CN114814011B - Method for identifying storage time of Daqu - Google Patents

Method for identifying storage time of Daqu Download PDF

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CN114814011B
CN114814011B CN202210381958.4A CN202210381958A CN114814011B CN 114814011 B CN114814011 B CN 114814011B CN 202210381958 A CN202210381958 A CN 202210381958A CN 114814011 B CN114814011 B CN 114814011B
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daqu
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storage time
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lactic acid
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CN114814011A (en
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韩莹
牟明月
涂华彬
杨玉波
陈定崑
程艳波
何姗姗
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Kweichow Moutai Co Ltd
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Abstract

The invention belongs to the technical field of white spirit manufacturing, and particularly relates to a method for identifying the storage time of Daqu, which is characterized in that the storage time of Daqu is identified according to the content of lactic acid in Daqu and the content of flavor substance components, wherein the flavor substance components comprise acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde. According to the method, the lactic acid content and the content information of the flavor substance components in the Daqu with different storage times are analyzed, and the Daqu with different storage times is found to have certain difference in the lactic acid content and the content of the flavor substance components, so that the method for identifying the Daqu storage time based on the difference of the lactic acid content and the content of the flavor substance components is established, the method can judge the Daqu storage time more scientifically and accurately, and meanwhile, a novel identification method is provided for a large number of Daqus with unknown storage times, and the identification method is high in accuracy and good in identification effect.

Description

Method for identifying storage time of Daqu
Technical Field
The application relates to the technical field of white spirit manufacturing, in particular to a method for identifying Daqu storage time.
Background
Daqu is a raw material for brewing wine, and is mainly a starter prepared by crushing raw materials such as barley, wheat, pea and the like, adding water, stirring and treading the raw materials into blocks, and fermenting and storing the blocks. Therefore, the Chinese white spirit is said that the quality of Daqu directly affects the quality of white spirit.
In the process of making the Daqu, the storage time is one of the important factors influencing the finished Daqu, and the Daqu can be continuously fermented in the storage process, namely, the so-called "Daqu post-fermentation". In particular, in the production application of high-temperature Daqu, the Daqu is generally required to have storage time of at least about six months so as to ensure the stability of the Daqu quality, but research shows that the storage time is too short or too long so as to obviously influence the Daqu quality, thereby influencing the subsequent wine production. At present, the storage time of the Daqu is determined by the time of entering and exiting the warehouse of the first batch of Daqu and the time of grinding the yeast during opening the warehouse, but the actual storage time of the Daqu is inconsistent and the storage time is determined by on-site production management due to the fact that the Daqu stored in Qu Cang is not used for entering or exiting the warehouse of the same batch in most cases, and a scientific and accurate determination method is lacked. Accordingly, the inventors have recognized that there is a need to provide a method for scientifically and accurately determining the storage time of Daqu.
Disclosure of Invention
In order to scientifically and accurately judge the Daqu storage time, the invention provides a method for identifying the Daqu storage time.
In one aspect, the present invention provides a method of identifying the storage time of Daqu based on the lactic acid content of Daqu and the content of flavor components including at least one of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde.
In some embodiments, the flavor component includes a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde.
In some embodiments, the invention provides a method of identifying the storage time of Daqu comprising the steps of:
s1, obtaining the content of lactic acid in Daqu and the content of each flavor substance component;
s2, inputting the content of the lactic acid and the content of each flavor substance component into a judging model to obtain the storage time of the Daqu; the discrimination model is a functional relation between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component.
In some embodiments, in step S1, each of the flavor components includes at least one of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde.
In some embodiments, in step S1, the flavor component comprises a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde.
In some embodiments, the functional relationship is a linear model, y=xa, Y is the storage time of the Daqu; x is a data set formed by the content of lactic acid in Daqu and the content of flavor substance components; a is a discrimination coefficient between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component.
In some embodiments, the discrimination coefficients are constant data sets composed of corresponding coefficients LD1, LD2, LD3, LD4 of different dimensions, where LD1 is a corresponding coefficient of a first dimension, LD2 is a corresponding coefficient of a second dimension, LD3 is a corresponding coefficient of a third dimension, and LD4 is a corresponding coefficient of a fourth dimension; the discrimination coefficient is a= [ LD1 LD2 LD3 LD4].
