CN115235927A - Method for screening wheat suitable for brewing and starter propagation - Google Patents
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Abstract
The invention discloses a method for screening wheat suitable for brewing and starter propagation, and belongs to the technical field of brewing. The method utilizes a single grain hardness meter to measure and analyze the hardness of the high-purity wheat grains, clarifies the relation between the standard deviation and the average number of the hardness of the single grains, establishes a single-grain hardness consistency determination model, and can be used for quickly evaluating the hardness consistency of the mixed wheat sample. The method comprises the steps of measuring a wheat sample to be detected by using a single grain hardness tester, obtaining the standard deviation and the average of the hardness of the sample, and determining the hardness consistency of the wheat sample to be detected according to a judgment model. The method provided by the invention can quickly and accurately evaluate the hardness consistency of the wheat sample.
Description
Technical Field
The invention relates to a method for screening wheat suitable for brewing and starter propagation, and belongs to the technical field of brewing.
Background
The hardness of wheat grains reflects the texture of wheat endosperm, namely the tight combination of protein and starch, and determines the energy consumption of milling and the size and breakage rate of starch granules. According to the hardness of grains, the wheat can be divided into hard wheat, mixed wheat and soft wheat. The wheat grain hardness is an important index for evaluating the wheat quality. For example, GB1351-2008 uses grain hardness as a main index for evaluating wheat quality, uses hardness index to replace cutin rate and flour quality rate as characterization indexes of softness and hardness of wheat, the hardness index of soft wheat is not higher than 45, the hardness index of hard wheat is not lower than 60, and the wheat with the hardness index between 45 and 60 is mixed wheat. The grain hardness is the best evaluation index for determining the processing quality and the final use of the wheat. Besides being used as food for processing flour, soft wheat is also a high-quality starter propagation raw material for the wine industry.
With the increasing consumption of Maotai-flavor liquor, the demand of wheat for brewing and yeast making is rising year by year. The soft wheat suitable for brewing wine and making yeast has low hardness of grains, is easy to absorb moisture, can be rolled into plum blossom BAN with a rotten core and a rotten skin, and is beneficial to the growth of microorganisms. Because the koji is prepared by manual trampling, the hardness difference between grains is required to be small, and hard grains cannot be mixed particularly. In recent years, the scale and standardization level of wheat production is continuously improved, but the quality difference of wheat products is still large. Although many large wine enterprises establish own brewing wheat production base to ensure the quality of products, the quality difference of the products, especially the over-large grain hardness difference, can be caused due to the mixing of varieties, the difference of production conditions and the like. The brewing and yeast making wheat not only requires that the average grain hardness accords with the soft wheat, but also requires that the consistency of the grain hardness is good, otherwise, the yeast making quality is influenced.
The method for measuring the hardness of the wheat grains mainly comprises a grinding volume method, a grinding time method (GT), a particle index method (PSI), a cuticle rate method, a near infrared spectroscopy (NIR), a single-grain characteristic measuring instrument method (SKCS), an electron microscope direct observation method (SEM) and the like. Among them, 3 methods such as PSI, NIR, and SKCS are more commonly used. In the wheat purchasing process, chinese enterprises generally adopt a particle index method (PSI) to measure the average hardness of grains, and research and develop a corresponding JYDB 100 type wheat hardness instrument, but the single grain hardness cannot be detected and the consistency of the grain hardness cannot be evaluated. Therefore, in the process of purchasing the brewing and starter propagation wheat, whether the average grain hardness meets the requirement or not can be judged only by the average grain hardness.
Disclosure of Invention
[ problem ] to
The technical problem to be solved by the invention is that manual yeast treading is carried out in the brewing and yeast making process of Maotai-flavor liquor, which requires small hardness difference among wheat grains for yeast making and good consistency of grain hardness, and in the existing brewing and yeast making wheat purchasing process, technicians can only know the average grain hardness and cannot screen out the wheat with good consistency of grain hardness.
