A method for intelligent cultivation of Daqu fermentation process through multi-level parameter analysis and regulation of standardized process

Abstract

The present invention discloses a method for intelligent cultivation of Daqu fermentation process through multi-level parameter analysis and regulation of standardized process, and belongs to the field of winemaking technology. In order to solve the problem that Daqu production is restricted by seasonal environmental temperature factors and promote the "intelligent and modern" production of winemaking, the present invention characterizes and divides the qu embryo forming and qu block fermentation processes by multi-level parameters, and establishes a quality evaluation system for high-quality high-temperature Daqu process; by analyzing the correlation between environmental factors and substrate consumption, microbial species abundance, metabolic enzyme activity and RQ in the qu block fermentation process, a multi-stage fermentation kinetic model is established; an intelligent software control system is used to set reference values ​​according to the fermentation kinetic model to feedback and adjust the environmental parameters in the Daqu fermentation process, better reproduce the production parameters, and realize the digital management of the qu room, thereby achieving the effect of real-time recording of Daqu fermentation conditions, reducing quality fluctuations caused by artificial production, and reducing artificial participation.

Description

Technical Field

[0001] The present invention belongs to the technical field of wine making, and in particular relates to a method for intelligent cultivation of Daqu fermentation technology through multi-level parameter analysis and regulation of a standardized process. Background Art

[0002] Liquor plays an important role in traditional Chinese culture. Daqu is the fermentation starter for liquor brewing, and the unique flavor substances in Daqu determine the aroma of liquor. The outstanding aroma, elegant and delicate, mellow body, and long aftertaste of sauce-flavor liquor are inseparable from the sauce-flavor Daqu. The characteristics of high-temperature and medium-high-temperature Daqu that are different from other qu are that the fermentation "top temperature" can reach 65°C, which will inhibit the growth of yeast and mold, and form a unique microbial community structure with heat-resistant bacteria and mold. The fermentation method of traditional Daqu is poorly controllable, and the environmental temperature and humidity control is greatly affected by climate and other factors. Therefore, the intelligent upgrade of Daqu fermentation is necessary and urgently needed, including the construction of hardware equipment and intelligent software systems in the fermentation room, and the digital realization of the precision and feedback control of multi-level parameters of fermentation. Finally, on the premise of adhering to the core technology of traditional brewing, the digital analysis and scientific innovation of traditional experience are realized.

[0003] The study found that raw materials are an important factor affecting the fermentation process and product quality. The difference in nutritional components of different raw materials is closely related to the quality and function of fermentation products. The production of traditional Daqu usually uses wheat or barley and peas. After being crushed and moistened with water, Daqu mother powder is inoculated and pressed into qu embryos. It is placed in the qu room. The humidity and temperature of the qu room are ventilated and adjusted according to different growth stages. After 20 to 40 days of fermentation, it becomes new qu, and then it is converted into old qu after being properly stored for 2-6 months. There are a few precedents and foundations for using current intelligent means to improve Daqu production technology. Xiao Xiao et al. (Research on the temperature field and intelligent control system application of liquor qu room) optimized the qu room structure through simulation analysis, designed the qu room control system, and finally achieved the qu room temperature requirement of ±0.5℃ at the target temperature after testing, realizing the transformation of the qu room hardware facilities from traditional production to intelligent information production, and also improving the quality of finished liquor qu and production efficiency.

[0004] Daqu is mainly composed of chemical components (material system), microorganisms (bacterial system) and biological enzymes (enzyme system). From the perspective of chemical components, starch, fat and crude protein are dominant; the microorganisms of its natural inoculation and fermentation mainly come from the mother koji and the environment. Changes in environmental factors (temperature, humidity) cause the succession and differences of the microbial community in Daqu. The microorganisms are mainly composed of molds (Aspergillus), bacteria (Bacillus, Lactobacillus, Acetobacter and Clostridium, etc.) and yeasts (Saccharomyces cerevisiae). The purpose of cultivating various microorganisms in Daqu and the brewing process is to use the enzyme system produced by microorganisms to carry out large and complex biochemical reactions. The enzyme system is mainly dominated by saccharification and liquefaction type amylase, saccharification type amylase, maltase, acid protease, cellulase, hemicellulase, tannase, pectinase, etc. Daqu metabolic enzymes mainly originate from the metabolism of microorganisms during the koji-making process. Ren Fei et al. (Study on the saccharification kinetics of Luzhou-flavor Daqu) determined the saccharification kinetics of Daqu saccharifying enzymes, studied the effects of different reaction conditions (substrate concentration, enzyme dosage, temperature, pH) on the saccharification reaction rate of Daqu, established a metabolic enzyme kinetic model, and provided theoretical parameters for the production process control of Luzhou-flavor Daqu liquor.

[0005] It is precisely because the multi-level physical and chemical parameters and microbial metabolic parameters of high-temperature Daqu determine the quality of the fermentation matrix at the microscopic level, so the high-quality high-temperature Daqu production model can be more clearly constructed by establishing the initial and process control and evaluation system. Shen Caihong et al. (patent number: CN 102206566 A) reasonably controlled the raw material particle size and fermentation multi-level parameters during the first and second turning of the koji to obtain the target matrix. Lu Jun et al. (Study on the influence of the innovation of the koji room "no straw, no turning of the warehouse, temperature and humidity control" on the microbial composition of high-temperature Daqu) used the Illumina platform to study the microbial community composition of traditional koji room Daqu, temperature and humidity control koji room Daqu and production straw.

