Method for detecting water absorption rate of moistened grain based on multi-dimensional texture characteristics

Abstract

The invention mainly relates to the technical field of wine brewing, and provides a wet grain water absorption detection method based on multi-dimensional texture characteristics in order to rapidly and accurately evaluate the water absorption state of wet grain particles. The core of the method is as follows: a plurality of sample particles are selected to measure the texture characteristics and the water absorption of each sample after grain moistening by measuring the water absorption and the internal moisture, the correlation between the water absorption of the moistened grain and each texture characteristic is analyzed, and the texture characteristics obviously related to the water absorption of the moistened grain are used as key texture characteristics; the method comprises the following steps: establishing a moist grain water absorption prediction model based on key texture characteristics, determining the key texture characteristics of a to-be-tested sample, and predicting the moist grain water absorption of the to-be-tested sample based on the established moist grain water absorption prediction model, so that the influence of surface free water on the evaluation of the moist grain water absorption state is eliminated, and the moist grain particle water absorption state can be rapidly and accurately evaluated. And the moistened grain sample does not need to be dried, so that the energy consumption in the moistened grain moisture detection link is reduced.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of brewing, and particularly relates to a method for detecting the water absorption rate of moistened grains based on multi-dimensional texture characteristics. Background Art

[0002] In the production of sauce-flavored liquor, the raw materials are called sand. The two grain inputs are respectively called the first sand input and the rough sand input, and both the first sand input and the rough sand input require moistening of the grains. The starch in brewing grains is the main source of ethanol and many flavor substances. The change of flavor depends to a large extent on the water absorption state of grain particles during the moistening process. Sufficient water absorption is the key prerequisite for ensuring good gelatinization of grains, which is beneficial to the smooth progress of subsequent saccharification, fermentation and other technological processes. At present, the industry generally uses the overall moisture content of the raw grains after moistening as an index to measure the water absorption state of the particles. However, due to reasons such as the free water attached to the surface of the particles and the lag of the data results in the conventional moisture content detection results, it is impossible to timely and accurately reflect the effect of the moistening process and the true water absorption state inside the grain particles. At the same time, the method of detecting the moisture content after drying the grains will cause a large amount of energy consumption in the drying stage, which does not conform to the concept of green manufacturing. Therefore, establishing a more accurate, energy-saving and rapid detection and evaluation method for the quality of moistened grains is of great significance for improving the overall quality of brewed white liquor. Summary of the Invention

[0003] The technical problem to be solved by the present invention is: to provide a method for detecting the water absorption rate of moistened grains based on multi-dimensional texture characteristics, aiming to quickly and accurately evaluate the water absorption state of moistened grain particles.

[0004] The technical solution adopted by the present invention to solve the above technical problems is: A method for detecting the water absorption rate of moistened grains based on multi-dimensional texture characteristics, the method comprising: Step S1: Select multiple samples after moistening the grains, and respectively measure the texture characteristics and water absorption rate of each sample; Step S2: Analyze the correlation between the water absorption rate of the moistened grains and various texture characteristics, take the texture characteristics significantly correlated with the water absorption rate of the moistened grains as key texture characteristics, and establish a prediction model for the water absorption rate of the moistened grains based on the key texture characteristics; Step S3: Measure the key texture characteristics of the待测样品 (to-be-detected sample), and calculate the water absorption rate of the moistened grains of the待测样品 (to-be-detected sample) based on the established prediction model for the water absorption rate of the moistened grains.

[0005] Further, the texture characteristics include the volume, hardness, viscosity, elasticity, chewiness, adhesiveness, cohesiveness and resilience of the sample.

[0006] Further, the measurement of the texture characteristics in step S1 includes: Step S11: Place the sample into the detection container, select a probe according to the required experimental conditions and install it on the instrument; Step S12: Perform pretreatment on the sample according to the testing requirements of each textural property, and select the textural property testing method; Step S13: Perform textural property testing, preprocess the obtained textural property data, and take the average value of multiple measurements as the final measurement result of the textural property.

[0007] Furthermore, the determination of sample water absorption rate in step S1 includes: recording the weight of the grain sample before soaking as m0, and after soaking, removing the free water on the surface of the soaked grain particles in a 35℃ oven and weighing the grain, recording the grain weight as m1, and the soaking water absorption rate = (m1-m0) / m0×100%.

[0008] Furthermore, step S3 includes: obtaining the correlation degree between grain water absorption rate and each textural characteristic based on Pearson correlation analysis, determining key textural characteristics based on the correlation degree, performing multiple linear stepwise regression analysis with key textural characteristics as independent variables and grain water absorption rate as dependent variable, and establishing a grain water absorption rate prediction model.