In some implementations, the correspondence coefficient LD1 is:
Figure BDA0003592186560000021
in some implementations, the correspondence coefficient LD2 is:
Figure BDA0003592186560000022
in some implementations, the correspondence coefficient LD3 is:
Figure BDA0003592186560000031
in some implementations, the correspondence coefficient LD4 is:
Figure BDA0003592186560000032
in some embodiments, the flavor component is a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde, the discrimination coefficients are as follows:
Figure BDA0003592186560000033
in some embodiments, the flavor component is a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde, the functional relationship is as follows:
Figure BDA0003592186560000041
the X is a data set formed by the contents of lactic acid, acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde in Daqu; y is the storage time of the Daqu corresponding to X.
In some embodiments, the method for constructing the discriminant model includes the steps of:
s2.1, taking the content of the obtained lactic acid and the content of the flavor substance component as characteristic variables, and randomly dividing the Daqu corresponding to the characteristic variables into training data and data to be detected;
s2.2, importing the storage time of the Daqu corresponding to the training data and the content data of the lactic acid and the flavor substance components into R software to obtain a judging model of the functional relation between the storage time of the Daqu and the lactic acid content in the Daqu and the content of the flavor substance components.
In some embodiments, the amount of training data is greater than 70% of the total amount of the Daqu sample.
In some embodiments, the functional relationship is constructed by an LDA algorithm in the R software package MASS.
In some embodiments, the construction process of the discriminant model is specifically as follows:
firstly, importing a data set into a program, carrying out standardization and centralization on the data set, training the standardized and centralized data set into a set and a test set in a random mode by utilizing a subset () function, and thus completing the preparation of training data;
and secondly, importing training data into a MASS software package, using LDA () function to take the Daqu storage time in the training set as an independent variable and other variables as dependent variables, and constructing an LDA (laser direct structuring) discrimination model for discriminating the Daqu storage time.
In some embodiments, the other variables are the lactic acid of the Daqu and the content of each flavor component during the construction of the discriminant model.
In some embodiments, in the construction process of the discriminant model, the input value of the lda () function is a data set constructed by the storage time of different daqus and the content data of lactic acid and each flavor substance component in the daqus.
In some embodiments, the output value of the lda () function is a functional relationship of the discriminant model.
In some embodiments, the construction method further comprises the steps of:
s2.3, verification of a discrimination model: and re-introducing the content data of the lactic acid of the training data and the content data of each flavor substance component into the judging model for carrying out the judgment again to obtain the Daqu storage time corresponding to the training data.
In some embodiments, the method for constructing a discriminant model further comprises the steps of:
s2.4, judging the storage time of the data to be tested: and importing the data to be detected into the discrimination model to obtain the Daqu storage time corresponding to the data to be detected.
In some embodiments, the content of the lactic acid is determined by high performance liquid chromatography methods; the content of the flavor substance component is measured by a gas chromatography-mass spectrometry analysis method;
in some embodiments, the step of determining the lactic acid content is as follows:
(1) Adding a Daqu sample and ultrapure water into a centrifuge tube, and sucking supernatant after oscillation, ultrasonic and centrifugation, and diluting to obtain a diluent;
(2) Filtering the diluted solution by a membrane filtration method to obtain filtrate;
(3) The filtrate was removed to a high performance liquid chromatograph for measurement.
In some embodiments, the addition ratio of the Daqu sample to ultrapure water in step (1) is 1 g/2 ml.
In some embodiments, the dilution factor in step (1) is 10-fold.
In some embodiments, in step (3), the lactic acid content is determined using an external standard method.
In some embodiments, the determination of the content of the flavor component is as follows:
1) Adding a Daqu sample and an ethanol water solution into a centrifuge tube, and sucking a supernatant into a sample bottle after shaking, ultrasonic treatment and centrifugation;
2) Adding NaCl into the sample bottle until the solution is saturated;
3) Adding an extractant into the saturated solution in the step 2), vortex, oscillation, extraction and centrifugation, then sucking an upper organic phase, and measuring the upper organic phase by adopting a gas chromatography-mass spectrometry technology.
