Analysis method and application of various monosaccharide substances in yeast for making hard liquor

Abstract

The invention belongs to the field of detection and analysis, and particularly relates to a method for analyzing multiple monosaccharide substances in yeast for making hard liquor and application. The analysis method comprises the following steps: 1, pretreatment: sequentially sterilizing, extracting, centrifuging, diluting and filtering a yeast sample to obtain a liquid to be detected; 2, detecting and analyzing: detecting and analyzing monosaccharide substances in the liquid to be detected based on an ion chromatography technology; wherein the extraction comprises the following steps: adding a methanol aqueous solution with the volume concentration of 11-19% into a yeast sample subjected to sterilization treatment, boiling in a boiling water bath for 5-15 minutes, rapidly cooling, carrying out vortex oscillation for 5-20 seconds, and boiling in a boiling water bath again for 5-15 minutes; the monosaccharide substances comprise rhamnose, arabinose, glucose, cane sugar, xylose and fructose. By adopting the analysis method, the accurate quantitative analysis of rhamnose, arabinose, glucose, cane sugar, xylose and fructose in the yeast sample can be simultaneously realized, and complicated treatment such as derivatization is not needed.

Description

Technical Field

[0001] The present application belongs to the field of detection and analysis, and in particular, relates to an analysis method and application of multiple monosaccharide substances in Daqu. Background Art

[0002] The sauce-flavored Daqu contains abundant carbohydrates such as starch, oligosaccharides, and glucose, which are important nutrient sources for microbial metabolism. The commonly used method for analyzing carbohydrates is titration, which is mainly for three major types of substances: reducing sugars, dextrins, and starch. The analysis method for reducing sugars in wineries is the traditional titration method. The accuracy of this method is affected by pH and temperature, and the reagents need to be strictly controlled. The accuracy of the determination of high-concentration reducing sugars is limited. At the laboratory level, the commonly used analysis methods for carbohydrates are high-performance liquid chromatography-mass spectrometry (HPLC-MS), derivatization combined with gas chromatography-mass spectrometry (GC-MS), pyrolysis gas chromatography (PGC), high-performance anion exchange chromatography-pulsed amperometry (HPAEC-PAD), etc.

[0003] At present, the analysis of sugar substances in Daqu is usually carried out by derivatization combined with gas chromatography-mass spectrometry. Shi Yalin et al. used derivatization-gas chromatography-mass spectrometry to identify arabinose, xylose, galactose, glucose, maltose and trehalose from Maotai-flavor Daqu. However, this method currently has problems such as the complexity and time-consuming sample pretreatment, limited controllability of the derivatization reaction, toxicity and cost of the derivatization reagent, interference of the derivatization reagent and quantitative limitation.

[0004] Based on this, the present application aims to provide a method for analyzing multiple monosaccharide substances in Maotai-flavor Daqu. Summary of the invention

[0005] The purpose of this application is to provide an analysis method and application of various monosaccharide substances in Daqu.

[0006] According to the first aspect of the present application, the present application provides an analysis method and application of multiple monosaccharide substances in Daqu, and the analysis method comprises the following steps:

[0007] 1. Pretreatment: sterilize, extract, centrifuge, dilute and filter the Daqu to be analyzed in sequence to obtain the test solution;

[0008] 2. Detection and analysis: Detection and analysis of monosaccharides in the test solution based on ion chromatography technology;

[0009] The extraction comprises: adding a methanol aqueous solution with a volume concentration of 11-19% to the sterilized Daqu, boiling it in a boiling water bath for 5-15 minutes, rapidly cooling it, vortexing it for 5-20 seconds, and boiling it again in a boiling water bath for 5-15 minutes; the monosaccharide substances comprise rhamnose, arabinose, glucose, sucrose, xylose and fructose.

[0010] Ion chromatography is a high-performance liquid chromatography technique that can be used to separate and analyze ionic substances in different matrix samples. It can be used to analyze substances including ions, sugars, amino acids, organic acids (bases), etc. At present, ion chromatography has been used to analyze sugar substances in infant formula milk powder and beverages, but there is no report on the use of ion chromatography for the analysis of monosaccharides in Daqu. Moreover, the present application has found through research that the method for detecting sugar substances in samples disclosed in the prior art cannot detect compounds other than glucose in the present application. Based on this, the present application proposes a method for analyzing multiple monosaccharides in Maotai-flavor Daqu based on ion chromatography technology. The present application optimizes the pretreatment of Daqu samples. For example, Daqu contains a large number of microorganisms and enzyme substances. During the storage of Daqu, a slow formation and consumption of sugar substances may occur. Based on this, the present application innovatively proposes a method of sterilization before detection, which achieves the effect of inactivating enzyme activity while sterilizing, so that the content of Daqu sugar substances measured subsequently is the true content; for example, in the extraction step of the pretreatment, the present application first boils the Daqu sample, vortexes it, and then boils it for extraction, so that a variety of monosaccharides in the Daqu can be extracted to the maximum extent. In addition, the present application further optimizes the concentration of the extraction solvent, using 11 The Daqu sample is extracted with a methanol aqueous solution of %-19%, so that the above-mentioned monosaccharides in the Daqu sample can be extracted as much as possible; at the same time, the analytical method provided in the present application does not require complex treatments such as derivatization, and ion chromatography injection can be used for analysis, and the analytical method is used to detect and analyze the corresponding monosaccharides in the Daqu, which can achieve a lower detection concentration. For example, the minimum detection concentration of arabinose is 0.22 μg / mL, which is lower than 1.20 μg / mL reported in the existing literature, and the minimum detection concentration of xylose is 0.20 μg / mL, which is lower than 2.50 μg / mL reported in the existing literature, while fucose and fructose are both detected for the first time.

[0011] In some embodiments of the present application, in the extraction step, the solid-liquid ratio between the Daqu and the methanol aqueous solution is 1:6 to 1:14 in g / ml. Specifically, in some embodiments of the present application, the solid-liquid ratio between the Daqu and the methanol aqueous solution can be 1:6, 1:7, 1:8, 1:9, 1:10, 1:11, 1:12, 1:13, 1:14 or a range consisting of any two of the above values ​​in g / ml. The present application has been experimentally verified that the solid-liquid ratio between the Daqu and the methanol aqueous solution will greatly affect the final analysis results of monosaccharide substances. Further limiting the solid-liquid ratio to the above range can make monosaccharide substances such as rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu be detected as much as possible.

[0012] In some embodiments of the present application, in step 1, the sterilization conditions include: sterilization at 100-120°C for 10-30 min. Specifically, in some embodiments of the present application, the sterilization temperature can be 100°C, 101°C, 102°C, 103°C, 104°C, 105°C, 106°C, 107°C, 108°C, 109°C, 110°C, 111°C, 112°C, 113°C, 114°C, 115°C, 116°C, 117°C, 118°C, 119°C, 120°C or a range consisting of any two of the above values. Specifically, in some embodiments of the present application, the sterilization time can be 10min, 11min, 12min, 13min, 14min, 15min, 16min, 17min, 18min, 19min, 20min, 21min, 22min, 23min, 24min, 25min, 26min, 27min, 28min, 29min, 30min or a range consisting of any two of the above values. Further limiting the sterilization temperature and time within the above range can better achieve the detection and analysis of monosaccharides in Daqu.

[0013] In some embodiments of the present application, in step one, the centrifugation includes: centrifuging the extracted Daqu extract at 3000-5000 r / min for 5-15 min, taking the supernatant after the first centrifugation and centrifuging it again at 8000-15000 r / min for 2-10 min to obtain the supernatant. Specifically, in some embodiments of the present application, the rotation speed of the centrifugation may be 3000r / min, 3100r / min, 3200r / min, 3300r / min, 3400r / min, 3500r / min, 3600r / min, 3700r / min, 3800r / min, 3900r / min, 4000r / min, 4100r / min, 4200r / min, 4300r / min, 4400r / min, 4500r / min, 4600r / min, 4700r / min, 4800r / min, 4900r / min, 5000r / min or a range consisting of any two of the above values. Specifically, in some embodiments of the present application, the time of the first centrifugation can be 5min, 6min, 7min, 8min, 9min, 10min, 11min, 12min, 13min, 14min, 15min or a range consisting of any two of the above values. Specifically, in some embodiments of the present application, the speed of the secondary centrifugation can be 8000r / min, 8500r / min, 9000r / min, 9500r / min, 10000r / min, 10500r / min, 11000r / min, 11500r / min, 12000r / min, 12500r / min, 13000r / min, 13500r / min, 14000r / min, 14500r / min, 15000r / min or a range consisting of any two of the above values. Specifically, in some embodiments of the present application, the time of the secondary centrifugation can be 2 min, 3 min, 4 min, 5 min, 6 min, 7 min, 8 min, 9 min, 10 min, or a range consisting of any two of the above values. Further limiting the speed and time of the primary centrifugation and the secondary centrifugation within the above range can better achieve the detection and analysis of monosaccharides in Daqu.

[0014] In some embodiments of the present application, the dilution includes: diluting the supernatant after centrifugation; the dilution multiple is 50-150 times. Specifically, in some embodiments of the present application, the dilution multiple can be 50 times, 60 times, 70 times, 80 times, 90 times, 100 times, 110 times, 120 times, 130 times, 140 times, 150 times, or a range consisting of any two of the above values.

[0015] In some embodiments of the present application, the filtration includes: filtering the extract obtained by dilution; the filtration includes: filtering with a 0.45 μm filter membrane.

[0016] In some embodiments of the present application, in step 2, the detection and analysis of the test liquid based on ion chromatography technology includes: separation, elution, qualitative and quantitative analysis of monosaccharide substances in the test liquid based on an ion chromatograph equipped with a pulsed amperometric detector.

[0017] In some embodiments of the present application, in the detection and analysis step, a gradient elution method is used to classify and elute monosaccharides in Daqu; the A eluent used for the classification elution is a 140-160mM NaOH solution, and the B eluent used for the classification elution is H2O. Specifically, in some embodiments of the present application, the concentration of the NaOH solution of the A eluent can be 140mM, 141mM, 142mM, 143mM, 144mM, 145mM, 146mM, 147mM, 148mM, 149mM, 150mM, 151mM, 152mM, 153mM, 154mM, 155mM, 156mM, 157mM, 158mM, 159mM, 160mM or a range consisting of any two of the above values. Further use of the above-mentioned eluent A and eluent B can better classify and elute the monosaccharide substances in the Daqu, thereby achieving better detection and analysis results.

