Microbial solid-state fermentation method and system for fish feed

Abstract

The invention relates to a microbial solid-state fermentation method and system for fish feed, and the fermentation method comprises the following steps: raw material pretreatment: mixing fermented soybean meal, krill meal, enteromorpha powder, wheat bran and a functional premix according to the weight percentage, and carrying out pulsation vacuum sterilization; solid-state fermentation: putting the pretreated raw materials into a solid-state fermentation tank, controlling the pH value, temperature, humidity and ventilatory capacity, and fermenting; determining a fermentation end point, namely determining the fermentation end point according to physicochemical and biological indexes; according to the preparation method, by optimizing the raw material formula and the fermentation process and adding the functional components, the nutritional value, palatability and safety of the feed are remarkably improved, meanwhile, the breeding cost is reduced, the breeding period is shortened, the breeding benefits are improved, and the quality of fish meat is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fish feed fermentation, and in particular to a microbial solid-state fermentation method and system for fish feed. Background Art

[0002] Fish is a basic and important nutritional supplement for humans. To improve the performance of freshwater fish and prevent bacterial diseases, an early approach was to add antibiotics, such as quinolone, nitrofurans, and bacitracin zinc, to their feed. This simple approach achieved the desired results, but it also had negative side effects: 1) Unmetabolized antibiotics accumulate in the fish, causing excessive, sometimes severe, levels of antibiotics and endangering consumer health; 2) Long-term use of antibiotic feed additives can weaken the immune system of freshwater fish, leading to the development of drug resistance in pathogens, increasing the risk of disease outbreaks and making disease control more difficult.

[0003] Microbial fermented feed refers to feed that uses plant-based agricultural and sideline products as the main raw materials under human-controlled conditions. Through the metabolism of microorganisms, the anti-nutrient factors in plant, animal, and mineral substances are decomposed and synthesized to produce biological feed or feed raw materials that are more edible and nutrient-absorbent for livestock and poultry and have no toxic effects. Discussions on microbial fermented feed began in the 1960s abroad, and industrial production gradually took shape. Discussions on microbial fermented feed in my country began in the 1990s (mainly the production of yeast protein feed). Due to various limitations, the research and development of microbial fermented feed has only been truly in-depth in recent years. Microbial fermented feed has the following characteristics:

[0004] 1. Improve feed utilization and reduce breeding costs. Fermented feed effectively breaks down feed ingredients into glucose and amino acids through biochemical reactions, shortening the feed conversion chain in the animal's digestive tract. This allows the various effective ingredients in the feed to be quickly and effectively absorbed and utilized, thereby improving feed utilization. The increase in crude fat and organic acids after fermentation increases the energy content of the diet, which is also a factor in improving weight gain and feed conversion rate. At the same time, the various live microorganisms in the fermented feed can absorb large amounts of organic and inorganic nitrogen that is difficult for animals to utilize, converting it into a variety of bacterial proteins, namely protein feed, significantly increasing the nutrient content of the feed and reducing feed costs.

[0005] 2. After fermentation, the feed produces a natural sour aroma, which can stimulate the appetite of animals, promote the secretion of digestive juices, increase the activity of digestive enzymes, accelerate the decomposition of feed nutrients, thereby promoting the absorption of nutrients, and comprehensively improving the feed intake and feeding speed of the fish and shrimp.

[0006] 3. Due to the unique nature of aquaculture, when a variety of beneficial bacteria are introduced into the water, they rapidly degrade the large amounts of organic matter (such as leftover bait and animal excrement) and toxic and harmful gases (such as ammonia and hydrogen sulfide) that accumulate in the water through oxidation, ammonification, nitrification, denitrification, and nitrogen fixation. This degradation process produces a variety of inorganic salts that are directly utilized by phytoplankton in the water. The abundant phytoplankton further degrades harmful substances, improving various indicators of the aquaculture water and thus purifying the water quality. This promotes the growth of fish and shrimp. Aquatic animals spend a long period of time in water, which is greatly affected by environmental factors and increases their exposure to harmful bacteria. Establishing a virtuous cycle of microbial systems in the intestines of fish and shrimp is one of the most effective ways to resist pathogenic infections.

[0007] At the same time, the beneficial bacteria in the microbial fermentation feed itself is a high-protein substance. At the same time, metabolism in the animal intestine can produce a variety of beneficial factors that help the animal digest nutrients, thereby promoting the growth, development and weight gain of aquatic animals. For example, Bacillus can secrete a variety of digestive enzymes (protease, amylase, lipase, etc.) to promote the digestion and absorption of nutrients; yeast can produce amino acids and multiple vitamins (K, C, B1, B2, pantothenic acid, niacin, biotin, inositol and folic acid, etc.) for the animal body to use. Yeast can also promote the production of phytase and improve the utilization rate of phosphorus, thereby achieving the purpose of improving the quality of fish meat.

[0008] With the application of intelligent optimization algorithms (such as multi-objective particle swarm optimization (MOPSO)) and artificial intelligence (AI) in feed formulation optimization, researchers have attempted to use alternative protein sources (such as cottonseed meal, rapeseed meal, fermented soybean dregs, etc.) in combination with microbial fermentation and enzymatic hydrolysis technologies to improve the protein digestibility, fiber degradation rate, fattening efficiency, and storage stability of feed. However, there are no good application methods. Existing microbial fermentation technologies lack precise regulation. If the microbial fermentation process does not meet the standards, it will affect the nutrient absorption of feed and even produce harmful substances in severe cases. Existing processes mainly rely on fixed temperature or manual regulation, lack intelligent control mechanisms, and are difficult to dynamically optimize the enzymatic hydrolysis efficiency. To this end, the present invention proposes a microbial solid-state fermentation method and system for fish feed. Summary of the Invention

[0009] The present invention aims to solve the technical problems existing in the prior art and provides a microbial solid-state fermentation method and system for fish feed, which can effectively reduce the fat content of fish, improve muscle quality, shorten the breeding cycle and improve feed conversion rate.

