Maotai-flavor liquor pit sealing mud evaluation model construction method and pit sealing mud evaluation method

Abstract

The invention provides a construction method of a Maotai-flavor liquor pit sealing mud evaluation model and a pit sealing mud evaluation method. The construction method comprises the following steps: 1, obtaining physicochemical indexes of different pit sealing mud samples and simultaneously obtaining sensory score data of the different pit sealing mud samples; 2, performing correlation analysis on the obtained physicochemical indexes corresponding to the different pit sealing mud samples and the sensory score data to obtain correlation data between the physicochemical indexes of the different pit sealing mud samples and the sensory score data; 3, screening the physicochemical indexes according to the correlation data to obtain modeling indexes; and 4, constructing an evaluation model according to the modeling indexes. Based on two aspects of physicochemical indexes and sensory scoring, two aspects of objective data and subjective evaluation are considered, so that the evaluation accuracy is improved; important modeling indexes are screened according to correlation analysis, it is ensured that the model contains factors having significant influence on the quality of pit mud, and the complexity of the model is simplified while the effectiveness of the model is improved.

Description

Technical Field

[0001] The invention belongs to the field of cellar mud quality assessment, and in particular relates to a method for constructing a cellar mud evaluation model for Maotai-flavor liquor and a cellar mud evaluation method. Background Art

[0002] The cellar-sealing mud used in Maotai-flavor liquor production is a special type of soil used to seal the fermentation vessels (i.e., cellars). It plays a key role in the liquor brewing process. This mud is typically used during the fermentation phase of Maotai-flavor liquor production. Specifically, after the prepared raw materials (i.e., mash) are placed in the cellar, the cellar is sealed with mud to ensure an environment suitable for anaerobic fermentation.

[0003] The main functions of cellar mud include: Sealing: Preventing outside air from entering the cellar and maintaining an oxygen-free environment inside, which is crucial for the anaerobic fermentation process; Microbial carrier: Cellar mud is rich in a variety of microorganisms that are beneficial to winemaking, such as bacteria, molds, yeasts, etc. These microorganisms participate in complex material metabolism processes and provide rich flavor components for the wine; Influence on wine quality: The quality of cellar mud and the types and quantity of microorganisms in it are directly related to the quality of the upper layer of mash, and therefore have an important impact on the flavor of the final product.

[0004] Sealing mud is typically made from viscous purple-red or yellow clay from specific regions. These clays have excellent adhesion and sealing properties, contain few impurities, and meet the standards required for winemaking. For example, the purple-red clay from Maotai Town in Renhuai is very suitable for cellar sealing.

[0005] Generally speaking, after seven fermentation cycles for Maotai-flavor liquor, the sealing mud is discarded, and new soil is used to make the sealing mud for the next round of production. To improve the recycling rate of sealing mud and reduce the exploitation of natural raw mud, the sealing mud with good quality after seven rounds can continue to be used. For sealing mud of poor quality, some companies will also take measures to repair the sealing mud. However, the evaluation method for the repaired sealing mud currently mainly uses manual sensory evaluation, but this method is very labor-intensive. Among other evaluation methods, the quality evaluation standards of the cellar mud mainly include cellar age, sensory indicators, and physical and chemical indicators. The evaluation system is relatively extensive and cannot fully and objectively reflect the quality of the cellar mud. In addition, the existing cellar mud evaluation models are mainly for the cellar mud of Luzhou-flavor liquor, and there are fewer evaluation models for the sealing mud of Maotai-flavor liquor. The determination of the cellar mud of Luzhou-flavor liquor often requires the determination of microbial indicators, but the measurement of microbial indicators is relatively complex.

[0006] Therefore, it is necessary to provide an evaluation method that is more reasonable in evaluation system, simple in evaluation index measurement, and targeted at the sealing mud of sauce-flavor liquor to evaluate the quality of the reused cellar mud. Summary of the Invention

[0007] Based on this, the purpose of this application is to provide a targeted evaluation method for the sealing mud of Maotai-flavor liquor.

[0008] To achieve the above objectives, the present application provides a method for constructing a cellar mud evaluation model for Maotai-flavor liquor, the technical solution adopted comprising the following steps:

[0009] 1. Obtaining physical and chemical indicators of different cellar-sealing mud samples and simultaneously obtaining sensory scoring data of the different cellar-sealing mud samples;

[0010] 2. performing correlation analysis on the physical and chemical indicators corresponding to the different cellar-sealing mud samples obtained and the sensory score data, to obtain correlation data between the physical and chemical indicators and the sensory score data of the different cellar-sealing mud samples;

[0011] 3. Screen the physical and chemical indicators based on the correlation data to obtain the modeling indicators;

[0012] 4. Construct an evaluation model based on the modeling indicators.

[0013] In some embodiments, in step 2, performing correlation analysis on the physical and chemical indicators corresponding to the different sealing mud samples obtained and the sensory score data includes: performing correlation analysis using a Pearson correlation analysis method.

[0014] In some embodiments, screening the physical and chemical indicators according to the correlation data includes: selecting evaluation indicators with the top 5 Pearson correlation coefficient r values.

[0015] In some embodiments, an evaluation indicator with a Pearson correlation coefficient r≥9.7 is selected.

[0016] In some embodiments, the modeling indicators include: TCOD and clay ratio.

[0017] In some embodiments, the modeling indicators include: TCOD, total nitrogen, ammonia nitrogen, MBC and clay ratio.

[0018] In some embodiments, the manual scoring evaluation criteria include: when the sealing mud has extremely poor viscosity, is black, has obvious roughness, is easy to dry and crack, and has a strong wine lees smell and fishy smell, the score is 0.