[0009] Furthermore, the prediction model for the water absorption rate of red sorghum after soaking is: Y=1.358X1-0.014X2-170.104X3+72.545X4+15.926, where Y is the water absorption rate after soaking; X1 is the volume after soaking; X2 is the hardness after soaking; X3 is the resilience after soaking; and X4 is the cohesion after soaking.

[0010] The beneficial effects of this invention are: by establishing a predictive model for grain moisture absorption rate by identifying key textural properties that are significantly related to the moisture absorption rate of different types of grains, the moisture absorption rate of grains can be monitored. This effectively eliminates the influence of free water on the evaluation of the moisture absorption state of grains, and can effectively and accurately evaluate the moisture absorption state of grains. The detection results are objective and reliable. Furthermore, it does not require drying of the grain samples, is simple to operate, enables rapid detection of moisture absorption rate, and saves energy consumption generated by moisture detection in the grain moistening process. This is beneficial for enterprises to optimize and promote the grain moistening process in a timely manner. Attached Figure Description

[0011] Figure 1 This is a flowchart of the grain moisture absorption rate detection method based on multidimensional texture properties described in this invention. Figure 2 This example compares the water absorption rate predicted by the established grain water absorption rate prediction model with the actual water absorption rate. Detailed Implementation

[0012] The core idea of ​​the grain moisture absorption rate detection method based on multidimensional textural properties described in this invention to solve the above-mentioned technical problems is as follows: after moistening the grain, multiple sample particles are selected to measure the textural properties and moisture absorption rate of each sample by measuring the relationship between moisture absorption rate and internal moisture. The correlation between moisture absorption rate and each textural property is analyzed. Textural properties that are significantly related to moisture absorption rate are identified as key textural properties. Based on the key textural properties, a moisture absorption rate prediction model is established. The key textural properties of the sample to be tested are measured, and the moisture absorption rate of the sample to be tested is predicted based on the established moisture absorption rate prediction model.

[0013] like Figure 1 As shown, the grain moisture absorption rate detection method based on multidimensional texture properties of the present invention includes: Step S1: Determine the textural properties of multiple samples after grain soaking.

[0014] After weighing and soaking the grain, the sample was placed in a cylindrical testing container. The cylindrical probe of the TA.XTC-18 texture analyzer was used. The testing parameters were: speed 1 mm / s before testing, speed 2 mm / s during testing, testing mode 10 gf, and test deformation 6%. The sample was tested multiple times for eight textural characteristics: volume, hardness, viscosity, elasticity, chewiness, adhesiveness, cohesion, and resilience. The arithmetic mean of the textural characteristics was taken as the test result.

[0015] Step S2: After soaking the grain, select multiple grains as samples to determine the water absorption rate of the grain.

[0016] The surface moisture of the moistened grain sample was removed with absorbent paper, and then dried in a 35℃ oven for 5 minutes to ensure that there was no moisture on the surface of the grain. The weight m1 of the moistened grain grain after removing the surface moisture was measured. The water absorption rate of the moistened grain was calculated as (m1-m0) / m0×100%. The water absorption rate test results were checked to see if they conformed to a normal distribution. The test results that conformed to a normal distribution were taken as reliable data.

[0017] Simultaneously, the internal moisture content of the moistened grain samples without surface moisture was determined by drying them in a 120℃ oven to constant weight. Specifically, the normality of the measured water absorption rate was tested, and the calculated p-value was 0.076, greater than 0.05, indicating that the water absorption rate basically conforms to a normal distribution and can be used as valid data. Based on the internal moisture content of multiple batches of grains and the measured water absorption rate, the relationship between the internal moisture content 'a' after moistening and the measured water absorption rate 'b' was fitted as a = 0.773 + 1.021b - 0.005b 2 R 2 =0.941, indicating that the water absorption rate of moistened grain is strongly correlated with the internal moisture content of moistened grain, and the water absorption rate of moistened grain can be used as an evaluation indicator for the quality of moistened grain.

[0018] Step S3: Based on Pearson correlation analysis, obtain the correlation degree between grain water absorption rate and each textural characteristic. Based on the correlation degree, determine the key textural characteristics. Use the key textural characteristics as independent variables and grain water absorption rate as dependent variable to conduct multiple linear stepwise regression analysis to establish a grain water absorption rate prediction model.

[0019] Step S4: Determine the key textural properties of the sample to be tested, and calculate the water absorption rate of the sample to be tested based on the established grain water absorption rate prediction model.

[0020] This embodiment further explains the grain moisture absorption detection method based on multidimensional texture properties described in this invention, based on the prediction of water absorption rate of red sorghum.

[0021] Step S1: Weigh 30g (m0) of uniformly mixed, plump sorghum grains. After processing with a grain-moistening process, batch-number the samples and place them in a cylindrical testing container. Use the columnar probe of a TA.XTC-18 texture analyzer. The testing parameters are: speed 1mm / s before testing, speed 2mm / s during testing, testing mode 10gf, and deformation 6%. Each sample is tested three times, and the arithmetic mean is taken. Eight textural characteristics of the samples are tested: volume, hardness, viscosity, elasticity, chewiness, adhesiveness, cohesion, and resilience, as well as the water absorption rate of each sample. Specific values ​​are shown in Table 1.

