Ancient-method maltose mixed boiling process

Abstract

The invention relates to the technical field of boiling, in particular to an ancient-method maltose mixed boiling process which comprises the following steps: soaking barley, draining water, and placing in a shade place to obtain germinated barley; after the sorghum is soaked, putting the sorghum into a steaming drawer, and uniformly steaming to obtain a steamed raw material; mincing malt of germinated barley, uniformly mixing with the cooking raw materials, and fermenting in a preset fermentation environment to obtain fermentation liquid; filtering to obtain wort, and concentrating the wort with soft fire to obtain concentrated wort; obtaining an optimal boiling condition according to an index triangulation algorithm, and mixing and boiling the concentrated wort to obtain maltose; and the maltose is naturally cooled and solidified, and cutting is performed after the maltose is completely cooled. By optimizing the boiling conditions, the production cycle is remarkably shortened, the production efficiency is improved, unnecessary energy consumption and raw material waste are reduced, the production cost is reduced, and fluctuation in the production process can be reduced through accurate boiling condition control.

Description

Technical Field

[0001] The invention relates to the technical field of boiling, in particular to an ancient method of mixing and boiling maltose. Background Art

[0002] Maltose has a profound influence in Chinese culture. It is not only a delicacy, but also a source of energy. In ancient times, people often regarded maltose as a snack that refreshed the mind and increased vitality. In traditional Chinese medicine, maltose is believed to be able to harmonize the spleen and nourish the stomach, and is especially beneficial to the spleen and stomach of children. It is sweet and warm in nature, and has the effects of nourishing the spleen and stomach, promoting fluid and removing dryness, moistening the lungs and relieving coughs. In the famous Xiaojianzhong Decoction and other prescriptions, maltose is reused as the main medicine, which has created a precedent for the use of food therapy to treat chronic diseases for later generations.

[0003] In the process of boiling maltose, temperature and stirring frequency have a great influence on the quality of maltose, including its viscosity. Boiling slowly over low heat can heat the sugar solution evenly and avoid local overheating. The stirring frequency helps to evenly mix the ingredients in the sugar solution, promote water evaporation and uniform reaction. If the stirring frequency is too low, the sugar solution may burn at the bottom of the pot, affecting the taste and quality; if the stirring frequency is too high, too much air may be introduced, which will have a certain impact on the texture of the maltose. The process parameters of the sugar boiling process are difficult to control, resulting in more defective products after the maltose is boiled, wasting production resources. Summary of the invention

[0004] The invention provides an ancient maltose mixing and boiling process, which is used to solve the defect that the maltose quality is low due to the unstable process parameters in the maltose production process.

[0005] The present invention provides a traditional maltose mixing and boiling process, comprising:

[0006] S1: After soaking the barley, drain the water and place it in a cool place, spray water regularly to keep it moist, and obtain germinated barley.

[0007] S2: After soaking, the sorghum is placed in a steamer and evenly steamed to obtain a cooking material.

[0008] S3: chopping the malt of the germinated barley and uniformly mixing it with the cooking raw materials, and fermenting it under a preset fermentation environment to obtain a fermented liquid.

[0009] S4: filtering the fermented liquid to remove impurities to obtain wort, and concentrating the wort over a low heat to obtain concentrated wort.

[0010] S5: using an exponential trigonometric optimization algorithm to calculate the optimum boiling conditions, and mixing and boiling the concentrated malt juice according to the optimum boiling conditions to obtain maltose.

[0011] S6: Allow the maltose to cool naturally and solidify, and then cut it after it is completely cooled.

[0012] According to the traditional maltose mixing and boiling process provided by the present invention, in step S3, the specific steps of maltose fermentation include:

[0013] S31: Real-time collection of process parameters during the fermentation process, including fermentation temperature, humidity, pH value, and nutrient concentration.

[0014] S32: Calculate the estimated fermentation time according to the process parameters.

[0015] S33: Determine whether the estimated fermentation time exceeds a preset optimal fermentation time range, and if so, adjust the process parameters to ensure that the estimated fermentation time is within the optimal fermentation time range.