[0019] When the sealing mud has poor viscosity, is black, has a rough feel, is easy to dry and crack, and has a strong smell of lees and fishy odor, the score is 20.

[0020] When the sealing mud has poor viscosity, is darker, slightly rough, easy to dry and crack, and has a slight smell of lees and fishy smell, the score is 40.

[0021] When the sealing mud has average viscosity, is slightly black, has a slightly strong water retention capacity and is more prone to cracking, and has a wine lees smell but no obvious fishy smell, the score is 60.

[0022] When the sealing mud is sticky, red, soft, has strong water retention capacity and is not easy to crack, and has no obvious smell, the score is 80.

[0023] When the sealing mud has good viscosity, is reddish, soft, has strong water retention capacity, is not easy to crack, and has no smell, the score is 100.

[0024] In some embodiments, the physical and chemical indicators include at least one of clay percentage, moisture content, pH value, TCOD, POXC, MBC, total nitrogen, ammonia nitrogen, humus, available potassium, available phosphorus, caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, and methanobacteria.

[0025] In some embodiments, in step one, the evaluation indicators include physical property indicators, chemical property indicators and microbial indicators that comprehensively reflect the structural stability, organic matter content, microbial activity and nutrient supply capacity of the sealing mud.

[0026] In some embodiments, the physical property indicators include: clay ratio, moisture content, and pH value.

[0027] In some embodiments, the physical property index includes: clay ratio.

[0028] In some embodiments, the chemical property indicators include: TCOD, POXC, MBC, total nitrogen, ammonia nitrogen, humus, available potassium, and available phosphorus.

[0029] In some embodiments, the chemical property indicators include: TCOD, MBC, total nitrogen, and ammonia nitrogen.

[0030] In some embodiments, the microbial indicators include caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, and methanobacteria.

[0031] In step 4, the construction of the evaluation model according to the modeling indicators includes: determining the weights of the various modeling indicators through the hierarchical analysis method, and constructing the evaluation model according to the determined weights.

[0032] In some embodiments, in step four, the construction of the evaluation model based on the modeling indicators includes: taking TCOD, total nitrogen, ammonia nitrogen, MBC and clay proportion as indicators at the same level, comparing and assigning values ​​to the indicators with each other to construct a judgment matrix, calculating the weight vector of each modeling indicator according to the judgment matrix, and obtaining the weight of each modeling indicator according to the weight vector.

[0033] In some embodiments, the weights of each indicator determined according to the hierarchical analysis method include: the weight of each indicator determined according to the hierarchical analysis method includes: the weight of TCOD is 0.307, the weight of total nitrogen is 0.099, the weight of ammonia nitrogen is 0.049, the weight of MBC is 0.049, and the weight of clay ratio is 0.494.

[0034] In some embodiments, the evaluation model is Y=0.307X1+0.099X2+0.049X3+0.049X4+

[0035] 0.4955X5; X1 is TCOD, X2 is total nitrogen, X3 is ammonia nitrogen, X4 is MBC, and X5 is the proportion of clay.

[0036] In some embodiments, in step one, the cellar sealing mud samples include cellar sealing mud samples with different degrees of use.

[0037] In some embodiments, in step one, the different cellar sealing mud samples include: original cellar sealing mud, 1st round cellar sealing mud, 2nd round cellar sealing mud, 3rd round cellar sealing mud, 4th round cellar sealing mud, 5th round cellar sealing mud, 6th round cellar sealing mud and discarded cellar sealing mud.

[0038] On the other hand, the present application provides a method for evaluating the sealing mud of Maotai-flavor liquor, the evaluation method comprising the following steps:

[0039] 1. Obtain the physical and chemical index data of the cellar mud to be evaluated;

[0040] 2. Input the acquired physical and chemical index data into the evaluation model constructed by the aforementioned construction method, and evaluate the cellar sealing mud to be evaluated based on the output results of the evaluation model;

[0041] Among them, the physical and chemical indicators include: TCOD, total nitrogen, ammonia nitrogen, MBC and clay ratio.

[0042] In some embodiments, the evaluation of the cellar mud to be evaluated based on the output results of the evaluation model includes: when the output Y value is 90-100, the corresponding quality description is: the cellar mud has excellent quality, reaching the level of one-round or original cellar mud; when the output Y value is 85-90, the cellar mud has very good quality, reaching the level of two-round cellar mud; when the output Y value is 60-85, the cellar mud has good quality, reaching the level of three-round cellar mud; when the output Y value is 40-60, the cellar mud has average quality, reaching the level of four-round cellar mud; when the output Y value is 30-40, the cellar mud has poor quality, reaching the level of five-round cellar mud; when the output Y value is 10-30, the cellar mud has very poor quality, reaching the level of six-round cellar mud; when the output Y value is 0-10, the cellar mud has extremely poor quality, similar to discarded cellar mud.

[0043] Beneficial effects: The model construction method adopted in this application is based on data of physical and chemical indicators and sensory scores, and takes into account both scientific measurement data and human perceptual experience, providing a more comprehensive evaluation perspective and improving the accuracy and reliability of the evaluation; through the correlation analysis between physical and chemical indicators and sensory scores, important modeling indicators are screened according to the obtained correlation data to ensure that the final established model mainly contains key factors that have a significant impact on sensory quality, which improves the effectiveness of the model while simplifying the complexity of the model; the model can more accurately evaluate the quality of the cellar mud or the degree of restoration of the cellar mud after treatment, and can explain why some cellar mud samples score higher, and which factors have a greater impact on quality, thereby guiding process improvements in production. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a fitting diagram of the model constructed in Example 1 of the present invention;

[0045] Figure 2 This is the fitting diagram of the model constructed in Comparative Example 1 of the present invention;

[0046] Figure 3 This is the fitting diagram of the model constructed in Comparative Example 2 of the present invention;

[0047] Figure 4 This is the fitting diagram of the model constructed in Comparative Example 3 of the present invention. DETAILED DESCRIPTION

[0048] The following is a detailed description of the technical solution of the present invention, which does not limit the scope of protection of the present invention. Non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the scope of protection of the present invention.