In some embodiments, the aqueous ethanol solution has a mass fraction of 40%; the addition ratio of the Daqu sample to the ethanol water solution is 1 g/2 ml.
In some embodiments, the ratio of the amount of supernatant aspirated to the addition of the Daqu sample in step 1) is 1ml:1g.
In some embodiments, in step 3), the extractant is diethyl ether; the ratio of the addition amount of the extractant to the sucking amount of the supernatant liquid is 1ml:5ml.
In some embodiments, in step 3), the content of the flavor component is determined using an external standard method.
In another aspect, the present invention provides an apparatus for identifying a storage time of Daqu, the apparatus comprising:
the data acquisition module is used for acquiring the content of lactic acid in the Daqu and the content of each flavor substance component;
the judging module is used for inputting the content of the lactic acid and the content of each flavor substance component into a judging model to obtain a judging result of the Daqu storage time; the judging model is a functional relation between the content of the lactic acid and the content of each flavor substance component and the storage time of the Daqu; the flavor substance component comprises at least one of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde and lactic acid.
In some embodiments, the flavor component includes a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde, lactic acid.
In some embodiments, the functional relationship is a linear model, y=xa; y is the storage time of Daqu; x is a data set formed by the content of lactic acid in the Daqu and the content of each flavor substance component; a is the discrimination coefficient between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component.
In some embodiments, the discrimination coefficients are constant data sets composed of corresponding coefficients LD1, LD2, LD3, LD4 of different dimensions, where LD1 is a corresponding coefficient of a first dimension, LD2 is a corresponding coefficient of a second dimension, LD3 is a corresponding coefficient of a third dimension, and LD4 is a corresponding coefficient of a fourth dimension; the discrimination coefficient is a= [ LD1 LD2 LD3 LD4].
In some implementations, the correspondence coefficient LD1 is:
Figure BDA0003592186560000061
in some implementations, the correspondence coefficient LD2 is:
Figure BDA0003592186560000062
in some implementations, the correspondence coefficient LD3 is:
Figure BDA0003592186560000063
in some implementations, the correspondence coefficient LD4 is:
Figure BDA0003592186560000071
in some embodiments, the flavor component is a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde, the discrimination coefficients are as follows:
Figure BDA0003592186560000072
in some embodiments, the flavor component is a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde, the functional relationship is as follows:
Figure BDA0003592186560000073
the X is a data set formed by the contents of lactic acid, acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde in Daqu; y is the storage time of the Daqu corresponding to X.
In yet another aspect, the invention provides the use of the method in identifying the storage time of Daqu.
In some embodiments, the Daqu is a high temperature Daqu, which refers to a Daqu that requires continuous fermentation at a high temperature stage during production.
In some embodiments, the high temperature yeast is Maotai-flavor liquor yeast.
In summary, the present application includes at least one of the following beneficial technical effects:
(1) According to the method, the lactic acid content and the content information of the flavor substance components in the Daqu with different storage times are analyzed, and the Daqu with different storage times is found to have certain difference in the lactic acid content and the content of the flavor substance components, so that a Daqu storage time identification method based on the difference of the lactic acid content and the content of the flavor substance components is established, and the identification accuracy of the Daqu storage time corresponding to training data is up to 97.2% through verification, and the identification accuracy of the Daqu storage time corresponding to data to be tested is up to 87%, so that the method has high feasibility for identifying the Daqu storage time, and further provides a new idea for perfecting Daqu production management and evaluation systems.
(2) Compared with the conventional Daqu production management method and evaluation system, the sampling detection steps are relatively simple, the detection speed is relatively faster, the Daqu storage time can be judged more scientifically and accurately by constructing the Daqu storage time judgment model, meanwhile, a novel identification method is provided for a large amount of Daqus with unknown storage time, the accuracy of the identification method provided by the invention is close to 90%, and the judgment effect is very good.