[0016] According to the traditional maltose mixing and boiling process provided by the present invention, in step S32, the calculation formula of the estimated fermentation time is expressed as:

[0017] t=ω1×f(X)+ω2×f(b)

[0018]

[0019]

[0020] Where t is the fermentation time, ω1 and ω2 are weight parameters, f(pH) is the relative pH value, f(DO is the relative dissolved oxygen value, T1 is the fermentation temperature, pH is the pH value, n is the number of nodes in the hidden layer of the neural network, X is the final bacterial concentration, X0 is the initial bacterial concentration, μ0 is the growth rate constant at standard temperature, Ea is the activation energy, R is the gas constant, μmax is the maximum specific growth rate, S is the nutrient concentration, Ks is the half-saturation constant, bi is the bias of the hidden layer, σ is the activation function, ωoi is the weight from the hidden layer to the output layer, bo is the bias of the output layer, and ωij is the weight from the input layer to the hidden layer.

[0021] According to the traditional maltose mixing and boiling process provided by the present invention, in step S32, the relative pH value is calculated according to the optimal pH value as follows:

[0022]

[0023] Where a is a constant, pH opt is the optimum pH value.

[0024] The formula for calculating the relative dissolved oxygen value based on dissolved oxygen is expressed as:

[0025]

[0026] In the formula, K DOis a constant related to dissolved oxygen, and DO is dissolved oxygen.

[0027] According to a traditional maltose mixing and boiling process provided by the present invention, in step S5, the specific steps of mixing and boiling include:

[0028] S51: Collect environmental data during the cooking process, the environmental data including cooking temperature, syrup viscosity, stirring frequency and cooking time.

[0029] S52: Preprocess the environmental data, including data normalization and variance homogeneity.

[0030] S53: The environmental data were analyzed using multivariate analysis of variance to obtain the viscosity of maltose.

[0031] S54: Calculate the optimum cooking conditions based on the viscosity using an exponential trigonometric algorithm.

[0032] According to the traditional maltose mixing and boiling process provided by the present invention, in step S53, the viscosity calculation formula is expressed as:

[0033]

[0034] In the formula, y is the viscosity of maltose, t is the time, T2 is the cooking temperature, and f is the stirring frequency.

[0035] According to the traditional maltose mixing and boiling process provided by the present invention, in step S54, the specific steps of using the exponential trigonometric algorithm to obtain the optimal boiling conditions include:

[0036] S541: Determine the initial combination data of boiling conditions, and calculate the value range of the boiling condition data.

[0037] S542: Initialize the boiling condition combination data.

[0038] S543: Use the exploration formula to update the position of the boiling condition combination data.

[0039] S544: Use a development formula to update the position of the boiling condition combination data, where the development formula includes a first development formula and a second development formula.

[0040] S545: Use the fitness function to evaluate whether the viscosity of the syrup meets expectations, and adjust the boiling conditions according to the current boiling conditions to obtain the optimal boiling conditions.

[0041] According to the traditional maltose mixing and boiling process provided by the present invention, in step S553, the exploration formula is expressed as:

[0042]

[0043] In the formula, is the jth position of the optimal solution, and represents the jth position of the ith solution in the current iteration and subsequent iterations, q1 and rij represent random numbers in the [0,1] interval, d1 and d2 represent the moving steps of the algorithm in the search space, and α1 is the parameter of the exponential function.

[0044] According to the traditional maltose mixing and boiling process provided by the present invention, in step S554, the first development formula is expressed as:

[0045]

[0046] Where q3 and q4 represent random numbers in the interval [0,1].

[0047] The second development formula is expressed as:

[0048]

[0049]

[0050] Where c is the function parameter, d1 and d2 are the moving steps of the algorithm in the search space.

[0051] The present invention also provides a traditional method of mixing and boiling maltose, comprising: in step S555, the fitness function formula is expressed as:

[0052]

[0053] Where x is the combined data of cooking conditions and μ is the mean.

[0054] The invention provides an ancient maltose mixed boiling process, which analyzes environmental data by using multivariate variance analysis and calculates the optimal boiling conditions by using an exponential trigonometric algorithm, thereby solving the defect of low maltose quality caused by unstable process parameters in the maltose production process, and achieving the following beneficial effects:

[0055] The present invention adopts an exponential trigonometric algorithm, which can find the optimal solution in a short time. By optimizing the boiling conditions, the production cycle can be significantly shortened and production efficiency can be improved. Unnecessary energy consumption and waste of raw materials can be reduced, and production costs can be reduced. Accurate control of boiling conditions helps to reduce fluctuations in the production process and ensure the stability and consistency of product quality. It helps enterprises better meet market demand and improve customer satisfaction.

