Grain steaming effect evaluation method and system
Through the near-infrared gelatinization degree rapid detection model and multi-step comprehensive analysis method, the accuracy and consistency problems of the existing steamed grain effect evaluation method are solved, a more efficient and accurate steamed grain effect evaluation is achieved, and the wine making process is ensured. Process controllability.
Patent Information
- Application Number
- CN202411458937.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-18
- Publication Date
- 2025-01-21
AI Technical Summary
The existing methods for steamed grain effect evaluation have poor accuracy and consistency, which mainly relies on experienced brewers to make subjective judgments, making it difficult to ensure the standardization and controllability of the evaluation.
The near-infrared gelatinization degree rapid detection model is used to detect the gelatinization degree of grains, and combined with the steps of sensory evaluation, cellar fermentation, steaming wine, etc., the effect of steaming grains is determined through comprehensive analysis of multiple indicators.
It improves the efficiency and accuracy of grain gelatinization detection, enhances the accuracy and standardization of steamed grain effect evaluation, and ensures the controllability of the production process and product quality.
Smart Images
Figure CN119334900A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of wine brewing technology, and in particular to a method and system for evaluating grain steaming effect. Background Art
[0002] Liquor is a traditional solid-state fermented distilled liquor. As one of the six major liquors in the world, it has formed a unique brewing process and wine style during its development. Grain steaming is the process of steaming and boiling the moistened grain raw materials. The main purpose is to make the starch granules absorb water, swell, rupture and gelatinize, so that its structure changes from an ordered state to a disordered state for microbial utilization and metabolism. The specific process of grain steaming mainly includes: soaking the grain (such as sorghum) in water to make its water content reach a certain proportion to prepare for subsequent steaming, and then steaming the soaked sorghum and other grain raw materials in the steamer. The high-temperature steam makes the starch granules gelatinize, which is convenient for subsequent fermentation and brewing. Grain steaming plays a vital role in the winemaking process. Through the expansion and gelatinization of starch, sterilization and improvement of grain structure, it provides a good basic condition for subsequent fermentation and brewing.
[0003] The traditional method of evaluating the effect of steaming grain mainly relies on sensory judgment by experienced brewing workers through pinching with hands, smelling with nose, observing with eyes, etc. The evaluation criteria of this method are mostly subjective descriptions such as "no raw core inside, cooked but not rotten", etc. The standards are vague and easily affected by personnel experience and subjectivity. It is not conducive to the control of the degree of steaming grain and the stability of the brewing process, nor is it conducive to large-scale production. In addition, the evaluation of the steaming effect only relies on the gelatinization degree of grain, which also has the problem of poor accuracy and consistency. Summary of the invention
[0004] The present application aims to solve the problem of poor accuracy and consistency in existing grain steaming effect evaluation methods, and proposes a grain steaming effect evaluation method and system.
[0005] The technical solution adopted by this application to solve the above technical problems is:
[0006] In a first aspect, the present application provides a method for evaluating the effect of steaming grains, the method comprising:
[0007] Obtaining a steamed grain sample, detecting the grain gelatinization degree of the grain sample based on a pre-constructed near-infrared gelatinization degree rapid detection model, and performing a sensory evaluation on the grain sample to obtain a first sensory score;
[0008] The grain sample is mixed with dregs and put into a cellar, and taken out of the cellar after the fermentation period in the cellar is over, and the acidity and starch content of the dregs are respectively detected before and after the fermentation period in the cellar, and the acidity change and the starch content change are determined;
[0009] The fermented mash is steamed in a steamer to obtain base wine, the yield of the base wine is determined, wine samples are taken for sensory evaluation to obtain a second sensory score, and the fusel oil content of the base wine is detected;
[0010] The steaming effect of the grain sample is determined according to the grain gelatinization degree, the first sensory score, the acidity change, the starch content change, the wine yield, the second sensory score and the fusel oil content.
[0011] Furthermore, the method for constructing the near-infrared gelatinization degree rapid detection model includes:
[0012] Obtain grain samples after steaming under different steaming parameters, collect spectral data of each grain sample respectively, and use manual physical and chemical methods to determine the degree of gelatinization of each grain sample;
[0013] After preprocessing the spectral data, the preprocessed spectral data and the measured grain gelatinization degree are used as basic data, and a near-infrared gelatinization degree rapid detection model is constructed based on the basic data and the near-infrared partial least squares method.