[0010] The present invention solves the above-mentioned technical problems with the following technical solutions: a method and system for microbial solid-state fermentation of fish feed; the method comprises the following steps:

[0011] S1: raw material pretreatment, comprising mixing fermented soybean meal (40%), krill meal (15%), enteromorpha powder (10%), wheat bran (25%), and functional premix (10%) according to weight percentage, and sterilizing by pulse vacuum;

[0012] S2: solid-state fermentation, placing the pretreated raw materials in a solid-state fermentation tank, controlling pH, temperature, humidity and ventilation, and fermenting;

[0013] S3: Fermentation endpoint determination, based on physical, chemical and biological indicators;

[0014] S4: Post-processing, drying the fermentation product and adding functional ingredients.

[0015] Furthermore, in the microbial solid-state fermentation method for fish feed, near-infrared spectroscopy is used to monitor the fermentation progress online during the solid-state fermentation step, including: irradiating the fermentation liquid with near-infrared light and measuring the intensity of the transmitted or reflected light; different wavelengths of light are absorbed differently by different chemical bonds, thereby obtaining a near-infrared spectrum of the fermentation liquid; and analyzing the characteristics of the spectrum to obtain the content of various components in the fermentation liquid;

[0016] A calibration model between near-infrared spectroscopy and key parameters of the fermentation process was established using partial least squares regression:

[0017] Data preprocessing: centralize and standardize spectral data and key parameters;

[0018] Extract principal components: Use nonlinear iterative partial least squares algorithm to extract principal components of spectral data and key parameters;

[0019] Establish a regression model: establish a linear regression model between the principal components and key parameters;

[0020] X=TP T +E;

[0021] Where X is the spectral data matrix, T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix;

[0022] Y=UQ T +F;

[0023] Where Y is the key parameter matrix, U is the score matrix of Y, Q is the loading matrix of Y, and F is the residual matrix;

[0024] U = TB;

[0025] B is the regression coefficient matrix.

[0026] Furthermore, in the microbial solid-state fermentation method for fish feed, after obtaining the contents of various components in the fermentation liquid, MPC is applied to optimize the solid-state fermentation process and a dynamic model of the solid-state fermentation process is constructed. In the dynamic model:

[0027] State Space:

[0028] x(k+1)=A*x(k)+B*u(k)+w(k);

[0029] y(k)=C*x(k)+v(k);

[0030] Where x(k) is the state vector at time k, u(k) is the control output vector at time k, y(k) is the output vector at time k, w(k) and v(k) are the process noise and measurement noise, and A, B, and C are system matrices.

[0031] Predicted output: Predict the state and output of N moments in the future based on the state space;

[0032] Y=S_x*x(k)+S_u*U

[0033] Where Y is the output vector N moments in the future, U is the control output vector N moments in the future, S_x and S_u are matrices derived from the system matrices A, B, and C;

[0034] Objective function:

[0035] J = (R_y-Y) T *Q(R_y-Y)+U T *R*U;

[0036] Among them, R_y is the desired output target, Q and are weight matrices, and T is the target time.

[0037] Furthermore, the microbial solid-state fermentation method for fish feed controls pH, temperature, humidity and ventilation according to a dynamic model:

[0038] Select control variables: pH, temperature, humidity, and ventilation as control variables;

[0039] Select controlled variables: target product, pH, and microbial concentration as controlled variables;

[0040] Define the objective function: microbial production and its product production as the target;

[0041] The growth mechanism of microorganisms is:

[0042] μ=μ_max*(S / (K_s+S))

[0043] Where μ is the specific growth rate of the microorganism, μ_max is the maximum specific growth rate, S is the substrate concentration, and K_s is the half-saturation constant;

[0044] According to the dynamic model of solid-state fermentation process:

[0045] J=-Q_1*BCAA(k+N)+Q_2*(pH(k+N)-pH_target)^2+R*Δu(k)^2

[0046] Where BCAA(k+N) is the branched-chain amino acid concentration at N time points in the future, pH(k+N) is the pH value at N time points in the future, pH_target is the desired pH value, Δu(k) is the increment of the control input, and Q_1, Q_2, and R are weight coefficients.

[0047] Furthermore, in the microbial solid-state fermentation method for fish feed, in step S1, the pulse vacuum sterilization conditions are 115°C for 15 minutes, followed by cooling to 85°C for 30 minutes to retain 30% of the activity of the native flora.

[0048] Furthermore, in the microbial solid-state fermentation method for fish feed, in step S2, an intermittent spraying system is used to maintain a moisture content of 40-45%; the ventilation strategy is micro-aeration at 0.2 vvm per hour to maintain an O2 concentration of >18%.

[0049] Furthermore, in the microbial solid-state fermentation method for fish feed, in step S3, the physical and chemical indicators are pH ≤ 4.8, reducing sugar < 2%, and free amino acid increment ≥ 80%; the biological indicators are spore formation rate > 95% and yeast viable count ≥ 1×10^9 CFU / g.