[ solution ]
The invention provides a method for screening wheat suitable for brewing and starter propagation, which comprises the following steps:
(1) Detecting the grain hardness of a wheat sample
Measuring the hardness of the wheat sample by using a single grain hardness tester, and calculating to obtain the average number and standard deviation of the grain hardness of the wheat sample;
(2) Calculating the mean standard deviation (S)
If the average number of the hardness of the wheat grains is less than 23, substituting the average number (X) into a regression equation S = -0.0166X +13.2737, and calculating to obtain S; substituting the mean number into regression equation S =3.7575E-05X if the mean number of wheat grain hardnesses is greater than or equal to 23 3 -6.7350E-03X 2 +0.3424X +8.1184, and calculating to obtain S;
(3) Selecting wheat for brewing and making yeast
And (3) comparing the standard deviation obtained in the step (1) with the S obtained in the step (2), and screening out a wheat sample with the standard deviation not more than S +2.2 for brewing and starter propagation.
In one embodiment of the present invention, the wheat middlings in step (1) are removed of impurities and crushed grains, and the water content is in the range of 11-13%. Moisture content exceeding 15% has a great influence on the screening results of low-hardness soft wheat.
In one embodiment of the invention, in step (1), each sample is tested 2 times repeatedly, each time 300 pieces are tested, the average number of the 2 times is taken as the average grain hardness and the standard deviation of the sample, the difference of the average grain hardness of the two times is not more than 2, and the standard deviation is not more than 0.5, otherwise, the measurement is carried out again.
In one embodiment of the invention, the average grain hardness of the wheat sample of step (1) is in the range of-7.20-80.23.
In one embodiment of the invention, the wheat sample with average grain hardness not more than 45 and standard deviation not more than S +2.2 is selected in the step (3) and is used for making wine and yeast.
[ advantageous effects ]
The method utilizes a Perten SKCS4100 instrument to detect and obtain the grain hardness of single grains of the wheat, and then utilizes the average number and standard deviation of the hardness of the single grains to establish a model for representing the consistency of the hardness of the wheat grains. And (3) determining the kernel hardness of the sample to be tested, calculating the average value, substituting the average value into the model, and screening the sample to be tested to obtain the wheat suitable for brewing and starter propagation.
The Perten SKCS4100 is used for detecting a sample only in a few minutes, has good repeatability and can quickly and accurately identify the hardness and the consistency of the wheat grains.
Drawings
FIG. 1 is a scatter plot of standard deviation and mean of individual grain hardness for 818 wheat samples.
FIG. 2 is the relationship between the standard deviation and the average value of the hardness of single grains of 793 samples of high-purity wheat varieties.
FIG. 3 is a graph showing the relationship between the increase in the standard deviation of hardness (Y) of samples having different mixing ratios and the difference in the hardness (X) between the original sample and the different sample.
Detailed Description
The following examples used a single grain hardness tester (Perten SKCS 4100) capable of detecting the hardness of a single grain, which was capable of detecting 300 grains at a time, and determining that the samples were "SOFT (SOFT)", "mixed Material (MIXTED)" and "HARD (HARD)", and the average grain hardness and standard deviation were obtained, and the data were directly displayed by a computer, with good reproducibility and large detectable hardness range. The instrument automatically separates each sample through a vacuum separation disc, the samples fall into a weighing hopper and are weighed, the samples fall into an insulating meniscus crushing device from the hopper, and the corresponding relation between crushing force and time and the conductivity between a crushing rotating wheel and the meniscus device are recorded when grains enter a meniscus gap to be crushed and flattened. The microprocessor transmits a series of data of each seed grain to the main control computer, including sample weight, weigher stability, crushing force peak value, average conductivity, crushing area, crushing length and the like. The computer calculates the hardness index, the grain weight, the grain diameter, the grain moisture and the like of the single grain, and calculates the average number and the standard deviation of corresponding indexes. The master computer will check the series of data for each sample against certain criteria and reject the data in question, i.e. invalid data.