[0014] Furthermore, the grain steaming parameters at least include the amount of water used to moisten the grain, the grain steaming time and the steam flow rate.
[0015] Furthermore, the method for manually determining the degree of gelatinization of each grain sample by physical and chemical means is an enzymatic hydrolysis method.
[0016] Furthermore, the spectral data is preprocessed, specifically including: performing smoothing preprocessing, vector normalization and first-order derivative on the spectral data.
[0017] Furthermore, a near infrared gelatinization degree rapid detection model is constructed based on the basic data and near infrared partial least squares method, specifically including:
[0018] The basic data is divided into a training set and a validation set. The training set is used to train the near-infrared partial least squares method to obtain an initial detection model, and the validation set is used to verify the accuracy of the initial detection model. If the accuracy verification passes, the initial detection model is used as a near-infrared gelatinization degree rapid detection model.
[0019] Further, the steaming effect of the grain sample is determined, specifically including:
[0020] According to the relationship between the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content and the grain steaming effect, the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content are quantified to obtain quantitative scores, and the grain steaming effect of the grain sample is determined based on the quantitative scores.
[0021] Furthermore, the steaming effect of the grain sample is determined according to the quantitative results, specifically including:
[0022] The weighted sum of the quantitative results of the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content is used as the grain steaming effect score, and the grain steaming effect of the grain sample is determined based on the grain steaming effect score.
[0023] Further, determining the grain steaming effect of the grain sample according to the grain steaming effect score specifically includes:
[0024] When the grain steaming effect score is in the first range, the grain steaming effect of the grain sample is judged to be excellent; when the grain steaming effect score is in the second range, the grain steaming effect of the grain sample is judged to be first-level; when the grain steaming effect score is in the third range, the grain steaming effect of the grain sample is judged to be second-level; when the grain steaming effect score is in the fourth range, the grain steaming effect of the grain sample is judged to be unqualified.
[0025] In a second aspect, the present application provides a system for evaluating the effect of steaming grains, wherein the system is used to implement the method for evaluating the effect of steaming grains as described in the first aspect.
[0026] The beneficial effects of the present application are as follows: the method and system for evaluating the effect of grain steaming provided in the present application detect the degree of grain gelatinization through a near-infrared rapid gelatinization detection model, thereby improving the efficiency and accuracy of grain gelatinization detection; and the grain samples after steaming are put into cellar for fermentation and steamed in a steamer, and the grain steaming effect of the grain sample is comprehensively determined by using the grain gelatinization degree and first sensory score of the grain sample, the acidity change and starch content change of the mash before and after entering the cellar, and the wine yield, second sensory score and fusel oil content of the base wine, thereby improving the accuracy and standardization of the evaluation of the grain steaming effect and ensuring the controllability of the production process and product quality. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Figure 1 A schematic flow chart of a method for evaluating the effect of steaming grains provided in an embodiment of the present application;
[0028] Figure 2 An original spectrum corresponding to a spectrum data provided in an embodiment of the present application;
[0029] Figure 3 A spectrum corresponding to the preprocessed spectral data provided in an embodiment of the present application;
[0030] Figure 4 A relationship curve diagram between the degree of gelatinization of grains and its quantitative score provided in an embodiment of the present application. DETAILED DESCRIPTION
[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.
[0032] The technical solution of the embodiment of the present application is applicable to the application scenario where the steaming effect of steamed grains in the brewing process needs to be judged. Since the evaluation of the steaming effect of grains currently relies on experienced brewing workers, the standard is vague and easily affected by personnel experience and subjectivity, and the accuracy and standardization are poor.
[0033] In order to solve the above problems, the inventors conducted a series of studies, taking grain gelatinization degree as a single factor variable, tracking actual production, and conducting investigations and preliminary experiments on production indicators that affect grain steaming to wine production. At the beginning of the experiment, a preliminary correlation analysis was conducted on production indicators such as steamed sorghum moisture content, steamed sorghum gelatinization degree, and total acid and total esters of base wine. According to the analysis results, several key indicators of liquor production with high correlation, such as grain sensory evaluation, physical and chemical properties of entering and leaving the cellar, base wine yield, base wine sensory score, and base wine fusel oil content, were finally selected as key influencing factors for analysis.