[0018] In some embodiments of the present application, the flow rate of the eluent is 0.5-1.5 mL / min. Specifically, in some embodiments of the present application, the flow rate of the eluent can be 0.5 mL / min, 0.6 mL / min, 0.7 mL / min, 0.8 mL / min, 0.9 mL / min, 1.0 mL / min, 1.1 mL / min, 1.2 mL / min, 1.3 mL / min, 1.4 mL / min, 1.5 mL / min or a range consisting of any two of the above values.

[0019] In some embodiments of the present application, the conditions of gradient elution are: 0-20min, 1-3% A liquid + 97-99% B liquid; 20-30min, A liquid rises to 100% in a linear manner, and B liquid drops to 0%; 30-35min, A liquid and B liquid are returned to the initial state in a linear manner, A liquid drops from 100% to 1-3% in a linear manner, and B liquid rises from 0% to 97-99% in a linear manner; preferably, the conditions of gradient elution are: 0-20min, 2% A liquid + 98% B liquid; 20-30min, A liquid rises to 100% in a linear manner, and B liquid drops to 0%; 30-35min, A liquid and B liquid are returned to the initial state in a linear manner, A liquid drops from 100% to 2% in a linear manner, and B liquid rises from 0% to 98% in a linear manner. Further adopting the above-mentioned gradient elution conditions can better enable the various monosaccharide substances in Daqu to be classified and eluted, and achieve better detection and analysis effects.

[0020] In some embodiments of the present application, the ion chromatography column used for separation is a carbohydrate analysis chromatography column CarboPac PA20 (3x 150mm); preferably, the chromatographic conditions used for separation include: separation column temperature of 25-35°C; injection volume of 15-25μL; Au as the working electrode, Ag / AgCl as the reference electrode, and the detection waveform of the electrochemical detector: carbohydrates (standard quad); preferably, the chromatographic conditions used for separation include: separation column temperature of 30°C; injection volume of 20μL.

[0021] In some embodiments of the present application, the qualitative analysis includes: after the elution is completed, a standard is used to compare the retention time to conduct a qualitative analysis of each sugar substance in the Daqu.

[0022] In some embodiments of the present application, the quantitative analysis includes: quantifying the monosaccharide substances in the test liquid based on the external standard method, and further calculating the content of the monosaccharide substances in the Daqu based on the quantitatively obtained content data of the monosaccharide substances in the test liquid.

[0023] In some embodiments of the present application, the content of the monosaccharide substance in Daqu is calculated by the following formula: the content of the monosaccharide substance in Daqu is N, N=n*dilution multiple of Daqu*20 / (2*(1-water content of Daqu)); wherein n is the content of the monosaccharide substance in the test liquid.

[0024] According to the second aspect of the present application, the present application also provides a method for distinguishing Daqu at different stages, the method comprising the following steps:

[0025] (1) obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified based on any analysis method described in the first aspect of the present application;

[0026] (2) Based on the quadratic discriminant analysis, the fast discrimination rules corresponding to the different stages of Daqu are obtained;

[0027] (3) Comparing and analyzing the measured values ​​with the quick discrimination rules, and discriminating the large-scale music stage based on the comparative analysis results.

[0028] In some embodiments of the present application, the stages to which the Daqu belongs include the first-turn Daqu, the second-turn Daqu and the demolition Daqu.

[0029] In some embodiments of the present application, the determining the stage of the Daqu based on the comparative analysis result includes:

[0030] If the glucose content is >10mg / g, sucrose >2.5mg / g and fructose >2.8mg / g, it is judged as a first-turn Daqu.

[0031] If the rhamnose content is 0.03mg / g≤≤0.06mg / g, 0.6mg / g≤arabinose≤1.0mg / g and glucose <10mg / g, it is judged as the second-turnover Daqu; if the glucose content is <1mg / g, the sucrose content is <0mg / g or the arabinose content is <0.1mg / g, it is judged as the Daqu in the warehouse demolition stage.

[0032] According to the third aspect of the present application, the present application also provides a method for distinguishing Daqu at different stages, the method comprising the following steps:

[0033] Based on any one of the analysis methods described in the first aspect of the present application, obtain the corresponding content data information of rhamnose, arabinose, glucose, sucrose, and fructose in the Daqu to be identified;

[0034] Construct discriminant functions based on quadratic discriminant analysis;

[0035] Substitute the obtained content data information of rhamnose, arabinose, glucose, sucrose and fructose into the discriminant function for calculation to obtain the discriminant function value, and discriminate the Daqu stage based on the discriminant function value.

[0036] During the study, it was found that in the discrimination method provided in the present application, when the discriminant function was constructed using the content data information of rhamnose, arabinose, glucose, sucrose, xylose and fructose, it was found that the final accuracy was not high. The present application further optimized the parameters and found that when the discriminant function was constructed using the content data information of rhamnose, arabinose, glucose, sucrose and fructose, the final discrimination accuracy of the Daqu stage was significantly improved.

[0037] In some embodiments of the present application, constructing a discriminant function based on quadratic discriminant analysis includes:

[0038] Obtain the content data of rhamnose, arabinose, glucose, sucrose and fructose corresponding to Daqu at different stages;

[0039] Calculate the general function corresponding to the quadratic discriminant analysis.

[0040] In some embodiments of the present application, the calculation includes: calculating the mean, covariance matrix and prior probability of each monosaccharide content corresponding to the Daqu in each stage; based on the calculated mean, further calculating the difference between the corresponding sample and the mean; calculating the inverse of the covariance matrix; calculating the determinant of the covariance matrix; based on the calculated difference between the sample and the mean, the inverse of the covariance matrix and the determinant of the covariance matrix, further calculating the discriminant function value.

[0041] In some embodiments of the present application, the discriminating the stage of the Daqu based on the discriminant function value includes: comparing the obtained discriminant function values, and discriminating the category of the Daqu to be discriminated as the stage corresponding to the maximum discriminant function value.

[0042] According to the fourth aspect of the present application, the present application also provides a method for distinguishing the type of wheat used in Daqu, the distinguishing method comprising the following steps:

[0043] 1) obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified based on any analysis method described in the first aspect of the present application;

[0044] 2) Based on the quadratic discriminant analysis, obtain the rapid discrimination rules for the wheat category used in Daqu;

[0045] 3) Compare and analyze the measured values ​​with the rapid discrimination rules, and discriminate the type of wheat used for Daqu based on the comparative analysis results.

[0046] In some embodiments of the present application, the types of wheat used for the Daqu include: Yangmai, Quanmai, Tianmin, Fanmai, and Zhengmai.

[0047] In some embodiments of the present application, if the glucose content is <8.5 mg / g, the sucrose content is >3.2 mg / g, and the rhamnose content is <0.04 mg / g, it is judged as Yang wheat; if the glucose content is >15 mg / g and the sucrose content is >3.5 mg / g, it is judged as Quan wheat; if the sucrose content is <1.5 mg / g and the xylose content is >0.45 mg / g and the glucose content∈[8,10] mg / g, it is judged as Tianmin; if the glucose content is >10 mg / g, the sucrose content is >2.5 mg / g and the xylose content is <0.4 mg / g, it is judged as Pan wheat; if the sucrose content is >3.5 mg / g and the arabinose content is >1.0 mg / g, it is judged as Zheng wheat.

[0048] According to the fifth aspect of the present application, the present application also provides a method for distinguishing the type of wheat used in Daqu, the distinguishing method comprising the following steps:

[0049] Based on any analysis method described in the first aspect of the present application, the corresponding content data information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified is obtained; a discriminant function is constructed based on quadratic discriminant analysis; the obtained content data information of rhamnose, arabinose, glucose, sucrose, xylose and fructose is substituted into the discriminant function for calculation to obtain the discriminant function value, and the type of wheat used for the Daqu is discriminated based on the discriminant function value.

[0050] In some embodiments of the present application, constructing a discriminant function based on quadratic discriminant analysis includes:

[0051] Obtain the content data information of rhamnose, arabinose, glucose, sucrose, xylose and fructose corresponding to Daqu at different stages; calculate the general function corresponding to the secondary discriminant analysis.

[0052] In some embodiments of the present application, the calculation includes: calculating the mean, covariance matrix and prior probability of each monosaccharide content corresponding to the Daqu in each stage; based on the calculated mean, further calculating the difference between the corresponding sample and the mean; calculating the inverse of the covariance matrix; calculating the determinant of the covariance matrix; based on the calculated difference between the sample and the mean, the inverse of the covariance matrix and the determinant of the covariance matrix, further calculating the discriminant function value.

[0053] In some embodiments of the present application, the discriminating the wheat category used for the Daqu based on the discriminant function value includes: comparing the obtained discriminant function values, and discriminating the wheat category used for the Daqu to be discriminated as the wheat category corresponding to the maximum discriminant function value.

[0054] According to the sixth aspect of the present application, the present application also provides a method for distinguishing Daqu at different stages, the method comprising the following steps:

[0055] Based on any analysis method described in the first aspect of the present application, the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified is obtained; based on Fisher discriminant analysis, the discriminant function corresponding to the Daqu at different stages is obtained; the content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified is substituted into the discriminant function of the Daqu at the corresponding stage, and the discriminant value of the Daqu at the corresponding stage is calculated; the stage of the Daqu to be identified is discriminated based on the discriminant value.

[0056] In some embodiments of the present application, the discriminant function corresponding to the Daqu in different stages is obtained based on Fisher discriminant analysis, and the Daqu in different stages includes the Daqu in the first turning stage, the Daqu in the second turning stage and the Daqu in the warehouse dismantling stage.