Example 1 construction of a model for screening wheat suitable for brewing koji from 793 samples of high purity wheat variety
The relation between the standard deviation and the average number of single grain hardness detected by Perten SKCS4100 in an uncontaminated pure line wheat variety sample is clarified through the embodiment, and a method for evaluating the consistency of the wheat grain hardness by using the standard deviation is found.
The specific process is as follows:
1. preparation of wheat samples
818 parts of wheat variety samples mainly come from examined wheat varieties and stable high-generation strains, no or a small amount of mixed plants are identified by planting in field plots, impurities are removed before flowering, manual harvesting and threshing are carried out, harvesting or threshing is stopped from mixing, uniform drying is carried out (the moisture range reaches 11-13%), impurities and broken grains are removed, about 100 g of samples are randomly extracted from each sample, and the samples are stored for later use.
2. Detection of wheat grain hardness
After the wheat is subjected to after-ripening period (after being harvested for 40 days), the kernel hardness test is started.
During detection, firstly, a switch of a Perten SKCS4100 and a matched computer is opened, a test program of a computer desktop is clicked, preheating is carried out for 30 minutes, and the number of detection samples is set to be 300. And fully and uniformly mixing the samples to be measured, randomly taking more than 300 grains, placing the grains into a sample hopper at one time, closing a door of the sample hopper, and automatically feeding the samples into a grain hopper. Inputting the Sample number in the test interface, clicking the buttons of 'Run Sample' and 'Continue', starting the measurement, displaying the number of 300 grains (setting the number of grains) by the computer, and finishing the detection. And after the measurement of all samples is finished, cleaning the residual grains and sample residues in the sample bucket, exiting the program, and closing the instrument and the computer.
The mean and standard deviation of the kernel hardness index, the hardness type of the sample (SOFT/MIXTED/HARD), and the mean moisture were recorded. Each sample was tested 2 times, with an average grain hardness difference of no more than 2 and a standard deviation of no more than 0.5 for 2 times, otherwise re-testing. And calculating the average grain hardness and standard deviation for 2 times, and storing the average grain hardness and standard deviation into an Excel file.
3. Data auditing and culling
Taking the average of the hardness of single grains of the sample as an abscissa axis and the standard deviation as an ordinate axis, and using Excel as a scatter diagram (figure 1), points which are free outside a concentrated distribution area appear in the diagram, mostly have larger standard deviations, and are mainly caused by impure varieties or mixed, and the samples corresponding to the points are removed. Sample 818 was tested in total, 25 were rejected (open circles in the figure), and 793 were retained for subsequent data analysis. 793 parts of higher-purity variety samples, 502 parts of soft wheat samples with the grain hardness of less than or equal to 45 (brewing and starter-making wheat belongs to soft wheat).
793 the average number of the high-purity variety samples is in a range of-7.02-80.23, the standard deviation is in a range of 10.24-15.67, and the samples are continuously distributed. 793 the samples of high-purity varieties basically have no mixture of different varieties, the production conditions of the same sample are similar, and the difference of the hardness of single grains is mainly determined by the characteristics of the varieties and is related to the grain difference of the varieties such as size, shape, plumpness and the like, so the samples belong to samples with consistent grain hardness.
4. Determination of the mean Standard deviation (S) of high purity samples of different hardness
The difference of the hardness values of the single grains can reflect the consistency of the hardness of the grains of the wheat sample, and the numerical values representing the variation in statistics mainly have standard deviation and variation coefficient. Perten SKCS4100 detection results show that the average number of the sample grain hardness and the variation coefficient are not in a direct proportion relation, so that the variation coefficient cannot be used for evaluating the sample hardness consistency, and only the standard deviation can be used. The scatter diagram shows that the standard deviation of the hardness of the single grain of the sample is not independent from the average value of the hardness of the single grain, and the standard deviation and the average value of the hardness of the single grain of the sample have a certain relation, which indicates that the standard deviation of the sample is different along with the difference of the average values of the hardness of the single grain.
