Method for predicting quality of Maotai-flavor Daqu

Abstract

The invention relates to the technical field of wine making, and particularly provides a Maotai-flavor yeast quality prediction method, which comprises the following steps of: obtaining the acidity and saccharifying power value of yeast in a whole bin by adopting a V-shaped sampling method and a mean value calculation method of each layer at the yeast making end and delivery stage, and calculating the yeast quality according to a storage duration-physical and chemical data calculation equation of the yeast, so as to predict the yeast quality of the Maotai-flavor yeast. According to the method, the physical and chemical index values such as the acidity and the saccharifying power of the whole-bin yeast can be accurately obtained through a small amount of sampling after yeast making is finished; and 2, the acidity and saccharifying power values of the batch of yeast during brewing after storage can be measured and calculated in combination with accurate physical and chemical index data of the yeast discharged from the warehouse.

Description

Technical Field

[0001] The invention relates to the technical field of winemaking, and specifically proposes a method for predicting the quality of Maotai-flavor Daqu. Background Art

[0002] Sauce-flavored Daqu is an important raw material and auxiliary material for brewing sauce-flavored liquor. It provides rich aroma, its precursors and fermentation microorganisms for the sauce-flavor fermentation process. Its usage in the entire production cycle of sauce-flavored liquor is close to the total amount of sorghum for brewing. Therefore, the quality of Sauce-flavored Daqu is an important factor affecting the quality of sauce-flavored liquor. Regulatory measures need to be taken in advance for Daqu of poor quality.

[0003] In order to identify the quality of Daqu, the traditional sauce-flavored Daqu quality evaluation method of "smelling" and "observing the color" was developed in the production of traditional sauce-flavored liquor. This type of method is highly subjective and empirical. Later, with the development and application of detection technology, most wine companies began to use physical and chemical indicators such as koji acidity and saccharification power for evaluation, which improved the scientificity and accuracy of koji quality evaluation to a certain extent.

[0004] However, the current evaluation of Daqu is usually carried out after it is shipped out of the warehouse and before it is stored. The single-room koji-making and fermentation warehouse used for actual production has spatial differences in temperature and humidity. The traditional random sampling method cannot cover the complexity of the entire warehouse, resulting in large fluctuations in physical and chemical indicators. At the same time, Daqu needs to be stored and aged for 3-6 months before use. Microbial metabolism continues during this period. The traditional method only relies on detection at the time of shipment and cannot predict key indicators after storage. If an abnormality is detected in the Daqu sample after crushing and mixing before brewing, and the fermentation parameters have been fixed, the control measures that can be taken are very limited.

[0005] Therefore, it is very important to accurately obtain the physical and chemical index data of the whole warehouse Daqu at the end of koji making and predict the physical and chemical index data of the batch of Daqu after the end of storage, but there is currently a lack of corresponding prediction methods. Summary of the Invention

[0006] In view of this, the present invention proposes a method for accurately obtaining the physical and chemical index data of the whole warehouse Daqu at the end of koji making, and predicting the physical and chemical index data of Daqu after the end of storage. The method can obtain accurate comprehensive acidity and saccharification power data of the whole warehouse Daqu through a small amount of sampling and testing when leaving the warehouse. At the same time, the comprehensive acidity and saccharification power data of the Daqu in the warehouse when it is brewed after storage are predicted through a prediction model, which provides strong support for timely adjusting the koji / koji storage / brewing parameters according to the quality of Daqu and ensuring the stability of the original wine quality.

[0007] The technical solution of the present invention is achieved as follows: the present invention provides a method for predicting the quality of sauce-flavored Daqu, which adopts a spatial V-shaped sampling method and a layer mean calculation method at the stage of finishing the koji making and unloading the koji to obtain the acidity and saccharification power values ​​of the Daqu in the entire warehouse, and then obtains the acidity and saccharification power values ​​of the Daqu when it is used for brewing based on the calculation equation of "storage time-physical and chemical data" of the Daqu.

[0008] In some embodiments, specific prediction methods include: (1) At the end of koji making and the stage of leaving the warehouse, three-dimensional gradient sampling is performed based on the thermodynamic distribution of the fermentation warehouse; (2) Calculate the acidity y0 and saccharification capacity z0 of the whole warehouse Daqu based on the sampling data; (3) Select a prediction model based on the duration of the ambient temperature > 20°C during the storage period to calculate the acidity y and saccharification power z during brewing after storage; The three-dimensional gradient sampling includes: Width direction: Select representative columns of the high-temperature zone at the edge of the fermentation chamber, the low-temperature zone in the center, and the transition zone; Length direction: sampling at the thermodynamically stable point of a representative column; Height direction: Drill in layers to obtain curved samples of the top high-temperature dry layer, the middle core layer and the bottom low-temperature and high-humidity layer.