[0049] The cellar sealing mud samples used in the embodiments of the present invention and the comparative examples include: original cellar sealing mud, 1st round cellar sealing mud, 2nd round cellar sealing mud, 3rd round cellar sealing mud, 4th round cellar sealing mud, 5th round cellar sealing mud, 6th round cellar sealing mud and discarded cellar sealing mud.

[0050] The test methods for the indicators in the embodiments of the present invention and the comparative examples are as follows:

[0051] 1. TCOD assay:

[0052] TCOD, or total chemical oxygen demand, is determined using a rapid digestion method, including:

[0053] 1) Solution preparation

[0054] H2SO4-Ag2SO4 solution: Take 5g of silver sulfate and add it to 500mL of concentrated sulfuric acid. Let it stand for 1-2 days until the silver sulfate is completely dissolved before use.

[0055] Potassium dichromate solution: Dry potassium dichromate at 120±2℃ to constant weight, weigh 24.5154g potassium dichromate and place it in a beaker. Then add 600mL of water and slowly add 100mL of concentrated sulfuric acid while stirring. After dissolving and cooling, transfer the solution to a 1000mL volumetric flask, dilute to the mark with water, and shake well.

[0056] Chemical oxygen demand (COD) standard solution: Dry potassium hydrogen phthalate at 105-110°C to constant weight. Dissolve 2.1274 g of potassium hydrogen phthalate in 250 mL of water. Transfer this solution to a 500 mL volumetric flask, dilute to the mark with distilled water, and shake well. This yields a standard solution with a COD value of 5000 mg / L.

[0057] 2) Standard curve determination

[0058] Prepare 100, 200, 400, 600, 800, and 1000 mg / L standard solutions respectively and take 2 mL of each and add 10

[0059] mL digestion tube, then add 4mL H2SO4-Ag2SO4 and 1mL 1 / 6K2Cr2O7 in sequence. After shaking,

[0060] Digest at 5°C for 15 minutes. Cool to room temperature in a water bath and then measure the absorbance. Plot a standard curve using the average absorbance versus COD value.

[0061] 3) TCOD determination

[0062] Take 1 mL of the mud-water mixture and dilute it to 100 times the original volume in a 250 mL beaker. Place the beaker in a rotor and stir it magnetically. Then, use a 5 mL syringe to take 2 mL of the mud-water mixture into a digestion tube. Then, add each solution in sequence according to the preparation method and measure the TCOD.

[0063] 2. Ammonia nitrogen determination:

[0064] The Nessler reagent spectrophotometric method was used to determine the following:

[0065] 1) Solution preparation

[0066] Nessler's reagent: Weigh 16.0g sodium hydroxide (NaOH), dissolve in 50mL water, and cool to room temperature. Weigh 7.0g potassium iodide (KI) and 10.0g mercuric iodide (HgI2), dissolve in water, and then slowly add this solution to the above 50mL water under stirring.

[0067] mL of sodium hydroxide solution, dilute to 100 mL with water. Store in a polyethylene bottle, tightly covered with a rubber stopper or polyethylene cap, and store in a dark place.

[0068] Potassium sodium tartrate solution, ρ = 500 g / L: Weigh 50.0 g of potassium sodium tartrate (KNaC.H4O6·4H2O) and dissolve it in 100 mL of water. Heat to boil to drive off ammonia, cool thoroughly, and dilute to 100 mL.

[0069] Ammonia nitrogen standard stock solution, ρ N =1000g / mL: weigh 3.8190g ammonium chloride (NH4C1, high-grade purity, at 100-1

[0070] 05℃ for 2h), dissolved in water, transferred into a 1000mL volumetric flask, and diluted to the mark.

[0071] Ammonia nitrogen standard working solution, ρ N = 10 g / mL: Pipette 5.00 mL of ammonia nitrogen standard stock solution into a 500 mL volumetric flask and dilute to the mark. Prepare immediately before use.

[0072] 2) Standard curve determination:

[0073] In eight 50 mL colorimetric tubes, add 0.00, 0.50, 1.00, 2.00, 4.00, 6.00, 8.00 and 10.00

[0074] mL of ammonia standard working solution, the corresponding ammonia nitrogen contents are 0.0, 5.0, 10.0, 20.0, 40.0, 60.0, 8

[0075] For 0.0 and 100 μg, add water to the mark. Add 1.0 mL of potassium sodium tartrate solution and shake well. Then add 1.0 mL of Nessler's reagent and shake well. After standing for 10 minutes, measure the absorbance at 420 nm and plot a calibration curve.

[0076] 3) Determination of ammonia nitrogen content:

[0077] Take 1 mL of mud-water mixture, add 10 mL of KCl solution (1 M), and after extraction for 1 hour, take the supernatant and add it to each solution in turn according to the preparation mark method to determine the ammonia nitrogen content.

[0078] 3. Total nitrogen determination: alkaline potassium persulfate digestion UV spectrophotometry, including:

[0079] 1) Solution preparation:

[0080] Alkaline potassium persulfate solution: Weigh 40.0g of potassium persulfate and dissolve it in 600mL of water (you can heat it in a 50°C water bath until completely dissolved). Separately, weigh 15.0g of sodium hydroxide and dissolve it in 300mL of water. After the sodium hydroxide solution cools to room temperature, mix the two solutions and dilute to 1000mL. Store in a polyethylene bottle.

