Method for predicting the hue angle of steamed tea leaves and method for predicting the hue angle of crude tea leaves

Abstract

This invention provides a method for predicting the hue angle of steamed leaves when producing crude tea from fresh leaves. The method uses the hue angle of the steamed leaves during crude tea production as the dependent variable, and calculates the hue angle of the steamed leaves using a correlation equation derived as the independent variable in a multiple regression analysis. [Solution] A method for predicting the hue angle of steamed leaves when raw leaves are steamed to produce crude tea, comprising a correlation equation derivation step in which a correlation equation is derived by performing multiple regression analysis with the following four explanatory variables: the ratio of the content of two types of chlorophyll a and chlorophyll b in the raw leaves, the total content of the two types of chlorophyll a and chlorophyll b in the raw leaves (mmol / 100gDW), the pH of the steamed leaves, and the steaming time of the raw leaves (seconds), and the hue angle of the steamed leaves as the dependent variable, and the hue angle of the steamed leaves is derived. The hue angle of the steamed leaves is calculated by inputting the four explanatory variables measured for the raw leaves and steamed leaves before they become crude tea into the correlation equation derived in the correlation equation derivation step.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the hue angle of steamed tea leaves and a method for predicting the hue angle of crude tea. [Background technology]

[0002] Traditionally, the degree of green color has been evaluated using a sensory evaluation item called "color luster" as a quality indicator for green tea, and this has a significant impact on market prices. When comparing the "color luster" score obtained through sensory evaluation with measurements using colorimetric instruments, a significant positive correlation has been confirmed between color luster and hue angle values. Furthermore, it has been found that chlorophyll (Chl) and its derivatives are important pigments that determine color luster and hue angle in tea leaves.

[0003] The chlorophyll (Chl) and its derivative content varies greatly depending on cultivation methods such as shading and nitrogen fertilization, as well as the green tea manufacturing process. To date, tea leaf color has only been evaluated from the individual aspects of cultivation and manufacturing. However, in recent years, shading has become increasingly common not only for traditional shaded teas such as tencha and gyokuro, but also for sencha production to enhance its green color and umami. Therefore, in order to cultivate and process green tea with good color, it is necessary to evaluate leaf color from two aspects: greening due to shading and discoloration due to heating during manufacturing. For example, while it is clear that chlorophyll ((Chl): hereafter abbreviated as appropriate) content and the rate of conversion from chlorophyll to pheophytin ((Phy): hereafter abbreviated as appropriate) are related to the color luster and hue angle of tea leaves, the relationships between these factors have not been clarified. In other words, the problem is that the relationships between the factors influencing color luster or hue angle, which are evaluation axes for the overall leaf color of various tea varieties through cultivation and processing, have not been clarified.

[0004] Previously, regarding the evaluation of tea leaf color, Non-Patent Literature 1 used samples of commercially available green tea (matcha, gyokuro, sencha, and deep-steamed sencha) categorized by tea type and grade (high, medium, and low), measured color difference values ​​using a colorimeter, and analyzed the relationship with quality scores obtained through sensory evaluation. The results showed that color values ​​are effective for evaluating the quality and classifying the grade of green tea, and the correlation between hue angle and chlorophyll and its derivative content, as well as the rate of change from chlorophyll to pheophytin, was mentioned based on simple regression analysis.

[0005] Furthermore, Non-Patent Literature 2 discusses the relationship between the evaluation of the luster of crude tea varieties and strains, colorimetric values, pigment content, and pH. Specifically, it analyzes the correlation between the luster score of crude tea, pigment content, and pheophytin change rate, and mentions a positive correlation between the luster score and chlorophyll content, and a negative correlation with pheophytin content, based on simple regression analysis.

[0006] However, while Non-Patent Documents 1 and 2 had clarified that chlorophyll and its derivative content, as well as the conversion rate from chlorophyll to pheophytin, are related to the color and hue angle of tea leaves, the relationships between these factors were not clearly defined. In particular, Non-Patent Document 1 evaluated commercially available green tea by tea type, and Non-Patent Document 2 evaluated it as a change in leaf color during processing. Consequently, it was difficult to evaluate the overall leaf color of various tea varieties from cultivation to processing.