[0006] At present, although there are some analyses on the effects of raw material parameters and environmental variables on Daqu fermentation, there are few reports on the process scheme of upgrading the fermentation parameters of high-temperature Daqu to "digitalization and intelligence" and combining microbial abundance and metabolic laws to feedback and regulate Daqu fermentation. Therefore, it is urgent to establish a primary and secondary parameter rating system for high-temperature Daqu and a microbial metabolic kinetics model to feedback and regulate the core control parameters of the smart qu room, so as to form a set of key intelligent core solutions for the standardization, controllability and quality of high-temperature Daqu, thereby greatly improving production efficiency, reducing labor intensity, ensuring the stability of Daqu quality, and promoting the intelligent transformation and upgrading of modern efficient brewing of Maotai liquor. Summary of the invention

[0007] The purpose of the present invention is to address the deficiencies in the prior art and provide a method for determining the intelligent control of high-temperature Daqu fermentation process by standardizing process multi-level parameters and substrate consumption, microbial growth and metabolic enzyme kinetic models. This method can not only solve the problem that Daqu production is restricted by seasonal environmental temperature factors, but also greatly promote the subsequent "intelligent and modern" production of winemaking.

[0008] The present invention is achieved through the following technical solutions:

[0009] In the first aspect, the present invention relates to the division of core control parameters of intelligent fermentation of high-quality Daqu and the analysis of microbial abundance and metabolism at different stages of the fermentation process, and finally establishes a multi-level parameter evaluation system to clarify the dynamic changes of raw material quality and microorganisms during the preparation and fermentation of koji embryos. The establishment method comprises the following steps:

[0010] The first and second level core parameters of Daqu fermentation from different manufacturers and regions are analyzed, including two modules: koji embryo preparation and koji block fermentation. The first level parameters of koji embryo preparation include koji embryo raw material quality and forming indicators, covering wheat raw material quality, wheat tempering moisture, inoculation ratio and buckling parameters; further, wheat raw material quality is attributed to the quality traits of wheat grains, including five quality traits of fat content, sedimentation value, fiber content, water absorption rate and germination rate, wheat tempering moisture refers to the water added in the wheat tempering step, inoculation ratio includes koji mother addition ratio and water percentage, buckling parameters include buckling times and pressing thrust of koji embryo. The first level parameters of process control in the evaluation system of koji block fermentation process mainly cover the initial fermentation environment temperature and humidity indicators, koji block temperature and humidity, fermentation environment air component percentage and formed RQ change parameters.

[0011] The secondary parameters of the koji embryo preparation process include the physical property change index of the koji block, and further, the physical property change index of the koji block is reflected in the hardness, elasticity, fluffiness, viscosity and resilience of the koji block; the secondary parameters of the koji block fermentation process include microbial abundance, metabolic function index and substrate consumption index. Furthermore, the substrate consumption index in the fermentation process is reflected in the changes in the crude starch, crude protein, fat and cellulose content of the raw materials; the abundance of dominant microbial species is aimed at molds in the fungal community, lactic acid bacteria and Bacillus in the bacterial community, and brewer's yeast in the yeast community. The functional metabolic indicators mainly include - saccharifying enzyme, liquefaction enzyme, protease, amylase, lipase, cellulase. Furthermore, the secondary microbial parameters should be combined with the primary parameter of koji temperature to be divided into three periods: the pre-suspension period, the middle-upper period and the post-suspension period. The specific core microorganisms show a trend of increasing and decreasing in the three periods.

[0012] In one embodiment of the present invention, the fat content of the above-mentioned wheat raw material is 1.15-1.22%, the sedimentation value is 27.63-28.96mL, the germination rate is 71.32-77.55%, the fiber content is 1.78-2.14%, and the water absorption rate is 55.21-57.6%; the moisture ratio of the moistened wheat is 5-8% of the total raw material; the addition of the koji mother in the fermentation raw material inoculation ratio accounts for 6-8% of the total ratio and the moisture accounts for 37-45% of the total ratio; the number of bending times is 4-5 times per minute, and the pressing thrust is 5500-6200N; the hardness of the koji block varies in the range of 2365.96-6262.36 / g, the elasticity varies in the range of 0.48-0.72, the fluffiness varies in the range of 0.23-0.48, the viscosity varies in the range of 628-1210, and the recovery force varies in the range of 0.03-0.07.

[0013] In one embodiment of the present invention, the above-mentioned ambient temperature and humidity refer to the koji room temperature of 25-38°C, the koji room humidity of 85.65-90.78%, the koji block temperature and humidity refer to the koji top temperature of 60-62°C, the koji block moisture of 10.85-38%, the fermentation environment air composition refers to the oxygen concentration fluctuation range of 23.5-28.8%, the carbon dioxide concentration fluctuation range of 44.86-86.44%, and the RQ range fluctuates between 0.8-2.

[0014] In one embodiment of the present invention, the specific indicators of the microbial abundance, metabolic function index and substrate consumption index are pre-slow-stage bacteria, fungi and yeast, accounting for 70-80%, 20-30% and 8-10% of the total microorganisms, respectively, the basic fermentation index saccharification power is 480-520 mg / (gh), liquefaction power is 6-6.7 g / (gh), fermentation power is 0.8-3.3 g / (g.72.h), and metabolic enzyme activity indicators are amylase, protease, lipase and cellulase, accounting for 30-35%, 45-60%, 15-20% and 5-8% of the overall metabolism, respectively; mid-stage bacteria, fungi and yeast account for 70-80%, 20-30% and 8-10% of the total microorganisms, respectively; ... mid-stage bacteria, fungi and yeast account for 70-80%, 20-30% and 8-10% of the total microorganisms, respectively; mid-stage bacteria, fungi and yeast account for 70-80%, 20-30% and 8-10% of the total microorganisms, respectively; mid-stage bacteria, fungi and yeast account for 70-80%, 20-30% and The mother accounts for 40-50%, 30-40% and 8-10% of the total microorganisms respectively. The basic fermentation index is saccharification power of 448-489mg / (gh), liquefaction power of 5.2-6.5g / (gh), fermentation power of 0.7-2.88g / (g.72.h), and metabolic enzyme activity indicators are amylase, protease, lipase and cellulase, accounting for 20-25%, 40-55%, 10-12% and 3-4% of the total metabolism respectively; the late-stage bacteria, fungi and yeast account for 38-55%, 36-47% and 3-6% of the total microorganisms respectively, and the basic fermentation index is saccharification power of 356-377mg / (gh), liquefaction capacity is 7-7.5g / (gh), fermentation capacity is 1-2.12g / (g.72.h), metabolic enzyme activity indicators are amylase, protease, lipase and cellulase, accounting for 40-55%, 35-48%, 15-18% and 2-5% of the total metabolism respectively; in the substrate consumption index, the starch content in the pre-stagnation period is 65.8-67.8%, the glutenin content accounts for 469.644-468.7856μg / mL, the crude cellulose content in the carbon source accounts for 1.55-1.62%, and the crude fat content is 1.678-1.858%; the starch content in the mid-stagnation period is 55.6- The content of starch in the late fermentation period was 42.8-33.6%, the content of glutenin was 143.557-162.225 μg / mL, the content of crude cellulose in the carbon source was 1.12-1.14%, and the content of crude fat was 0.17-0.25%; among them, the contents of the four kinds of crude proteins (albumin, globulin, alcohol-soluble protein and glutenin) extracted gradually decreased with the fermentation time and finally stabilized.