[0050] Furthermore, in the microbial solid-state fermentation method for fish feed, in step S4, the drying method is low-temperature airflow drying to make the moisture content ≤9%; the functional ingredients include 0.5% tea polyphenol-β-cyclodextrin embedding material and 3% conjugated linoleic acid microcapsules.

[0051] In another aspect, a microbial solid-state fermentation system for fish feed is provided, which is applied to any one of the microbial solid-state fermentation methods for fish feed, and the system comprises:

[0052] Raw material pretreatment unit:

[0053] A mixing device for receiving and mixing fermented soybean meal, krill meal, enteromorpha meal, wheat bran and functional premix;

[0054] Pulsating vacuum sterilization equipment is used to sterilize the mixed raw materials. The equipment can accurately control the temperature and vacuum degree to achieve a pulsating sterilization process;

[0055] Solid-state fermentation unit:

[0056] Solid-state fermentation tank with adjustable material layer thickness, automatic turning rake, temperature control system, humidity control system and ventilation system;

[0057] Temperature control system: used to accurately control the temperature inside the fermentation tank according to the set temperature gradient;

[0058] Humidity control system: including intermittent spraying device, which can maintain the moisture content of the fermentation material within the range of 40-45%;

[0059] Aeration system: provides micro-aeration at 0.2vvm per hour and is equipped with an oxygen concentration sensor to monitor and control the oxygen concentration in the tank to be above 18% in real time;

[0060] Fermentation endpoint determination unit:

[0061] pH meter, reducing sugar meter, and amino acid analyzer are used to monitor physical and chemical indicators during the fermentation process;

[0062] Microscope, hemocytometer, and plate counter for detecting biological indicators during the fermentation process;

[0063] Near-infrared spectroscopy online monitoring equipment is used to monitor the fermentation process in real time and adjust process parameters;

[0064] Post-processing unit:

[0065] Low-temperature air flow dryer, used to dry the fermentation product to a moisture content of ≤9%;

[0066] Additive mixing equipment, used to evenly mix functional ingredients with the dried fermentation product;

[0067] Control unit:

[0068] PLC control system, used to centrally control and monitor the operating parameters of each unit to realize the automated fermentation process;

[0069] The data acquisition and analysis system is used to record and analyze data during the fermentation process and provide a basis for process optimization.

[0070] The beneficial effects of the present invention are:

[0071] The present invention improves the palatability and nutritional value of feed by adding tea polyphenol-β-cyclodextrin embedding material to absorb and degrade geosmin, reducing the fishy smell value of feed by 67% and increasing the feed intake of fish; the fermentation product contains 2.3% branched-chain amino acids (BCAA), which promotes muscle protein synthesis, increases shear force by 25%, reduces drip loss by 40%, and improves the taste and texture of fish meat; and 3% conjugated linoleic acid (CLA) microcapsules are compounded to activate the PPARα fatty acid oxidation pathway, reduce visceral fat deposition by 35%, and reduce the fat content of fish bodies.

[0072] The present invention improves production efficiency and economic benefits: shortens the breeding cycle: the slimming cycle is shortened from 45 days to 32 days, shortening the time by 28%, thereby accelerating the time it takes for fish to be put on the market; reduces the feed coefficient: the feed coefficient is reduced from 1.6 to 1.38, saving 14% of the feed volume and reducing the breeding cost; increases the premium space: the premium space is increased from +￥2 / kg to +￥5.8 / kg, increasing the profit by 190%, thereby increasing the income of the breeders.

[0073] The present invention also uses MPC to control the fermentation process: MPC uses a dynamic model to predict future states and adjusts control variables based on the prediction results; this allows parameters such as temperature, humidity, and ventilation to be dynamically optimized according to actual conditions, thereby improving fermentation efficiency.

[0074] The present invention also provides a two-stage pH control strategy: ensuring bacterial activity: an initial pH of 6.5 helps maintain bacterial activity; achieving self-sterilization: the pH naturally drops to 4.5 to achieve self-sterilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 1 is a schematic flow chart of a microbial solid-state fermentation method for fish feed in one embodiment;

[0076] Figure 2 This is a schematic diagram of the module structure of a microbial solid-state fermentation system for fish feed in one embodiment. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0078] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0079] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0080] In one embodiment, Figure 1 As shown, the present application provides a microbial solid-state fermentation method for fish feed, comprising the following steps:

[0081] S1: raw material pretreatment, comprising mixing fermented soybean meal (40%), krill meal (15%), enteromorpha powder (10%), wheat bran (25%), and functional premix (10%) according to weight percentage, and sterilizing by pulse vacuum;

[0082] S2: solid-state fermentation, placing the pretreated raw materials in a solid-state fermentation tank, controlling pH, temperature, humidity and ventilation, and fermenting;

[0083] S3: Fermentation endpoint determination, based on physical, chemical and biological indicators;

[0084] S4: Post-processing, drying the fermentation product and adding functional ingredients.

[0085] Furthermore, in step S1, the pulse vacuum sterilization condition is 115°C for 15 minutes, and then cooled to 85°C for 30 minutes to retain 30% of the activity of the native bacterial flora.

[0086] Furthermore, in step S2, an intermittent spraying system is used to maintain a moisture content of 40-45%; the aeration strategy is micro-aeration at 0.2 vvm per hour to maintain an O2 concentration of >18%.

[0087] Furthermore, in step S3, the physical and chemical indicators are pH ≤ 4.8, reducing sugar < 2%, and free amino acid increment ≥ 80%; the biological indicators are spore formation rate > 95%, and yeast viable count ≥ 1×10^9 CFU / g.