[0081] Potassium nitrate standard stock solution, ρN = 100 mg / L: Weigh 0.7218 g of potassium nitrate and dissolve it in water. Transfer the solution to a 1000 mL volumetric flask, dilute to the mark with water, and mix thoroughly. Add 2 mL of chloroform as a protective agent and store at 4°C in the dark.

[0082] Potassium nitrate standard working solution, ρN = 10.0 mg / L: Measure 10.00 mL of potassium nitrate standard stock solution into a 100 mL volumetric flask, dilute to the mark with water, mix well, and prepare before use.

[0083] 2) Standard curve determination:

[0084] Measure 0.00, 0.20, 0.50, 1.00, 3.00, and 7.00 mL of potassium nitrate standard solution into a 25 mL ground-glass stoppered colorimetric tube. The corresponding total nitrogen (N) contents are 0.00, 2.00, 5.00, 10.0, 30.0, and 70.0 μg, respectively. Dilute to 10.00 mL with water, then add 5.00 mL of alkaline potassium persulfate solution and securely stopper the tube. Place the colorimetric tube in a high-pressure steam sterilizer and heat until the top pressure valve opens to purge air. Close the valve and continue heating to 120°C, starting the timer and maintaining the temperature between 120-124°C for 30 minutes. Allow to cool naturally, open the valve to vent air, remove the outer cap, remove the colorimetric tube, and cool to room temperature. Press the stopper to mix the liquid in the tube by inverting it three times.

[0085] Add 1.0 mL of hydrochloric acid solution (1 + 9) to each colorimetric tube, dilute with water to the 25 mL mark, cap and mix thoroughly. Measure the absorbance at 220 nm and 275 nm on a UV spectrophotometer using water as a reference, and plot a calibration curve.

[0086] 3) Total nitrogen determination:

[0087] Take 1 mL of mud-water mixture, add 10 mL of HCl solution (1 M), and after extraction for 1 hour, take the supernatant and add it to each solution in sequence according to the preparation mark method to determine its total nitrogen content.

[0088] 4. pH determination: potentiometric method

[0089] Weigh 10.0 g of soil sample into a 50 mL tall beaker or other suitable container and add 25 mL of water. Seal the container with parafilm or plastic wrap and vigorously stir with a magnetic stirrer for 2 minutes. Let stand for 30 minutes and measure the pH within 1 hour using a pH meter.

[0090] 5. Determination of clay particle ratio: laser particle size analyzer

[0091] The clay fraction, defined as the proportion of particles less than 2 μm in the pit mud, was measured using a Mastersizer 2000 laser particle size analyzer. The process involved ultrasonicating the pit mud sample for 5 minutes, preheating the particle size analyzer for 15-20 minutes, and then activating the liquid circulation system. The test program was set, with the refractive index set to 1.60 and each test duration set to 25 seconds. The pit mud sample was then added to the sample cell, and the test program was run. Three measurements were taken, and the average value was calculated.

[0092] 6. MBC Assay

[0093] MBC (microbial biomass carbon) is measured using the following method: Weigh 10g of fresh soil sample and place it in a 25ml beaker. Place the beaker in a vacuum desiccator, along with two or three 15ml beakers containing ethanol-free chloroform. Place a small amount of anti-boiling glass beads in each beaker, along with a small beaker containing a NaOH solution to absorb CO2 released during fumigation. Add a small amount of water to the bottom of the desiccator to maintain humidity. Cover the vacuum desiccator, evacuate with a vacuum pump, and allow the chloroform to boil for 5 minutes. Close the vacuum desiccator and incubate at 25°C in the dark for 24 hours. The fumigated soil is extracted with a K2SO4 solution of a specified concentration, and the organic carbon content of the extract is measured using an automated organic carbon analyzer. First, measure the total organic carbon (C1) in the fumigated extract. Then, perform the same extraction procedure on an equal amount of unfumigated soil and measure the total organic carbon (C0). The calculation formula of MBC is: MBC = (C1-C0) × KEC, where KEC is the fumigation efficiency factor, usually ranging from 0.38 to 0.45.

[0094] 7. POXC Assay

[0095] POXC, or potassium permanganate oxidizable carbon, is determined using the closed culture alkali absorption method. 10 g of soil sample is placed in a beaker, adjusted to 50% of field capacity with distilled water, and placed in a 1 L culture bottle together with a beaker containing 2 mL of 0.5 mol / L NaOH. The sample is closed and cultured at 21°C for 10 days. The CO2 absorbed by NaOH is then titrated with 1.5 mol / L BaCl and 0.1 mol / L HCl to determine the potential mineralizable carbon content.

[0096] 8. Determination of moisture content:

[0097] Dry the covered container and lid at (105±5)℃ for 1h, cool slightly, cover with lid, and then place in a desiccator to cool for at least 45min. Measure the mass m0 of the covered container to an accuracy of 0.01g. Use a sample spoon to transfer 30-40g of fresh soil sample to a weighed covered container, cover the container, and measure the total mass m1 to an accuracy of 0.01g. Remove the container lid, place the container and the fresh soil sample in an oven, and dry them at (105±5)℃ to constant weight. Dry the container lid at the same time. Cover the container lid and place it in a desiccator to cool for at least 45min. Immediately after taking it out, measure the total mass m2 of the covered container and dried soil to an accuracy of 0.01g. According to the formula, the moisture content W H2O =(m1-m2) / (m2-m0)×100 to calculate the moisture content.