[0007] Furthermore, Patent Document 1 discloses a tea quality evaluation device that irradiates a tea sample with light of an excitation wavelength in the ultraviolet region and estimates the content of various components in the sample by multivariate analysis according to the magnitudes of multiple fluorescence intensities. However, Patent Document 1 only estimates the content of various components in the sample and does not evaluate the color, which represents the degree of greenness of the tea leaves to be manufactured, nor does it take into account the influence of the contained components on the manufacturing of the tea leaves. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2023-069387 [Non-patent literature]

[0009] [Non-Patent Document 1] Katsunori Kohata, Yoichi Yamashita, Yuichi Yamaguchi, and Hideki Horie. 2001. Research Report of the Vegetable and Tea Research Station 16:9-18. [Non-Patent Document 2] Hitoshi Ijichi, Akihiko Tokuda 2015. Evaluation of rough brown color in tea varieties and strains, and its relationship with colorimetric values, pigment content, and pH. Tea Research Report 119:7-11 [Overview of the project] [Problems that the invention aims to solve]

[0010] While it has been clear that chlorophyll and its derivative content, as well as the rate of conversion from chlorophyll to pheophytin, are related to the color and hue angle of tea leaves, the relationships between these factors have not yet been adequately clarified. For example, Non-Patent Document 1 evaluated commercially available green tea by tea type, and Non-Patent Document 2 evaluated it as a change in leaf color during processing. However, there was a problem in that the influence of various factors such as different tea varieties, the characteristics of fresh and steamed leaves due to cultivation methods such as shading and fertilization, and the steamed leaf manufacturing process on the leaf color of tea leaves had not been evaluated.

[0011] Therefore, in view of the above problems, the present invention aims to provide a method for predicting the hue angle of steamed leaves, which clarifies the influence of various factors such as the properties of raw leaves and steamed leaves and the manufacturing process on the hue angle of steamed leaves when producing crude tea from raw leaves, by performing multiple regression analysis, and also clarifies the contribution of each factor to the hue angle of steamed leaves. Furthermore, the present invention provides a method for predicting the hue angle of crude tea, which predicts the hue angle of crude tea from the hue angle of steamed leaves calculated using a method for predicting the hue angle of steamed leaves. [Means for solving the problem]

[0012] The features of the present invention are listed below. (1) A method for predicting the hue angle of steamed leaves when raw leaves are steamed to make steamed leaves and crude tea is produced from the steamed leaves, comprising a correlation equation derivation step in which a correlation equation is derived by performing multiple regression analysis with the following four explanatory variables: the ratio of the content of two types of chlorophyll a and chlorophyll b in the raw leaves, the total content of the two types of chlorophyll a and chlorophyll b in the raw leaves (mmol / 100g DW (dry weight)), the pH of the steamed leaves, and the steaming time (seconds) of the raw leaves, and the hue angle of the steamed leaves as the dependent variable, and the hue angle of the steamed leaves is calculated by inputting the four explanatory variables measured for the raw leaves and steamed leaves before they become crude tea into the correlation equation derived in the correlation equation derivation step, thereby calculating the hue angle of the steamed leaves. (2) The method for predicting the hue angle of steamed leaves as described in (1), wherein the multiple regression analysis derives the correlation equation by the stepwise method. (3) The method for predicting the hue angle of steamed leaves as described in (1) or (2), wherein the steaming conditions for steaming the raw leaves are in the range of 40 seconds to 120 seconds. (4) A method for predicting the hue angle of steamed leaves according to (1), (2), or (3), wherein the hue angle of the steamed leaves is in the range of 95° or more and 110° or less. A method for predicting the hue angle of crude tea, which predicts the hue angle of crude tea based on the hue angle of steamed leaves calculated by the method for predicting the hue angle of steamed leaves described in any one of (1) to (4). [Effects of the Invention]