[0015] In the second aspect, the present invention relates to the establishment and fitting application of a fermentation kinetic model based on a smart qufang, and establishes a functional kinetic model of substrate consumption, microbial growth, and metabolic enzyme generation from raw material evaluation parameters and fermentation process parameters.

[0016] The substrate consumption in the Daqu fermentation process is mainly used for bacterial growth and maintaining cell metabolic activities. The consumption kinetic model of starch, fat (carbon source) and crude protein (nitrogen source) in the substrate is established by fitting the Boltzmann model. The model formula is: The Daqu microbial growth is mainly focused on the core microbial flora, including Aspergillus in fungi, Bacillus and lactic acid bacteria in bacteria, and Saccharomyces cerevisiae in yeast. The model is established by fitting the DoseResp model, and the model formula is: The Daqu metabolic enzymes include proteases, amylases, lipases and cellulases. There are three types of relationships between product generation rate and cell growth rate: product generation coupled with bacterial growth, product generation partially coupled with bacterial growth and product generation uncoupled with bacterial growth. The model is established by fitting using the SGompertz model, and the model formula is y=αe-e (-k(x-xc)) .

[0017] It is further defined that the establishment of the kinetic model and the fermentation environment are established and analyzed in stages. The pre-suspension period mainly consumes the carbon source in the substrate to promote the growth of mold and yeast, and produce protease and amylase; the middle period mainly consumes carbon source and nitrogen source to promote the proliferation of bacteria to produce protease, amylase and lipase; in the post-suspension period, bacteria consume carbon and nitrogen sources to produce a small proportion of protease, amylase, lipase and cellulase.

[0018] In a third aspect, the present invention also relates to the debugging of environmental factors of the intelligent koji room and the parameter analysis of the actual fermentation process, and the process comprises the following steps:

[0019] Raw material pretreatment and koji embryo forming: The high-temperature koji usually uses wheat as the main raw material, and the raw material pretreatment module mainly includes wheat screening and tempering. During actual fermentation, a batch of raw materials requires about 500 kg. According to the quality characteristics of wheat grains in the above-mentioned wheat raw material quality evaluation system combined with the above-mentioned substrate consumption kinetic model, wheat production varieties with carbon sources and nitrogen sources within the optimal threshold range are preferred, and the cylindrical primary cleaning screen vibrates and grades to screen the materials according to particle size. The water addition ratio of the tempering part is 5-8% of the total raw materials. A spray-type watering controller is connected in series to strongly water the wheat to ensure that the water effectively penetrates into the grains and is evenly distributed, and the raw material quality index of the first-level parameter in the fermentation parameters is accurately calculated. The koji embryo forming module mainly includes raw material crushing, mixing and buckling forming; the koji mother added to the fermentation raw materials accounts for 6-8% of the total ratio and the moisture accounts for 37-45%, which is evenly mixed by the mixer and sent to the buckling machine. The buckling machine adopts intelligent bionic treading to simulate the strength and number of manual treading to achieve anthropomorphic buckling and accurately quantify the first-level parameters of the fermentation parameters, namely, the moisture content of the moistened wheat and the inoculation ratio.

[0020] Qufang fermentation: Qufang fermentation module mainly includes temperature and humidity, O2 , CO 2 The temperature and humidity control sensors and O 2 , CO 2 The sensor sends the real-time status of the koji room environment and the temperature and humidity of the koji blocks to the logic feedback automatic control system, which calculates the temperature and moisture changes of the koji blocks at different stages of fermentation (pre-slow period, mid-slow period, and post-slow period) and the overall fermentation respiration of the koji blocks (0 2 , CO 2 The above kinetic model was combined with multiple experiments to adjust the environment and the temperature and humidity of the block, O 2 , CO 2 The content relationship is fitted to get RQ, and the blower system responds to real-time feedback adjustment; further, the first-level parameter environment control temperature in the pre-delay period is in the range of 30-45℃, and the fan humidification keeps the environmental humidity >90%, the first-level parameter environment temperature in the mid-delay period slowly rises to the range of 48-58℃, and the fan humidification keeps the environmental humidity in the range of 90%-85%, and the first-level parameter environment control temperature in the post-delay period slowly drops to the range of 55-35℃, and the fan humidification keeps the environmental humidity in the range of 90%-85%. In addition, when abnormal values ​​appear, the multi-layer timed exchange and koji block flipping of the unmanned solid-state fermentation room are finally obtained. The control scheme of multi-level parameters of the intelligent fermentation method is finally obtained.

[0021] Further defined, the fermentation respiration intensity is measured by RQ. 2 , CO 2 It is an intuitive indicator of the content relationship, and uses an artificial intelligence system to identify the fermentation status of the koji block and predict the fermentation trend. It connects the control and characterization parameters in the primary and secondary indicators, adjusts the control parameters, responds to the characterization parameters, and ultimately studies the kinetic relationship between environmental factors and substrate consumption, microbial growth and metabolic activities, and the laws of change over time to react to the fermentation characteristics and connect the timed koji turning to achieve temperature and humidity balance, generating a set of intelligent koji feedback control strategies.