[0034] Based on this, the technical scheme of the present application is proposed. In an embodiment of the present application, a grain sample after steaming is obtained, the grain gelatinization degree of the grain sample is detected based on a pre-constructed near-infrared gelatinization rapid detection model, and the grain sample is subjected to a sensory evaluation to obtain a first sensory score; the grain sample is mixed with dregs and put into a cellar, and taken out of the cellar after the fermentation period in the cellar is over, and the acidity and starch content of the dregs are respectively detected before entering the cellar and after taking out of the cellar, and the acidity change and the starch content change are determined; the fermented dregs are steamed in a steamer to obtain basic wine, the wine yield of the basic wine is determined, a wine sample is taken for sensory evaluation to obtain a second sensory score, and the fusel oil content of the base wine is detected; the steaming effect of the grain sample is determined according to the grain gelatinization degree, the first sensory score, the acidity change, the starch content change, the wine yield, the second sensory score and the fusel oil content.
[0035] Specifically, the embodiment of the present application pre-constructs a near-infrared rapid detection model for gelatinization, obtains grain samples after grain cooking, uses the near-infrared rapid detection model for gelatinization to detect the gelatinization degree of the grain samples, and determines the first sensory score of the grain samples, and then uses the grain samples for fermentation in a cellar and steaming wine in a steamer. After fermentation in the cellar, the changes in acidity and starch content of the mash before and after entering the cellar are determined. After steaming wine in the steamer, the yield, second sensory score and fusel oil content of the base wine are determined. Finally, the determined gelatinization degree, first sensory score, change in acidity, change in starch content, yield, second sensory score and fusel oil content of the grain samples are used to comprehensively determine the steaming effect of the grain samples. Since the embodiment of the present application detects the gelatinization degree of grain through a near-infrared gelatinization rapid detection model, the efficiency and accuracy of grain gelatinization degree detection are improved, and the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content are comprehensively considered when evaluating the grain steaming effect, thereby improving the accuracy and standardization of the grain steaming effect evaluation and ensuring the controllability of the production process and product quality.
[0036] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.
[0037] See also Figure 1 The method for evaluating the steaming effect of grain provided in the embodiment of the present application comprises the following steps:
[0038] S1. Obtain a steamed grain sample, detect the degree of gelatinization of the grain sample based on a pre-constructed near-infrared rapid gelatinization detection model, and perform a sensory evaluation on the grain sample to obtain a first sensory score.
[0039] In the actual winemaking process, a portion of the grain after steaming can be taken as a grain sample, and then the gelatinization degree can be detected using a near-infrared gelatinization rapid detection model.
[0040] In the embodiment of the present application, the method for constructing the near-infrared gelatinization degree rapid detection model may specifically include:
[0041] Obtain grain samples after steaming under different steaming parameters, collect spectral data of each grain sample, and use manual physical and chemical methods to determine the degree of gelatinization of the grain samples;
[0042] After preprocessing the spectral data, the preprocessed spectral data and the measured grain gelatinization degree are used as basic data, and a near-infrared gelatinization degree rapid detection model is constructed based on the basic data and the near-infrared partial least squares method.
[0043] The grain steaming parameters at least include the amount of water used to moisten the grain, the grain steaming time and the steam flow rate. The method for manually determining the degree of gelatinization by physical and chemical means may be an enzyme hydrolysis method.
[0044] In practical applications, grain samples were taken after steaming under different steaming parameters, spectral data were collected on a near-infrared spectrometer, and the degree of gelatinization of each grain sample was determined manually by physical and chemical methods. Then, the collected spectral data and the measured degree of gelatinization were used as basic data, and a near-infrared partial least squares method was used to construct a near-infrared gelatinization rapid detection model.
[0045] In the embodiment of the present application, the preprocessing of the spectral data mainly includes smoothing preprocessing, vector normalization and first-order derivative. In practical applications, the spectral data can be preprocessed using SpectrumDH software. Figure 2 shows an original spectrum corresponding to a spectral data, Figure 3 shows a spectrum corresponding to the preprocessed spectral data, Figure 3 The spectral region with the richest information is in the range of 4000-10000, and the principal component dimension is 8, which can be used to establish a fast detection model for grain near-infrared gelatinization degree. Preprocessing can eliminate some of the effects of noise, random errors, and baseline drift to improve the accuracy and precision of the model.