[0057] In some embodiments of the present application, the discriminant function includes:

[0058] The discriminant function of Daqu in the first turning stage is: δ1(x)=0.321*N(rhamnose)+0.135*N(arabinose)+0.456*N(glucose)+0.234*N(sucrose)+0.102*N(xylose)+0.087*N(fructose)-5.231;

[0059] The discriminant function of Daqu in the second turning stage is: δ2(x) = -0.213*N(rhamnose)-0.087*N(arabinose)+0.321*N(glucose)+0.456*N(sucrose)-0.123*N(xylose)+0.231*N(fructose)-3.987;

[0060] The discriminant function of Daqu in the unpacking stage is: δ2(x) = 0.102*N(rhamnose) + 0.234*N(arabinose) - 0.156*N(glucose) + 0.034*N(sucrose) + 0.567*N(xylose) - 0.321*N(fructose) - 2.154;

[0061] In the above discriminant function, N is the content of the corresponding monosaccharide substance, and the unit is mg / g.

[0062] The step of discriminating the stage of the large-scale music to be discriminated based on the discriminant value comprises: comparing the obtained discriminant values, and discriminating the category of the large-scale music to be discriminated as the stage corresponding to the maximum discriminant value.

[0063] According to the seventh aspect of the present application, the present application also provides a method for distinguishing the type of wheat used in Daqu, the distinguishing method comprising the following steps:

[0064] Obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified based on any analysis method described in the first aspect of the present application;

[0065] Based on Fisher discriminant analysis, the discriminant functions corresponding to different wheat categories were obtained;

[0066] Substitute the content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified into the discriminant function of the corresponding wheat category, and calculate the discriminant value of the corresponding wheat category;

[0067] The wheat category used for the Daqu to be identified is discriminated based on the discriminant value.

[0068] In some embodiments of the present application, the wheat categories include: Yangmai, Quanmai, Tianmin, Fanmai, and Zhengmai.

[0069] In some embodiments of the present application, the discriminant functions corresponding to different wheat categories include:

[0070] Wheat: δ1(x) = 0.212*N(rhamnose) + 0.145*N(arabinose) + 0.356*N(glucose) + 0.178*N(Nsucrose) + 0.098*N(Nxylose) + 0.103*N(fructose) - 4.678;

[0071] Wheat: δ2(x) = -0.153*N(rhamnose)-0.098*N(arabinose)+0.234*N(glucose)+0.321*N(sucrose)-0.112*N(xylose)+0.187*N(fructose)-3.897;

[0072] Tianmin: δ3(x) = 0.087*N(rhamnose) + 0.204*N(arabinose) - 0.146*N(glucose) + 0.064*N(sucrose) + 0.456*N(xylose) - 0.213*N(fructose) - 2.564;

[0073] Pan-wheat: δ4(x)=0.175*N(rhamnose)+0.058*N(arabinose)-0.213*N(glucose)+0.159*N(sucrose)+0.234*N(xylose)+0.078*N(fructose)-1.987;

[0074] Zheng wheat: δ5(x) = -0.201*N(rhamnose)-0.108*N(arabinose)+0.123*N(glucose)+0.231*N(sucrose)+0.125*N(xylose)+0.321*N(fructose)-0.765;

[0075] In the above discriminant function, N is the content of the corresponding monosaccharide substance, and the unit is mg / g.

[0076] In some embodiments of the present application, the discriminating the wheat category used for the Daqu to be discriminated based on the discriminant value includes: comparing the obtained discriminant values, and discriminating the wheat category used for the Daqu to be discriminated as the wheat type corresponding to the maximum discriminant value.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The present application provides an analysis method for a variety of monosaccharide substances in sauce-flavored Daqu based on ion chromatography technology. The present application first optimizes the pretreatment of Daqu samples, and adopts the analysis method provided by the present application, without the need for complex treatments such as derivatization, and can be analyzed by ion chromatography injection, and the analysis method provided by the present application is used to detect and analyze the corresponding monosaccharide substances in Daqu, which can achieve a lower detection concentration, such as the minimum detection concentration of arabinose is 0.22μg / mL, which is lower than the 1.20μg / mL reported in the existing literature, and the minimum detection concentration of xylose is 0.20μg / mL, which is lower than the 2.50μg / mL reported in the existing literature, and fucose and fructose are both detected for the first time. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0080] Figure 1 The content range of monosaccharide substances in the first-turned Daqu sample in an embodiment of the present application;

[0081] Figure 2 The content range of monosaccharide substances in the double-turned Daqu sample in one embodiment of the present application;

[0082] Figure 3 The content range of monosaccharide substances in the Daqu sample at the unpacking stage in one embodiment of the present application;

[0083] Figure 4 The results of the effects of different volume concentrations of methanol aqueous solution on the extraction of monosaccharides in Daqu samples in Comparative Example 1-1 of the present application;

[0084] Figure 5 The results of the effects of different material-liquid ratios on the extraction of monosaccharides from Daqu samples in Comparative Examples 1-2 of this application;

[0085] Figure 6The results of the effect of sterilization on the extraction of monosaccharide substances in Daqu samples in comparative examples 1-3 of the present application;

[0086] Figure 7 These are the results of the effects of different shaking methods on the extraction of monosaccharides in Daqu samples in Comparative Examples 1-4 of the present application. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field based on the present application belong to the scope of protection of the present application.

[0088] Example 1

[0089] This embodiment provides a specific implementation method of a method for analyzing multiple monosaccharide substances in Maotai-flavor Daqu based on ion chromatography technology based on the invention content of this application. The analysis method comprises the following steps:

[0090] 1. Pre-treatment:

[0091] Weigh 2 g of different Daqu samples into a 50 mL centrifuge tube with a sealing gasket, put on the lid but not tighten it, and put it into a sterilizer (Hirayama, HVE-50, Japan) at 115°C for 20 min; take it out and cool it to room temperature, add 20 mL of 15% methanol aqueous solution, boil it in a boiling water bath for 10 min, vortex it for 10 s after rapid cooling, and boil it again for 10 min; centrifuge it at 4000 r / min for 10 min, take 1 mL of supernatant and centrifuge it at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter it with a 0.45 μm filter membrane;

[0092] 2. Detection and analysis:

[0093] Qualitative analysis: ICS-5000 ion chromatograph equipped with pulsed amperometric detector was used to analyze the sugar substances in Daqu: Carbohydrate analysis column CarboPac PA20 (3x 150mm) was used to separate the sugar substances in Daqu, the column temperature was 30°C, the injection volume was 20μL, Au was used as the working electrode, Ag / AgCl was used as the reference electrode, and the detection waveform of the electrochemical detector was: carbohydrates (standard quad); the sugar substances in Daqu were classified and eluted by gradient elution, and the A eluent used for elution was 150mM NaOH, eluent B is H2O; the flow rate of eluting liquid A is 1mL / min, the flow rate of liquid B is 0.4mL / min, and the gradient elution conditions are: 0-20min, 2% liquid A + 98% liquid B; 20-30min, liquid A is increased to 100% in a linear manner, and liquid B is reduced to 0%, that is, 100% liquid A + 0% liquid B; 30-35min, liquid A and liquid B are returned to the initial state in a linear manner, that is, liquid A is reduced from 100% to 2%, and liquid B is increased from 0% to 98%, that is, 2% liquid A + 98% liquid B; after the elution is completed, the retention time is compared with the standard to conduct qualitative analysis of various sugar substances in Daqu;

[0094] Quantitative analysis: Based on the external standard method, the sugar substances (fucose (rhamnose), arabinose, glucose, sucrose, xylose and fructose) in Daqu were quantitatively analyzed; a certain concentration of rhamnose, arabinose, glucose, sucrose, xylose and fructose standard solutions were prepared with ultrapure water, and the samples were analyzed according to the above method. The standard curve was established by mass concentration-peak area, and the mass concentration with a chromatographic peak signal-to-noise ratio greater than 3 was determined as the detection limit, and the signal-to-noise ratio greater than 10 was determined as the quantitative limit, as shown in Table 1. Based on the established standard curve, the mass concentration (n) of the corresponding monosaccharide in the test solution was obtained, and the sugar substance content (N) in Daqu was calculated based on the following formula: N = n*dilution factor*20 / (2*(1-water content)), and the water content was measured by a moisture meter.

[0095] Table 1 Standard curve

[0096]

[0097]

[0098] The calculated results of the content of sugar substances (N) in the Daqu samples are shown in Table 2:

[0099] Table 2 Results of sugar content in different Daqu samples

[0100]

[0101] Example 2

[0102] This example is based on Example 1 and provides an analysis method for sugar substances in Maotai-flavor Daqu at different stages. The analysis method steps are as follows:

[0103] 1. Pre-treatment:

[0104] Weigh 2 g of Daqu samples at different stages (Daqu samples of first turning, Daqu samples of second turning, Daqu samples of unpacking stage) into a 50 mL centrifuge tube with a sealing gasket, put on the lid but do not tighten it, put it into a sterilizer and sterilize it at 115 ° C for 20 min, take it out and cool it to room temperature, add 20 mL of 15% methanol aqueous solution, boil it in a boiling water bath for 10 min, vortex it for 10 s after rapid cooling, boil it again for 10 min, centrifuge it at 4000 r / min for 10 min, take 1 mL of supernatant and centrifuge it at 10000 r / min for 5 min, dilute the supernatant obtained after high-speed centrifugation 100 times, and filter it with a 0.45 μm filter membrane;

[0105] 2. Detection and Analysis

[0106] Qualitative analysis: same as Example 1;

[0107] Quantitative analysis: Same as Example 1.