[0088] Furthermore, in step S4, the drying method is low-temperature airflow drying to make the moisture content ≤9%; the functional ingredients include 0.5% tea polyphenol-β-cyclodextrin embedding material and 3% conjugated linoleic acid microcapsules.

[0089] In this embodiment, the solid-state fermentation method aims to improve the nutritional value, palatability and functionality of fish feed through the action of microorganisms, and ultimately improve the growth performance and health of fish;

[0090] Raw materials:

[0091] Fermented soybean meal provides the carbon and nitrogen sources required for microbial growth. At the same time, fermented soybean meal may contain more small molecule peptides and amino acids that are easily absorbed by fish.

[0092] Krill meal is rich in protein, astaxanthin, etc., which improves the nutritional value and coloring effect of the feed.

[0093] Enteromorpha powder is rich in polysaccharides, minerals, etc., and may have the effect of enhancing immunity.

[0094] Wheat bran provides carbon source and crude fiber, regulating the structure and aeration of the feed.

[0095] Functional premixes contain vitamins, minerals, enzyme preparations, probiotics, etc., which enhance the nutritional and functional properties of the feed. At the same time, the taste can be adjusted according to different fish species to achieve the purpose of being suitable for various fish.

[0096] Pulsating vacuum sterilization: uses high temperature to kill most of the bacteria, but retains the activity of a portion (30%) of the native flora. This practice is to utilize the dominant species in the native flora, or to simulate the natural fermentation process. Pulsating vacuum sterilization is gentler than simple high-temperature sterilization and can better retain the nutrients of the raw materials.

[0097] Environmental parameters:

[0098] The thickness of the material layer affects the air permeability and heat dissipation effect. 25cm can not only ensure sufficient contact of the fermentation materials, but also avoid anaerobic and high temperature problems caused by too thick a material layer; the automatic turning rake helps to mix the materials and ensure the uniformity of fermentation.

[0099] Temperature gradient: 50°C (0-36h) → 45°C (36-60h) → room temperature (60-72h). Microorganisms require different temperatures at different growth stages. An initial high temperature (50°C) may promote the rapid growth of certain heat-resistant bacteria, while a mid-stage cooling (45°C) may be more conducive to the metabolism and product accumulation of the target bacteria. A late cooling to room temperature can inhibit the growth of other bacteria and stabilize the fermentation product.

[0100] Humidity control: Intermittent spraying system (maintains moisture content at 40-45%). Humidity is an important factor for microbial growth; solid-state fermentation requires appropriate moisture to ensure microbial metabolic activity; intermittent spraying can prevent the material from being too dry or too wet.

[0101] Aeration strategy: micro-aeration at 0.2 vvm per hour (O2 concentration > 18%). Most beneficial bacteria are aerobic and require oxygen for effective metabolism. 0.2 vvm means that the volume of air introduced per minute is 0.2 times the volume of the fermenter. Maintaining an O2 concentration above 18% can ensure the oxygen supply of microorganisms.

[0102] Fermentation endpoint judgment:

[0103] Physical and chemical indicators:

[0104] pH≤4.8: During the fermentation process, microorganisms will produce organic acids, causing the pH value to drop; a pH value lower than 4.8 indicates that the fermentation has reached a certain level, and the accumulation of organic acids helps inhibit the growth of miscellaneous bacteria.

[0105] Reducing sugar <2%: Reducing sugar is the product of microbial decomposition of carbohydrates. A decrease in reducing sugar content indicates that the carbon source has been fully utilized.

[0106] Free amino acid increment ≥80%: Protein is broken down into small molecular peptides and amino acids, which are easier for fish to absorb and utilize; the free amino acid increment is an indicator of the degree of protein decomposition.

[0107] Biological indicators:

[0108] Spore formation rate > 95%: Spores are dormant bodies formed by certain bacteria under adverse conditions; a high spore formation rate indicates that the fermentation product has good stability and can withstand environmental conditions such as dryness and high temperature.

[0109] The number of viable yeast cells is ≥1×10^9 CFU / g: Yeast is a commonly used feed probiotic that can improve the intestinal flora of fish and enhance immunity; the number of viable bacteria is an indicator of the activity of probiotics.

[0110] Further processing:

[0111] Fishy smell removal and enhanced odor: 0.5% tea polyphenol-β-cyclodextrin embedding adsorbs and degrades geosmin, reducing fishy odor levels detected by the electronic nose by 67%. Tea polyphenols have antioxidant properties, and β-cyclodextrin can embed odorants such as geosmin, reducing fishy odor.

[0112] Muscle firming: The fermentation product contains 2.3% branched-chain amino acids (BCAA), which stimulate muscle protein synthesis, increase shear force by 25%, and reduce drip loss by 40%. BCAA is an important raw material for muscle protein synthesis and can improve the quality of fish meat.

[0113] Fat metabolism: Compounded with 3% conjugated linoleic acid (CLA) microcapsules, it activates the PPARα fatty acid oxidation pathway and reduces visceral fat deposition by 35%. CLA can promote fat decomposition and reduce fat accumulation.

[0114] Fermentation drying: Low-temperature airflow drying (≤55°C), moisture ≤9%: Low-temperature drying can maximize the retention of active ingredients in the fermentation product and avoid high-temperature damage; low moisture content can prevent mildew and corruption.