[0009] In some embodiments, in three-dimensional gradient sampling: The edge high temperature zone is the row ≤0.5m away from the warehouse wall; The central low-temperature zone is the row with a distance ≥ L / 2 from the warehouse wall, where L is the warehouse width; The transition zone is the column between the high temperature zone at the edge and the low temperature zone in the center.

[0010] In some embodiments, the top high-temperature layer is a block layer ≤0.3 m away from the top surface of the qu pile; the middle core layer is a block layer 0.3-0.6 m away from the top of the qu pile; and the bottom low-temperature layer is a block layer >0.6 m away from the top of the qu pile.

[0011] In some embodiments, the thermodynamic stability point is determined by the following formula: , where N is the total number of blocks in a single column.

[0012] In some embodiments, the number of sampling points of the thermodynamically stable point satisfies: , where k edge 、k trans 、k center The number of columns represents the edge area, transition area, and central area respectively, and each column has at least two layers in the vertical direction.

[0013] In some embodiments, in step (2), the calculation formula for the whole-warehouse acidity y0 is: , The calculation formula for the whole warehouse saccharification power z0 is: , in, is the average acidity of the top high-temperature layer, is the average acidity of the middle core layer, is the average acidity of the bottom low-temperature layer, is the average saccharification power of the top high-temperature layer, is the average saccharification power of the middle core layer, is the average saccharification power of the bottom low-temperature layer, 、 、 are the weight coefficients of each layer respectively.

[0014] In some embodiments, the weight coefficient satisfies: , , ,in 、 、 are the average temperatures of the top, middle and bottom parts respectively.

[0015] In some embodiments, the saccharification capacity prediction model of step (3) includes: if >20℃ for ≥90 days, then If the temperature is >20℃ for <90 days, then , x is the number of storage days.

[0016] In some embodiments, the acidity prediction model of step (3) includes: if >20°C for ≥90 days, then If the temperature is >20℃ for <90 days, then , x is the number of storage days.

[0017] In some embodiments, the top high-temperature layer is a block layer ≤0.6 m from the top surface of the qu pile; the middle core layer is a block layer 0.6-1.0 m from the top of the qu pile; and the bottom low-temperature layer is a block layer >1.0 m from the top of the qu pile.

[0018] The present invention has the following beneficial effects compared to the prior art: The present invention enables, firstly, accurate measurements of physical and chemical indicators such as acidity and saccharification capacity for the entire warehoused koji by sampling a small amount at the end of koji production. Secondly, by combining accurate physical and chemical indicator data for the koji leaving the warehouse, the acidity and saccharification capacity of the batch of koji can be calculated for use in winemaking after storage. By knowing the accurate physical and chemical data for the koji leaving the warehouse and the predicted physical and chemical data after storage, effective guidance can be provided for the accurate identification and evaluation of koji quality, as well as for regulating koji quality during storage and adjusting process parameters for use in winemaking. DETAILED DESCRIPTION

[0019] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] It should be noted that when an element is referred to as being “fixed on” or “disposed on” another element, it may be directly on the other element or indirectly on the other element. When an element is referred to as being “connected to” another element, it may be directly connected to the other element or indirectly connected to the other element.

[0021] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the present invention belong. If the definitions set forth in this section are contrary to or otherwise inconsistent with definitions set forth in the patents, patent applications, published patent applications, and other publications incorporated herein by reference, the definitions listed in this section take precedence over the definitions incorporated herein by reference.

[0024] Unless otherwise specified, the methods used in the following examples are conventional methods. The materials, reagents, and instruments used are conventional materials, reagents, and instruments in the art, unless otherwise specified, and can be obtained commercially by those skilled in the art.

[0025] When an amount, concentration or other value or parameter is expressed as a range, a preferred range or a range defined by a series of upper preferred values ​​and lower preferred values, this should be understood as specifically disclosing all ranges formed by any pairing of any upper range limit or preferred value with any lower range limit or preferred value, regardless of whether the range is disclosed alone. For example, when a range "1 to 5" is disclosed, the described range should be interpreted as including the ranges "1 to 4", "1 to 3", "1 to 2", "1 to 2 and 4 to 5", "1 to 3 and 5", etc. When a numerical range is described herein, unless otherwise stated, the range is intended to include its endpoints and all integers and fractions within the range. In the present specification and claims, range definitions may be combined and / or interchanged, and if not otherwise stated, such ranges include all subranges contained therein.