[0108] Combination Figure 1-3 The results of the content range of sugar substances in the Daqu samples at the first turning over, second turning over and warehouse unpacking stages are shown as follows: in the Daqu samples at the first turning over stage, the content of rhamnose is 0.027-0.083 mg / g, the content of arabinose is 0.47-1.25 mg / g, the content of glucose is 8.07-18.93 mg / g, the content of sucrose is 0.93-4.12 mg / g, the content of xylose is 0.23-0.98 mg / g, and the content of fructose is 1.18-4.26 mg / g; in the Daqu samples at the second turning over stage, the content of rhamnose is 0.020-0.064 mg / g, the content of arabinose is 0.20-1.3 7mg / g, glucose content is 3.59-14.72mg / g, sucrose content is 0.17-4.58mg / g, xylose content is 0.20-0.59mg / g, and fructose content is 1.12-4.23mg / g; in the Daqu samples at the unpacking stage, the content of rhamnose is 0-0.075mg / g, the content of arabinose is 0.00038-0.084mg / g, the content of glucose is 0.029-0.56mg / g, the content of sucrose is 0-0.13mg / g, the content of xylose is 0.0011-0.12mg / g, and the content of fructose is 0.019-0.51mg / g. According to the above analysis results, as the Daqu goes from the first turn, the second turn to the unpacking stage, the content of the six different monosaccharides shows an overall downward trend.

[0109] This embodiment further analyzes the differences in the content of various sugar substances in the Daqu samples at the first turning, second turning and unpacking stages by Welch's variance test, and specifically performs the difference analysis based on the following code:

[0110] “importpandas

[0111] from algorithm import differentiation_analysis

[0112] data_x=pandas.Series(["A","B","A","B","A"],name="A")

[0113] data_y=pandas.DataFrame({"B":[1,2,3,4,5]})

[0114] print(differentiation_analysis.variance_analysis(data_x,data_y))”

[0115] The results of the difference analysis are shown in Table 3:

[0116] Table 3 Results of difference analysis

[0117]

[0118]

[0119] Note: ***, **, * represent 1%, 5%, 10% significance levels respectively

[0120] According to the above difference analysis results, there were significant differences (p<0.05) in the contents of six sugars, namely rhamnose, arabinose, glucose, sucrose, xylose and fructose, in the Daqu samples at the first turning over, second turning over and warehouse unpacking stages.

[0121] Comparative Example 1

[0122] Based on Example 1, this comparative example further explored the effects of different concentrations of methanol aqueous solution on the analysis results.

[0123] The details are as follows:

[0124] 1. Pre-treatment:

[0125] Weigh 2 g of koji sample into a 50 mL centrifuge tube with a sealing gasket, put it into a sterilizer, and sterilize it at 115°C for 20 min; take it out and cool it to room temperature, add 20 mL of methanol aqueous solution with different contents (0%, 5%, 10%, 15%, 20%, 25%, 30%), boil it in a boiling water bath for 10 min, vortex it for 10 s after rapid cooling, and boil it again for 10 min; centrifuge it at 4000 r / min for 10 min, take 1 mL of supernatant and centrifuge it at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter it with a 0.45 μm filter membrane, and keep it at -20°C for testing.

[0126] 2. Detection and Analysis

[0127] Qualitative analysis: same as Example 1;

[0128] Quantitative analysis: Same as Example 1.

[0129] The results are as follows Figure 4 As shown, taking glucose as an example, using methanol-water solutions of different concentrations to extract sugar substances in Daqu will affect the final analysis results. For example, when the methanol extraction concentration is 15%, the glucose content is the highest, which is 0.25 mg / g.

[0130] Comparative Example 2

[0131] Based on Example 1, this comparative example further explored the effects of different material-liquid ratios on the analysis results.

[0132] The details are as follows:

[0133] 1. Pre-treatment:

[0134] Weigh 2 g of koji sample into a 50 mL centrifuge tube with a sealing gasket, put it into a sterilizer, and sterilize it at 115°C for 20 min; take it out and cool it to room temperature, add 15% methanol aqueous solution, and the solid-liquid ratio is set to: 1:4, 1:5, 1:10, 1:15, 1:20, respectively, boil it in a boiling water bath for 10 min, vortex it for 10 s after rapid cooling, and boil it again for 10 min; centrifuge it at 4000 r / min for 10 min, take 1 mL of supernatant and centrifuge it at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter it with a 0.45 μm filter membrane, and keep it at -20°C for testing.

[0135] 2. Detection and Analysis

[0136] Qualitative analysis: Same as implementation 1;

[0137] Quantitative analysis: Same as Example 1.

[0138] The results are as follows Figure 5As shown, taking glucose as an example, using different solid-liquid ratios to extract sugar substances in Daqu will affect the final analysis results. For example, when the extraction solid-liquid ratio is 1:10, the glucose content is the highest, which is 0.25 mg / g.

[0139] Comparative Example 3

[0140] Based on Example 1, this comparative example further explored the effect of sterilization in the pre-treatment step on the analysis results.

[0141] The details are as follows:

[0142] 1. Pre-treatment:

[0143] Treatment 1 (sterilization): Weigh 2 g of different Daqu samples into 50 mL centrifuge tubes with sealing gaskets, put on the lid but not tighten it, and sterilize in a sterilizer (Hirayama, HVE-50, Japan) at 115°C for 20 min; take out and cool to room temperature, add 20 mL of 15% methanol aqueous solution, boil in a boiling water bath for 10 min, vortex for 10 s after rapid cooling, and boil again for 10 min; centrifuge at 4000 r / min for 10 min, take 1 mL of supernatant and high-speed centrifuge at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter with a 0.45 μm filter membrane;

[0144] Treatment 2 (not sterilized): Weigh 2 g of different Daqu samples, directly add 20 mL of 15% methanol aqueous solution, boil in a boiling water bath for 10 min, quickly cool and vortex for 10 s, boil again for 10 min; centrifuge at 4000 r / min for 10 min, take 1 mL of supernatant and centrifuge at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter with a 0.45 μm filter membrane;

[0145] 2. Detection and analysis:

[0146] Qualitative analysis: same as Example 1;

[0147] Quantitative analysis: Same as Example 1.

[0148] The results are as follows Figure 6 As shown in the figure, with xylose, sucrose and fructose as the analysis objects, the contents of these three substances detected in Daqu after sterilization were higher than those before sterilization. After sterilization, the contents of xylose, sucrose and fructose were 0.009 mg / g, 0.180 mg / g and 0.388 mg / g respectively; when not sterilized, the contents of xylose, sucrose and fructose in Daqu were 0.003, 0.158 and 0.318 mg / g respectively. It can be seen that whether the Daqu sample is sterilized in the pretreatment will have a certain impact on the analysis results.

[0149] Comparative Example 4

[0150] Based on Example 1, this comparative example further explored the effects of different oscillation methods in the pre-treatment step on the analysis results.

[0151] The details are as follows:

[0152] 1. Pre-treatment:

[0153] Treatment 1 (vortexing): Weigh 2 g of different Daqu samples into 50 mL centrifuge tubes with sealing gaskets, put on the lid but not tighten it, and sterilize in a sterilizer (Hirayama, HVE-50, Japan) at 115°C for 20 min; take out and cool to room temperature, add 20 mL of 15% methanol aqueous solution, boil in a boiling water bath for 10 min, vortex for 10 s after rapid cooling, and boil again for 10 min; centrifuge at 4000 r / min for 10 min, take 1 mL of supernatant and high-speed centrifuge at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter with a 0.45 μm filter membrane;

[0154] Treatment 2 (homogenization): Weigh 2 g of different Daqu samples into a 50 mL centrifuge tube with a sealing gasket, put on the lid but not tighten it, and put it into a sterilizer (Hirayama, HVE-50, Japan) at 115°C for 20 min; take it out and cool it to room temperature, add 20 mL of 15% methanol aqueous solution, boil it in a boiling water bath for 10 min, quickly cool it and homogenize it with a homogenizer for 10 s, boil it again for 10 min; centrifuge it at 4000 r / min for 10 min, take 1 mL of the supernatant and high-speed centrifuge it at 10000 r / min for 5 min; dilute the supernatant obtained after high-speed centrifugation 100 times; filter it with a 0.45 μm filter membrane;

[0155] 2. Detection and analysis:

[0156] Qualitative analysis: same as Example 1;

[0157] Quantitative analysis: Same as Example 1.

[0158] The results are as follows Figure 7 As shown in the figure, taking rhamnose and arabinose as the analysis objects, the content after vortex oscillation treatment is slightly higher than that of homogenizer homogenization. After vortex oscillation treatment, the content of rhamnose and arabinose is 0.111 and 0.181 mg / g respectively. After homogenization treatment, the content of rhamnose and arabinose is 0.107 and 0.090 mg / g respectively. The content of rhamnose increases by 3.6% and the content of arabinose increases by 55.8%. It can be seen that the use of different oscillation methods in the pretreatment of Daqu samples will have a certain impact on the analysis results.

[0159] Comparative Example 5

[0160] Based on Example 1, this comparative example further explored the effect of different gradient elutions in the pre-treatment step on the analysis results.

[0161] The details are as follows:

[0162] 1. Pretreatment: same as in Example 1;

[0163] 2. Detection and analysis:

[0164] Qualitative analysis: Different elution conditions were used for analysis. Elution condition 1 (Example 1) was: 150 mM NaOH for eluent A and H2O for eluent B; the flow rate of eluting A solution was 1 mL / min and the flow rate of B solution was 0.4 mL / min. From 0 to 20 min, 2% A solution + 98% B solution was used. From 20 to 30 min, A solution was increased to 100% in a linear manner and B solution was decreased to 0%, i.e., 100% A solution + 0% B solution. From 30 to 35 min, A solution and B solution were returned to the initial state in a linear manner, i.e., A solution was decreased from 100% to 2% and B solution was increased from 0% to 98%, i.e., 2% A solution + 98% B solution. Elution condition 2 was: 150 mM NaOH solution for eluent A and 150 mM B solution for eluent B. The NaOH solution contains 1M sodium acetate; the eluent flow rate is 1mL / min, and the elution conditions are: 0-1.3min, 93% A solution + 7% B solution, 1.3-10min, A solution is reduced to 82% in a linear manner, B solution is increased to 18%, 10-19min, A solution is reduced to 78% in a linear manner, B solution is increased to 22%, and finally at 19-20min, it returns to the initial state in a linear manner, that is, 93% A solution + 7% B solution; the other conditions are the same as in Example 1;

[0165] Quantitative analysis: Same as Example 1.

[0166] The test results are as follows: only glucose was repeatedly detected in the Daqu sample using elution condition 2. It can be seen that using different elution conditions to analyze monosaccharides in Daqu will have a significant impact on the test results.