[0022] Beneficial effects of the present invention:

[0023] (1) Standardize the primary parameters of high-quality high-temperature Daqu, build a high-quality high-temperature Daqu evaluation system, combine traditional and modern fermentation indicators, and strive to concretize multi-level parameters on the basis of traditional high-quality high-temperature Daqu, so as to get rid of the current situation of complete reliance on manual experience for raw material quality indicators and environmental control indicators.

[0024] (2) By analyzing the abundance and metabolic functions of high-temperature Daqu fermentation microorganisms, it is clear that environmental variables are the driving force for microbial changes during Daqu fermentation. The main purpose is to clarify the trends of Daqu substrate consumption, microbial growth, and metabolic functional enzymes during the fermentation process to establish a kinetic model, ultimately better reproducing the secondary parameters of fermentation.

[0025] (3) Based on the kinetic model established for intelligent fermentation of Daqu, real-time feedback is provided to adjust the temperature and humidity in the qu room to affect the carbon and nitrogen source consumption of wheat, microbial proliferation and metabolite accumulation in the three main fermentation stages: the pre-slow period, the middle-slow period and the post-slow period. This shows the main framework of the fermentation process and improves the accuracy of the model in predicting the intelligent fermentation process, thus forming a set of control strategies based on the kinetic model.

[0026] (4) Intelligent fermentation can adjust the Daqu fermentation according to the set value and external environmental parameters, and can better reproduce the production parameters. At the same time, the real-time data monitoring system can feedback the data trend and help record the fermentation of Daqu, which can reduce the quality fluctuation caused by manual production, reduce the degree of manual participation, realize the standardization, automation and intelligence of the solid-state brewing industry, establish a new Daqu fermentation equipment system, and enhance the scientific and technological content and competitive advantage of liquor companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention provides a technical roadmap for the intelligent cultivation method of Daqu fermentation process through multi-level parameter analysis and regulation of standardized processes. DETAILED DESCRIPTION

[0028] The technical solutions described in the present invention will be described clearly and completely below in conjunction with the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0029] Example 1: Establishment of a system for evaluating the quality of koji raw materials and their forming

[0030] In this embodiment, the primary and secondary indicators of the traditional fermented Daqu in the koji preparation stage are clearly and specifically divided. Specifically, the core parameters of the raw materials of Daqu from different manufacturers and regions are analyzed before and during fermentation, and a koji raw material quality and molding evaluation system is established. Raw material samples and koji are collected from wineries in multiple regions. The raw materials AE are collected from five regions. The sampling time points are 0, 2, 4, 8, 12, 14, 18, 24, and 30 days, and the temperature and humidity monitoring time interval is 1-2 days. The preparation of koji embryo includes the first and second level parameters in the raw material evaluation system. The first level parameters in the raw material evaluation system are mainly for the raw material preparation stage before the fermentation process, covering wheat raw material quality (composition), moisture content of tempered wheat, inoculation ratio and buckling parameters; further, the quality of wheat raw materials is aimed at 5 quality traits of wheat grains, including fat content (1.15-1.22%), fiber content (1.78-2.14%), water absorption rate (55.21-57.6%), sedimentation value (27.63-28.96mL) and germination rate (71.32-77.55%); moisture content of tempered wheat includes The amount of water added in the tempering step is 5-8% for traditional tempering; the inoculation ratio is the amount of koji added to the total raw materials, and the amount of koji added in traditional high-temperature fermentation is 6-8%, and the water accounts for 37-45% of the total ratio; and the koji embryo pressing adopts intelligent bionic treading to simulate the strength and frequency of manual treading, and the optimal number of pressings is determined to be 4-5 times per minute, and the pressing thrust is 5500-6200N. Under the process parameter conditions, the surface pulp extraction effect area ratio (X) of the large koji is 85.66%, and the uniformity factor of the moisture content inside the large koji (Y) is 6.370, achieving the optimal effect of anthropomorphic pressing. The secondary parameters in the raw material evaluation system are mainly used to analyze the density (fluffiness) of the koji during the fermentation process. The physical property analysis method is mainly used to focus on the hardness, elasticity, fluffiness, viscosity, and resilience of the mechanical performance indicators. Among the physical property parameters of the koji obtained by the traditional fermentation method, the hardness is in the range of 2365.96-6262.36 / g, the elasticity is in the range of 0.48-0.72, the fluffiness is in the range of 0.23-0.48, the viscosity is in the range of 628-1210, and the resilience is in the range of 0.03-0.07.

[0031] Table 1 Analysis results of quality traits of wheat grains from different raw materials

[0032]

[0033] Table 2 Analysis results of mechanical properties of curved blocks at different sampling time points

[0034]

[0035] Example 2: Establishment of a quality evaluation system for the fermentation process of koji blocks

[0036] In this embodiment, the first and second level indicators of the fermentation stage in the traditional fermentation method are clearly and specifically divided. Specifically, the physical, chemical and quality core indicators of Daqu from different manufacturers and regions are analyzed during the fermentation period. Samples are collected from multiple regional wineries. The sampling time points are 0, 2, 4, 8, 12, 14, 18, 24, and 30 days, and the temperature and humidity monitoring time interval is 1-2 days. Qu block fermentation includes environmental monitoring indicators in the first level parameters and quality characterization indicators in the second level parameters. The environmental monitoring parameters in the fermentation process evaluation system mainly cover the initial fermentation environment temperature and humidity indicators, qu block temperature and humidity, the proportion of air components in the fermentation environment, and the formed RQ change parameters. The experiment shows that the temperature of the traditional high-temperature large koji room is 25-38℃, and the top temperature of the koji block can reach 60-62℃; the humidity of the koji room is 85.65-90.78%, and the moisture of the koji block is 10.85-38%; the oxygen concentration fluctuates between 23.5-28.8%, and the carbon dioxide concentration fluctuates between 44.86-86.44%; the RQ range fluctuates between 0.8-2. The abundance and metabolic function indicators of microorganisms in the quality characterization indicators reversely affect the core control parameters of fermentation, and the feedback characterization includes the carbon and nitrogen source content of the substrate matrix that changes with the fermentation time, which is specifically reflected in the changes in the raw material crude starch, crude protein, fat, and cellulose content of the substrate consumption index during the fermentation process. The changes in the abundance of microorganisms are divided into the determination of the proportion of microorganisms-molds, bacteria and yeasts, and the microbial metabolic function indicators are divided into saccharification power, liquefaction power, fermentation power, and important enzymes of microbial metabolism include protease, amylase, lipase, and cellulase.