[0026] Example 1 We selected a fermentation tank and used both the "V-shaped sampling method + layer mean calculation" and random sampling methods. Based on the sampled Daqu acidity and saccharification data, we calculated the average Daqu data for the entire tank. We then mixed, crushed, sampled, and tested the Daqu from the entire tank to obtain accurate physical and chemical data.

[0027] The parameters of the fermentation chamber are as follows:

[0028] 3D gradient sampling Sampling point positioning: , take the 26th song in each column.

[0029] Sampling point coordinates and temperature detection:

[0030] Stratified means and weights:

[0031] Whole warehouse benchmark value: W=0.294+0.377+0.328=0.999

[0032]

[0033] Hot season model calculation

[0034]

[0035] Actual measurement verification:

[0036] Control group: Traditional random sampling method

[0037] Example 2 We selected a fermentation tank and used both the "V-shaped sampling method + layer mean calculation" and random sampling methods. Based on the sampled Daqu acidity and saccharification data, we calculated the average Daqu data for the entire tank. We then mixed, crushed, sampled, and tested the Daqu from the entire tank to obtain accurate physical and chemical data.

[0038] The parameters of the fermentation chamber are as follows:

[0039] 3D gradient sampling Sampling point positioning: , take the 26th song in each column.

[0040] Sampling point coordinates and temperature detection:

[0041] Stratified means and weights:

[0042] Whole warehouse benchmark value: W=0.287+0.373+0.339=0.999

[0043]

[0044] Cold season model calculation

[0045]

[0046] Actual measurement verification:

[0047] Control group: Traditional random sampling method

[0048] The depths stated in the above table refer to the distance from the top surface of the koji pile.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting the quality of Maotai-flavor Daqu, characterized in that: The steps include: (1) At the end of koji making and the stage of leaving the warehouse, three-dimensional gradient sampling is performed based on the thermodynamic distribution of the fermentation warehouse; (2) Calculate the acidity y0 and saccharification capacity z0 of the whole warehouse Daqu based on the sampling data; (3) Select a prediction model based on the duration of the ambient temperature > 20°C during the storage period to calculate the acidity y and saccharification power z during brewing after storage; The three-dimensional gradient sampling includes: Width direction: Select representative columns of the high-temperature zone at the edge of the fermentation chamber, the low-temperature zone in the center, and the transition zone; Length direction: sampling at the thermodynamically stable point of a representative column; Height direction: Drill in layers to obtain curved samples of the top high-temperature dry layer, the middle core layer and the bottom low-temperature and high-humidity layer.

2. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein In 3D gradient sampling: The edge high temperature zone is the row ≤0.5m away from the warehouse wall; The central low-temperature zone is the row with a distance ≥ L / 2 from the warehouse wall, where L is the warehouse width; The transition zone is the column between the high temperature zone at the edge and the low temperature zone in the center.

3. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein The thermodynamic stability point is determined by the following formula: , where N is the total number of blocks in a single column.

4. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein The number of sampling points of the thermodynamically stable point satisfies: , where k edge 、k trans 、k center The number of columns represents the edge area, transition area, and central area respectively, and each column has at least two layers in the vertical direction.

5. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein In step (2), the calculation formula for the whole warehouse acidity y0 is: The calculation formula of the whole warehouse saccharification power z0 is: ,in, is the average acidity of the top high-temperature layer, is the average acidity of the middle core layer, is the average acidity of the bottom low-temperature layer, is the average saccharification power of the top high-temperature layer, is the average saccharification power of the middle core layer, is the average saccharification power of the bottom low-temperature layer, 、 、 are the weight coefficients of each layer, W=( + + ).

6. The method for predicting the quality of Maotai-flavor Daqu according to claim 5, wherein: The weight coefficient satisfies: , , in 、 、 are the average temperatures of the top, middle and bottom parts respectively.

7. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein: The saccharification capacity prediction model of step (3) includes: if the temperature is >20℃ for ≥90 days, then If the temperature is >20℃ for <90 days, then , x is the number of storage days.

8. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein: The acidity prediction model of step (3) includes: if the temperature is >20℃ for ≥90 days, then If the temperature is >20℃ for <90 days, then , x is the number of storage days.

9. The method for predicting the quality of Maotai-flavor Daqu according to claim 1, wherein: The top high-temperature layer is the block layer ≤0.6m away from the top surface of the qu pile; the middle core layer is the block layer 0.6-1.0m away from the top of the qu pile; the bottom low-temperature layer is the block layer >1.0m away from the top of the qu pile.

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