Detailed Description
The technical solution of the present invention is further illustrated by the following specific examples, which do not represent limitations on the scope of the present invention. Some insubstantial modifications and adaptations of the invention based on the inventive concept by others remain within the scope of the invention.
Example 1A method for constructing a discrimination model for discriminating Daqu storage time
S1, obtaining content information of lactic acid and each flavor substance component in Daqu;
in this example, the Daqu sample was derived from high temperature Daqu with a storage time of 0-8 months provided by Maotai liquor, inc., guizhou, 153 total, and the lactic acid content and the content of each flavor component in the Daqu sample were measured:
s1.1 acquisition of the content of lactic acid in Daqu sample
Adding 5.0g of Daqu powder sample and 10mL of ultrapure water into a centrifuge tube, absorbing supernatant after oscillation, ultrasound and centrifugation, diluting the supernatant by 10 times to obtain a diluent, filtering the diluent by a membrane filtration method to obtain filtrate, and then transferring the filtrate into a high performance liquid chromatography analyzer for lactic acid analysis, wherein the lactic acid content is determined by an external standard method, thus obtaining the data of the lactic acid content in the Daqu sample.
S1.2 acquisition of the content of each flavor component in the Daqu sample
Adding 10g of Daqu powder sample and 20mL of 40% ethanol aqueous solution into a centrifuge tube, oscillating, ultrasonic and centrifuging to absorb 10mL of supernatant in a sample bottle, adding NaCl into the sample bottle until the solution is saturated, adding 2mL of NaCl serving as an extractant, extracting an upper organic phase after vortex, oscillation and extraction centrifugation, analyzing the upper organic phase by adopting a gas chromatography-mass spectrometry technology to obtain flavor substance component information, and determining the content of the flavor substance component by adopting an external standard method to obtain content data of the flavor substance component in the Daqu sample.
The content data of lactic acid and flavor components corresponding to the storage time of 153 Daqu samples are obtained through the S1.1 and the S1.2, and the flavor components in the Daqu samples comprise acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde, namely the content data of lactic acid, acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde corresponding to the storage time of 153 Daqu samples are obtained through the S1.1 and the S1.2.
S2, constructing a Daqu storage time discrimination model;
s2.1, taking the content information of lactic acid and flavor substances in the Daqu sample obtained in the step S1 as characteristic variables, and randomly dividing 153 Daqu samples corresponding to the characteristic variables into 107 training data and 46 data to be tested, wherein the number of the training data accounts for about 70% of the total amount of the samples;
s2.2, importing storage time of the Daqu sample corresponding to the 107 training data and lactic acid content data and content data information of each flavor substance component in the Daqu sample into an LDA algorithm in an R software package MASS to construct a Daqu storage time judging model;
the LDA algorithm (Linear Discriminant Analysis ) in the R software package MASS used in this embodiment projects the high-dimensional pattern sample into the best discrimination vector space to achieve the effect of extracting classification information and compressing feature space dimensions, and ensures that the pattern sample has the largest inter-class distance and the smallest intra-class distance in the new subspace after projection, i.e. the pattern has the best separability in the space, thereby realizing separation discrimination on the complex sample.