[0013] According to the present invention, when producing crude tea from raw leaves, it is possible to clarify the influence of various factors such as the properties of raw leaves and steamed leaves and the manufacturing process on the color and hue angle of steamed leaves by performing multiple regression analysis, and to provide a method for predicting the hue angle of steamed leaves by clarifying the contribution of each factor to the hue angle of steamed leaves. In addition, we can provide a method for predicting the hue angle of crude tea, which predicts the hue angle of crude tea corresponding to the hue angle of steamed leaves calculated using a method for predicting the hue angle of steamed leaves. [Brief explanation of the drawing]

[0014] [Figure 1] Figure 1 is a flow diagram showing each manufacturing step from fresh leaves to crude tea manufacturing. [Figure 2] Figure 2 is a diagram showing the hue angle after performing each step for manufacturing crude tea in the method for predicting the hue angle of steamed leaves according to the present invention. [Figure 3] Figure 3 is a scatter diagram, where (a) is a scatter diagram when plotting the hue angle of crude tea against the hue angle of fresh leaves, and (b) is a scatter diagram when plotting the hue angle of crude tea against the hue angle of steamed leaves (steamed tea leaves). [Figure 4] Figure 4 is a scatter plot, where (a) is a scatter plot when plotting predicted values against actually measured values in a training data set for the hue angle of steamed leaves, and (b) is a scatter plot when plotting predicted values against actually measured values in a test data set for the hue angle of steamed leaves. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, a method for predicting the hue angle of steamed leaves and a method for predicting the hue angle of crude tea according to embodiments of the present invention will be described in detail.

[0016] Hereinafter, the present invention will be specifically described with reference to the drawings. A method for manufacturing crude tea involves drying tea leaves such that components such as catechins in the tea leaves can be eluted. Heretofore, the degree of greenness of crude tea has been evaluated by sensory evaluation in terms of "color and luster", and furthermore, it has been confirmed that the hue angle value measured using a colorimeter has a significant positive correlation with the "color and luster" score. It has been known so far that there is a correlation between the hue angle of steamed leaves and crude tea, the content of chlorophyll and derivatives thereof, and the rate of conversion from chlorophyll to pheophytin. It is also known that there is a correlation between the color and luster score of crude tea and pH, and there is also a correlation between the conversion rate from chlorophyll to pheophytin and pH. However, the change in hue angle from fresh leaves to crude tea via steamed leaves has not been studied.

[0017] The manufacturing process of crude tea according to the method for predicting the hue angle of steamed leaves of the present invention is shown below. Figure 1 is a flowchart showing each stage of the manufacturing process from raw tea leaves to crude tea. As shown in Figure 1, the process begins with a fresh leaf collection step S1, in which raw leaves are first collected. Next, a steaming step S2 is performed to steam the raw leaves and deactivate the enzymes, followed by a kneading step S3 to soften the steamed leaves produced in the steaming step S2, and then a crude tea finishing step S4 to finish the steamed leaves after the kneading step S3 into crude tea. At this time, a cooling step to cool the steamed leaves and a drying step to dry the steamed leaves can be inserted as appropriate within or after each step. These manufacturing steps consist of a series of steps from the fresh leaf collection step S1 to the steaming step S2, the kneading step S3, and finally the crude tea finishing step S4 to produce crude tea. Through these steps, the shape, color, aroma, and flavor of the crude tea can be optimized, and high-quality crude tea can be obtained.

[0018] In the fresh leaf harvesting process S1, the freshness of the fresh leaves harvested from open-field or covered cultivation is crucial, and to prevent oxidation, a steaming process S2 is quickly carried out to heat the fresh leaves with steam. In the steaming process S2, steaming with steam inactivates the oxidizing enzymes in the fresh leaves, preventing oxidation and deterioration of the leaves. The steaming process S2 helps to maintain the vibrant green color characteristic of green tea. In the steaming process S2, the steaming temperature and time can be appropriately adjusted depending on the type of fresh leaves and the type of tea. In the steaming conditions of this invention, in order to quickly inactivate the oxidizing enzymes in the fresh leaves with 100°C steam, it is preferable to steam the tea leaves at a temperature of 90-100°C for a required time of 40-120 seconds.