[0022] Table 1. Texture data of red sorghum samples after soaking. Step S2: Based on the Pearson correlation coefficient, the correlation between various textural properties and grain water absorption rate was analyzed. The results of the correlation analysis are shown in Table 2.

[0023] Table 2 Correlation analysis between water absorption rate and textural properties of grains Based on the textural properties that are significantly correlated with the water absorption rate of red sorghum, a multiple linear stepwise regression analysis was conducted, and the water absorption rate prediction model was established, as shown in Table 3. The volume, hardness, resilience, and cohesion of the grain particles in the moistened grain sample are the key textural properties for predicting the water absorption rate of the moistened grain.

[0024] Table 3 Multiple Regression Analysis Table The regression equation is: Y = 1.358X1 - 0.014X2 - 170.104X3 + 72.545X4 + 15.926, where Y is the water absorption rate after soaking; X1 is the volume after soaking; X2 is the hardness after soaking; X3 is the resilience after soaking; and X4 is the cohesion after soaking. Multiple linear regression analysis showed that the four textural properties—volume, hardness, resilience, and cohesion—significantly affected the regression prediction model. The p-values ​​were all less than 0.05, indicating significance at the 0.05 level. The Durbin-Watson value approached 2, and the F-value p < 0.001, indicating good significance. Therefore, the model meets the requirements. R 2 =0.825, adjusted R 2 =0.798, which is close to 1, indicating a good model fit. Regarding variable collinearity, the VIF is less than 5 for all variables, suggesting a well-constructed model.

[0025] Figure 2 To compare the predicted water absorption rates of some batches of moistened grain samples with the measured values ​​based on the water absorption rate prediction model, the volume, hardness, resilience, and cohesion of the moistened grain sample particles are key textural properties that determine the water absorption rate prediction, contributing 79.8% to the predicted water absorption rate. Figure 2 As shown, the relative error between the model's predicted values ​​and the measured values ​​is 3.46%, with an average accuracy of 96.54%. The model is stable, highly accurate, and has relatively small errors.

Claims

1. A method for detecting the water absorption rate of grains based on multidimensional textural properties, characterized in that, The method includes: Step S1: After soaking the grain, select multiple samples and determine the textural properties and water absorption rate of each sample. Step S2: Analyze the correlation between grain water absorption rate and various textural properties, identify textural properties that are significantly correlated with grain water absorption rate as key textural properties, and establish a grain water absorption rate prediction model based on key textural properties. Step S3: Determine the key textural properties of the sample to be tested, and calculate the water absorption rate of the sample to be tested based on the established grain water absorption rate prediction model.

2. The method for detecting grain water absorption rate based on multidimensional textural properties according to claim 1, characterized in that, The textural properties include the sample's volume, hardness, viscosity, elasticity, chewiness, adhesiveness, cohesiveness, and resilience.

3. The method for detecting grain water absorption rate based on multidimensional textural properties according to claim 1, characterized in that, The determination of textural properties in step S1 includes: Step S11: Place the sample into the detection container, select the probe according to the required experimental conditions, and install it into the instrument; Step S12: Perform pretreatment on the sample according to the testing requirements of each textural property, and select the textural property testing method; Step S13: Perform textural property testing, preprocess the obtained textural property data, and take the average value of multiple measurements as the final measurement result of the textural property.

4. The method for detecting grain water absorption rate based on multidimensional textural properties according to claim 1, characterized in that, The step S1 for determining the water absorption rate of the sample includes: recording the weight of the grain sample before soaking as m0, and after soaking, removing the free water on the surface of the grain particles in a 35℃ oven and weighing it, recording the grain weight as m1. The water absorption rate of the grain is calculated as (m1-m0) / m0×100%.

5. The method for detecting grain water absorption rate based on multidimensional textural properties according to claim 1, characterized in that, Step S3 includes: obtaining the correlation degree between grain water absorption rate and each textural characteristic based on Pearson correlation analysis, determining key textural characteristics based on the correlation degree, performing multiple linear stepwise regression analysis with key textural characteristics as independent variables and grain water absorption rate as dependent variable, and establishing a grain water absorption rate prediction model.

6. The method for detecting the water absorption rate of grains based on multidimensional textural properties according to any one of claims 1-5, characterized in that, The prediction model for the water absorption rate of red sorghum after soaking is: Y = 1.358X1 - 0.014X2 - 170.104X3 + 72.545X4 + 15.926, where Y is the water absorption rate after soaking; X1 is the volume after soaking; X2 is the hardness after soaking; X3 is the resilience after soaking; and X4 is the cohesion after soaking.

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