In this embodiment, the process of constructing the Daqu storage time discrimination model by using the LDA algorithm in the R software package MASS is specifically as follows:
introducing the content data set of the lactic acid and the flavor substance components corresponding to the storage time of the Daqu samples obtained in the step S1 into a program, carrying out standardization and centralization treatment on the storage time of 153 Daqu samples and the content data of the corresponding lactic acid and the flavor substance components, dividing the standardized and centralized 153 Daqu samples into 70% training sets and 30% test sets in a random manner by using a subset () function, namely randomly dividing 153 Daqu samples corresponding to characteristic variables into 107 training data and 46 to-be-tested data, wherein the quantity of the training data accounts for about 70% of the total quantity of the samples, and thus completing the preparation of the training data;
the training data is imported into a MASS software package, in this embodiment, the input value of the LDA algorithm in the R software package MASS is a data set constructed by the storage time of different Daqu samples and the content data of lactic acid and each flavor component in the Daqu samples, that is, the input value of the LDA algorithm in the R software package MASS is a data set constructed by the storage time of 107 Daqu samples and the content data of lactic acid and each flavor component in the Daqu samples, that is, the training set; constructing an LDA discrimination model by using LDA () function and taking the storage time of the Daqu in the training set as independent variables and the contents of other variables such as lactic acid and flavor components in the Daqu sample as dependent variables, wherein the output value of the LDA algorithm in the R software package MASS is the functional relation of the discrimination model, namely the discrimination model of the functional relation between the storage time of the Daqu sample and the lactic acid content data and the flavor component content data in the Daqu sample is obtained and is used for discriminating the storage time of the Daqu;
the functional relation of the discrimination model constructed by the steps is specifically as follows:
Y＝XA
the functional relation is a linear model, and Y is the storage time of Daqu; x is a data set formed by the content of lactic acid in the Daqu and the content of each flavor substance component; a is a discrimination coefficient between the storage time of the Daqu and the content of lactic acid and flavor components in the Daqu.
A is a constant data set composed of corresponding coefficients LD1, LD2, LD3, LD4 of different dimensions, a= [ LD1 LD2 LD3 LD4], where LD1 is a corresponding coefficient of a first dimension, LD2 is a corresponding coefficient of a second dimension, LD3 is a corresponding coefficient of a third dimension, and LD4 is a corresponding coefficient of a fourth dimension, i.e., a functional relationship:
Y＝＝[LD1 LD2 LD3 LD4]×X
when the flavor substance components in the Daqu sample are the combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde, the numerical values of the corresponding coefficients LD1, LD2, LD3 and LD4 are specifically shown in the table 1:
table 1 specific numerical table of corresponding coefficients LD1, LD2, LD3, LD4
Figure BDA0003592186560000101
Figure BDA0003592186560000111
That is to say,
Figure BDA0003592186560000112
the constant data set of the discrimination coefficient a composed of the corresponding coefficients LD1, LD2, LD3, LD4 of different dimensions is specifically expressed as follows:
Figure BDA0003592186560000113
the above functional relationship is specifically expressed as follows:
Figure BDA0003592186560000114
x in the functional relation is a data set formed by the contents of lactic acid, acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde in Daqu; y is the storage time of the Daqu corresponding to X.
S3, verifying a Daqu storage time judging model;
and (3) re-introducing the data information of the content of the lactic acid and each flavor substance component of 108 training data obtained by the random distribution after the normalization and the centralization treatment into a discrimination model constructed by an LDA algorithm in an R software package MASS for performing a return judgment to obtain the Daqu storage time corresponding to the 108 training data, and evaluating the discrimination accuracy of the Daqu storage time discrimination model, wherein the result is shown in the following table 2:
TABLE 2 return judgment result table of Daqu storage time judgment model on training data
Figure BDA0003592186560000121
From the return judgment results obtained in table 2, it can be seen that after 107 training data are reintroduced into the judgment model constructed by the LDA algorithm in the R software package MASS, only three training data have judgment errors, and the judgment accuracy reaches 104/107=97.2%, which indicates that the judgment model constructed in step S2 has higher judgment accuracy and better judgment effect on the storage time of the Daqu.
S4, judging storage time of the Daqu sample;
the 46 pieces of data to be measured, which are obtained through the normalization and the centralization processing and are randomly distributed, are imported into a discrimination model constructed by an LDA algorithm in an R software package MASS, discrimination of storage time of a Daqu sample is carried out, and specific results are shown in the following table 3:
table 3 discrimination results table of data to be measured by Daqu storage time discrimination model
Figure BDA0003592186560000122
Figure BDA0003592186560000131
As can be seen from the discrimination results obtained in the above table 3, the number of the Daqu samples of the data to be measured is 46, the accuracy of the discrimination model to the data to be measured is 40/46=87%, and the results further indicate that the discrimination accuracy of the constructed discrimination model to the storage time of the Daqu samples to be measured is higher, and the discrimination effect is good;
in addition, as can be seen from table 3, the storage time of the Daqu sample with error in the discrimination result is different from the actual storage time by only one month in most cases, and the discrimination result is not deviated from the actual result to a great extent, so that the discrimination model for discriminating the Daqu storage time constructed by the method has high feasibility.