[0098] Example 1: A method for constructing a model for evaluating the sealing mud of Maotai-flavor liquor

[0099] This embodiment provides a method for evaluating the sealing mud of Maotai-flavor liquor, comprising the following steps:

[0100] 1. Obtaining the physical and chemical indicators of original, discarded, and first to sixth rounds of cellar sealing mud

[0101] The physical and chemical indicators corresponding to the original cellar-sealing mud, 1st round of cellar-sealing mud, 2nd round of cellar-sealing mud, 3rd round of cellar-sealing mud, 4th round of cellar-sealing mud, 5th round of cellar-sealing mud, 6th round of cellar-sealing mud and abandoned cellar-sealing mud were measured respectively: TCOD, POXC, MBC, total nitrogen, ammonia nitrogen content, pH value, clay ratio, moisture content, humus, caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, methanobacteria, fast-acting potassium and available phosphorus, and the above-mentioned physical and chemical indicator data of the original, abandoned and 1st to 6th round of cellar-sealing mud were obtained.

[0102] The obtained physical and chemical index data results are shown in Table 1 and Table 2 below:

[0103] Table 1 TCOD, POXC, MBC, total nitrogen, ammonia nitrogen content, pH value, clay content, and moisture content of the sealing mud at each stage

[0104]

[0105] Table 2 Data on humus, caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, methanobacteria, available potassium and available phosphorus in the sealing mud at each stage

[0106]

[0107]

[0108] 2. Obtain sensory scoring data for original, discarded, and sealed pit mud from each round

[0109] According to the sensory scoring standards for the sealing mud in Table 3, sensory scores were given to the original, discarded and each round of pit mud.

[0110] Table 3 Sensory scoring criteria for sealing mud

[0111]

[0112] According to the above scoring standards, the original, various rounds and discarded sealing mud of the sauce-flavor liquor cellar mud were sensory scored, and the results were: original sealing mud 100 points, 1st round sealing mud 95 points, 2nd round sealing mud 90 points, 3rd round sealing mud 85 points, 4th round sealing mud 40 points, 5th round sealing mud 30 points, 6th round sealing mud 10 points and discarded sealing mud 0 point.

[0113] 3. Indicator Correlation Analysis and Screening

[0114] The correlation analysis was conducted between the physical and chemical indicators of the original, discarded and sealed pit muds of each round and the comprehensive scores of the pit mud.

[0115] The correlation analysis method uses the Pearson correlation coefficient to analyze the correlation between the eight evaluation indicators of the cellar mud and the sensory scores. The analysis results are expressed as the correlation coefficient r. The results are shown in the following table:

[0116] Table 4: Correlation coefficients between physical and chemical indicators of sealing mud and sensory scores

[0117] Evaluation indicators r-value Evaluation indicators r-value Evaluation indicators r-value Evaluation indicators r-value TCOD -1.00 POXC -0.93 MBC -0.97 Total nitrogen -0.99 Ammonia nitrogen content -0.97 pH -0.89 Clay ratio -0.97 Moisture content 0.30 humus -0.95 Hexanoic acid bacteria -0.65 Actinomycetes 0.68 Butyric acid bacteria -0.46 lactic acid bacteria -0.057 Methanobacterium - Fast-acting potassium -0.85 Available phosphorus -0.73

[0118] In the above table, there is no r value for Methanobacterium because Methanobacterium was not detected in the sealing mud of Maotai-flavor liquor.

[0119] The Pearson correlation coefficient r ranges from -1 to 1. The absolute value represents the degree of correlation, while the sign represents whether it is positive or negative. A positive value indicates a positive correlation, while a negative value or a value of 0 indicates no correlation. The values ​​of r for each indicator and the degree of correlation are shown in Table 5 below:

[0120] Table 5: r values ​​and correlations

[0121] Degree of correlation r value range Perfect positive correlation r＝1 Perfect negative correlation r＝-1 Positive linear correlation 0<r<1 Negative linear correlation -1<r<0 Not relevant r＝0

[0122] 4. Construction of Pit Mud Evaluation Model

[0123] 1) Establish a hierarchical model:

[0124] TCOD, total nitrogen, ammonia nitrogen, MBC, and clay fraction with good correlation in the correlation analysis in step 2 were taken as indicators for establishing the model.

[0125] 2) Construct a judgment matrix:

[0126] The experts in evaluating the quality of pit mud used the nine-point scaling method shown in Table 6 to compare each indicator with all other indicators in pairs to determine the relative importance of each indicator. The resulting relative importance judgment matrix is ​​shown in Table 7.

[0127] Table 6 Scaling method description

[0128]

[0129] Table 7 Judgment Matrix

[0130] TCOD Total nitrogen Ammonia nitrogen MBC Clay ratio TCOD 1 3 7 7 0.5 Total nitrogen 0.333 1 2 2 0.2 Ammonia nitrogen 0.143 0.5 1 1 0.111 MBC 0.143 0.5 1 1 0.111 Clay ratio 2 5 9 9 1

[0131] 3) Calculate the weight vector:

[0132] First, normalize the judgment matrix according to formula 1-1, and then obtain the eigenvector of the normalized data according to formula 1-2.

[0133]

[0134] Where A represents the judgment matrix, Represents the elements in the normalized judgment matrix. A ij represents the importance score of element i relative to element j, represents the feature vector, and n represents the number of indicators.

[0135] The obtained eigenvectors are shown in Table 8 below.