[0019] Next, a kneading and pressing process S3 is performed to equalize the moisture content of the steamed leaves, shape them, and extract the components. The kneading and pressing process S3 can be divided into several steps as appropriate, and one example is shown below. First, a rough kneading process S31 is performed, in which excess moisture is removed from the tea leaves with hot air while stirring, and the steamed leaves are softened by light kneading pressure. In the rough kneading process S31, the tea leaves can be dried by stirring them at a temperature of approximately 36°C for a required period of 35 minutes. Next, a kneading process S32 is performed, in which the tea leaves are kneaded under pressure. In the kneading process S32, the tea leaves are kneaded under pressure for 20 minutes without applying heat to the tea leaves, which allows the moisture in the tea leaves to be evenly distributed and softened. Next, the intermediate rolling process S33 is carried out to dry the tea leaves. In the intermediate rolling process S33, the tea leaves are dried at a temperature of approximately 36°C for 40 minutes using hot air and rolling pressure while being stirred, which helps to homogenize the components within the steamed leaves. Next, the refining process S34 is carried out to shape the tea leaves. In the refining process S34, the tea leaves can be dried and shaped into needles at a temperature of approximately 37°C for 40 minutes.

[0020] Next, the tea leaves after the kneading process S3 are dried to produce crude tea in the crude tea finishing process S4. The resulting crude tea is a "roughly finished semi-product" with improved shelf life. In the next process, the crude tea is shaped, separated, roasted, etc., to become the finished product (finished tea). Crude tea can be produced through the above processes (S1 to S4).

[0021] These manufacturing processes greatly influence the internal quality and appearance of the tea. Furthermore, by finely adjusting each process, it is possible to produce different types of green tea. Until now, crude tea has been evaluated using a sensory evaluation called "color luster" and "hue angle," one of the evaluation items in the CIE color space, measured with colorimeters and other measuring instruments.

[0022] Color and luster is a concept that sensually describes the rectangular characteristics of an object's color, such as its beauty, vividness, and gloss. In food products, especially green tea, it is considered important in quality evaluation. In green tea production, the degree and brightness of the green color of the unprocessed tea leaves are important, and a vibrant green color is desired. It has been known that the color and luster of crude tea can vary depending on the variety and characteristics of the raw materials, cultivation conditions (shading conditions, amount of fertilizer), steaming or heating temperature and time in the manufacturing process, and the pressure or time of rolling. Further, the hue angle is an index for quantitatively representing color, and is indicated as an angle (°) representing a color attribute. The hue angle is calculated based on the international standard for CIE color spaces representing colors. Specifically, the measured color is assigned the value a on the a-axis (red-green direction) of the CIELab color space * and the value b on the b-axis (yellow-blue direction) * from which the hue angle h * = tan - (b * / a * ) can be calculated. The directionality of a color can be accurately expressed by this hue angle. In general, the hue angle represents the position of a color: in the hue angle range of 90° to 180°, a higher value indicates a position closer to the green direction, and a lower value indicates a position closer to the yellow direction. Color luster is mainly sensory, while the hue angle is a quantitative evaluation by a measuring instrument, and these are mutually related. It is known that the hue angle can be applied to the evaluation of fresh leaves, steamed leaves, and crude tea.

[0023] Accordingly, the hue angles of fresh leaves, steamed leaves, crude tea, and leaves after each production process thereof were measured. Figure 2 is a diagram showing the hue angle after each step of producing crude tea in the method for predicting the hue angle of steamed leaves according to the present invention. For the varieties shown in Figure 2, fresh leaves of different tea varieties were prepared. The hue angle of tea leaves was measured using a spectrocolorimeter (CM-5, manufactured by Konica Minolta, Inc.) by placing a transparent petri dish filled with powdered tea leaves on a target mask with φ=30 mm and performing reflectance measurement, whereby CIE (International Commission on Illumination) L * a * b * The hue angle was calculated from the color system. As shown in Figure 2, it can be seen that in each tea production process, the hue angle of tea leaves gradually decreases from fresh leaves to crude tea, and the difference between tea varieties is substantially determined in the steaming step S2, and the difference in hue angle remains unchanged through to the crude tea finishing step S4. Therefore, Figure 2 shows that when measuring the hue angle at each stage of the manufacturing process using raw leaves of a certain variety, the hue angle of crude tea shows a high correlation with the steamed leaves after the steaming process S2, indicating that the steaming process S2 is important in determining the hue angle of crude tea.