Example 2A method of identifying Daqu storage time
Based on example 1, this example provides a method for identifying the storage time of Daqu, comprising the following specific steps:
s1, obtaining the content of lactic acid in Daqu and the content of each flavor substance component;
s2, inputting the content of the lactic acid and the content of each flavor substance component into a judging model to obtain the storage time of the Daqu; the judging model is a functional relation between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component;
the method for obtaining the lactic acid content of the Daqu and the content of each flavor component in the step S1 is the same as that of the example 1;
the above-mentioned discrimination model is the discrimination model constructed in example 1, namely, is the functional relation between the storage time of Daqu and the lactic acid content and the contents of each flavor substance component in Daqu; the functional relation is a linear model, Y=XA, and Y is the storage time of the Daqu; x is a data set formed by the content of lactic acid in the Daqu and the content of each flavor substance component; a is a distinguishing coefficient between the storage time of the Daqu and the content of lactic acid and each flavor substance component in the Daqu, A is a constant data set composed of corresponding coefficients LD1, LD2, LD3 and LD4 of different dimensions, A= [ LD1 LD2 LD3 LD4], wherein LD1 is a corresponding coefficient of a first dimension, LD2 is a corresponding coefficient of a second dimension, LD3 is a corresponding coefficient of a third dimension, and LD4 is a corresponding coefficient of a fourth dimension, namely, the functional relation is:
Y＝＝[LD1 LD2 LD3 LD4]×X
the flavor components of the Daqu in this example are acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, and combinations of benzaldehyde, LD1, LD2, LD3, LD4 are specifically as follows:
Figure BDA0003592186560000141
the constant data set of the discrimination coefficient a composed of the corresponding coefficients LD1, LD2, LD3, LD4 of different dimensions is specifically expressed as follows:
Figure BDA0003592186560000142
the functional relationship is specifically expressed as follows:
Figure BDA0003592186560000143
x in the functional relation is a data set formed by the contents of lactic acid, acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde in Daqu; y is the storage time of the Daqu corresponding to X.
And (2) introducing the contents of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde and lactic acid in the Daqu sample obtained in the step (S1) into a discrimination model constructed by an LDA algorithm in a R software package MASS, namely a functional relation, so as to obtain the storage time of the Daqu sample corresponding to the contents of the input acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde and lactic acid, and realizing the discrimination of the Daqu storage time.
Example 3A device for identifying Daqu storage time
The embodiment provides a device for identifying the storage time of Daqu, which comprises:
the data acquisition module is used for acquiring the content of lactic acid in the Daqu and the content of each flavor substance component;
the judging module is used for inputting the content of the lactic acid and the content of each flavor substance component into a judging model to obtain a judging result of the Daqu storage time; the distinguishing model is a functional relation between the content of the lactic acid and the content of each flavor substance component and the storage time of the Daqu;
the flavor components in this example include combinations of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde; of course, the flavor component may also include at least one of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde.
It is important to note here that the foregoing embodiments are limited to the particular embodiments disclosed as the best mode contemplated for carrying out the invention, and that these embodiments are presented in a manner that will enable one of ordinary skill in the art to more fully understand the principles and operation of the invention and that are not intended to limit the invention in any way nor to limit the scope of the invention in any way any one of ordinary skill in the art to which it pertains.