[0136] Table 8 Feature vectors

[0137] TCOD Total nitrogen Ammonia nitrogen MBC Clay ratio Eigenvector TCOD 0.2763 0.3 0.35 0.35 0.26015 1.53647 Total nitrogen 0.09201 0.1 0.1 0.1 0.10406 0.49607 Ammonia nitrogen 0.03951 0.05 0.05 0.05 0.05775 0.24727 MBC 0.03951 0.05 0.05 0.05 0.05775 0.24727 Clay ratio 0.55264 0.5 0.45 0.45 0.52029 2.47293

[0138] The eigenvectors in Table 7 are normalized according to formulas 1-3 to obtain the weights Ni of each indicator: The weights of each indicator are shown in Table 9 below:

[0139]

[0140] Table 9 Indicator weights

[0141] index Weight <![CDATA[X1:TCOD]]> 0.307 <![CDATA[X2: Total Nitrogen]]> 0.099 <![CDATA[X3: Ammonia nitrogen]]> 0.049 <![CDATA[X4:MBC]]> 0.049 <![CDATA[X5: Clay particle proportion]]> 0.494

[0142] 4) Consistency test:

[0143] Due to the complexity of each research problem and the diversity of the evaluation subjects, it is very likely that inconsistent and contradictory conclusions will appear when comparing the importance of each evaluation indicator. Therefore, in order to meet the consistency requirements of the judgment matrix and make the research results scientific and reasonable, it is necessary to perform a consistency test and calculate the consistency ratio CR value.

[0144] According to formulas 1-4, 1-5, and 1-6, CI = 0.00488 is obtained, and RI = 1.12 is found, so CR = 0.00436 < 0.1, indicating that the judgment matrix has satisfactory consistency. Therefore, the weights obtained in step 3) pass the consistency test.

[0145] CR=CI / RI (Formula 1-4)

[0146]

[0147]

[0148] Where CI: represents the consistency index; CR: represents the random consistency ratio; RI: represents the random consistency index, which has different values ​​according to the number of elements n; λmax: represents the maximum eigenvalue of the judgment matrix A.

[0149] 5) Build an evaluation model:

[0150] Use the weights that pass the consistency test to build an evaluation model: the resulting model is:

[0151] Y=0.307X1+0.099X2+0.049X3+0.049X4+0.4955X5

[0152] 5. Validation of the Pit Mud Evaluation Model

[0153] A scatter plot was made with the calculated comprehensive score as the horizontal axis and the sensory evaluation score as the vertical axis, and a regression curve was fitted. The fitted curve is shown as follows: Figure 1 As shown, the R 2 =0.98766, which proves that the model can accurately evaluate the degree of restoration after pit mud treatment.

[0154] Example 2: A method for evaluating the sealing mud of Maotai-flavor liquor

[0155] The method comprises the following steps: obtaining evaluation index values ​​of the cellar sealing mud to be evaluated, where the evaluation indexes include: X1: TCOD, X2: total nitrogen, X3: ammonia nitrogen, X4: MBC and X5: clay ratio; inputting the evaluation index values ​​into the evaluation model constructed in Example 1, and evaluating the cellar sealing mud to be evaluated based on the output results of the evaluation model.

[0156] When the output Y value is 90-100, the corresponding quality description is: the quality of the cellar mud is excellent, reaching the level of the first round or original cellar mud; when the output Y value is 85-90, the quality of the cellar mud is very good, reaching the level of the second round of cellar mud; when the output Y value is 60-85, the quality of the cellar mud is good, reaching the level of the third round of cellar mud; when the output Y value is 40-60, the quality of the cellar mud is average, reaching the level of the fourth round of cellar mud; when the output Y value is 30-40, the quality of the cellar mud is poor, reaching the level of the fifth round of cellar mud; when the output Y value is 10-30, the quality of the cellar mud is very poor, reaching the level of the sixth round of cellar mud; when the output Y value is 0-10, the quality of the cellar mud is extremely poor, similar to that of discarded cellar mud.

[0157] Comparative Example 1

[0158] Based on Example 1, the difference between this comparative example and Example 1 is that the index used to construct the evaluation model does not include the clay ratio, and the other conditions used are the same as those in Example 1, including the following steps:

[0159] 1. Determine the physical and chemical indicators of the original, abandoned and 1 to 6 rounds of pit mud: TCOD, POXC, MBC, total nitrogen, ammonia nitrogen content, pH value, moisture content, humus, caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, methanobacteria, fast-acting potassium, and available phosphorus.

[0160] 2. Sensory evaluation of pit mud: same as in Example 1.

[0161] 3. Index correlation analysis and screening: Same as Example 1.

[0162] 4. Construction of Pit Mud Evaluation Model

[0163] 1) Establish a hierarchical model: select TCOD, total nitrogen, ammonia nitrogen, MBC, and moisture content as indicators for establishing the model.

[0164] 2) Constructing a judgment matrix: The resulting judgment matrix is ​​shown in Table 10 below:

[0165] Table 10 Judgment Matrix of Comparative Example 1

[0166] TCOD Total nitrogen Ammonia nitrogen MBC pH TCOD 1 3 7 7 0.5 Total nitrogen 0.333 1 2 2 0.2 Ammonia nitrogen 0.143 0.5 1 1 0.111 MBC 0.143 0.5 1 1 0.111 pH 2 5 9 9 1

[0167] 3) Calculate the eigenvectors and weights. The obtained eigenvectors are shown in Table 11 below:

[0168] Table 11 Feature vectors of comparative example 1

[0169] TCOD Total nitrogen Ammonia nitrogen MBC pH Eigenvector TCOD 0.276319 0.3 0.35 0.35 0.260146 1.536465 Total nitrogen 0.092014 0.1 0.1 0.1 0.104058 0.496073 Ammonia nitrogen 0.039514 0.05 0.05 0.05 0.057752 0.247266 MBC 0.039514 0.05 0.05 0.05 0.057752 0.247266 pH 0.552639 0.5 0.45 0.45 0.520291 2.47293

[0170] The weights of each indicator are shown in Table 12 below:

[0171] Table 12 Index weights for comparative example 1

[0172] index Weight TCOD 0.307293 Total nitrogen 0.099215 Ammonia nitrogen 0.049453 MBC 0.049453 pH 0.494586

[0173] 4) Consistency test: The obtained weights pass the consistency test.