[0056] The present invention makes more scientific and accurate decisions based on the results of multivariate variance analysis and exponential trigonometric algorithm, which helps enterprises better cope with market changes and improve competitiveness. Through precise analysis and calculation, the trial and error cost in the production process is reduced, so that enterprises can find the best production plan more quickly and improve overall benefits. Through precise data analysis, the present invention helps enterprises better understand the resource consumption in the production process, and through optimizing resource allocation, improve resource utilization efficiency and reduce maltose production costs. Through precise environmental data analysis and viscosity prediction, the present invention helps to improve the quality of maltose and improve the stability of maltose viscosity.

[0057] The present invention calculates the most suitable boiling conditions to ensure that the wort reacts in the best state, thereby shortening the boiling time and improving production efficiency. The present invention accurately controls the boiling conditions to obtain a maltose product with more consistent color, taste and sweetness, improves product quality, reduces production costs, meets higher market demands, and enhances the market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a traditional maltose mixing and boiling process provided by an embodiment of the present invention;

[0059] Figure 2 The present invention provides a schematic flow chart of a traditional maltose mixing and boiling process. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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.

[0061] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is considered to be "fixed to" another component, it may be directly fixed on the other component or there may be a central component at the same time.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0063] Example 1: Figure 1-Figure 2 As shown, an ancient method of mixing and boiling maltose provided by an embodiment of the present invention mainly comprises the following steps:

[0064] S1: Material selection: Select fresh, plump, and pest-free barley of the year as the germination material. Soak the barley in clean water for about 12 hours to allow the barley to fully absorb water and swell. Drain the soaked barley, spread it on a germination tray or ordinary tray, cover it with gauze or tissue paper, and spray water regularly to keep it moist. During the germination process, maintain appropriate temperature and humidity, generally at 20-25℃ and 80%-90% humidity. After 3-5 days of growth, the barley will grow 3-4 cm long malt, which can be crushed evenly with a mortar.

[0065] S2: Choose starch-rich raw materials such as glutinous rice, rice, millet or sorghum. Wash the raw materials and remove impurities and dust. Soak the washed raw materials in clean water for 7-8 hours in cold water in winter and 3-4 hours in summer to allow the raw materials to fully absorb water and swell. Put the soaked raw materials into the steamer and use a wooden stick to poke a few air holes so that the steaming is more even. Use firewood to steam the rice. Put the steamed glutinous rice into warm water and stir evenly.

[0066] S3: Use a knife or a low-speed juicer to chop the germinated barley malt. Chopped malt helps the saccharifying enzyme to better contact and react with the starch in the raw materials. Choose glutinous rice, rice or other starchy raw materials that have been steamed and dried to a suitable temperature. The raw materials after steaming should be soft and easy to mix with malt. Mix the chopped malt with the steamed raw materials evenly. During the mixing process, make sure that the malt is in full contact with the raw materials so that the saccharifying enzyme can evenly act on the starch in the raw materials. The ratio of barley to glutinous rice is 1:10. This ratio should be adjusted according to actual conditions to ensure the best saccharification effect.

[0067] During the fermentation process, it is necessary to maintain appropriate temperature and humidity. The general temperature is controlled between 30-35℃, and the humidity is maintained at about 80%-90%. These conditions help the saccharifying enzyme to fully play its role and promote the conversion of starch to maltose. The fermentation time is generally 2-3 days, and the specific time depends on the type of raw materials, the activity of malt and the conditions of the fermentation environment. During the fermentation process, it is necessary to check the fermentation situation regularly to ensure that the fermentation process proceeds smoothly. Under suitable conditions, saccharifying enzymes will act on the starch in the raw materials and decompose it into oligosaccharides such as maltose. Some intermediates and by-products, such as lactic acid and acetic acid, will be produced in this process, which have a certain effect on the flavor and quality of maltose. During the fermentation process, the fermentation situation needs to be closely observed. If abnormal fermentation or unpleasant odor is found, the fermentation conditions should be adjusted in time or corresponding measures should be taken. At the same time, the fermentation needs to be stirred regularly to promote the full contact and reaction between the saccharifying enzyme and the raw materials. When the fermentation liquid becomes clear and presents a certain sweetness, it means that the fermentation is complete. At this time, the fermentation is stopped and subsequent filtration and concentration operations are carried out. Select fermentation tanks and reactors with excellent stirring and heating functions. These equipment can provide a more uniform and stable fermentation environment, which is conducive to the growth of microorganisms and the accumulation of metabolites. Fermentation equipment should have good aseptic operation performance and sealing performance to prevent contamination by external microorganisms. The sterility of the fermentation process can be ensured through regular disinfection and sterilization, as well as the use of sterile air and sterile culture medium.