[0046] In an embodiment of the present application, in order to improve the accuracy of the near-infrared gelatinization degree rapid detection model, a method for constructing a near-infrared gelatinization degree rapid detection model based on the basic data and the near-infrared partial least squares method may specifically include:
[0047] The basic data is divided into a training set and a validation set. The training set is used to train the near-infrared partial least squares method to obtain an initial detection model, and the validation set is used to verify the accuracy of the initial detection model. If the accuracy verification passes, the initial detection model is used as a near-infrared gelatinization degree rapid detection model.
[0048] Specifically, the initial detection model is verified through the verification set. If the accuracy of the initial detection model reaches the preset conditions, the training is completed and the near-infrared gelatinization degree rapid detection model is obtained. If the accuracy of the initial detection model does not reach the preset conditions, the above steps are repeated for retraining until the accuracy reaches the preset conditions.
[0049] The embodiment of the present application uses near-infrared spectroscopy technology to model the degree of gelatinization of grains, thereby realizing online, rapid, and non-destructive detection of the degree of gelatinization of grains, greatly improving detection efficiency, reducing labor intensity, and facilitating the processing of a large number of grain samples to establish an evaluation method and system for the effect of steaming grains, and allowing the detection results to be quickly used for feedback production, thereby facilitating the adjustment and optimization of process parameters.
[0050] In an embodiment of the present application, the sensory evaluation of the grain sample may specifically be performed by asking an experienced winemaker from a winery to perform a sensory evaluation to obtain a first sensory score of the grain sample. The first sensory score corresponding to the sensory evaluation may refer to Table 1.
[0051] Table 1 Correspondence between sensory evaluation and first sensory score
[0052] Sensory evaluation First sensory evaluation Sometimes raw, average stickiness, hard to tender 23 Sometimes raw, average stickiness, hard to tender 23 Occasionally raw, good stickiness, average tenderness 26 Occasionally raw, good stickiness, average tenderness 26 No raw core, good stickiness, good tenderness 28 No raw core, good stickiness, good tenderness 29 No raw core, good stickiness, good tenderness 29 No raw core, good stickiness, good tenderness 30 No raw core, good stickiness, good tenderness 30 No rawness, slightly sticky, good tenderness 29 Sometimes raw, sticky, soft and tender 23 Sometimes raw, sticky, soft and tender 23
[0053] The higher the first sensory score, the better the steaming effect.
[0054] S2. Mix the grain sample with dregs and put it into a cellar, and take it out of the cellar after the fermentation period in the cellar is over. Before putting it into the cellar and after taking it out of the cellar, respectively, detect the acidity and starch content of the dregs, and determine the changes in the acidity and starch content.
[0055] In practical applications, a certain amount of grain samples are mixed with distiller's grains, spread out to dry, and then koji powder is added to the cellar to test the acidity and starch content of the fermented grains before entering the cellar. After the fermented grains are fermented in the cellar, the acidity and starch content of the fermented grains are tested. Based on the absolute difference in the acidity and starch content of the fermented grains before and after entering the cellar, the change in acidity and starch content of the fermented grains before and after entering the cellar can be calculated.
[0056] S3. Steaming the fermented mash in a steamer to obtain base wine, determining the yield of the base wine, taking wine samples for sensory evaluation to obtain a second sensory score, and detecting the fusel oil content of the base wine.
[0057] After the fermented mash is steamed in a steamer, the total weight and alcohol content of the base wine are determined, and the yield of the base wine is converted. Wine tasters are then invited to taste and score the base wine to obtain a second sensory score. Finally, the base wine is tested for its physical and chemical properties and the fusel oil content is measured.
[0058] S4. Determine the steaming effect of the grain sample according to the grain gelatinization degree, the first sensory score, the acidity change, the starch content change, the wine yield, the second sensory score and the fusel oil content.
[0059] In the embodiment of the present application, the method for determining the steaming effect of the grain sample may specifically include:
[0060] According to the relationship between the grain gelatinization degree, the first sensory score, the change in acidity, the change in starch content, the wine yield, the second sensory score and the fusel oil content and the grain steaming effect, the grain gelatinization degree, the first sensory score, the change in acidity, the change in starch content, the wine yield, the second sensory score and the fusel oil content are quantified to obtain a quantitative score, and the weighted sum of the quantified scores of the grain gelatinization degree, the first sensory score, the change in acidity, the change in starch content, the wine yield, the second sensory score and the fusel oil content is used as the grain steaming effect score, and the grain steaming effect of the grain sample is determined according to the grain steaming effect score.