[0037] Furthermore, the starch utilization of the substrate matrix during the fermentation process was determined by chemical methods, and it was calculated that the starch content decreased from 67.8% to 58.3% with the fermentation time, showing a trend of gradual decrease. The contents of the four crude proteins extracted (albumin, globulin, alcohol-soluble protein, and glutenin) gradually decreased with the fermentation time and finally stabilized. The glutenin content accounted for the largest proportion and decreased from 468.7856μg / mL to 227.65263μg / mL. The crude cellulose in the carbon source was extracted and separated by acid-base digestion of the sample, and the content decreased from 1.62% to 1.28%; and the crude fat in the wheat nitrogen source utilization was extracted by using anhydrous ether or petroleum ether and then evaporating the solvent to obtain the crude fat, and the crude fat content decreased from 1.858% to 0.43%.

[0038] The change trend of fungi, bacteria and yeast obtained by microbial plate culture was that the pre-slow-stage bacteria accounted for 70-80% of the total microorganisms, fungi accounted for 20-30%, and yeast accounted for 8-10%. The basic fermentation index saccharification power was 520-480mg / (gh), liquefaction power was 6-6.7g / (gh), and fermentation power was 0.8-3.3g / (g.72.h); the metabolic enzyme activity index was amylase accounting for 30-35% of the total metabolism, protease accounting for 45-60%, lipase accounting for 15-20%, and cellulase accounting for 5-8%; the mid-stage bacteria accounted for 40-50% of the total microorganisms, fungi accounted for 30-40%, and yeast accounted for 8-10%. The basic fermentation index saccharification power was 448-489mg / (gh), liquefaction power was 5.2-6.5 g / (gh), and the fermentation capacity is 0.7-2.88g / (g.72.h); the metabolic enzyme activity indicators are amylase accounting for 20-25% of the overall metabolism, protease accounting for 40-55%, lipase accounting for 10-12%, and cellulase accounting for 3-4%; it decreases in the later period of the post-delay period, and the three interact and restrict each other. Bacteria account for 38-55% of the total microorganisms, fungi account for 36-47%, and yeast accounts for 3-6%. The basic fermentation indicators are saccharification power at 356-377mg / (gh), liquefaction power at 7-7.5g / (gh), and fermentation power at 1-2.12g / (g.72.h); the metabolic enzyme activity indicators are amylase accounting for 40-55% of the overall metabolism, protease accounting for 35-48%, lipase accounting for 15-18%, and cellulase accounting for 2-5%.

[0039] Example 3: Establishment and analysis of the pre-fermentation phase kinetic model

[0040] The pre-fermentation slow period is the temperature rising stage, which mainly consumes the carbon source in the substrate to promote the growth of mold and yeast, and produces protease and amylase; the growth of bacteria, substrate consumption and product generation are in a linked state. From the parameter result data, the three are interconnected and correspond to each other. The substrate consumption kinetic model uses the Boltzmann model to establish a fitting equation for the substrate consumption (starch and fat) and calculate the fitting coefficient. The fitting results show that R 2 The fitting coefficients are 0.99875 and 0.9968, respectively. They are relatively high and can better describe the starch content and consumption during the fermentation process of Daqu pre-fermentation. The substrate consumption rate is directly related to the bacterial cell generation, which is a typical growth "coupling" type. The OD values ​​of mold and yeast cell growth were fitted using the DoseResp model to establish the kinetic equation and calculate the fitting coefficient. The fitting coefficient R 2The results showed that the Dose Resp model was 0.99504 and 0.9785. Therefore, the Dose Resp model can be used to fit the growth of mold and yeast in the pre-fermentation stage of Daqu in the experiment. The SGompertz model was used to perform nonlinear fitting on the production of proteases and amylases during the fermentation process. The fitting coefficients were 0.9987 and 0.9765, both of which had good fitting effects. The growth rate of proteases and amylases produced by the metabolism of molds and yeasts increased rapidly during the logarithmic growth period, and the content of metabolites tended to stabilize after 5 days of fermentation, and the metabolic value reached the maximum value at 7 days. The pre-fermentation slow period is in the growth and metabolic adaptation stage, and the bacterial metabolic system adapts to the new environment and proliferates rapidly. However, due to the small amount of bacteria, the growth is relatively slow, and the differences in substrate consumption and product generation are not obvious.

[0041] Table 3 Models used in the pre-fermentation period and their fitted kinetic equations and fitting coefficients

[0042]

[0043]

[0044] Example 4: Establishment and analysis of the dynamic model of the fermentation stage

[0045] The fermentation mid-term refers to the stage when the temperature reaches the top temperature of fermentation, which mainly consumes carbon and nitrogen sources to promote bacterial growth and produce proteases, amylases and lipases; the bacteria are in the exponential growth phase, and the bacteria reproduce in large quantities, so the substrate and products are also consumed and generated in large quantities; the substrate consumption kinetic model uses the Boltzmann model to establish a fitting equation for the substrate consumption (starch and fat) and calculate the fitting coefficient. The fitting results show that R 2 The fitting coefficients are 0.9789 and 0.9899, ​​which are relatively high and can better describe the starch and consumption during the fermentation of Daqu. The substrate consumption rate is directly related to the bacterial production, which is a typical growth "coupling" type. The OD values ​​of mold and yeast cell growth were established using the Dose Resp model to establish the fitting kinetic equation and calculate the fitting coefficient. The fitting coefficient R 2 The fitting coefficients were 0.9858 and 0.9722. Therefore, the DoseResp model can be used to fit the bacterial growth in the pre-fermentation stage of Daqu in the experiment. The SGompertz model was used to perform nonlinear fitting on the production of protease, amylase and lipase during the fermentation process. The fitting coefficients were 0.9899 and 0.9966, both of which had good fitting effects.

