluation system to select watermelon materials with excellent flavor, excellent flavor, average flavor, poor flavor, and extremely poor flavor; S14 uses high-resolution optical equipment to collect images of the outer skin within the orthographic projection range of the watermelon navel under standardized light source conditions; S15 uses image processing technology to extract the hue information of the watermelon's outer skin. The hue data is represented by the H value in the HSV color space. S16 performs correlation analysis on the extracted hue data and the watermelon flavor grade data obtained in step S13, and uses statistical methods or machine learning models to establish a mapping relationship between hue and flavor grade to obtain a fourth flavor evaluation system.

7. A method for evaluating watermelon flavor according to claim 6, characterized in that: The association analysis specifically includes: presetting hue ranges to correspond to different flavor characteristics, green with a hue value of 120-150 degrees indicates fresh or unripe watermelon; yellow with a hue value of 40-70 degrees indicates a more mature watermelon; other hue ranges correspond to different flavor characteristics; and applying regression analysis to predict the flavor level corresponding to the hue data.

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