[0068] S31: Real-time collection of temperature, humidity, pH value, dissolved oxygen during the fermentation process, and automatic adjustment according to the preset logic. High-precision sensors and instruments are installed in the fermentation tanks and reactors to monitor the key parameters of the fermentation process in real time. These sensors and instruments can accurately reflect the actual situation of the fermentation environment and provide reliable data support for the control system.

[0069] S32: The control system dynamically adjusts the fermentation conditions based on the real-time monitored data through the feedback mechanism. The monitoring and control system is integrated into the fermentation equipment to realize the intelligent and automatic control of the equipment. Through the integrated control system, the fermentation process can be fully monitored and precisely controlled, and the stability and controllability of the fermentation can be improved.

[0070] The relationship between temperature, pH value, dissolved oxygen, and nutrient concentration during fermentation is expressed as follows:

[0071] t=ω1×f(X)+ω2×f(b)

[0072]

[0073] Wherein, t is set as fermentation time, T is fermentation temperature, pH is pH value, DO is dissolved oxygen, N is nutrient parameter, among which, X is final bacterial concentration, X0 is initial bacterial concentration, μ0 refers to growth rate constant at standard temperature, Ea is activation energy, R is gas constant, μmax is maximum specific growth rate, S is nutrient concentration, Ks is half-saturation constant, a is constant, pHopt is optimum pH value, KDO is constant related to dissolved oxygen, n is number of nodes in hidden layer of neural network, is weight from input layer to hidden layer, bi is bias of hidden layer, σ is activation function, ωoi is weight from hidden layer to output layer, bo is bias of output layer.

[0074] S33: Determine whether the estimated fermentation time exceeds a preset optimal fermentation time range, and if so, adjust the process parameters to ensure that the estimated fermentation time is within the optimal fermentation time range.

[0075] Adjust the pH value of the fermentation broth to keep it within the optimal range for microbial growth and metabolism, optimize the fermentation process and increase the fermentation rate.

[0076] Increase the dissolved oxygen content in the fermentation broth, promote the growth and metabolism of microorganisms, and thus shorten the fermentation time. The size and distribution of bubbles in the fermentation broth can improve the efficiency of dissolved oxygen. Nutrients are the basis for the growth and metabolism of microorganisms. By optimizing the concentration of nutrients to meet the needs of microbial growth and metabolism, the fermentation rate can be increased and the fermentation time can be shortened.

[0077] Fed-batch fermentation refers to the periodic addition of nutrients to the fermentation broth during the fermentation process to maintain the growth and metabolic needs of the microorganisms. Continuous fermentation is the continuous discharge of the fermentation broth from the fermenter, while adding fresh nutrients to maintain a stable fermentation state. In actual operation, the concentration and proportion of nutrients such as carbon sources, nitrogen sources, and inorganic salts are reasonably adjusted according to the physiological characteristics of the microorganisms and the needs of the fermentation process. Improve the fermentation process, optimize the fermentation mode, and use fed-batch fermentation to fully utilize the growth and metabolic potential of microorganisms, increase the fermentation rate, and shorten the fermentation time.

[0078] By real-time monitoring of key parameters in the fermentation process and calculating the fermentation time based on these parameters, abnormal situations can be discovered and handled in a timely manner to ensure the stability and controllability of the fermentation process. By analyzing and processing the monitoring data, the fermentation conditions can be further optimized and the fermentation rate can be increased.

[0079] S4: After fermentation is complete, pour the liquid in the container into gauze for filtering, filter out the rice grains, remove the malt residue and other impurities, and retain the clear wort. Pour the filtered liquid into a pot and concentrate it over medium-low heat. Stir constantly during the concentration process to prevent the liquid from sticking to the pot and burning. As the water evaporates, the liquid will gradually become thicker.