[0046] Table 4 Models used in the middle and end stage of fermentation and their fitting kinetic equations and fitting coefficients

[0047]

[0048]

[0049] Example 5: Establishment and analysis of the delayed phase kinetic model after the fermentation stage

[0050] The post-fermentation slow period is the stage when the temperature begins to drop, and a large number of heat-sensitive microorganisms go dormant or die. The post-slow period is mainly when heat-resistant bacteria (heat-resistant Bacillus) consume carbon and nitrogen sources to produce a small amount of protease, amylase, lipase, and cellulase. The strain is in a state of fermentation and maintenance until the end, and the total amount of microorganisms begins to decrease; the substrate consumption kinetic model uses the Boltzmann model to establish a fitting equation for the substrate consumption (starch and fat) and calculate the fitting coefficient. The fitting results show R 2 The fitting coefficients are 0.9678, 0.9822 and 0.9524, which are relatively high and can better describe the consumption of starch, fat and protein during the slow fermentation after Daqu. However, the substrate consumption rate is still nonlinearly directly related to the bacterial production, while the product production and bacterial growth are still "coupled" in the same direction. The OD value of the thermotolerant Bacillus bacterial growth was established using the DoseResp model to establish a fitting kinetic equation and calculate the fitting coefficient. The fitting coefficient R 2 The value of Dose Resp model was 0.9885, so the Dose Resp model can be used to fit the growth of heat-resistant bacteria in the late stage of Daqu in the experiment. The SGompertz model was used to perform nonlinear fitting on the production of protease, amylase, lipase and cellulase during the fermentation process; the fitting coefficients were 0.9899, ​​0.9966, 0.9855 and 0.9963, all of which had good fitting effects. This shows that although the growth of bacteria and the accumulation of metabolites in the late slow fermentation period are slower than before, the fitting results show that the three factors reduce the fermentation rate and metabolic rate in the same direction over time.

[0051] Table 5 The models used in the post-fermentation period and their fitting kinetic equations and fitting coefficients

[0052]

[0053]

[0054] Example 6: Forming a set of quality control strategies for curved blank molding based on the dynamic model

[0055] In this embodiment, a set of control strategies for the preparation of koji embryos for intelligent fermentation is generated by specifically utilizing the koji embryo raw material quality and molding evaluation system established in Embodiments 1-5 and combining it with a fermentation kinetics model.

[0056] Raw material pretreatment and koji embryo forming: The high-temperature koji usually uses wheat as the main raw material, and the raw material pretreatment module mainly includes wheat screening and tempering. During the actual pilot fermentation, a batch of raw materials requires about 500 kg. The quality characteristics of wheat grains in the wheat raw material quality evaluation system in Example 1 are combined with the substrate consumption kinetic model of Examples 3-5 to better describe the changes and interrelationships in the process of substrate consumption, microbial growth and product generation and fermentation. The formula is used to select wheat production varieties with carbon sources and nitrogen sources within the optimal threshold range. The wheat raw material evaluation system selects raw materials with fat content of 1.66-1.58%, sedimentation value of 28.66-27.45 mL, germination rate of 72.44-76.83%, fiber content of 1.82-2.34% and water absorption rate of 56.33-58.85%. The cylindrical primary cleaning screen vibrates and classifies the materials according to particle size. The water addition ratio of the moistening part is 5-8% of the total raw materials. The series spray water controller strongly waters the wheat to ensure that the water effectively penetrates into the grains and is evenly distributed, and the raw material quality index of the first-level parameter in the intelligent fermentation parameters is accurately calculated. The koji embryo forming module mainly includes raw material crushing, mixing and buckling forming; the koji mother in the fermentation raw materials accounts for 6-8% of the total ratio and the water accounts for 37-48%. It is evenly mixed by the mixer and sent to the buckling machine. The buckling machine uses simulation experiments to obtain a buckling frequency of 55-6 times per minute. When the pressing thrust reaches 6200-6500N, it can achieve an anthropomorphic buckling effect. The physical properties of the koji obtained by the intelligent fermentation method include hardness of 2347.552-5768.155 / g; elasticity of 0.78547-0.9345; fluffiness of 0.747-0.998; viscosity of 626.9685-1378.24; and resilience of 0.058-0.1025. Compared with the fermented koji obtained by the traditional fermentation method, the fluffiness of the koji obtained by the intelligent fermentation method is significantly improved by 40%-50%, and the elasticity and resilience are also significantly improved by nearly 10%-20%. The intelligent fermentation method improves the mass and heat transfer performance of the koji itself during the fermentation stage.

[0057] Example 7: Forming a set of intelligent block fermentation process control strategies based on kinetic models

[0058] This embodiment specifically utilizes the koji fermentation process quality evaluation system established in Embodiments 1-5 in combination with the fermentation kinetics model to generate a set of intelligent fermentation control strategies for Daqu.