[0174] 5) Construct an evaluation model: Use the weights that pass the consistency test to construct an evaluation model.

[0175] 5. Validation of the Pit Mud Evaluation Model

[0176] A scatter plot was made with the calculated comprehensive score as the horizontal axis and the sensory evaluation score as the vertical axis, and a regression curve was fitted. The fitted curve is as follows: Figure 2 As shown, the R 2 =0.63327, indicating that the accuracy of the model in evaluating the restoration degree after pit mud treatment is not high.

[0177] Comparative Example 2

[0178] This comparative example constructs a corresponding model based on the method recorded in the prior art "Determination of Pit Mud Quality Evaluation Index and Its Weight Based on Analytic Hierarchy Process" and performs model verification, comprising the following steps:

[0179] 1. Model Construction

[0180] The weight set of the index system recorded in "Determination of Pit Mud Quality Evaluation Indicators and Their Weights Based on the Analytic Hierarchy Process" is shown in Table 13 below:

[0181] Table 13 Weights of the indicator system in Example 1

[0182]

[0183] The model constructed based on the above weights is:

[0184] Y=0.08×(0.1X1+0.62X2+0.28X3)+0.46×(0.19X4+0.08X5+0.06X6+0.05X7+0.21X8+0.

[0185] 42X9)+0.46×(0.55X 10 +0.21X 11 +0.06X 12 +0.06X 13 +0.12X 14 )

[0186] Where X1-X 14 They are color, smell, feel, humus, ammonia nitrogen, available potassium, available phosphorus, pH value, moisture, caproic acid bacteria, methanobacteria, actinomycetes, butyric acid bacteria, and lactic acid bacteria.

[0187] 2. Model Validation

[0188] The values ​​of various indicators of the sealing mud in each round of Maotai-flavor liquor were brought into the model for verification.

[0189] In view of the aforementioned sensory indicators of color, smell, and feel, this comparative example uses the method shown in Table 14 below to measure the sealing mud of each round of Maotai-flavor liquor. The measurement results are as follows:

[0190] Table 14 Measurement methods for color, smell and feel of the sealing mud of Maotai-flavor liquor

[0191]

[0192]

[0193] The color, smell and feel scores measured according to the above method are shown in Table 15 below:

[0194] Table 15 Measurement scores of color, smell and feel of the sealing mud of Maotai-flavor liquor

[0195] original One round Second round Three rounds Four rounds Five rounds Six rounds Abandoned Color 100 94.2 88.7 82.9 54.8 29.1 6.9 0 odor 100 97.8 92.3 85 40.8 34.2 5.5 0 feel 100 96.4 91.5 83.3 47.6 34.7 10 0

[0196] The values ​​of caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, methanobacteria, humus, ammonia nitrogen, available potassium, available phosphorus, pH, and moisture have been measured in Example 1. The values ​​of various indicators of the sealing mud of each round of Maotai-flavor liquor are brought into the model, and a scatter plot is made with the comprehensive score calculated by the model as the abscissa and the sensory evaluation score as the ordinate, and a regression curve is fitted to obtain Figure 3 , the fitting curve R 2 =0.70326, indicating that the accuracy of the model in evaluating the restoration degree after pit mud treatment is not high.

[0197] Comparative Example 3:

[0198] Based on Example 1, the difference between this comparative example and Example 1 is that the indicator used to construct the evaluation model does not include TCOD, and the other conditions used are the same as those in Example 1, including the following steps:

[0199] 1. Determination of physical and chemical indicators of original, discarded, and 1st to 6th round pit mud: Same as Example 1.

[0200] 2. Sensory evaluation of pit mud: same as in Example 1.

[0201] 3. Index correlation analysis and screening: Same as Example 1.

[0202] 4. Construction of Pit Mud Evaluation Model

[0203] 1) Establish a hierarchical model: select clay percentage, total nitrogen, ammonia nitrogen, and MBC as indicators for establishing the model.

[0204] 2) Constructing a judgment matrix: The resulting judgment matrix is ​​shown in Table 10 below:

[0205] Table 16 Judgment Matrix of Comparative Example 3

[0206] Total nitrogen Ammonia nitrogen MBC Clay ratio Total nitrogen 1 2 2 0.2 Ammonia nitrogen 0.5 1 1 0.111 MBC 0.5 1 1 0.111 Clay ratio 5 9 9 1

[0207] 3) Calculate the eigenvectors and weights: The obtained eigenvectors are shown in Table 17 below.

[0208] Table 17 Feature vectors of comparative example 3

[0209] Normalization Total nitrogen Ammonia nitrogen MBC Clay ratio Eigenvector Total nitrogen 0.142857 0.153846 0.153846 0.140647 0.591196 Ammonia nitrogen 0.071429 0.076923 0.076923 0.078059 0.303334 MBC 0.071429 0.076923 0.076923 0.078059 0.303334 Clay ratio 0.714286 0.692308 0.692308 0.703235 2.802136

[0210] The weights of each indicator are shown in Table 12 below:

[0211] Table 18 Index weights of Example 4

[0212] index Weight Total nitrogen 0.147799 Ammonia nitrogen 0.075833 MBC 0.075833 Clay ratio 0.700534

[0213] 4) Consistency test: The obtained weights pass the consistency test.

[0214] 5) Construct an evaluation model: Use the weights that pass the consistency test to construct an evaluation model.