[0024] We measured the relationship between the hue angles of fresh and steamed leaves of multiple varieties of plants grown in open fields and under cover cultivation. Figure 3 shows two scatter plots: (a) a scatter plot of the hue angle of unrefined tea against the hue angle of fresh tea leaves, and (b) a scatter plot of the hue angle of unrefined tea against the hue angle of steamed tea leaves. In Figure 3, n represents the number of data points, and r represents the correlation coefficient (r-value). r quantifies the strength of the correlation between two sets of data; a value closer to 1 indicates a strong positive correlation, while a value closer to -1 indicates a strong negative correlation. A value closer to zero indicates a weak correlation. The results in Figure 3(a) show that the hue angles of the fresh leaves of various varieties grown in open fields and under cover cultivation have a small correlation coefficient r of 0.28 with the hue angles of crude tea, indicating a low correlation. In contrast, the hue angles of steamed leaves (heated steamed leaves) of various varieties grown in open fields and under cover cultivation show a high correlation with the hue angles of crude tea, as shown in Figure 3(b), with a correlation coefficient r of 0.92. Therefore, it can be seen that the hue angle of crude tea is determined by the steaming process used to produce steamed leaves, regardless of whether the tea variety is grown in open fields or under cover.

[0025] Traditional regression analysis typically involved simple linear regression, where one dependent variable was analyzed using one independent variable. Therefore, in this invention, by using four factors measured as influencing the hue angle of steamed leaves as explanatory variables and performing multiple regression analysis with the hue angle of steamed leaves as the dependent variable, it is possible to determine the magnitude of the influence of the explanatory variables on the dependent variable by determining partial regression coefficients, etc., and derive a correlation equation. Multiple regression analysis uses multiple explanatory variables to predict the dependent variable, creating a correlation equation to quantitatively represent the relationship between them. In contrast, simple regression analysis uses only one explanatory variable to predict the dependent variable. Therefore, multiple regression analysis can resolve phenomena involving multiple intertwined factors all at once. It can also clarify the contribution of each factor and identify which factors are important. This allows for efficient use of data and improved prediction accuracy.

[0026] The general correlation equation (1) derived from multiple regression analysis can be expressed as follows: Y = a0 + a1X1 + a2X2 + a3X3 + a4X4: Correlation equation (1) Here, Y is the dependent variable, X1, X2, X3, and X4 are the explanatory variables. a0 is the constant term, a1, a2, a3, and a4 represent the partial regression coefficients for the explanatory variables. Using this correlation equation (1), for example, the dependent variable Y can be predicted based on new data X1, X2, X3, and X4 of the explanatory variables. Furthermore, by calculating the standardized partial regression coefficients for each partial regression coefficient, the magnitude of the influence of each factor on the dependent variable can be analyzed. Note that the a0 constant term represents the value of the dependent variable when all explanatory variables are zero, and represents the baseline value within the entire model.

[0027] In the correlation equation derivation step for deriving correlation equation (1) in the present invention, the hue angle of steamed leaves is used as the dependent variable. As factors influencing this hue angle of steamed leaves, for example, as characteristics of raw leaves, the total content of chlorophyll a and b in raw leaves and the content ratio of chlorophyll a and b in raw leaves (chlorophyll a / b) were considered, and multiple regression analysis was performed.

[0028] Furthermore, the multiple regression analysis according to the present invention employs a stepwise method (incremental method) to create an optimal regression model for the dependent variable. This method reduces the complexity of the model and allows for the efficient selection of important explanatory variables even when there are many explanatory variables, thereby maximizing predictive performance. In the stepwise method (increase / decrease method), explanatory variables were determined based on the correlation AIC (Analytic Correlation Index), an index representing the accuracy of the correlation equation obtained by adding or removing variables at each step. Correlation equation (1) was created by applying the index with the smallest AIC when all four factors were included. Since high correlation between explanatory variables reduces the reliability of the model, multicollinearity can be reduced by checking the Variance Expansion Factor (VIF). The Variance Expansion Factor (VIF) is an index that quantitatively evaluates multicollinearity, i.e., whether explanatory variables are correlated with each other. A value closer to 1 is better, but it is desirable that it be at least 5 or less.