[0061] Since the effect of grain steaming improves with the increase of grain gelatinization degree, the best effect is achieved at about 75% to 80%. If the grain gelatinization degree continues to increase, the effect of grain steaming will deteriorate. Therefore, the quantitative score of grain gelatinization degree increases first and then decreases with the increase of grain gelatinization degree. The higher the quantitative score of grain gelatinization degree, the better the grain steaming effect. Figure 4 A relationship curve diagram between the degree of grain gelatinization and its quantitative score is shown. For the acidity change, the smaller the acidity change, the better the grain steaming effect, so the smaller the acidity change, the higher the corresponding quantitative score. For the starch content change, the greater the starch content change, the better the grain steaming effect, so the greater the starch content change, the higher the corresponding quantitative score. For the wine yield, the greater the wine yield, the better the grain steaming effect, so the greater the wine yield, the higher the corresponding quantitative score. The second sensory score is based on the score of professional wine tasters. The higher the second sensory score, the better the grain steaming effect, so the higher the quantitative score of the second sensory score, the better the grain steaming effect. For the fusel oil content, the smaller the fusel oil content, the better the grain steaming effect, so the smaller the fusel oil content, the higher the corresponding quantitative score.
[0062] Based on the relationship between the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content and the grain steaming effect, they were quantified to obtain the quantitative scores of grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content, and finally the grain steaming effect score was calculated according to the weighted sum of all quantitative scores. The calculation formula is as follows:
[0063] X=Aa+Bb+Cc+Dd+Ee+Ff+Gg;
[0064] Among them, X represents the grain steaming effect score, A represents the quantitative score of grain gelatinization degree, a represents the weight of grain gelatinization degree, B represents the quantitative score of the first sensory score, b represents the weight of the first sensory score, C represents the quantitative score of acidity change, c represents the weight of acidity change, D represents the quantitative score of starch content change, d represents the weight of starch content change, E represents the quantitative score of wine yield, e represents the weight of wine yield, F represents the quantitative score of the second sensory score, f represents the weight of the second sensory score, G represents the quantitative score of fusel oil content, and g represents the weight of fusel oil content.
[0065] It can be understood that after obtaining the grain steaming effect score, the grain steaming effect can be judged according to the grain steaming effect score. The higher the grain steaming effect score, the better the grain steaming effect. In the embodiment of the present application, the grain steaming effect can be rated according to the range of the grain steaming effect score, as follows:
[0066] When the grain steaming effect score is in the first range, the grain steaming effect of the grain sample is judged to be excellent; when the grain steaming effect score is in the second range, the grain steaming effect of the grain sample is judged to be first-level; when the grain steaming effect score is in the third range, the grain steaming effect of the grain sample is judged to be second-level; when the grain steaming effect score is in the fourth range, the grain steaming effect of the grain sample is judged to be unqualified.
[0067] Among them, the first range, the second range, the third range, and the fourth range can be set according to actual conditions. For example, in the embodiment of the present application, when 100≥X≥90, the grain steaming effect is judged to be excellent; when 90>X≥80, the grain steaming effect is judged to be level one; when 80>X≥70, the grain steaming effect is judged to be level two; when 70>X, the grain steaming effect is judged to be unqualified.
[0068] In summary, the method for evaluating the effect of grain steaming provided in the embodiment of the present application detects the gelatinization degree of grain through a near-infrared rapid gelatinization degree detection model, thereby improving the efficiency and accuracy of grain gelatinization degree detection; and the grain samples after steaming are put into cellar for fermentation and steamed in a steamer, and the grain steaming effect of the grain sample is comprehensively determined by using the grain gelatinization degree and first sensory score of the grain sample, the acidity change and starch content change of the mash before and after entering the cellar, and the wine yield, second sensory score and fusel oil content of the base wine, thereby improving the accuracy and consistency of the grain steaming effect evaluation and ensuring the controllability of the production process and product quality.