[0167] Example 3

[0168] This embodiment is based on Example 2, and provides a method for classifying and predicting the different stages of Maotai-flavor Daqu. For Maotai-flavor Daqu, it needs to go through three processes: one warehouse turning, two warehouse turning, and warehouse dismantling. The microorganisms and enzyme activities corresponding to each process are not exactly the same. Based on the preliminary exploration and analysis in Example 2, the Daqu samples in the first, second and warehouse turning stages have significant differences in the contents of six sugars: rhamnose, arabinose, glucose, sucrose, xylose and fructose. This embodiment further takes the content of sugar substances in Daqu as the starting point, based on the difference in the content of sugar substances in Daqu, to achieve discriminant analysis of Daqu at different stages. In this embodiment, the units of the content of monosaccharides involved are all mg / g, and the specific steps are as follows:

[0169] 1. Obtain the contents of rhamnose, arabinose, glucose, sucrose and fructose in the Daqu samples at the first turning over, second turning over and unpacking stages respectively: the same as in Example 2;

[0170] 2. Construct a discriminant function based on quadratic discriminant analysis (QDA). The specific steps are as follows:

[0171] import numpy as np

[0172] (1) Determine the mean vector: For each category, calculate the mean vector of its features as follows:

[0173] mu_1 = np.array([0.048, 0.932, 11.352, 2.673, 2.982]) #1

[0174] mu_2 = np.array([0.043,0.817,9.874,2.156,2.511]) #2

[0175] mu_3 = np.array([0.032, 0.064, 0.254, 0.008, 0.186])# unwind

[0176] Based on the above method, the mean vector (μ k ) The results are shown in Table 4:

[0177] Table 4

[0178] stage rhamnose arab glucose sucrose fructose 1 turn 0.048 0.932 11.352 2.673 2.982 2 flip 0.043 0.817 9.874 2.156 2.511 Unwinding 0.032 0.064 0.254 0.008 0.186

[0179] (2) Covariance matrix: For each category, calculate the covariance matrix of its features, as follows:

[0180] sigma_1 = np.array([

[0181] [0.0002,0.0010,0.0123,0.0031,0.0018],

[0182] [0.0010,0.0456,0.2314,0.0621,0.0382],

[0183] [0.0123,0.2314,5.3421,1.4321,0.8923],

[0184] [0.0031,0.0621,1.4321,0.3845,0.2314],

[0185] [0.0018,0.0382,0.8923,0.2314,0.1456] ])

[0187] sigma_2=np.array([

[0188] [0.0003,-0.0020,0.0154,0.0042,0.0021],

[0189] [-0.0020,0.0512,0.2456,0.0712,0.0412],

[0190] [0.0154,0.2456,6.1234,1.5123,0.9123],

[0191] [0.0042,0.0712,1.5123,0.4123,0.2512],

[0192] [0.0021,0.0412,0.9123,0.2512,0.1623] ])

[0194] sigma_3=np.array([

[0195] [0.0015,0.0080,0.0231,0.0012,0.0056],

[0196] [0.0080,0.0123,0.0456,0.0023,0.0123],

[0197] [0.0231,0.0456,0.1234,0.0056,0.0234],

[0198] [0.0012,0.0023,0.0056,0.0008,0.0012],

[0199] [0.0056,0.0123,0.0234,0.0012,0.0089] ])

[0201] Based on the above method, the calculated covariance matrix (∑k) is as follows:

[0202] 1st turn stage:

[0203]

[0204] 2nd stage:

[0205]

[0206] Unwinding stage:

[0207]

[0208] (3) Calculate the prior probability of each category: The prior probability refers to the probability of each category appearing in the population, which is usually estimated based on the sample ratio of each category in the training data set. Specifically, the prior probability reflects the probability distribution of each category itself before specific data is observed. It is as follows:

[0209] pi_1=0.37#1 turn

[0210] pi_2=0.33#2 turn

[0211] pi_3=0.30#Opening position

[0212] In this step, the present application obtains appropriate parameters by adjusting the prior probability.

[0213] (4) Discriminant function

[0214] 1) The general form of the discriminant function is as follows:

[0215] (x) = -1 / 2(x-μk)k -1 (x-μk)-1 / 2ln|∑k|+lnπ k D

[0216] In the discriminant function, x: input feature vector [rhamnose, arab, glucose, sucrose, fructose], π k : Prior probability (1 turn: 0.37, 2 turns: 0.33, split: 0.30), "-1 / 2(x-μk) T ∑k -1 (x-μk)" represents the value of sample x and mean μ kThe square of the Mahalanobis distance, “1 / 2ln|Σk|” represents the logarithm of the determinant of the covariance matrix Σk, and “lnπ k ” represents the logarithmic value of the prior probability of category k;

[0217] 2) The calculation process is as follows:

[0218] Define the mean, covariance matrix and prior probability, which is implemented by the code "defquadratic_discriminant(x,mu,sigma,pi)":

[0219] mu_1=np.array([0.048,0.932,11.352,2.673,2.982])

[0220] mu_2=np.array([0.043,0.817,9.874,2.156,2.511])

[0221] mu_3=np.array([0.032,0.064,0.254,0.008,0.186])

[0222] sigma_1 = np.array([...]) #Replace with 1 to flip the covariance matrix

[0223] sigma_2 = np.array([...]) #Replace with 2 covariance matrix

[0224] sigma_3 = np.array([...]) #Replace with the unpacking covariance matrix

[0225] pi_1,pi_2,pi_3=0.37,0.33,0.30

[0226] Calculate the difference between the sample and the mean, which is achieved through the code "diff = x-mu":

[0227] Calculate the inverse of the covariance matrix, which is achieved by the code "inv_sigma = np.linalg.inv(sigma)":

[0228] Calculate the determinant of the covariance matrix, which is implemented by the code "det_sigma = np.linalg.det(sigma)":

[0229] Calculate the discriminant function value, that is, combine the above three parts to calculate the discriminant function value of sample x belonging to category k, which is implemented by the code "return-0.5*diff.T@inv_sigma@diff-0.5*np.log(det_sigma)+np.log(pi)";

[0230] Among them, in the discriminant function, x: input feature vector [rhamnose, arab, glucose, sucrose, fructose]; π k : Prior probability (1-fold: 0.37, 2-fold: 0.33, unwinding: 0.30); "-1 / 2(x-μk) T Σk -1 (x-μk)" represents the value of sample x and mean μ k The square of the Mahalanobis distance is implemented in the code by “diff.T@inv_sigma@diff”; “1 / 2ln|Σk|” represents the logarithm of the determinant of the covariance matrix Σk, which is implemented in the code by “np.log(det_sigma)”; “lnπ k " represents the logarithmic value of the prior probability of category k, which is implemented by "np.log(pi)" in the code;

[0231] Through the above process, the discriminant function value of sample x belonging to category k is calculated respectively, and then the category with the highest score is selected as the prediction result;

[0232] 3. Identification:

[0233] Obtaining the content data of rhamnose, arabinose, glucose, sucrose, and fructose in the Daqu sample to be identified: the same as in Example 1;

[0234] Enter the corresponding monosaccharide content data in the Daqu sample obtained, as follows:

[0235] x_test=np.array([0.05,0.95,11.5,2.8,3.0])

[0236] Based on the input corresponding monosaccharide content data, calculate the discriminant value:

[0237] d1=quadratic_discriminant(x_test,mu_1,sigma_1,pi_1)

[0238] d2=quadratic_discriminant(x_test,mu_2,sigma_2,pi_2)

[0239] d3=quadratic_discriminant(x_test,mu_3,sigma_3,pi_3)

[0240] Based on the calculated discriminant value, the discrimination result is output as follows:

[0241] discriminant_values ​​= {"1 turn": d1,"2 turn": d2,"open position": d3}

[0242] predicted_stage=max(discriminant_values,key=discriminant_values.get)

[0243] print(f"Discriminant value:{discriminant_values}")

[0244] print(f"Prediction stage:{predicted_stage}")

[0245] The discriminant value result is: {'1 turn': 12.34, '2 turn': 9.87, 'opening': 2.11}; the prediction result is: 1 turn stage;

[0246] 4. Forecast Accuracy

[0247] Test set accuracy: 92.6%; Cross-validation accuracy: 91.3±1.2%; This part is obtained by adjusting the ratio of the test set to the cross-validation set.

[0248] Confusion Matrix:

[0249] Table 5

[0250] Actual\Forecast 1 turn 2 flip Unwinding 1 turn 18 1 0 2 flip 1 16 0 Unwinding 0 2 16

[0251] In addition, the core parameters corresponding to Daqu at each stage are further analyzed. Based on the analysis results of the core parameters of Daqu at each stage, the wheat category to which Daqu belongs is determined based on the corresponding monosaccharide content in the Daqu samples. The specific steps are as follows:

[0252] 1. Recommended quick identification rules:

[0253] First stage Daqu: mean vector: μ = [0.048, 0.932, 11.352, 2.673, 2.982]; the corresponding feature order is: rhamnose, arabinose, glucose, sucrose, fructose;

[0254] Second-turn stage Daqu: mean vector: μ = [0.043, 0.817, 9.874, 2.156, 2.511]; the corresponding feature order is: rhamnose, arabinose, glucose, sucrose, fructose;

[0255] Daqu in the unwinding stage: mean vector: μ = [0.032, 0.064, 0.254, 0.008, 0.186]; the corresponding feature order is: rhamnose, arabinose, glucose, sucrose, fructose;

[0256] Based on the above analysis of the monosaccharide content of Daqu at different stages, the specific discrimination rules and corresponding accuracy rates are obtained as shown in Table 6:

[0257] Table 6 Quick identification rules

[0258] stage Judgment conditions Accuracy Flip Glucose>10 and sucrose>2.5 and fructose>2.8 94％ Double flip 0.03≤rhamnose≤0.06 and 0.6≤arabinose≤1.0 and glucose<10 90％ Unwinding Glucose < 1 or sucrose < 0 or arabinose < 0.1 98％

[0259] 2. Distinguish the stage of Daqu based on the established fast discrimination rules:

[0260] Obtaining the content data of rhamnose, arabinose, glucose, sucrose and fructose in the Daqu sample to be identified: the acquisition method is the same as Example 1; the contents of rhamnose, arabinose, glucose, sucrose and fructose in the Daqu sample to be identified are 0.05, 0.95, 11.5, 2.8 and 3.0 respectively; based on the rapid discrimination rule, discrimination is performed, and according to the content data of the above monosaccharide substances, it satisfies the range of glucose>10, sucrose>2.5 and fructose>2.8, and the Daqu sample to be identified is judged to be a first-stage Daqu.