In order to avoid the influence of a large number of samples on the relationship analysis between the samples, 793 varieties of samples are subjected to data grouping according to the average number of the grain hardness, 17 groups are divided in total, the group distance is 5, and the average grain hardness and the average standard deviation of each group of samples are calculated (table 2). According to the AACC55-31 method, the tested samples mainly comprise 7 types of all 8 types, but only 1 sample of a very hard type (hardness is 80-90), and a very hard type (hardness is more than 90) is lacked, and the hardness of the samples is mainly concentrated between 25-70 according to grouping results, and 4 types of the samples are more, namely soft (25-34), medium soft (35-44), medium hard (45-64) and hard (65-80). From table 1, it can be found that the standard deviation of the grain hardness changes from slowly decreasing to slowly increasing and then greatly decreasing with the increase of the average number, and a curve regression equation suitable for the standard deviation and the average number is not fitted by using Excel software.
TABLE 1 grouping results of wheat sample kernel hardness
Further analysis in different regions shows that the kernel hardness is less than 23 and greater than 23 in different regression relations. When the hardness of the grains is less than 23, the standard deviation shows a linear decreasing change trend along with the increase of the hardness of the grains, the regression equation is S = -0.0166X +13.2737, the extremely significant level (P = 4.01E-03) is reached, and the coefficient R is determined 2 And the fitting degree is higher than 0.90. When the hardness of the grains is more than 23, the standard deviation shows a change trend of increasing firstly and then decreasing along with the increase of the hardness of the grains, and the change trend accords with a 3-degree polynomial curve, and the regression equation of the change trend is S =3.7575E-05X 3 -6.7350E-03X 2 +0.3424X +8.1184, reaching a very significant level (P = 3.18E-07), determining the coefficient R 2 =0.98, the degree of fit is extremely high. In the two regression equations, X is the average grain hardness of the variety sample, and S is the predicted standard deviation of a certain X value, namely the average standard deviation of high-purity samples with different hardness. When the kernel hardness is 23, S calculated using the two equations is substantially equal, both 12.9, and therefore 23 is the boundary, also the integer closest to the group 6 mean 23.47 in table 2. Fig. 2 shows the main results of the regression analysis.
If the average number of the hardness of the single grains is less than 23, taking the average number as X, substituting the X into a linear regression equation S = -0.0166X +13.2737, and calculating S. If the average is 23 or more, it is substituted as X into a 3 rd order polynomial regression equation S =3.7575E-05X 3 -6.7350E-03X 2 +0.3424X +, 8.1184, calculate S.
5. Determination of wheat grain hardness consistency
Taking the average of the hardness of each grain of 793 samples as X, substituting the X into a corresponding regression equation, calculating S, calculating the difference between the standard difference of the hardness of each grain sample and the corresponding S, and determining the boundary whether the hardness of the grains is consistent or not according to the distribution of the differences. The average of these differences was 0.006, the standard deviation was 0.939, the kurtosis was-0302, and the skewness was 0.118, as shown by the statistics of the descriptions of these differences, which fit into a normal distribution, close to a normalized normal distribution. 95% and 99% are 2 confidence probabilities commonly used in agricultural science research, and considering that the uncertain factors of agricultural production conditions are more, the boundary judgment is carried out according to the confidence probability 99%, the critical value of 1% of the right tail is 2.2, the receiving area is normally distributed with the value less than or equal to 2.2, and the negative area is greater than 2.2. Therefore, S +2.2 is used as a limit for evaluating the consistency of the hardness of the grains by using the standard deviation. If the standard deviation of the hardness of the sample grains is less than or equal to S +2.2, the consistency of the hardness of the grains is judged as 'consistent', otherwise, the hardness of the grains is 'inconsistent'.
In addition, because the brewing and starter propagation wheat belongs to soft wheat, according to the methods of GB1351-2008 and AACC55-31, the average grain hardness of the soft wheat is less than or equal to 45. The hardness type SOFT (SOFT) samples tested using the Perten SKCS4100 had no average grain hardness higher than 45, but there were also samples with average grain hardness ≦ 45 and hardness type mixed Material (MIXTED), mainly mixed with some HARD (HARD) samples. However, samples tested as blends could not be considered as "inconsistent" in hardness between kernels, since some blend samples with an average hardness around 45 also had a lower standard deviation.