[0115] In a specific embodiment, 1000 kg of fish feed is prepared by using fermented soybean meal: 40%, krill meal: 15%, enteromorpha powder: 10%, wheat bran: 25%, and functional premix: 10%;

[0116] Special additives: Tea polyphenol-β-cyclodextrin embedding compound: 0.5% (for removing fishy smell), conjugated linoleic acid (CLA) microcapsule: 3% (for fat metabolism).

[0117] After all the raw materials are mixed evenly, they are sterilized using a pulse vacuum sterilization method with the parameters set at 115°C for 15 minutes, then cooled to 85°C for 30 minutes.

[0118] Put it into the fermentation tank for fermentation and fermentation ends after reaching the fermentation end point:

[0119] Physical and chemical indicators: pH ≤ 4.8, reducing sugar < 2%, free amino acid increment ≥ 80%;

[0120] Biological indicators: spore formation rate>95%, yeast viable count ≥1×10^9 CFU / g;

[0121] After fermentation, low-temperature airflow drying is used, and the temperature is controlled at ≤55°C to reduce the moisture content to ≤9%, and the product is sealed and stored.

[0122] The key production parameters and test methods are shown in Table 1:

[0123] Process Parameter Standards Detection method Strain expansion Spore concentration ≥ 2×10^10 CFU / mL Hemocytometer + microscope observation Fermentation and drying Low temperature air flow drying (≤55℃), moisture ≤9% Rapid Moisture Meter Hygiene control Salmonella negative, mycotoxins <10μg / kg ELISA kits stability Accelerated test at 40℃ for 3 months, survival rate of live bacteria>80% Constant temperature and humidity chamber + plate counting

[0124] Table 1

[0125] After obtaining fermented fish feed, it is applied in feeding strategy:

[0126] Phased feeding plan:

[0127] Adaptation period (1-7 days): 20% fermented feed replaces conventional feed, and 0.1% complex enzyme preparation is added to promote adaptation.

[0128] Strengthening period (8-21 days): 40% replacement, fed 4 times a day (07:00 / 11:00 / 15:00 / 19:00).

[0129] Sprint period (15 days before leaving the pond): 60% replacement, and simultaneous use of bacterial solution to soak feed (10^8 CFU / mL, 30 minutes each time) to further improve the effect.

[0130] The experimental results are as follows:

[0131] Fish body fat content: 2.1% → 1.4%;

[0132] Muscle collagen: 12 mg / g → 18 mg / g;

[0133] DHA retention rate: 83% → 94%;

[0134] Feed coefficient: 1.6→1.38;

[0135] The fishy odor value detected by the electronic nose was reduced by 67%;

[0136] The shear force value is increased by 25% and the drip loss is reduced by 40%;

[0137] Visceral fat deposits were reduced by 35%.

[0138] The cost-benefit analysis is shown in Table 2:

[0139] project Conventional feed Fermented feed Comparative Advantages production costs ￥2,600 / ton ￥3,150 / ton +21％ Bait coefficient 1.65 1.42 Save 14% of feed Weight loss cycle 45 days 32 days 28% shorter time Premium space +￥2 / kg +￥5.8 / kg 190% more profit

[0140] Table 2

[0141] In summary, fermented feed is slightly higher than conventional feed (+21%), but the feed usage is reduced and the overall cost is lower; at the same time, high-quality sturgeon is sold at a higher price (+￥5.8 / kg), and the profit margin is increased.

[0142] In another embodiment, a two-stage pH control technology is adopted (initial pH 6.5 → naturally dropped to 4.5), which not only ensures the activity of the strain but also achieves self-sterilization. Fuzzy control can also be used to achieve two-stage pH control to achieve adaptive optimization of the fermentation process; the pH in the initial stage is set to 6.5 to provide an optimal environment for the rapid reproduction of dominant strains; then, by monitoring the concentration of fermentation metabolites (such as lactic acid, acetic acid) and bacterial diversity (based on metagenomic sequencing data), a fuzzy logic controller is used to dynamically adjust the pH drop rate to ensure that the pH gradually drops to 4.5; the fuzzy logic controller combines an expert experience knowledge base (based on historical fermentation data) and real-time data feedback, which can effectively inhibit the growth of miscellaneous bacteria, achieve self-sterilization, and maximize the metabolic activity of the target functional strain.

[0143] At the same time, an intelligent dynamic feeding strategy is provided to maximize the production of functional metabolites (such as branched-chain amino acids, γ-aminobutyric acid, etc.); after 36 hours of fermentation, the intelligent feeding system is started. The system uses a deep reinforcement learning model to dynamically adjust the feeding rate and total feeding amount of xylose based on real-time monitored fermentation parameters (such as dissolved oxygen, redox potential, biomass, metabolite concentration, etc.) and historical data. This deep reinforcement learning model learns the optimal feeding strategy by simulating various disturbances and responses during the fermentation process to cope with differences between different batches. In addition, the strategy also integrates a mathematical model based on metabolic flux analysis to predict the impact of different feeding schemes on metabolic pathways, thereby precisely controlling the synthesis of functional metabolites. Through this intelligent dynamic feeding, the yield and conversion rate of functional metabolites can be significantly improved, while reducing the production of by-products.

[0144] In one embodiment, near-infrared spectroscopy is used to monitor the fermentation progress online during the solid-state fermentation step, including: irradiating the fermentation broth with near-infrared light and measuring the intensity of the transmitted or reflected light; different wavelengths of light are absorbed differently by different chemical bonds, thereby obtaining a near-infrared spectrum of the fermentation broth; and analyzing the characteristics of the spectrum to determine the content of various components in the fermentation broth.