Claims (23)

1. A method for identifying the storage time of Daqu, characterized in that the storage time of Daqu is identified according to the content of lactic acid in Daqu and the content of flavor components, wherein the flavor components comprise a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde;
the method is characterized by identifying the following functional relation, wherein the functional relation is a linear model, the linear model is Y=XA, and Y is the storage time of Daqu; the X is a data set formed by the content of the lactic acid in the Daqu and the content of the flavor substance component; the A is a distinguishing coefficient between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component;
the discrimination coefficient is a constant data set composed of corresponding coefficients LD1, LD2, LD3 and LD4 in different dimensions, wherein LD1 is a corresponding coefficient in a first dimension, LD2 is a corresponding coefficient in a second dimension, LD3 is a corresponding coefficient in a third dimension, LD4 is a corresponding coefficient in a fourth dimension, and the discrimination coefficient is A= [ LD1 LD2 LD3 LD4];
the said
Figure FDA0004136841580000011
Said->
Figure FDA0004136841580000012
The said
Figure FDA0004136841580000013
Said->
Figure FDA0004136841580000014
The discrimination coefficient A is as follows:
Figure FDA0004136841580000021
the functional relationship is as follows:
Figure FDA0004136841580000022
wherein the Daqu is high temperature Daqu within 0-8 months.
2. A method for identifying the storage time of Daqu comprising the steps of:
s1, obtaining the content of lactic acid in Daqu and the content of each flavor substance component;
s2, inputting the content of the lactic acid and the content of each flavor substance component into a judging model to obtain the storage time of the Daqu; the judging model is a functional relation between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component;
in the step S1, the flavor components comprise a combination of acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol and benzaldehyde;
the functional relation is a linear model, Y=XA, and Y is the storage time of the Daqu; x is a data set consisting of the lactic acid content and the flavor component content of the Daqu; a is a distinguishing coefficient between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component;
the discrimination coefficient is a constant data set composed of corresponding coefficients LD1, LD2, LD3 and LD4 in different dimensions, wherein LD1 is a corresponding coefficient in a first dimension, LD2 is a corresponding coefficient in a second dimension, LD3 is a corresponding coefficient in a third dimension, LD4 is a corresponding coefficient in a fourth dimension, and the discrimination coefficient is A= [ LD1 LD2 LD3 LD4];
the said
Figure FDA0004136841580000031
Said->
Figure FDA0004136841580000032
Said->
Figure FDA0004136841580000033
The said
Figure FDA0004136841580000034
The discrimination coefficient A is as follows:
Figure FDA0004136841580000035
the functional relationship is as follows:
Figure FDA0004136841580000041
wherein the Daqu is high temperature Daqu within 0-8 months.
3. The method of claim 2, wherein the method for constructing the discriminant model comprises the steps of:
s2.1, taking the content of the obtained lactic acid and the content of the flavor substance component as characteristic variables, and randomly dividing the Daqu corresponding to the characteristic variables into training data and data to be detected;
s2.2, importing the storage time of the Daqu corresponding to the training data and the content data of the lactic acid and the flavor substance component into R software to obtain a judging model of a functional relation between the storage time of the Daqu and the content of the lactic acid and the content of the flavor substance component in the Daqu.
4. A method according to claim 3, wherein the amount of training data is more than 70% of the total amount of the Daqu sample.
5. A method as claimed in claim 3, wherein the functional relationship is constructed by an LDA algorithm in the R software package MASS.
6. The method of claim 5, wherein the construction process of the discriminant model is specifically as follows:
firstly, importing a data set into a program, carrying out standardization and centralization on the data set, and dividing the standardized and centralized data set into a training set and a testing set in a random mode by utilizing a subset () function so as to finish the preparation of training data;
and secondly, importing training data into a MASS software package, using LDA () function to take the Daqu storage time in the training set as an independent variable and other variables as dependent variables, and constructing an LDA (laser direct structuring) discrimination model for discriminating the Daqu storage time.
7. The method of claim 6, wherein the other variable is the content of the lactic acid and the flavor component of the Daqu during the construction of the discriminant model.
8. The method of claim 6, wherein the input values of the lda () function are data sets constructed from storage time of different daqus and content data of the lactic acid and the flavor components in the daqus during the construction of the discrimination model.
9. The method of claim 6 wherein the output value of the lda () function is a functional relationship of the discriminant model.
10. The method of claim 3, wherein the construction method further comprises the steps of:
s2.3, verification of a discrimination model: and re-introducing the content data of the lactic acid of the training data and the content data of each flavor substance component into the judging model for carrying out the judgment again to obtain the Daqu storage time corresponding to the training data.