[0029] As a result, the following four factors were derived as explanatory variables that have the greatest influence on the dependent variable: the ratio of the content of the two types of chlorophyll a and chlorophyll b in the raw leaves, the total content of the two types of chlorophyll a and chlorophyll b in the raw leaves (mmol / 100g DW), the pH of the steamed leaves, and the steaming time of the raw leaves (seconds). Furthermore, by setting X1 as the steaming time of the raw leaves (seconds), X2 as the pH of the steamed leaves, X3 as the total content of the two types of chlorophyll a and chlorophyll b in the raw leaves (mmol / 100g DW), and X4 as the ratio of the content of the two types of chlorophyll a and chlorophyll b in the raw leaves, and then calculating the partial regression coefficients a1, a2, a3, and a4, The correlation equation (1) can be derived as follows: Y = a0 + a1X1 + a2X2 + a3X3 + a4X4.

[0030] Furthermore, in the steamed leaf hue angle prediction method of the present invention, when newly producing steamed leaves and crude tea, the conditions for producing steamed leaves with the value of the objective variable can be derived by inserting the values ​​of the explanatory variables X1, X2, X3, and X4 into the correlation equation (1) that has already been derived. For example, by inserting the total content of two types of chlorophyll a and chlorophyll b in the raw leaves (mmol / 100gDW), which is influenced by the natural environment (X3), the content ratio of two types of chlorophyll a and chlorophyll b in the raw leaves (X4), as well as the pH of the steamed leaves (X2) and the steaming time of the raw leaves (seconds) (X1) into correlation equation (1), the predicted hue angle Y of the steamed leaves produced, which is the objective variable, can be calculated. Furthermore, for the explanatory variables X2, X3, and X4, which are influenced by the natural environment and are difficult to adjust artificially, the steaming time of the raw leaves (minutes) (X1), which can be easily adjusted artificially during the manufacturing process to keep the hue angle of the steamed leaves within the range of 95° to 110°, can be calculated. In this case, the steaming process, which greatly affects the hue angle of the steamed leaves, preferably involves steaming the tea leaves with steam to a temperature of 90°C to 100°C for 40 to 120 seconds.

[0031] Therefore, the method for predicting the hue angle of steamed leaves according to the present invention includes a correlation equation derivation step in which the target variable is clearly defined, and various factors are subjected to multiple regression analysis to derive a correlation equation that represents the relationship with the explanatory variables. By inputting the explanatory variables using the derived correlation equation, the hue angle of steamed leaves, which is the target variable, can be calculated and predicted. Furthermore, the method for predicting the hue angle of crude tea according to the present invention can predict the hue angle of crude tea because the hue angle of steamed leaves corresponds to the hue angle of crude tea. [Examples]

[0032] The present invention will be described in detail based on the following embodiments. However, the present invention is not limited to the embodiments shown below.

[0033] (Example 1) A mixed dataset of steamed leaves (574 in total), consisting of various varieties, cultivation (covering) conditions, and steaming time, obtained at the National Agriculture and Food Research Organization (Kanaya and Makurazaki Tea Research Center), was divided into training datasets (382 in total) and test datasets (192 in total) in a ratio of approximately 2:1 using the random function of the calculation software: Excel. Multiple regression analysis was performed using BellCurve for Excel (Social Information Service Co., Ltd.). Stepwise multiple regression analysis was performed on the training dataset, and a correlation equation (2) was created that represents the optimal multiple regression equation based on the AIC (Akaike Information Criterion).