[0145] A calibration model between near-infrared spectroscopy and key parameters of the fermentation process was established using partial least squares regression:

[0146] Data preprocessing: centralize and standardize spectral data and key parameters;

[0147] Extract principal components: Use nonlinear iterative partial least squares algorithm to extract principal components of spectral data and key parameters;

[0148] Establish a regression model: establish a linear regression model between the principal components and key parameters;

[0149] X=TP T +E;

[0150] Where X is the spectral data matrix, T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix;

[0151] Y=UQ T +F;

[0152] Where Y is the key parameter matrix, U is the score matrix of Y, Q is the loading matrix of Y, and F is the residual matrix;

[0153] U = TB;

[0154] B is the regression coefficient matrix.

[0155] During solid-state fermentation, near-infrared spectroscopy can be used to monitor a variety of key parameters, such as:

[0156] Component content: raw material consumption (such as starch, protein), metabolite production (such as amino acids, organic acids), water content, etc.

[0157] Fermentation process: By monitoring the changes in component content, the fermentation process and rate can be understood;

[0158] End point determination: Based on the content and change trend of key components, it can be determined whether the fermentation has reached the end point;

[0159] Quality control: Real-time monitoring of the fermentation process, timely adjustment of process parameters, and ensuring the stability of product quality;

[0160] In order to relate NIRS spectral data to key parameters of the fermentation process, a calibration model needs to be established;

[0161] Data preprocessing:

[0162] Spectral data preprocessing: correct spectral deviation, eliminate noise, and improve spectral resolution;

[0163] Principal component extraction:

[0164] The principal components of spectral data (X) and key parameter data (Y) were extracted simultaneously using a nonlinear iterative partial least squares algorithm;

[0165] The principal component is a linear combination of the original variables, which can summarize the main information of the original data and reduce the data dimension;

[0166] Regression model establishment:

[0167] Establish a linear regression model between principal components and key parameters;

[0168] The goal of PLSR is to find the component with the strongest relationship between X and Y and to establish a regression equation to determine the detection data currently presented in the near-infrared spectral data.

[0169] In this embodiment, near-infrared light is electromagnetic radiation with a wavelength between 780nm and 2500nm. The spectrum in this band corresponds to the overtones and combination bands of molecular vibrations, especially the vibrations of chemical bonds such as CH, OH, and NH. When near-infrared light is irradiated on a sample, certain wavelengths of light are absorbed by specific chemical bonds in the sample. The degree of absorption is directly related to the concentration of these chemical bonds. By measuring the intensity of transmitted or reflected light, the spectrum of the sample in the near-infrared band can be obtained. This spectrum is like a "fingerprint" of the sample, containing rich chemical information.

[0170] During the fermentation process, the concentrations of various components will change over time, such as: the consumption of raw materials (such as carbon sources, nitrogen sources); the accumulation of intermediate metabolites (such as organic acids, amino acids); the production of target products (such as enzymes, antibacterial substances); because these components have unique near-infrared absorption characteristics, their changes can be monitored in real time by analyzing the near-infrared spectrum of the fermentation broth.

[0171] Data example:

[0172]

[0173] Results: The changes in protein content, amino acid content and pH value during the fermentation process can be accurately predicted. Through online monitoring, the fermentation progress can be understood in real time, and process parameters (such as temperature, humidity, and ventilation) can be adjusted as needed to obtain the best fermentation effect.

[0174] In one embodiment, after obtaining the contents of various components in the fermentation broth, MPC is applied to the optimization of the solid-state fermentation process to construct a dynamic model of the solid-state fermentation process. In the dynamic model:

[0175] State Space:

[0176] x(k+1)=A*x(k)+B*u(k)+w(k);

[0177] y(k)=C*x(k)+v(k);

[0178] Where x(k) is the state vector at time k, u(k) is the control output vector at time k, y(k) is the output vector at time k, w(k) and v(k) are the process noise and measurement noise, and A, B, and C are system matrices.

[0179] Predicted output: Predict the state and output of N moments in the future based on the state space;

[0180] Y=S_x*x(k)+S_u*U

[0181] Where Y is the output vector N moments in the future, U is the control output vector N moments in the future, S_x and S_u are matrices derived from the system matrices A, B, and C;

[0182] Objective function:

[0183] J = (R_y-Y) T *Q(R_y-Y)+U T *R*U;

[0184] Among them, R_y is the desired output target, Q and are weight matrices, and T is the target time.

[0185] Furthermore, pH, temperature, humidity and ventilation are controlled according to the dynamic model:

[0186] Select control variables: pH, temperature, humidity, and ventilation as control variables;

[0187] Select controlled variables: target product, pH, and microbial concentration as controlled variables;

[0188] Define the objective function: microbial production and its product production as the target;

[0189] The growth mechanism of microorganisms is:

[0190] μ = μ_max*(S / (K_s+S));

[0191] Where μ is the specific growth rate of the microorganism, μ_max is the maximum specific growth rate, S is the substrate concentration, and K_s is the half-saturation constant;

[0192] According to the dynamic model of solid-state fermentation process:

[0193] J=-Q_1*BCAA(k+N)+Q_2*(pH(k+N)-pH_target)^2+R*Δu(k)^2

[0194] Where BCAA(k+N) is the branched-chain amino acid concentration at N time points in the future, pH(k+N) is the pH value at N time points in the future, pH_target is the desired pH value, Δu(k) is the increment of the control input, and Q_1, Q_2, and R are weight coefficients.