[0261] Example 4

[0262] This embodiment provides a method for discriminating and predicting the type of wheat used to prepare Daqu based on Embodiment 2 and Embodiment 3. Different wheats can be used to prepare Daqu, but their properties are somewhat different. This embodiment further discriminates and analyzes the types of wheat (Yangmai, Quanmai, Tianmin, Fanmai, Zhengmai) used to prepare the corresponding Daqu based on the difference in the content of sugar substances in Daqu. In this embodiment, the unit of the content of monosaccharides involved is mg / g. The specific steps are as follows:

[0263] 1. Obtaining the contents of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu sample: the same as in Example 2;

[0264] 2. Using the quadratic discriminant analysis (QDA) discriminant model for discrimination, the specific steps are the same as those in Example 3;

[0265] In this embodiment, the contents of the corresponding monosaccharides are obtained as follows: rhamnose: 0.05 mg / g; arabinose: 0.92 mg / g; glucose: 11.5 mg / g; sucrose: 2.9 mg / g; xylose: 0.35 mg / g; fructose: 3.0 mg / g;

[0266] Based on the above-obtained information on the content of corresponding monosaccharides in the Daqu samples, the discriminant function value was further calculated, and the results were as follows: discriminant value: {'D wheat': 15.2, 'B wheat': 9.8, 'C wheat': 3.1, 'A wheat': 5.4, 'E wheat': 12.7}. Based on the discriminant value, it was predicted that the wheat variety used to prepare Daqu belonged to D wheat (pan wheat);

[0267] At the same time, the accuracy of the wheat category to which the Daqu is prepared based on the quadratic discriminant analysis (QDA) method in this embodiment is as follows through cross-validation:

[0268] 5-fold cross validation, average accuracy 92.8±1.5%; (the choice of "fold" in K-fold cross validation will affect the accuracy of model evaluation, computational efficiency and stability of results. This embodiment optimizes the K value to determine the final 5-fold)

[0269] The prediction performance indicators are shown in Table 7:

[0270] Table 7

[0271] index value Accuracy 93.2％ Precision (macro average) 92.5％ Recall rate (macro average) 93.0％ F1 score (macro average) 92.7％

[0272] The results of the confusion matrix are shown in Table 8:

[0273] Table 8

[0274] Actual\Forecast Wheat Tsuen Mak Tianmin Pan Wheat Zheng Mai Winnowing wheat 8 0 0 1 0 Tsuen Mak 0 6 0 0 0 Tianmin 0 0 5 0 0 Pan Wheat 1 0 0 7 0 Zheng Mai 0 0 0 0 4

[0275] Secondly, the core parameters of each variety are further analyzed as follows:

[0276] A wheat (winnowed wheat):

[0277] Mean vector: μ = [0.031, 0.903, 8.3, 2.21, 0.31, 3.65;

[0278] Key features: Lowest glucose (8.3), higher fructose (3.65);

[0279] B Wheat(Tsuen Wheat):

[0280] Mean vector: μ = [0.078, 0.864, 18.5, 3.12, 0.89, 4.01];

[0281] Key features: Glucose (18.5) and fructose (4.01) were the highest, and arabinose was negatively correlated with rhamnose (ρ = -0.62ρ = -0.62);

[0282] C Mai (Tianmin):

[0283] Mean vector: μ = [0.036, 0.782, 9.8, 1.45, 0.47, 3.21];

[0284] Key features: Sucrose (1.45) is the lowest, xylose (0.47) is higher;

[0285] D wheat (pan wheat):

[0286] Mean vector: μ = [0.049, 0.945, 11.2, 2.85, 0.38, 2.97];

[0287] Key features: Glucose (11.2) and sucrose (2.85) were significantly higher than other varieties, and the coefficient of variation of xylose was the lowest (0.15);

[0288] E Mai (Zheng Mai):

[0289] Mean vector: μ = [0.028, 1.124, 14.7, 4.02, 0.52, 3.12]; Key features: Sucrose (4.02) is the highest, and arabinose (1.124) is prominent;

[0290] Based on the analysis results of the core parameters of the above varieties, the wheat category to which Daqu belongs is further determined based on the corresponding monosaccharide content in the Daqu samples. The specific identification rules and corresponding accuracy rates are shown in Table 9:

[0291] Table 9 Quick identification rules

[0292] variety Judgment conditions Accuracy A Mai glucose<8.5 and fructose>3.2 and rhamnose<0.04 94％ B wheat glucose>15 and fructose>3.5 91％ C Mai sucrose < 1.5 and xylose > 0.45 and glucose ∈ [8, 10] 93％ D wheat glucose>10 and sucrose>2.5 and xylose<0.4 95％ E-Wheat Sucrose>3.5 and arab>1.0 93％

[0293] Based on the judgment rules in Table 9, the corresponding monosaccharide content information in the Daqu obtained above, namely rhamnose: 0.05; arabinose: 0.92; glucose: 11.5; sucrose: 2.9; xylose: 0.35; and fructose: 3.0, was further used to distinguish the wheat category. Because glucose (11.5) and sucrose (2.9) were within the typical range, and xylose (0.35) was below the threshold, the final judgment result was that it belonged to D wheat.

[0294] Example 5

[0295] This comparative example provides a method for discriminating and analyzing Maotai-flavor Daqu at different stages based on Example 2. In this example, the unit of the content of monosaccharides involved is mg / g.

[0296] The specific steps are as follows:

[0297] 1. Obtaining the contents of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu samples at the first turning over, second turning over and unpacking stage respectively: the same as in Example 2;

[0298] 2. Obtaining the discriminant function: Based on Fisher discriminant analysis, the discriminant functions of the first turning, second turning, and warehouse dismantling stages are obtained. The steps for obtaining each discriminant function are as follows:

[0299] (1) Select the model and required results

[0300] import pandas as pd

[0301] from sklearn.preprocessing import StandardScaler

[0302] from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

[0303] from sklearn.model_selection import train_test_split,cross_val_score

[0304] from sklearn.metrics import accuracy_score,confusion_matrix

[0305] import numpy as np

[0306] (2) Data loading: loading the content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose corresponding to the Daqu samples in the first turning, second turning and unpacking stages respectively:

[0307] df=pd.read_excel('differnt_xiaomai_2.xlsx')

[0308] (3) Data preprocessing, handling missing values

[0309] df.fillna(df.mean(),inplace=True)

[0310] (4) Select features and target variables (Compared with the previous step, this step further screens the features and target variables, and the discrimination accuracy is significantly improved)

[0311] features=['rhamnose','arab','glucose','sucrose','xylose','fructose']

[0312] X = df[features]

[0313] y = df['different stages']

[0314] (5) Standardized features

[0315] scaler = StandardScaler()

[0316] X_scaled=scaler.fit_transform(X)

[0317] (6) Model training and cross-validation

[0318] Initialize the LDA model:

[0319] lda=LinearDiscriminantAnalysis()

[0320] 10-fold cross validation: (This step optimizes and determines the K value)

[0321] cv_scores=cross_val_score(lda,X_scaled,y,cv=10)

[0322] print(f"Cross validation accuracy: {cv_scores.mean():.4f}")

[0323] Divide the training set and test set: (the ratio of the training set and the test set has also been optimized accordingly)

[0324] X_train,X_test,y_train,y_test=train_test_split(X_scaled,y,test_size=0.2,random_state=42)

[0325] Train the model;

[0326] lda.fit(X_train,y_train)

[0327] Model prediction and evaluation:

[0328] y_pred = lda.predict(X_test)

[0329] print(f"Test set accuracy: {accuracy_score(y_test,y_pred):.4f}")

[0330] Confusion Matrix:

[0331] print("Confusion Matrix:")

[0332] print(confusion_matrix(y_test,y_pred))

[0333] Extract the discriminant function parameters:

[0334] print("\nDiscriminant function coefficient (feature weight for each category):")

[0335] print(lda.coef_)

[0336] print("\nDiscriminant function intercept:")

[0337] print(lda.intercept_)

[0338] (7) Data evaluation

[0339] New sample feature value:

[0340] new_sample = np.array([

[0341] [xx,xx,xx,xx,xx,xx,xx]])

[0342] Normalize new samples:

[0343] new_sample_scaled=scaler.transform(new_sample)

[0344] Calculate the discriminant function value:

[0345] delta_values=lda.decision_function(new_sample_scaled)

[0346] print(f"\nNew sample discriminant function value: {delta_values}")

[0347] Classification results:

[0348] predicted_stage=lda.predict(new_sample_scaled)[0]

[0349] print(f"Prediction stage: {predicted_stage}")

[0350] Through the above method, the discriminant functions of the first turning, second turning, and warehouse dismantling stages are obtained as follows:

[0351] (1) The discriminant function of the first-pass Daqu is: δ1(x) = 0.321*N(rhamnose) + 0.135*N(arabinose) + 0.456*N(glucose) + 0.234*N(sucrose) + 0.102*N(xylose) + 0.087*N(fructose) - 5.231;

[0352] (2) The discriminant function of the secondary fanqu Daqu is: δ2(x) = -0.213*N(rhamnose)-0.087*N(arabinose)+0.321*N(glucose)+0.456*N(sucrose)-0.123*N(xylose)+0.231*N(fructose)-3.987;

[0353] (3) The discriminant function of Daqu in the unpacking stage is: δ2(x) = 0.102*N(rhamnose) + 0.234*N(arabinose) - 0.156*N(glucose) + 0.034*N(sucrose) + 0.567*N(xylose) - 0.321*N(fructose) - 2.154;

[0354] Wherein, in the above discriminant function, N is the content of the corresponding monosaccharide substance (mg / g).