11. The method of claim 10, wherein the method of constructing the discriminant model further comprises the steps of:
s2.4, judging the storage time of the data to be tested: and importing the data to be detected into the discrimination model to obtain the Daqu storage time corresponding to the data to be detected.
12. The method of claim 2, wherein the lactic acid content is determined by high performance liquid chromatography; the content of the flavor component is determined by a gas chromatography mass spectrometry analysis method.
13. The method of claim 12, wherein the step of determining the lactic acid content is as follows:
(1) Adding a Daqu sample and ultrapure water into a centrifuge tube, and sucking supernatant after oscillation, ultrasonic and centrifugation, and diluting to obtain a diluent;
(2) Filtering the diluted solution by a membrane filtration method to obtain filtrate;
(3) The filtrate was removed to a high performance liquid chromatograph for measurement.
14. The method of claim 13, wherein the addition ratio of the Daqu sample to the ultrapure water in step (1) is 1 g/2 ml.
15. The method of claim 13, wherein the dilution factor in step (1) is 10 times.
16. The method of claim 13, wherein in step (3), the lactic acid content is determined using an external standard method.
17. The method of claim 2, wherein the step of determining the content of the flavor component is as follows:
1) Adding a Daqu sample and an ethanol water solution into a centrifuge tube, and sucking a supernatant into a sample bottle after shaking, ultrasonic treatment and centrifugation;
2) Adding NaCl into the sample bottle until the solution is saturated;
3) Adding an extractant into the saturated solution in the step 2), vortex, oscillation, extraction and centrifugation, then sucking an upper organic phase, and measuring the upper organic phase by adopting a gas chromatography-mass spectrometry technology.
18. The method of claim 17, wherein the aqueous ethanol solution has a mass fraction of 40%; the addition ratio of the Daqu sample to the ethanol water solution is 1 g/2 ml.
19. The method of claim 17, wherein the ratio of the amount of supernatant aspirated to the addition of the Daqu sample in step 1) is 1ml:1g.
20. The method of claim 17, wherein in step 3), the extractant is diethyl ether; the ratio of the addition amount of the extractant to the sucking amount of the supernatant liquid is 1ml:5ml.
21. The method of claim 17, wherein in step 3) the content of the flavoring component is determined using an external standard method.
22. Use of a method according to any one of claims 1-21 for identifying the storage time of Daqu.
23. An apparatus for identifying the storage time of a Daqu, said apparatus comprising:
the data acquisition module is used for acquiring the content of lactic acid in the Daqu and the content of each flavor substance component;
the judging module is used for inputting the content of the lactic acid and the content of each flavor substance component into a judging model to obtain a judging result of the Daqu storage time; the judging model is a functional relation between the content of the lactic acid and the content of each flavor substance component and the storage time of the Daqu; the flavor component comprises a combination of the flavor components including acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, caproic acid, 2, 3-butanediol, 2-furfuryl alcohol, benzyl alcohol, phenethyl alcohol, benzaldehyde, lactic acid;
the functional relation is a linear model, and is y=xa; y is the storage time of Daqu; x is a data set formed by the content of lactic acid in the Daqu and the content of each flavor substance component; a is a distinguishing coefficient between the storage time of the Daqu and the content of lactic acid in the Daqu and the content of each flavor substance component;
the discrimination coefficients are constant data sets composed of corresponding coefficients LD1, LD2, LD3 and LD4 in different dimensions, wherein LD1 is a corresponding coefficient in a first dimension, LD2 is a corresponding coefficient in a second dimension, LD3 is a corresponding coefficient in a third dimension, and LD4 is a corresponding coefficient in a fourth dimension; the discrimination coefficient is a= [ LD1 LD2 LD3 LD4];
the said
Figure FDA0004136841580000071
Said->
Figure FDA0004136841580000072
Said->
Figure FDA0004136841580000073
The said
Figure FDA0004136841580000074
The discrimination coefficients are as follows:
Figure FDA0004136841580000075
the functional relationship is as follows:
Figure FDA0004136841580000081
wherein the Daqu is high temperature Daqu within 0-8 months.
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