[0215] 5. Validation of the Pit Mud Evaluation Model

[0216] A scatter plot was made with the calculated comprehensive score as the horizontal axis and the sensory evaluation score as the vertical axis, and a regression curve was fitted. The fitted curve is shown as follows: Figure 4 As shown, the R 2 =0.825, indicating that the accuracy of the model in evaluating the degree of restoration after pit mud treatment is not high.

[0217] The present invention is described by way of certain embodiments. It will be appreciated by 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 invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to suit specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A method for constructing an evaluation model for sealing mud of Maotai-flavor liquor, characterized in that: The following steps are involved:

1. Obtaining physical and chemical indicators of different cellar-sealing mud samples and simultaneously obtaining sensory scoring data of the different cellar-sealing mud samples; 2. performing correlation analysis on the physical and chemical indicators corresponding to the different cellar-sealing mud samples obtained and the sensory score data, to obtain correlation data between the physical and chemical indicators and the sensory score data of the different cellar-sealing mud samples; 3. Screen the physical and chemical indicators based on the correlation data to obtain the modeling indicators; 4. Construct an evaluation model based on the modeling indicators.

2. The construction method according to claim 1, wherein In step 2, performing correlation analysis on the physical and chemical indicators corresponding to the different cellar-sealing mud samples obtained and the sensory score data includes: performing correlation analysis using the Pearson correlation analysis method.

3. The construction method according to claim 1, wherein The screening of the physical and chemical indicators according to the correlation data includes: selecting evaluation indicators with the top 5 Pearson correlation coefficient r values; Preferably, an evaluation index with a Pearson correlation coefficient r ≥ 9.7 is selected; Preferably, the modeling indicators include: TCOD and clay ratio; Preferably, the modeling indicators include: TCOD, total nitrogen, ammonia nitrogen, MBC and clay ratio.

4. The construction method according to claim 2, wherein: The manual scoring criteria include: if the sealing mud has extremely poor viscosity, is black, has obvious roughness, is easy to dry and crack, and has a strong lees smell and fishy odor, the score is 0; If the sealing mud has poor viscosity, is black, has a rough feel, is easy to dry and crack, and has a strong lees smell and fishy odor, the score is 20; If the sealing mud has poor viscosity, is dark, slightly rough, easily dries and cracks, and has a slight lees smell and fishy odor, the score is 40; If the sealing mud has average viscosity, is slightly black, has a slightly strong water retention capacity and is prone to cracking, and has a lees smell but no obvious fishy odor, the score is 60; When the sealing mud is sticky, red, soft, has strong water retention and is not easy to crack, and has no obvious smell, the score is 80; When the sealing mud has good viscosity, is reddish, soft, has strong water retention capacity, is not easy to crack, and has no smell, the score is 100.

5. The construction method according to claim 1, wherein: In step 1, the physical and chemical indicators include at least one of clay ratio, moisture content, pH value, TCOD, POXC, MBC, total nitrogen, ammonia nitrogen, humus, available potassium, available phosphorus, caproic acid bacteria, actinomycetes, butyric acid bacteria, lactic acid bacteria, and methanobacteria.

6. The construction method according to claim 1, wherein: In step 4, the construction of the evaluation model according to the modeling indicators includes: determining the weights of the various modeling indicators by means of a hierarchical analysis method, and constructing the evaluation model according to the determined weights; Preferably, the construction of the evaluation model based on the modeling indicators includes: taking TCOD, total nitrogen, ammonia nitrogen, MBC and clay proportion as indicators at the same level, comparing and assigning values ​​to the indicators to construct a judgment matrix, calculating the weight vector of each modeling indicator according to the judgment matrix, and obtaining the weight of each modeling indicator according to the weight vector.

7. The construction method according to claim 6, wherein: The weight of TCOD is 0.307, the weight of total nitrogen is 0.099, the weight of ammonia nitrogen is 0.049, the weight of MBC is 0.049, and the weight of clay fraction is 0.494; Preferably, the evaluation model is Y=0.307X1+0.099X2+0.049X3+0.049X4+0.4955X5; wherein X1 is TCOD, X2 is total nitrogen, X3 is ammonia nitrogen, X4 is MBC, and X5 is the clay fraction.

8. The construction method according to claim 1, wherein: In step one, the different cellar sealing mud samples include: original cellar sealing mud, 1st round cellar sealing mud, 2nd round cellar sealing mud, 3rd round cellar sealing mud, 4th round cellar sealing mud, 5th round cellar sealing mud, 6th round cellar sealing mud and discarded cellar sealing mud.

9. A method for evaluating the sealing mud of Maotai-flavor liquor, characterized in that: The steps include: Obtain the physical and chemical index data of the cellar mud to be evaluated; Inputting the acquired physical and chemical index data into the evaluation model constructed by the construction method according to any one of claims 1 to 8, and evaluating the cellar sealing mud to be evaluated based on the output result of the evaluation model; Among them, the physical and chemical indicators include: TCOD, total nitrogen, ammonia nitrogen, MBC and clay ratio.

10. The method according to claim 9, wherein The evaluation of the cellar mud to be evaluated based on the output results of the evaluation model includes: when the output Y value is 90-100, the corresponding quality description is: the cellar mud has excellent quality, reaching the level of one-round or original cellar mud; when the output Y value is 85-90, the cellar mud has very good quality, reaching the level of two-round cellar mud; when the output Y value is 60-85, the cellar mud has good quality, reaching the level of three-round cellar mud; when the output Y value is 40-60, the cellar mud has average quality, reaching the level of four-round cellar mud; when the output Y value is 30-40, the cellar mud has poor quality, reaching the level of five-round cellar mud; when the output Y value is 10-30, the cellar mud has very poor quality, reaching the level of six-round cellar mud; when the output Y value is 0-10, the cellar mud has extremely poor quality, similar to discarded cellar mud.

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