[0069] Based on the above technical solution, the embodiment of the present application further proposes a system for evaluating the effect of steaming grains, and the system is used to implement the method for evaluating the effect of steaming grains as described in the embodiment of the present application.
[0070] It can be understood that since the evaluation system for the steaming effect of grain described in the embodiment of the present application is a system for implementing the evaluation method for the steaming effect of grain described in the embodiment, for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method, which will not be repeated here.
Claims
1. A method for evaluating the effect of steaming grains, characterized in that: The method comprises: Obtaining a steamed grain sample, detecting the grain gelatinization degree of the grain sample based on a pre-constructed near-infrared gelatinization degree rapid detection model, and performing a sensory evaluation on the grain sample to obtain a first sensory score; The grain sample is mixed with dregs and put into a cellar, and taken out of the cellar after the fermentation period in the cellar is over, and the acidity and starch content of the dregs are respectively detected before and after the dregs are put into the cellar, and the acidity change and starch content change are determined; The fermented mash is steamed in a steamer to obtain base wine, the yield of the base wine is determined, wine samples are taken for sensory evaluation to obtain a second sensory score, and the fusel oil content of the base wine is detected; The steaming effect of the grain sample is determined according to the grain gelatinization degree, the first sensory score, the acidity change, the starch content change, the wine yield, the second sensory score and the fusel oil content.
2. The method for evaluating the steaming effect of grain according to claim 1, characterized in that: The method for constructing the near-infrared gelatinization degree rapid detection model comprises: Obtain grain samples after steaming under different steaming parameters, collect spectral data of each grain sample respectively, and use manual physical and chemical methods to determine the degree of gelatinization of each grain sample; After preprocessing the spectral data, the preprocessed spectral data and the measured grain gelatinization degree are used as basic data, and a near-infrared gelatinization degree rapid detection model is constructed based on the basic data and the near-infrared partial least squares method.
3. The method for evaluating the steaming effect of grain according to claim 2, characterized in that: The grain steaming parameters at least include the amount of water used to moisten the grain, the grain steaming time and the steam flow rate.
4. The method for evaluating the steamed grain effect according to claim 2, characterized in that: The method for manual physical and chemical determination of the degree of gelatinization of each grain sample is the enzymatic hydrolysis method.
5. The method for evaluating the steamed grain effect according to claim 2, characterized in that: The spectral data is preprocessed, specifically including: smoothing preprocessing, vector normalization and first-order derivative of the spectral data.
6. The method for evaluating the steamed grain effect according to claim 2, characterized in that: According to the basic data and based on the near infrared partial least squares method, a near infrared gelatinization degree rapid detection model is constructed, which specifically includes: The basic data is divided into a training set and a validation set. The training set is used to train the near-infrared partial least squares method to obtain an initial detection model, and the validation set is used to verify the accuracy of the initial detection model. If the accuracy verification passes, the initial detection model is used as a near-infrared gelatinization degree rapid detection model.
7. The method for evaluating the steamed grain effect according to claim 1, characterized in that: Determine the steaming effect of grain samples, including: According to the relationship between the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content and the grain steaming effect, the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content are quantified to obtain quantitative scores, and the grain steaming effect of the grain sample is determined based on the quantitative scores.
8. The method for evaluating the steamed grain effect according to claim 7, characterized in that: The steaming effect of the grain sample is determined based on the quantitative results, including: The weighted sum of the quantitative results of the grain gelatinization degree, first sensory score, acidity change, starch content change, wine yield, second sensory score and fusel oil content is used as the grain steaming effect score, and the grain steaming effect of the grain sample is determined based on the grain steaming effect score.
9. The method for evaluating the steamed grain effect according to claim 8, characterized in that: Determining the grain steaming effect of the grain sample according to the grain steaming effect score specifically includes: When the grain steaming effect score is in the first range, the grain steaming effect of the grain sample is judged to be excellent; when the grain steaming effect score is in the second range, the grain steaming effect of the grain sample is judged to be first-level; when the grain steaming effect score is in the third range, the grain steaming effect of the grain sample is judged to be second-level; when the grain steaming effect score is in the fourth range, the grain steaming effect of the grain sample is judged to be unqualified.
10. A system for evaluating the effect of steaming grains, characterized in that: The system is used to implement the method for evaluating the steaming effect of grains as described in any one of claims 1 to 9.