Finally selecting a wheat sample with the average grain hardness of less than or equal to 45 and the standard deviation of less than or equal to S +2.2 for brewing wine and making yeast.
Conclusion
Example 1 details the procedure for using 793 samples of high purity wheat variety to create a model for screening for wheat suitable for brewing koji. The relationship between the standard deviation and the average of the hardness of single grains is analyzed by utilizing 793 high-purity wheat variety samples through Excel software, 2 regression equations with high fitting degree are obtained, and therefore the average standard deviation (S) of the high-purity samples with different hardness is determined. The distribution of the standard deviation of the grain hardness of the samples and the difference value of the corresponding S not only conforms to the normal distribution, but also approaches to the normalized normal distribution, thereby illustrating the scientificity of S calculation. The critical value of the probability interval of 1% at the right tail of normal distribution is used as the limit of hardness consistency evaluation, and about 99% of 793 wheat variety samples are evaluated as hardness consistency.
Hardness consistency evaluations were performed on all 818 samples according to the method of the present invention, with 787 samples having hardness rated "consistent" at 96.2% and 31 parts "inconsistent" (table 3) at 3.8%, and these samples having higher hardness standard deviations, a minimum of 14.57 and a maximum of 20.37, all higher than the corresponding S + 2.2. Of the 793 high purity samples, 785 samples rated "consistent" in hardness, accounting for 99.0% and 8 "inconsistent" (numbers 24-31 in table 3), accounting for 1.0%, a minimum of 14.57 and a maximum of 15.67, all higher than the corresponding S + 2.2. 502 parts of the 818 wheat samples have average grain hardness less than or equal to 45 and hardness evaluation of consistency, and are suitable for making wine and yeast.
TABLE 2 hardness results for samples with "inconsistent" grain hardness
Example 2 evaluation of consistency of hardness of miscellaneous wheat samples using the model (screening conditions) established in example 1
1. Sample mixing and grain hardness detection
Samples of high-purity wheat varieties with 9 different average grain hardnesses and standard deviations are selected from example 1, the numbers A, B, C, D, E, F, G, H and K are substituted according to the average grain hardness from low to high, the consistency of the grain hardnesses is 'consistent', and the detected average grain hardness and standard deviations and the average standard deviation of the corresponding standard samples are listed in Table 3. The 9 samples are mixed in pairs according to the proportion that the number of the seeds of the abnormal samples accounts for 5 percent, 15 percent and 25 percent of the total number of 300 particles (the samples accounting for more than or equal to 25 percent of the obtained mixed samples are original samples, and the samples accounting for less than or equal to 25 percent of the obtained mixed samples are abnormal samples), so as to obtain 216 mixed sample combinations. The sample preparation and hardness testing were the same as in example 1, except that 300 samples were required.
TABLE 3 average and standard deviation of hardness of grains of the samples and average standard deviation of corresponding standard samples
2. Kernel hardness consistency evaluation of hybrid samples
According to the average hardness of grains of the hybrid sample, substituting regression equations S = -0.0166X +13.2737 (average is less than 23) and S =3.7575E-05X respectively 3 -6.7350E-03X 2 +0.3424X +8.1184 (average greater than or equal to 23) calculates S. Then, whether the hardness of the grains is consistent or not is determined according to the standard deviation of each mixed sample, and meanwhile, the standard deviation is compared with the original sample, and the variation of the average hardness and the standard deviation of the grains is analyzed. The results of kernel hardness detection and consistency evaluation are shown in table 4.
TABLE 4 detection of grain hardness and consistency evaluation results of the hybrid samples
Note: mixing combination 285A +15B represents that the original sample A285 particles are mixed with the different sample B15 particles, and the other similar; consistency 1 and 2 represent "consistent" and "inconsistent", respectively.