[0080] S5: Pour the concentrated liquid into a thick-bottomed pot and prepare to boil. The thick-bottomed pot prevents high heat from burning the liquid. Bring to a boil over high heat and then simmer over low heat. Stir constantly during the boiling process to ensure that the liquid is heated evenly. As the water evaporates further, the liquid will gradually thicken and appear amber or dark brown. When the liquid thickens and hangs on the sugar plate, flows down slowly in sheets and has the unique stringy texture of maltose, the boiling is complete. At this time, turn off the heat and pour the maltose into a mold to cool it down.

[0081] S51: Collect temperature changes, syrup viscosity, heat, stirring frequency and cooking time during the cooking process. The temperature sensor accurately measures the temperature of the syrup, while the viscosity sensor indirectly reflects its viscosity and state by measuring the flow rate or resistance of the syrup. During the cooking process, the data logging system will continuously collect and store data from the sensors. These data will form a complete time series, reflecting the various changes in the syrup cooking process.

[0082] S52: Preprocessing the collected data, including data normalization and variance homogeneity.

[0083] Z-score standardization is used to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. The formula for Z-score standardization is expressed as:

[0084]

[0085] Where Z is the standardized value, X is the original value, μ is the mean of the population data, and σ is the standard deviation of the population data.

[0086] Logarithmic transformation is used to normalize the data variance. For some positively skewed data whose variance increases with the increase of the mean, logarithmic transformation makes the variance tend to be stable. The formula for logarithmic transformation is:

[0087] P=lnX

[0088] Where P is the standardized value and X is the original value.

[0089] S53: The collected data were analyzed using multivariate analysis of variance to obtain the viscosity formula.

[0090] The formula for viscosity is:

[0091]

[0092] In the formula, y is the viscosity of maltose, t is the time, T is the temperature, F is the heat, and f is the stirring frequency.

[0093] S54: Calculate the optimum cooking conditions according to the viscosity using an exponential trigonometric algorithm so that the viscosity of the final syrup reaches an optimal value, which is a specific viscosity range or specific value determined according to product requirements or process standards.

[0094] S541: Determine the initial boiling experimental condition combination, and represent a potential solution to the optimization problem as a set of boiling condition combinations. Assuming that a function f(x1, x2, …, xn) with n variables is to be optimized, then a set of boiling condition combinations is represented as Where xi (i = 1, 2, ..., n) is the value of the variable, and each xi has its corresponding value range [Li, Ui].

[0095] The formula for determining the upper and lower limits is expressed as:

[0096]

[0097] Where Li and Ui represent the upper and lower limits of the expected search space, respectively. r1 and r2 are two coefficients randomly selected between 0 and 1. represents the jth position of the optimal solution obtained so far. xj represents the position of the suboptimal solution at the jth index.

[0098] The role of the exponential adjustment part: The exponential function plays a role in controlling the convergence speed when adjusting the boiling conditions. As the number of iterations t increases, the value of e-αt will gradually decrease, which means that the moving step size of the individual in the search space will gradually become smaller, which helps the algorithm to gradually converge to a better solution in the later stage.

[0099] The role of the trigonometric function: The trigonometric function brings periodicity and oscillation to the update of the individual. This makes the individual not move monotonically in one direction in the search space. This periodic movement helps the individual jump out of the local optimal solution. When the individual is trapped near the local optimal solution, the periodic oscillation of the trigonometric function gives the individual the opportunity to move to other areas and continue to search for a better solution.

[0100] Collaboration and competition between individuals: In addition to being guided by the global optimal individual, there is also a collaborative and competitive relationship between individuals. By calculating the distance between individuals, individuals that are closer will share information and compete in terms of fitness. If an individual finds that the fitness of the individuals around it is better, it will adjust its update strategy to get closer to the better individual faster.

[0101] S542: Initialization formula is:

[0102] x ij =L i +r ij (U i -Li )

[0103] Where rij is a random number uniformly distributed in the interval [0,1], Li and Ui represent the upper and lower limits of the expected search space, respectively.

[0104] S543: Use an exploration formula to update the position of the boiling condition combination data. The first exploration formula is expressed as:

[0105]

[0106]

[0107]

[0108]

[0109] In the formula, is the jth position of the optimal solution, and Indicates the jth position of the ith solution in the current iteration and subsequent iterations. T is the label of the current iteration. q1 and rij represent random numbers in the [0,1] interval. t and MaxI_ter represent the current iteration number and the total iteration number. d1 and d2 are usually used to represent the moving step size or adjustment range of the algorithm in the search space. α1 is the parameter of the exponential function.