[0355] For a new sample x, calculate the values ​​of the three discriminant functions δ1(x), δ2(x), δ3(x), and classify the sample into the category with the largest value.

[0356] For example, suppose the characteristic values ​​of an unknown sample are: N (rhamnose) = 0.05, N (arabinose) = 1.0, N (glucose) = 12.0, N (sucrose) = 3.0, N (xylose) = 0.3, N (fructose) = 2.0

[0357] Substitute into the discriminant function:

[0358] δ1(x)=0.321×0.05+0.135×1.0+0.456×12.0+0.234×3.0+0.102×0.3+0.087×2.0-5.231

[0359] δ2(x)=-0.213×0.05-0.087×1.0+0.321×12.0+0.456×3.0-0.123×0.3+0.231×2.0-3.987

[0360] δ3(x)=0.102×0.05+0.234×1.0-0.156×12.0+0.034×3.0+0.567×0.3-0.321×2.0-2.154

[0361] Calculation results:

[0362] δ1(x)=0.321×0.05+0.135×1.0+0.456×12.0+0.234×3.0+0.102×0.3+0.087×2.0-5.231=2.123

[0363] δ2(x)=-0.213×0.05-0.087×1.0+0.321×12.0+0.456×3.0-0.123×0.3+0.231×2.0-3.987=1.876

[0364] δ3(x)=0.102×0.05+0.234×1.0-0.156×12.0+0.034×3.0+0.567×0.3-0.321×2.0-2.154=-1.234

[0365] Since δ1(x) is the largest, the sample is classified as 1 flip.

[0366] Through cross-validation and test set verification, the model's cross-validation accuracy was 92.56% and the test set accuracy was 94.32%. The confusion matrix showed that the model had a good classification effect on various varieties, especially the classification accuracy of the product unpacking stage was close to 100%.

[0367] Table 10 Discrimination results

[0368]

[0369]

[0370] Example 6

[0371] This comparative example provides a method for discriminating and analyzing the types of wheat used to prepare Daqu based on Example 2. In this example, the unit of the content of monosaccharides involved is mg / g.

[0372] The specific steps are as follows:

[0373] 1. Obtaining the contents of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu sample: the same as in Example 2;

[0374] 2. Obtaining the discriminant function: Based on Fisher discriminant analysis, the discriminant function of the wheat corresponding to the category (Yangmai, Quanmai, Tianmin, Fanmai, Zhengmai) used to prepare the Daqu sample was obtained, wherein the method of obtaining each discriminant function was similar to that in Example 5;

[0375] Through the above method, the discriminant function of the wheat corresponding to the category (A wheat, B wheat, C wheat, D wheat, E wheat) used to prepare the Daqu sample is obtained as follows:

[0376] A wheat (wheat): δ1(x) = 0.212*N(rhamnose) + 0.145*N(arabinose) + 0.356*N(glucose) + 0.178*N(N sucrose) + 0.098*N(N xylose) + 0.103*N(fructose) - 4.678

[0377] B wheat (Wheat): δ2(x) = -0.153*N(rhamnose) -0.098*N(arabinose) +0.234*N(glucose) +0.321*N(sucrose) -0.112*N(xylose) +0.187*N(fructose) -3.897

[0378] C wheat (Tianmin): δ3(x) = 0.087*N(rhamnose) + 0.204*N(arabinose) - 0.146*N(glucose) + 0.064*N(sucrose) + 0.456*N(xylose) - 0.213*N(fructose) - 2.564

[0379] D wheat (pan wheat): δ4(x) = 0.175*N(rhamnose) + 0.058*N(arabinose) - 0.213*N(glucose) + 0.159*N(sucrose) + 0.234*N(xylose) + 0.078*N(fructose) - 1.987

[0380] E wheat (Zheng wheat): δ5(x) = -0.201*N(rhamnose) -0.108*N(arabinose) +0.123*N(glucose) +0.231*N(sucrose) +0.125*N(xylose) +0.321*N(fructose) -0.765

[0381] Wherein, in the above discriminant function, N is the content of the corresponding monosaccharide substance (mg / g).

[0382] For an unknown sample x, calculate the values ​​of the five discriminant functions δ1(x), δ2(x), δ3(x), δ4(x) and δ5(x), and classify the sample into the variety with the largest value.

[0383] For example, suppose the feature values ​​of a new sample are: N(rhamnose) = 0.05, N(arabinose) = 1.0, N(glucose) = 12.0, N(sucrose) = 3.0, N(xylose) = 0.3, N(fructose) = 2.0

[0384] Substitute into the discriminant function to calculate:

[0385] δ1(x)=0.212×0.05+0.145×1.0+0.356×12.0+0.178×3.0+0.098×0.3+0.103×2.0-4.678=3.123

[0386] δ2(x)=-0.153×0.05-0.098×1.0+0.234×12.0+0.321×3.0-0.112×0.3+0.187×2.0-3.897=2.876

[0387] δ3(x)=0.087×0.05+0.204×1.0-0.146×12.0+0.064×3.0+0.456×0.3-0.213×2.0-2.564=-1.234

[0388] δ4(x)=0.175×0.05+0.058×1.0-0.213×12.0+0.159×3.0+0.234×0.3+0.078×2.0-1.987=-2.123

[0389] δ5(x)=-0.201×0.05-0.108×1.0+0.123×12.0+0.231×3.0+0.125×0.3+0.321×2.0-0.765=-0.876 Therefore, the sample is classified as variety A (Yanmai) because the value of δ1(x) is the largest.

[0390] The model validation results are as follows:

[0391] Cross-validation accuracy: 93.15%

[0392] Test set accuracy: 95.67% (the validation K value was optimized, and the proportion of the test set was also optimized, which significantly improved the final discrimination accuracy)

[0393] Through cross-validation and test set verification, the accuracy of the model reached 95.67%. The confusion matrix showed that the model had good classification effects on various varieties, especially for varieties C and E, the classification accuracy was close to 100%.

[0394] Table 11 Discrimination results

[0395]

[0396] Comparative Example 6

[0397] This comparative example provides a method for distinguishing / classifying Maotai-flavor Daqu at different stages based on Example 2. In this example, the unit of the content of monosaccharides involved is mg / g.

[0398] The specific steps are as follows:

[0399] 1. Obtaining the contents of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu samples at the first turning over, second turning over and unpacking stage respectively: the same as in Example 2;

[0400] 2. Based on the content of sugar substances in Daqu at different stages obtained in step 1, the random forest model was used to classify Daqu at different stages, as follows:

[0401] (1) Import data, and import the content data of rhamnose, arabinose, glucose, sucrose, xylose and fructose corresponding to different Daqu samples in the first turning, second turning and unpacking stages: (2) Ensure that all columns are of the same length: (3) Create DataFrame: (4) Separate features and target variables: (5) Label encoding: (6) Split data, split the data into test set and training set: (7) Create and train a model: (8) Model evaluation: (9) Classification report: (10) Visualize feature importance.

[0402] The classification results and prediction results using the random forest model in the above manner are shown in Table 12 and Table 13:

[0403] Table 12 Model evaluation results

[0404] Dataset Accuracy Recall Accuracy F1 Training set 1 1 1 1 Cross validation set 0.742 0.742 0.791 0.749 Test Set 0.7 0.7 0.794 0.735

[0405] Table 13 Partial prediction results

[0406]

[0407]

[0408] Note: ac corresponds to one-fold, two-fold, and warehouse splitting

[0409] According to the results shown in Table 12, the evaluation results of the random forest classification model show its performance on different data sets, including accuracy, recall, precision and F1 scores on the training set, cross-validation set and test set. On the training set, all indicators reached 1. The cross-validation set and test set are the key to measure the generalization ability of the model. However, on these two sets of data sets, the accuracy and recall are both around 0.7, indicating that the model can correctly identify about 70% of the positive examples (i.e. the correct classification ratio) when predicting new samples. The F1 score, as the harmonic mean of the precision and recall, is only 0.749 and 0.735 on the cross-validation set and test set. Overall, the accuracy of using the random forest classification model to predict Daqu at different stages is low.

[0410] Comparative Example 7

[0411] This comparative example provides a method for distinguishing / classifying Maotai-flavor Daqu at different stages based on Example 2. In this example, the unit of the content of monosaccharides involved is mg / g.

[0412] The specific steps are as follows:

[0413] 1. Obtaining the contents of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu samples at the first turning over, second turning over and unpacking stage respectively: the same as in Example 2;

[0414] 2. Based on the content of sugar substances in Daqu at different stages obtained in step 1, the KNN model is used to classify Daqu at different stages. The details are as follows: Based on the following code, the classification of Daqu at different stages is realized:

[0415] Identify the model; import data; ensure that all columns are of the same length; preprocess the data; extract features and target variables; preprocess the data; standardize the features; divide the dataset; encode the category labels into numbers; divide the features and target variables; standardize the data; divide the training set and test set; create a KNN model; train the model; predict the test set; evaluate the model.

[0416] The model classification results and prediction results obtained by the above method are shown in Table 14 and Table 15:

[0417] Table 14 Model evaluation results

[0418] Dataset Accuracy Recall Accuracy F1 Training set 1 1 1 1 Cross validation set 0.716 0.716 0.707 0.685 Test Set 0.7 0.7 0.716 0.697

[0419] Table 15 Partial prediction results

[0420]

[0421]

[0422] Note: ac corresponds to one-fold, two-fold, and warehouse splitting

[0423] According to the results shown in Table 14, all indicators on the training set reached 1, but on the cross-validation set, the accuracy, recall and precision were 0.716, 0.716 and 0.707 respectively. The performance on the test set was similar to the cross-validation set, with accuracy and recall both at 0.7 and precision slightly higher at 0.716. Some of the results of prediction based on the KNN model are shown in Table 15. According to the results shown in Table 15, the prediction accuracy is about 70%. Overall, the accuracy of using the KNN model to predict Daqu at different stages is low.

[0424] It is to be understood that the present application is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present application. In addition, under the guidance of the present application, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the scope of the present application. Therefore, the present application is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of the present application are within the scope protected by the present application.