Through analysis of the grain hardness detection results of 3 different mixing ratios, the average number of standard deviations of 5%, 15% and 25% of the mixed samples is respectively increased by 2.5, 5.6 and 7.6 compared with the original sample, which shows that the standard deviation is obviously increased along with the increase of the mixing ratio, but the increase amplitude is continuously reduced. The standard deviation variation ranges of the samples with the 3 mixing ratios are 0.0-10.6, 0.1-20.6 and 0.2-25.9 respectively, and the larger variation interval is also related to the grain hardness difference between the original sample and the different sample. From the relationship (fig. 3) between the standard deviation increase value of the grain hardness of the 3 samples with different mixing ratios and the hardness difference value of the original sample and the different sample, the higher the mixing ratio is, the higher the standard deviation increase value is, the more significant quadratic polynomial relationship is formed between the standard deviation increase value and the hardness difference value, and along with the increase of the hardness difference value, the standard deviation increase value is in an ascending trend, and the ascending amplitude is continuously increased.
By analyzing the uniformity of the grain hardness of the samples with different mixing ratios and the hardness difference change of the two mixed samples (table 5), the 3 mixed ratios have samples with consistent grain hardness and inconsistent grain hardness, the maximum difference between the number of the consistent samples and the hardness of the mixed samples is reduced along with the increase of the mixing ratio, the hardness difference of the two types of mixed samples is more obvious, and the hardness difference of the 25 percent mixed sample is 21.2 as a limit.
TABLE 5 sample seed hardness uniformity and hardness differential variation for two mixed samples at different blending ratios
Conclusion
In example 2, the method established in the example is used to characterize the hardness consistency of the hybrid sample, thereby verifying the application effect of the screening method established in example 1. 9 high-purity wheat variety samples with different average grain hardness and standard deviation are selected, 216 mixed samples are constructed according to the mixing proportion of 5%, 15% and 25%, and the consistency of the grain hardness is characterized. Compared with the original sample, the increase of the standard deviation of the hardness of the grains of the hybrid sample and the change of the consistency are mainly related to the hardness difference value of the hybrid ratio and the mixed sample 2, the larger the hybrid ratio is and the hardness difference value between the mixed samples is, the larger the increase value of the standard deviation is, and the hardness consistency is poor, which shows that the hardness consistency of the hybrid sample can be better evaluated by the method.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by one skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A method for screening wheat suitable for brewing wine and making koji is characterized by comprising the following steps:
(1) Detecting the grain hardness of wheat samples
Determining the grain hardness of the wheat sample by using a single grain hardness tester, and calculating to obtain the average number and standard deviation of the grain hardness of the wheat sample;
(2) Calculating the mean standard deviation S
If the average number of the hardness of the wheat grains is less than 23, substituting the average number X into a regression equation S = -0.0166X +13.2737, and calculating to obtain S; substituting the mean number into regression equation S =3.7575E-05X if the mean number of wheat grain hardnesses is greater than or equal to 23 3 -6.7350E-03X 2 +0.3424X +8.1184, and calculating to obtain S;
(3) Selecting wheat for brewing yeast
And (3) comparing the standard deviation obtained in the step (1) with the S obtained in the step (2), and screening out a wheat sample with the standard deviation not more than S +2.2 for brewing and starter propagation.
2. The method according to claim 1, wherein the wheat sample in step (1) is subjected to removal of impurities and crushed grains, and the moisture content of the wheat sample is 11-13%.
3. The method according to claim 1, wherein in step (1), the test is repeated 2 times for each sample, 300 grains are tested, and the average grain hardness and standard deviation of the samples are taken as the average number of 2 times; the average grain hardness difference of 2 times does not exceed 2, and the standard deviation does not exceed 0.5, otherwise, the measurement is carried out again.
4. The method of claim 1, wherein the average grain hardness of the wheat sample of step (1) is in the range of-7.20-80.23.
5. The method of claim 1, wherein the wheat sample with average grain hardness of 45 or less and standard deviation of S +2.2 or less is selected in the step (3) for brewing koji making.
6. The method according to claim 1, wherein the wine is Maotai-flavor liquor.
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