[0195] In this embodiment, the core idea of ​​MPC is: Model prediction: using the dynamic model of the process to predict the behavior of the system in the future.

[0196] Optimization solution: Based on the prediction results, optimize the control actions in the future to meet the set goals and constraints.

[0197] Rolling optimization: In each control cycle, the above prediction and optimization process is repeated, and the first control action of the optimization result is applied to the system.

[0198] In state space:

[0199] x(k): The state vector at time k, which contains the key variables describing the system state. For example, in this solid-state fermentation process, these variables may include: pH, temperature, humidity, microbial concentration (e.g., concentration of Bacillus, yeast), and key metabolite concentrations (e.g., branched-chain amino acids (BCAAs) and reducing sugar concentrations).

[0200] u(k): The control input vector at time k, which contains the control variables that can be adjusted. For example: the amount of pH regulator added, heating / cooling power (to control temperature), spray water volume (to control humidity), ventilation volume

[0201] y(k): The output vector at time k, containing the variables to be monitored or controlled, such as pH, BCAA concentration, microbial concentration, etc.

[0202] The objective function is the core of MPC, which defines the control goal. The goal of MPC is to find a set of control inputs U that optimizes the objective function (e.g., reaches a preset value).

[0203] For example, taking the production of branched-chain amino acids as the goal, for simplicity, we only consider pH and BCAA concentration as state variables;

[0204] Initial conditions: pH = 6.5, temperature = 50°C, humidity = 42%, BCAA concentration = 0.5% and Bacillus concentration = 1x10^8 CFU / g

[0205] Control objectives: BCAA production reaches the preset value, pH is maintained at around 4.5, control period: 1 hour, prediction step: N = 12 hours.

[0206] x(k)=[pH(k);BCAA(k)];

[0207] u(k)=[Acid_add(k); Temperature(k); Humidity(k); Aeration(k)] (where Acid_add is the amount of acid added);

[0208] y(k)=[pH(k);BCAA(k)];

[0209] The system matrices A, B, and C need to be determined through experimental data or mechanistic modeling; here we assume they are known (this is a simplified example). For example, the B matrix may be as follows:

[0210] B=[-0.05, 0.001, 0, 0; 0.01, -0.005, 0.002];

[0211] Among them, pH is affected by the amount of acid added, temperature, humidity, and ventilation, and BCAA is affected by temperature, humidity, and ventilation;

[0212] This means that increasing the amount of acid added will lower the pH, increasing the temperature will favor BCAA production, excessive humidity will reduce BCAA production, and increasing ventilation will favor BCAA production;

[0213] Objective function:

[0214] J=-Q_1*BCAA(k+12)+Q_2*(pH(k+12)-4.5)^2+R1*ΔAcid_add(k)^2+R2*ΔTemperature(k)^2+R3*ΔHumidity(k)^2+R4*ΔAeration(k)^2

[0215] Where Q_1 = 100 (focus on BCAA production), Q_2 = 50 (focus on pH), R1 = 1, R2 = 0.5, R3 = 0.5, R4 = 0.2 (control input variation penalty)

[0216] Constraints: pH: 4.0 ≤ pH ≤ 7.0, Temperature: 40°C ≤ Temperature ≤ 55°C, Humidity: 35% ≤ Humidity ≤ 48%, Acid addition: 0 ≤ Acid_add ≤ 0.1 (unit: g / L), Aeration: 0.1 vvm <= Aeration <= 0.3 vvm (0.2 vvm in the file)

[0217] MPC solution process:

[0218] Step 1: During each control period (1 hour), measure the current pH value and BCAA concentration.

[0219] Step 2: Use the state-space model to predict pH and BCAA concentrations for the next 12 hours based on the current pH, BCAA concentration, and the assumed control input sequence.

[0220] Step 3: Under the premise of satisfying the constraints, find a set of control input sequences (acid addition amount, temperature, humidity, ventilation volume) to minimize the objective function J; this usually requires the use of an optimization algorithm (e.g., quadratic programming).

[0221] Step 4: Apply the first control action of the optimized control input sequence (i.e., the amount of acid added, temperature, humidity, and ventilation volume in the next hour) to the solid-state fermentation system.

[0222] Step 5: Wait for one control cycle (1 hour), then repeat Steps 1-4.

[0223] Numerical example:

[0224] Current status: pH = 6.0, BCAA = 0.6%

[0225] MPC solution results:

[0226] Acid_add(k)=0.05 g / L (acid added);

[0227] Temperature (k) = 52°C (elevated temperature);

[0228] Humidity(k)=40%(adjust humidity);

[0229] Aeration(k)=0.22vvm;

[0230] Based on the current pH and BCAA concentration, the MPC predicts the pH and BCAA concentration for the next 12 hours and adjusts acid addition, temperature, humidity, and aeration to maximize BCAA production and maintain a pH around 4.5.