[0059] Qufang fermentation: mainly includes temperature and humidity, O 2 , CO 2The control and monitoring of the pre-suspension period is carried out according to the established quality evaluation system for the fermentation process of the koji block. The first-level parameter environment control temperature is calibrated in the range of 30-45°C, and the fan humidification keeps the environmental humidity>90%. Then, according to the microbial abundance and metabolic enzyme parameters determined by the second-level parameters, the fermentation kinetic model established in Example 3 is combined with the Dose Resp model. When the second-level parameters of the input system are abnormal in range, various sensors on site collect data including temperature, humidity, O 2 and CO 2 The information is measured by the sensor, and the transmitter signal is converted and transmitted to the PLC. After receiving the collected data, the PLC uploads it to the host computer through the PROFINET field bus, and adjusts the temperature, humidity and gas concentration of the koji room by adjusting the intelligent panel that inputs the kinetic parameters to control the air blowing system. Similarly, the first-level parameter ambient temperature of the calibration period slowly rises to the range of 48-58°C, and the fan humidification keeps the ambient humidity drop range at 90%-85%. The second-level parameter is combined with the fermentation kinetic model established in Example 4, mainly for the bacterial biomass combined with the Dose Resp model; similarly, the control panel monitors the indicator range according to the numerical specifications input by the kinetic model, and the system issues an early warning when the bacterial biomass is abnormal. At this time, the differential numerical intervention is carried out in the form of turning the koji, and the upper and lower exchanges of the multi-layer koji blocks are realized by chain transmission. The temperature and humidity detection devices provided on each koji block groove device can detect the temperature, humidity, and gas concentration of the koji block at any time. 2 and CO 2 Concentration, and the ventilation device is controlled by PLC to supply air to the koji room, control the temperature and humidity inside the entire koji room, achieve an environment that is beneficial to bacteria, and ensure that the quality of koji blocks in different positions is consistent. For the post-suspension period, the primary parameter environment control temperature is slowly controlled to drop to the range of 55-35°C, and the fan humidification keeps the ambient humidity drop range at 90%-85%. The secondary parameter is combined with the fermentation kinetics model established in Example 5. The post-suspension period Dose Resp model quantifies heat-resistant bacteria (thermoresistant Bacillus) and the Boltzmann model describes the consumption of carbon and nitrogen sources that change over time. In the post-suspension period when the temperature drops, the sensor checks the physical parameters of each surface of the koji block and compares them with the standard values. If there is a deviation, the motor operation is controlled by PLC.

[0060] Furthermore, the control system calibrates and calculates the content of the koji temperature, moisture changes and fermentation respiration of the koji block in the pre-slow period, mid-slow period and post-slow period of fermentation. The ambient temperature of the outer side of the koji room close to the door of the intelligent fermentation method is 2°C lower than that of the inner side away from the door on average. The humidity in the fermentation area is controlled between 85-93%, and the humidity is maintained to increase by 2-10% in the end. Because the heat transfer performance of the koji fermented in the intelligent way is stable, the koji temperature is about 3-5°C lower than the top temperature of the traditional koji on average, and the number of "black koji and white koji" is reduced; the change in moisture is not much different and eventually reduced to 10-15%. In the physical property analysis, the fluctuating degree of the koji fermented in the intelligent way is 0.12-0.25 higher in elasticity and 0.24-0.36 higher in fluctuating degree than the traditional fermentation method. The influence of microbial respiration in the pre-slow period is greater than that of the traditional fermentation method. The gas concentration fluctuates between 23-56% during the overall fermentation period; the RQ range fluctuates between 0.8-2.

[0061] Furthermore, the preferred indicators in the intelligent culture program are that pre-slow-phase bacteria account for 40-50% of the total microorganisms, fungi account for 30-40%, and yeast account for 20-30%; the basic fermentation indicators are saccharification power at 363.57-181.97 mg / (gh), liquefaction power at 0.1-0.36 g / (gh), and fermentation power at 0.2-2.5 g / (g.72.h); the preferred indicators of enzyme activity are that amylase accounts for 25-32% of the overall metabolism, protease accounts for 42-58%, lipase accounts for 15-20%, and cellulase accounts for 3-5%. In the mid-stage, bacteria account for 70-80% of the total microorganisms, fungi account for 20-30%, and yeasts account for 8-10%; the basic fermentation indicators are saccharification power at 281.97-120.57 mg / (gh), liquefaction power at 0.369-1.6 g / (gh), and fermentation power at 0.8-3.2 g / (g.72.h); the preferred indicators of enzyme activity are amylase accounting for 20-25% of the overall metabolism, protease accounting for 55-65%, lipase accounting for 8-12%, and cellulase accounting for 1.5-3%. Post-slow-phase bacteria account for 50-60% of the total microorganisms, fungi account for 20-30%, and yeasts account for 3-10%; the basic fermentation indicators are saccharifying power at 120.57-118.65 mg / (gh), liquefaction power at 1.6-2.48 g / (gh), and fermentation power at 0.8-3.2 g / (g.72.h); the preferred indicators of enzyme activity are amylase accounting for 20-25% of the overall metabolism, protease accounting for 55-65%, lipase accounting for 8-12%, and cellulase accounting for 1.5-3% (see Table 6). In the early stage of substrate consumption, the starch content was 63.4-77.2%, the gluten content was 413.679-425.668 μg / mL, the crude cellulose content in the carbon source was 1.98-2.14%, and the crude fat content was 2.13-1.79%; in the middle stage, the starch content was 63.22-67.87%, the gluten content was 226.66-234.56 μg / mL, and the crude cellulose content in the carbon source was 1.88-2 .13%, crude fat content is 0.48-0.52%; starch content in the late delayed period is 32.17-45.66%, gluten content accounts for 145.33-155.76μg / mL, crude cellulose content in carbon source accounts for 1.77-1.98%, crude fat content is 0.165-0.248%; among them, the contents of the four extracted crude proteins (albumin, globulin, alcohol-soluble protein, glutenin) gradually decrease with the fermentation time and finally stabilize. According to the real-time parameter information of the fermentation process obtained in the control model, and based on the model of each link, the parameter optimization results corresponding to each link are obtained, and then the production process control parameters corresponding to each link are adjusted according to the parameter optimization results to achieve the expected optimization goals. This result shows that intelligent fermentation Daqu can not only realize digital regulation, but also ensure the high efficiency of fermentation from the perspective of microscopic analysis.