[0034] The hue angle of the steamed leaves, y(°), could be expressed by correlation equation (2). y = 67.952 - 0.059x1 + 6.751x2 + 6.226x3 - 1.735x4: Correlation equation (2) Here, let x1 be the steaming time (seconds), x2 be the pH of the steamed leaves, x3 be the total content of chlorophyll a and b in the raw leaves (mmol / 100g DW), and x4 be the ratio of chlorophyll a and b content in the raw leaves (chlorophyll a / b). This corresponds to the order of the magnitude of the influence. As a result, the factors of raw leaves that affect the hue angle of steamed leaves include the total content of chlorophyll a and b in raw leaves and the ratio of chlorophyll a and b content in raw leaves (chlorophyll a / b). Factors of steamed leaves that affect the hue angle of steamed leaves include the pH of steamed leaves. Factors in the manufacturing process include the steaming time of raw leaves. These four explanatory variables were identified. Although not explicitly stated here, all VIFs (Variance Expansion Factors) were less than 5, indicating that multicollinearity could be ignored. Table 1 shows the contribution of each factor to the hue angle; "-" indicates a negative contribution, and "+" indicates a positive contribution. The p-value is an indicator used to determine whether each coefficient is statistically significant. If the p-value is sufficiently small, it can be concluded that each coefficient has an influence on the dependent variable. The t-value can be calculated as (standardized partial regression coefficient / standard error), and if it is greater than or equal to a predetermined value, it means that the explanatory variable is valid. The results are shown in Table 1.

[0035] [Table 1]

[0036] This multiple regression analysis was applied to a test dataset to validate its effectiveness. Figure 4 shows (a) a scatter plot of predicted values ​​for the hue angle of steamed leaves against measured values ​​in the training dataset, and (b) a scatter plot of predicted values ​​for the hue angle of steamed leaves against measured values ​​in the test dataset. Here, the measured values ​​of the factors for each sample are input into the derived correlation equation (2) to calculate the steamed leaf hue angle y(°) as the predicted value(°). In Figure 4, the predicted value(°) is shown on the vertical axis and the measured value(°) is shown on the horizontal axis. The straight line indicates a 1:1 relationship. Furthermore, the adjusted coefficient of determination R is calculated as a result. 2 The training dataset yielded a score of 0.86, while the test dataset yielded a score of 0.84. Therefore, from the results in Figures 4(a) and 4(b), it was found that the measured values ​​(°) of the steamed leaves correspond to the predicted values ​​(°) obtained by correlation equation (2), indicating that the derived correlation equation (2) can be used to predict the hue angle of the steamed leaves. Similarly, since the hue angle of steamed leaves corresponds to the hue angle of crude tea, it was found that the hue angle of crude tea can be predicted by predicting the hue angle of steamed leaves.

Claims

1. A method for predicting the hue angle of steamed leaves when raw leaves are steamed to produce steamed tea leaves, and crude tea is produced from said steamed leaves, The ratio of the two types of chlorophyll a and chlorophyll b content in the raw leaves, the total content of the two types of chlorophyll a and chlorophyll b in the raw leaves (mol / 100g DW), the pH of the steamed leaves, and the steaming time (minutes) of the raw leaves were measured and used as four explanatory variables. The process includes a correlation equation derivation step in which a correlation equation is derived by performing multiple regression analysis with the hue angle of the steamed leaves as the dependent variable. A method for predicting the hue angle of steamed leaves, comprising inputting the four explanatory variables measured for the raw leaves and steamed leaves before they become crude tea into the correlation equation derived in the correlation equation derivation step, thereby calculating the hue angle of the steamed leaves.

2. The method for predicting the hue angle of steamed leaves according to claim 1, wherein the multiple regression analysis derives the correlation equation by the stepwise method.

3. The method for predicting the hue angle of steamed leaves according to claim 1, wherein the steaming conditions for steaming the raw leaves are in the range of 40 seconds to 120 seconds.

4. The method for predicting the hue angle of steamed leaves according to claim 1, wherein the hue angle of the steamed leaves is in the range of 95° or more and 110° or less.

5. A method for predicting the hue angle of crude tea, which predicts the hue angle of crude tea based on the hue angle of steamed leaves calculated by the method for predicting the hue angle of steamed leaves described in any one of claims 1 to 4.

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