[0110] The formula for updating the position in the second exploration phase is expressed as:

[0111]

[0112]

[0113] Where q2 represents a random number in the interval [0,1].

[0114] S544: Use the development formula to update the position of the boiling condition combination data. The first development formula is used in the early iteration stage of the algorithm. The algorithm focuses on exploring potential solutions in the neighborhood of the current point. Through the first development formula, the algorithm can generate a series of new candidate solutions, which are searched near the current best solution to ensure that new solution space areas can be explored. The first development formula uses exponential functions and trigonometric functions to adjust the position of the search individual, and combines some additional random and adaptive variables to improve the search efficiency.

[0115] In the second development phase, the algorithm focuses on further exploration and utilization of the high-quality solutions that have been discovered, that is, a more detailed search around these solutions to find the global optimal solution or a solution close to the global optimal solution. A more sophisticated update mechanism is used in the second development phase. This mechanism pays more attention to local exploration of the search space while maintaining search diversity. By gradually narrowing the search scope and increasing the focus on high-quality solutions, the algorithm can more effectively discover the global optimal solution in the second development phase.

[0116] The development formula in the exponential triangular optimization algorithm is divided into the first development formula and the second development formula, mainly to adapt to the different needs and strategies of the algorithm in the optimization process. By adopting different update mechanisms and conversion mechanisms in different development stages, the exponential triangular optimization algorithm can maintain efficient search performance throughout the search process and effectively find the global optimal solution or a solution close to the global optimal solution.

[0117] The first development formula is expressed as:

[0118]

[0119]

[0120] Where q3 and q4 represent random numbers in the interval [0,1].

[0121] The individual position update formula in the second development stage is:

[0122]

[0123]

[0124] Here, the coefficient c is determined by a clever combination of exponential and trigonometric functions, creating a unique and efficient mechanism.

[0125] S545: The fitness function is an evaluation mechanism used to assess whether the viscosity of the syrup meets expectations, and is a process for adjusting the cooking conditions according to the current cooking conditions.

[0126] The fitness function formula is expressed as:

[0127]

[0128] Where x is the combined data of cooking conditions and μ is the mean.

[0129] S6: Pour the cooked maltose into a pre-prepared mold or directly onto a platform coated with cooking oil, and let it cool and solidify naturally. After the maltose is completely cooled, cut it into small pieces or strips as needed and eat or store it.

[0130] An ancient maltose mixed boiling process, which analyzes environmental data using multivariate variance analysis and uses exponential trigonometric algorithm to calculate the optimal boiling conditions, solves the defect of low maltose quality caused by unstable process parameters in the maltose production process, and achieves the following beneficial effects:

[0131] The exponential triangle algorithm can significantly shorten the production cycle and improve production efficiency by optimizing the cooking conditions. It can reduce unnecessary energy consumption and waste of raw materials and reduce production costs. Accurate control of cooking conditions can help reduce fluctuations in the production process and ensure the stability and consistency of product quality. It can help companies better meet market demand and improve customer satisfaction.

[0132] Based on the results of multivariate variance analysis and exponential trigonometric algorithm, more scientific and accurate decisions can be made. This helps enterprises better cope with market changes and improve competitiveness. Through precise analysis and calculation, the trial and error costs in the production process are reduced, and enterprises can find the best production plan more quickly and improve overall benefits. The present invention helps enterprises better understand the resource consumption in the production process through precise data analysis. The present invention improves resource utilization efficiency and reduces production costs by optimizing resource allocation. The precise environmental data analysis and viscosity prediction of the present invention help to maintain consistent quality standards during the production process. No matter how the production conditions change, the viscosity of maltose can be kept stable by adjusting the process parameters.

[0133] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An ancient method of mixing and boiling maltose, characterized in that: include: S1: After soaking the barley, drain the water and place it in a cool place, spray water regularly to keep it moist, and obtain germinated barley; S2: After soaking, the sorghum is placed in a steamer and evenly steamed to obtain a steaming raw material; S3: chopping the malt of the germinated barley and uniformly mixing it with the cooking raw material, and fermenting it under a preset fermentation environment to obtain a fermented liquid; S4: filtering the fermented liquid to remove impurities to obtain wort, and concentrating the wort over a low heat to obtain concentrated wort; S5: using an exponential trigonometric optimization algorithm to calculate the optimum boiling conditions, and mixing and boiling the concentrated malt juice according to the optimum boiling conditions to obtain maltose; S6: allowing the maltose to cool naturally and solidify, and then cutting the maltose after it is completely cooled.