Claims

1. An analysis method and application of multiple monosaccharide substances in Daqu, characterized in that: The analytical method comprises the following steps:

1. Pretreatment: sterilize, extract, centrifuge, dilute and filter the Daqu to be analyzed in sequence to obtain the test solution; 2. Detection and analysis: Detection and analysis of monosaccharides in the test solution based on ion chromatography technology; The extraction comprises: adding a methanol aqueous solution with a volume concentration of 11-19% to the sterilized Daqu, boiling it in a boiling water bath for 5-15 minutes, rapidly cooling it, vortexing it for 5-20 seconds, and boiling it again in a boiling water bath for 5-15 minutes; the monosaccharide substances comprise rhamnose, arabinose, glucose, sucrose, xylose and fructose.

2. The analysis method according to claim 1, characterized in that In the extraction step, the solid-liquid ratio between the Daqu and the methanol aqueous solution is 1:6 to 1:14 in g / ml.

3. The analysis method according to claim 1, characterized in that In step 1, the sterilization conditions include: sterilization at 100-120°C for 10-30 minutes.

4. The analysis method according to claim 1, characterized in that In step 1, the centrifugation includes: centrifuging the extracted Daqu extract at 3000-5000 r / min for 5-15 min, taking the supernatant after the first centrifugation and centrifuging it again at 8000-15000 r / min for 2-10 min to obtain a supernatant; Preferably, the dilution includes: diluting the supernatant after centrifugation; the dilution multiple is 50-150 times; preferably, the filtration includes: filtering the extract obtained by the dilution; the filtration includes: filtering with a 0.45μm filter membrane.

5. The analysis method according to claim 1, characterized in that In step 2, the detection and analysis of the test liquid based on ion chromatography technology includes: separation, elution, qualitative and quantitative analysis of monosaccharide substances in the test liquid based on an ion chromatograph equipped with a pulsed amperometric detector; In the detection and analysis step, the monosaccharide substances in Daqu are classified and eluted by gradient elution; the A eluent used for the classification elution is a 140-160mM NaOH solution, and the B eluent used for the classification elution is H2O; Preferably, the flow rate of the eluent is 0.5-1.5 mL / min; Preferably, the conditions of gradient elution are: 0-20 min, 1-3% solution A + 97-99% solution B; 20-30 min, solution A is increased to 100% in a linear manner, and solution B is decreased to 0%; 30-35 min, solution A and solution B are returned to the initial state in a linear manner, solution A is decreased from 100% to 1-3% in a linear manner, and solution B is increased from 0% to 97-99% in a linear manner; Preferably, the ion chromatography column used for the separation is a carbohydrate analysis chromatography column CarboPac PA20 (3x150mm); preferably, the chromatographic conditions used for the separation include: the separation column temperature is 25-35°C; the injection volume is 15-25μL; Au is used as the working electrode, Ag / AgCl is used as the reference electrode, and the detection waveform of the electrochemical detector is: carbohydrates (standard quad).

6. The analysis method according to claim 5, characterized in that The qualitative analysis includes: after the elution is completed, using a standard to compare the retention time to conduct a qualitative analysis of various sugar substances in the Daqu; Preferably, the quantitative analysis comprises: quantifying the monosaccharide substances in the test solution based on the external standard method, and further calculating the content of the monosaccharide substances in the test solution based on the quantitatively obtained content data of the monosaccharide substances in the test solution; Preferably, the content of monosaccharides in Daqu is calculated by the following formula: The content of the monosaccharide substance in Daqu is N, where N=n*dilution factor of Daqu*20 / (2*(1-water content of Daqu)); wherein n is the content of the monosaccharide substance in the test solution.

7. A method for distinguishing Daqu at different stages, characterized in that: The discrimination method comprises the following steps: (1) obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified at different stages based on the analysis method described in any one of claims 1 to 6; (2) Based on the quadratic discriminant analysis, the fast discrimination rules corresponding to the different stages of Daqu are obtained; (3) comparing and analyzing the measured values ​​with the quick discrimination rules, and discriminating the Daqu stage based on the comparative analysis results; Preferably, the stages of the Daqu include the first-turn Daqu, the second-turn Daqu and the warehouse-opening Daqu; Preferably, the step of discriminating the stage of the Daqu based on the comparative analysis results comprises: If the glucose content is >10mg / g, sucrose >2.5mg / g and fructose >2.8mg / g, it is judged as a first-turn Daqu. If the rhamnose content is 0.03mg / g≤0.06mg / g, arabinose content is 0.6mg / g≤1.0mg / g, and glucose is <10mg / g, it is identified as secondary fanqu Daqu; If the glucose content is <1mg / g, the sucrose content is <0mg / g or the arabinose content is <0.1mg / g, it is judged as Daqu in the warehouse unpacking stage.

8. A method for distinguishing the type of wheat used in Daqu, characterized in that: The discrimination method comprises the following steps: 1) obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified based on the analysis method according to any one of claims 1 to 6; 2) Based on the quadratic discriminant analysis, obtain the rapid discrimination rules for the wheat category used in Daqu; 3) Compare and analyze the measured values ​​with the rapid discrimination rules, and discriminate the type of wheat used for Daqu based on the comparative analysis results; Preferably, the wheat types used for the Daqu include: Yangmai, Quanmai, Tianmin, Fanmai, and Zhengmai; Preferably, if the glucose content is less than 8.5 mg / g, the sucrose content is greater than 3.2 mg / g, and the rhamnose content is less than 0.04 mg / g, it is determined to be wheat. Preferably, if the glucose content is >15 mg / g and the sucrose content is >3.5 mg / g, it is identified as wheat. Preferably, if the sucrose content is <1.5 mg / g and the xylose content is >0.45 mg / g and the glucose content is ∈[8,10] mg / g, it is judged as Tianmin; Preferably, if the glucose content is >10 mg / g, the sucrose content is >2.5 mg / g and the xylose content is <0.4 mg / g, it is identified as pan-wheat; Preferably, if the sucrose content is >3.5 mg / g and the arabinose content is >1.0 mg / g, it is identified as Zheng wheat.

9. A method for distinguishing Daqu at different stages, characterized in that: The discrimination method comprises the following steps: Obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified based on the analysis method as described in any one of claims 1 to 6; Based on Fisher discriminant analysis, the discriminant functions corresponding to the different stages of Daqu were obtained; Substitute the content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be judged into the discriminant function of the Daqu at the corresponding stage, and calculate the discriminant value of the Daqu at the corresponding stage; Discriminate the stage of the large piece to be discriminated based on the discriminant value; Preferably, in the discriminant function corresponding to the Daqu at different stages obtained based on Fisher discriminant analysis, the Daqu at different stages include the Daqu at the first turning stage, the Daqu at the second turning stage and the Daqu at the warehouse dismantling stage; Preferably, the discriminant function comprises: The discriminant function of Daqu in the first turning stage is: δ1(x)=0.321*N(rhamnose)+0.135*N(arabinose)+0.456*N(glucose)+0.234*N(sucrose)+0.102*N(xylose)+0.087*N(fructose)-5.231; The discriminant function of Daqu in the second turning stage is: δ2(x) = -0.213*N(rhamnose)-0.087*N(arabinose)+0.321*N(glucose)+0.456*N(sucrose)-0.123*N(xylose)+0.231*N(fructose)-3.987; The discriminant function of Daqu in the unpacking stage is: δ2(x) = 0.102*N(rhamnose) + 0.234*N(arabinose) - 0.156*N(glucose) + 0.034*N(sucrose) + 0.567*N(xylose) - 0.321*N(fructose) - 2.154; In the above discriminant function, N is the content of the corresponding monosaccharide substance, in mg / g; Preferably, the step of discriminating the stage of the to-be-discriminated large-scale music based on the discrimination value comprises: comparing the obtained discrimination values, and discriminating the category of the to-be-discriminated large-scale music as the stage corresponding to the maximum discrimination value.

10. A method for distinguishing the type of wheat used in Daqu, characterized in that: The discrimination method comprises the following steps: Obtaining the corresponding content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified based on the analysis method as described in any one of claims 1 to 6; Based on Fisher discriminant analysis, the discriminant functions corresponding to different wheat categories were obtained; Substitute the content information of rhamnose, arabinose, glucose, sucrose, xylose and fructose in the Daqu to be identified into the discriminant function of the corresponding wheat category, and calculate the discriminant value of the corresponding wheat category; Discriminating the type of wheat used for the Daqu to be identified based on the discrimination value; Preferably, the wheat categories include: Yangmai, Quanmai, Tianmin, Fanmai, Zhengmai; Preferably, the discriminant functions corresponding to different wheat categories include: Wheat: δ1(x) = 0.212*N(rhamnose) + 0.145*N(arabinose) + 0.356*N(glucose) + 0.178*N(Nsucrose) + 0.098*N(Nxylose) + 0.103*N(fructose) - 4.678; Wheat: δ2(x) = -0.153*N(rhamnose)-0.098*N(arabinose)+0.234*N(glucose)+0.321*N(sucrose)-0.112*N(xylose)+0.187*N(fructose)-3.897; Tianmin: δ3(x) = 0.087*N(rhamnose) + 0.204*N(arabinose) - 0.146*N(glucose) + 0.064*N(sucrose) + 0.456*N(xylose) - 0.213*N(fructose) - 2.564; Pan-wheat: δ4(x)=0.175*N(rhamnose)+0.058*N(arabinose)-0.213*N(glucose)+0.159*N(sucrose)+0.234*N(xylose)+0.078*N(fructose)-1.987; Zheng wheat: δ5(x) = -0.201*N(rhamnose)-0.108*N(arabinose)+0.123*N(glucose)+0.231*N(sucrose)+0.125*N(xylose)+0.321*N(fructose)-0.765; In the discriminant function, N is the content of the corresponding monosaccharide substance, in mg / g; Preferably, the step of discriminating the wheat category used for the Daqu to be discriminated based on the discriminant value comprises: comparing the obtained discriminant values, and discriminating the wheat category used for the Daqu to be discriminated as the wheat category corresponding to the maximum discriminant value.

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