[0062] Table 6 can only optimize the parameter range of each control object at different stages of fermentation of the late koji block

[0063]

[0064]

[0065] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the claims.

Claims

1. A method for intelligent cultivation of Daqu fermentation process through multi-level parameter analysis and regulation of standardized process, characterized in that: The method is to combine the koji embryo raw material quality and molding evaluation system and the koji block fermentation process quality evaluation system established by analyzing the primary and secondary parameters of the traditional koji fermentation process with the multi-stage fermentation kinetic model of substrate carbon and nitrogen source consumption, microbial growth, and metabolic enzyme generation established by dividing the koji embryo fermentation into multiple stages, namely, the pre-slow stage, the middle-slow stage, and the post-slow stage, so as to realize the digital specification and control of the whole process of koji production from raw material pretreatment, molding and fermentation; The quality and forming evaluation system of the koji embryo raw material includes the primary parameters of wheat raw material quality, moisture content of wheat, inoculation ratio and buckling parameters, and the secondary parameters of the koji block hardness, elasticity, fluffiness, viscosity and recovery force; The primary parameters of the koji fermentation process quality evaluation system include the initial fermentation environment temperature and humidity, koji temperature and humidity, fermentation environment air component ratio and the formed RQ change parameters; In the multi-stage fermentation kinetic model, the Boltzmann model is used to describe the consumption of substrate carbon and nitrogen sources, the Dose Resp model is used to describe the growth of microorganisms, and the SGompertz model is used to describe the production of metabolic enzymes; finally, the optimized range of the secondary parameters of staged cultivation is obtained according to the model; The evaluation parameters of the wheat raw material quality include fat content, sedimentation value, fiber content, water absorption rate and germination rate; the inoculation ratio includes the addition ratio of koji mother and the water content; the buckling parameters include the buckling times and the pressing thrust; The fat content of the wheat raw material is 1.15-1.22%, the sedimentation value is 27.63-28.96 mL, the germination rate is 71.32-77.55%, the fiber content is 1.78-2.14%, and the water absorption rate is 55.21-57.6%; the moisture ratio of the moistened wheat is 5-8% of the total raw material; the addition of the koji mother in the fermentation raw material inoculation ratio accounts for 6-8% of the total ratio, and the moisture accounts for 37-45% of the total ratio; the number of buckling is 4-5 times per minute, and the pressing thrust is 5500-6200 N; the hardness of the koji block varies in the range of 2365.96-6262.36 / g, the elasticity varies in the range of 0.48-0.72, the fluffiness varies in the range of 0.23-0.48, the viscosity varies in the range of 628-1210, and the recovery force varies in the range of 0.03-0.07; The environmental temperature and humidity refer to the koji room temperature of 25-38°C and the koji room humidity of 85.65-90.78%; the koji block temperature and humidity refer to the koji top temperature of 60-62°C and the koji block moisture of 10.85-38%; the fermentation environment air composition refers to the oxygen concentration fluctuation range of 23.5-28.8%, the carbon dioxide concentration fluctuation range of 44.86-86.44%; the RQ range fluctuates between 0.8-2; The secondary parameters of the quality evaluation system of the koji fermentation process include microbial abundance, metabolic function index, and substrate consumption index. The specific indexes are that bacteria in the pre-suspension period, fungi and yeast account for 70-80%, 20-30% and 8-10% of the total microorganisms, respectively; the basic fermentation index saccharification power is 480-520 mg / (gh), liquefaction power is 6-6.7 g / (gh), and fermentation power is 0.8-3.3 g / (g.72.h); the metabolic enzyme activity indexes are amylase, protease, lipase and cellulase, which account for 30-35%, 45-60%, 15-20% and 5-8% of the overall metabolism, respectively; bacteria, fungi and yeast in the mid-suspension period account for 40-50%, 30-40% and 8-10% of the total microorganisms, respectively; the basic fermentation index saccharification power is 448-489 mg / (gh), liquefaction power is 5.2-6.5 g / (gh), and fermentation power is 0.7-2.88 g / (g.72.h), metabolic enzyme activity indicators are amylase, protease, lipase and cellulase, accounting for 20-25%, 40-55%, 10-12% and 3-4% of the total metabolism respectively; post-suspension bacteria, fungi and yeast account for 38-55%, 36-47% and 3-6% of the total microorganisms respectively, basic fermentation indicators saccharification power is 356-377 mg / (gh), liquefaction power is 7-7.5 g / (gh), fermentation power is 1-2.12 g / (g.72.h), metabolic enzyme activity indicators are amylase, protease, lipase and cellulase, accounting for 40-55%, 35-48%, 15-18% and 2-5% of the total metabolism respectively; in the substrate consumption index, the starch content in the pre-suspension period is 65.8-67.8%, and the gluten content accounts for 469.644-468.7856 μg / mL, the crude cellulose content in the carbon source accounted for 1.55-1.62%, and the crude fat content was 1.678-1.858%; the starch content in the middle stage was 55.6-58.3%, the glutenin content accounted for 215.55776-227.65263 μg / mL, the crude cellulose content in the carbon source accounted for 1.19-1.28%, and the crude fat content was 0.48-0.52%; the starch content in the late stage was 42.8-33.6%, the glutenin content accounted for 143.557-162.225 μg / mL, the crude cellulose content in the carbon source accounted for 1.12-1.14%, and the crude fat content was 0.17-0.25%.

2. The method according to claim 1, characterized in that The method is to combine the koji embryo raw material quality and molding evaluation system, the koji block fermentation process quality evaluation system and the multi-stage fermentation kinetic model, and adjust the temperature and humidity of the koji room by adjusting the intelligent panel to control the air blowing system. At the same time, when abnormal values ​​appear, the unmanned solid-state fermentation koji room performs multi-layer timed exchange and koji block flipping to ensure the balance of temperature and humidity of koji blocks in each layer and at different spatial positions, so that the koji blocks reach an environment that is beneficial to bacteria, thereby carrying out the koji training and fermentation process.

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