2. The traditional maltose mixing and boiling process according to claim 1, characterized in that: In step S3, the specific steps of maltose fermentation include: S31: Real-time collection of process parameters during the fermentation process, wherein the process parameters include fermentation temperature, humidity, pH value, and nutrient concentration; S32: Calculating the estimated fermentation time according to the process parameters; S33: Determine whether the estimated fermentation time exceeds a preset optimal fermentation time range, and if so, adjust the process parameters to ensure that the estimated fermentation time is within the optimal fermentation time range.

3. The traditional maltose mixing and boiling process according to claim 2, characterized in that: In step S32, the calculation formula of the estimated fermentation time is expressed as: t=ω1×f(X)+ω2×f(b) In the formula, t is set as the fermentation time, ω1 and ω2 are weight parameters, and f ( pH ) is the relative pH value, f ( DO is the relative dissolved oxygen value, T1 is the fermentation temperature, pH is the pH value, n is the number of nodes in the hidden layer of the neural network, X is the final bacterial concentration, X0 is the initial bacterial concentration, μ0 is the growth rate constant at standard temperature, Ea is the activation energy, R is the gas constant, μmax is the maximum specific growth rate, S is the nutrient concentration, Ks is the half-saturation constant, bi is the bias of the hidden layer, σ is the activation function, ωoi is the weight from the hidden layer to the output layer, bo is the bias of the output layer, and ωij is the weight from the input layer to the hidden layer.

4. The traditional maltose mixing and boiling process according to claim 3 is characterized in that: In step S32, the relative pH value is calculated according to the optimum pH value. The relative pH value formula is expressed as: In the formula, a is a constant, pHopt is the optimal pH value; The relative dissolved oxygen value is calculated based on the dissolved oxygen. The formula for the relative dissolved oxygen value is: Where KDO is the dissolved oxygen related constant and DO is the dissolved oxygen.

5. The traditional maltose mixing and boiling process according to claim 1, characterized in that: In step S5, the specific steps of mixing and boiling include: S51: Collecting environmental data during the cooking process, wherein the environmental data includes cooking temperature, syrup viscosity, stirring frequency and cooking time; S52: preprocessing the environmental data, the preprocessing including data normalization and variance homogeneity; S53: Analyzing the environmental data using multivariate variance analysis to obtain the viscosity of maltose; S54: Calculate the optimum cooking conditions using an exponential trigonometric algorithm according to the viscosity.

6. The traditional maltose mixing and boiling process according to claim 5, characterized in that: In step S53, the viscosity is calculated as follows: In the formula, y is the viscosity of maltose, t is the time, T2 is the cooking temperature, and f is the stirring frequency.

7. The traditional maltose mixing and boiling process according to claim 5 is characterized in that: In step S54, the specific steps of using the exponential trigonometric algorithm to obtain the optimal boiling conditions include: S541: Determine boiling condition combination data, and calculate the value range of the boiling condition combination data; S542: Initializing the boiling condition combination data; S543: using an exploration formula to update the position of the boiling condition combination data; S544: using a development formula to update the position of the boiling condition combination data, the development formula comprising a first development formula and a second development formula; S545: Use the fitness function to evaluate whether the viscosity of the syrup meets expectations, and adjust the boiling conditions according to the current boiling conditions to obtain the optimal boiling conditions.

8. The traditional maltose mixing and boiling process according to claim 7 is characterized in that: In step S553, the exploration formula is expressed as: In the formula, is the jth position of the optimal solution, and represents the jth position of the ith solution in the current iteration and subsequent iterations, q1 and rij represent random numbers in the [0,1] interval, d1 and d2 represent the moving steps of the algorithm in the search space, and α1 is the parameter of the exponential function.

9. The traditional maltose mixing and boiling process according to claim 7, characterized in that: In step S554, the first development formula is expressed as: Where q3 and q4 represent random numbers in the interval [0,1]; The second development formula is expressed as: Where c is the function parameter, d1 and d2 are the moving steps of the algorithm in the search space.

10. The traditional maltose mixing and boiling process according to claim 7, characterized in that: In step S555, the fitness function formula is expressed as: Where x is the combined data of cooking conditions and μ is the mean.

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