[0231] On the other hand, Figure 2 As shown, the present application also provides a microbial solid-state fermentation system for fish feed, which is applied to any of the microbial solid-state fermentation methods for fish feed, and the system comprises:

[0232] Raw material pretreatment unit 100:

[0233] A mixing device for receiving and mixing fermented soybean meal, krill meal, enteromorpha meal, wheat bran and functional premix;

[0234] Pulsating vacuum sterilization equipment is used to sterilize the mixed raw materials. The equipment can accurately control the temperature and vacuum degree to achieve a pulsating sterilization process;

[0235] Solid-state fermentation unit 200:

[0236] Solid-state fermentation tank with adjustable material layer thickness, automatic turning rake, temperature control system, humidity control system and ventilation system;

[0237] Temperature control system: used to accurately control the temperature inside the fermentation tank according to the set temperature gradient;

[0238] Humidity control system: including intermittent spraying device, which can maintain the moisture content of the fermentation material within the range of 40-45%;

[0239] Aeration system: provides micro-aeration at 0.2vvm per hour and is equipped with an oxygen concentration sensor to monitor and control the oxygen concentration in the tank to be above 18% in real time;

[0240] Fermentation endpoint determination unit 300:

[0241] pH meter, reducing sugar meter, and amino acid analyzer are used to monitor physical and chemical indicators during the fermentation process;

[0242] Microscope, hemocytometer, and plate counter for detecting biological indicators during the fermentation process;

[0243] Near-infrared spectroscopy online monitoring equipment is used to monitor the fermentation process in real time and adjust process parameters;

[0244] Post-processing unit 400:

[0245] Low-temperature air flow dryer, used to dry the fermentation product to a moisture content of ≤9%;

[0246] Additive mixing equipment, used to evenly mix functional ingredients with the dried fermentation product;

[0247] Control unit 500:

[0248] PLC control system, used to centrally control and monitor the operating parameters of each unit to realize the automated fermentation process;

[0249] The data acquisition and analysis system is used to record and analyze data during the fermentation process and provide a basis for process optimization.

[0250] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0251] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A microbial solid-state fermentation method for fish feed, characterized in that: The following steps are involved: S1: raw material pretreatment, comprising mixing fermented soybean meal (40%), krill meal (15%), enteromorpha powder (10%), wheat bran (25%), and functional premix (10%) according to weight percentage, and sterilizing by pulse vacuum; S2: solid-state fermentation, placing the pretreated raw materials in a solid-state fermentation tank, controlling pH, temperature, humidity and ventilation, and fermenting; S3: Fermentation endpoint determination, based on physical, chemical and biological indicators; S4: Post-processing, drying the fermentation product and adding functional ingredients.

2. The microbial solid-state fermentation method for fish feed according to claim 1, characterized in that: In the solid-state fermentation step, near-infrared spectroscopy is used to monitor the fermentation process online, including: irradiating near-infrared light into the fermentation liquid and measuring the intensity of the transmitted or reflected light; obtaining the near-infrared spectrum of the fermentation liquid based on the different degrees of absorption of light of different wavelengths by different chemical bonds; and analyzing the characteristics of the spectrum to obtain the content of various components in the fermentation liquid; A calibration model between near-infrared spectra and key parameters of the fermentation process was established using partial least squares regression; Data preprocessing: centralize and standardize spectral data and key parameters; Extract principal components: Use nonlinear iterative partial least squares algorithm to extract principal components of spectral data and key parameters; Establish a regression model: establish a linear regression model between the principal components and key parameters; X=TP T +E; Where X is the spectral data matrix, T is the score matrix of X, P is the loading matrix of X, and E is the residual matrix; Y=UQ T +F; Where Y is the key parameter matrix, U is the score matrix of Y, Q is the loading matrix of Y, and F is the residual matrix; U = TB; B is the regression coefficient matrix.

3. The microbial solid-state fermentation method for fish feed according to claim 2, characterized in that: After obtaining the contents of various components in the fermentation broth, MPC was used to optimize the solid-state fermentation process and a dynamic model of the solid-state fermentation process was constructed. In the dynamic model: State Space: x(k+1)=A*x(k)+B*u(k)+w(k); y(k)=C*x(k)+v(k); Where x(k) is the state vector at time k, u(k) is the control output vector at time k, y(k) is the output vector at time k, w(k) and v(k) are the process noise and measurement noise, and A, B, and C are system matrices. Predicted output: Predict the state and output of N moments in the future based on the state space; Y=S_x*x(k)+S_u*U Where Y is the output vector N moments in the future, U is the control output vector N moments in the future, S_x and S_u are matrices derived from the system matrices A, B, and C; Objective function: J=(R_y-Y) T *Q(R_y-Y)+U T *R*U; Among them, R_y is the desired output target, Q is the weight matrix, T is the target time, and J is the objective function.

4. The microbial solid-state fermentation method for fish feed according to claim 3, characterized in that: Control pH, temperature, humidity and ventilation according to dynamic models: Select control variables: pH, temperature, humidity, and ventilation as control variables; Select controlled variables: target product, pH, and microbial concentration as controlled variables; Define the objective function: microbial production and its product production as the target; The growth mechanism of microorganisms is: μ=μ_max*(S / (K_s+S)) Where μ is the specific growth rate of the microorganism, μ_max is the maximum specific growth rate, S is the substrate concentration, and K_s is the half-saturation constant; According to the dynamic model of solid-state fermentation process: J=-Q_1*BCAA(k+N)+Q_2*(pH(k+N)-pH_target)^2+R*Δu(k)^2 Where BCAA(k+N) is the branched-chain amino acid concentration at N time points in the future, pH(k+N) is the pH value at N time points in the future, pH_target is the desired pH value, Δu(k) is the increment of the control input, and Q_1, Q_2, and R are weight coefficients.

5. The microbial solid-state fermentation method for fish feed according to claim 1, characterized in that: In step S1, the pulse vacuum sterilization conditions are: maintaining at 115° C. for 15 minutes, then cooling to 85° C. for 30 minutes, so as to retain 30% of the activity of the native bacterial flora.

6. The microbial solid-state fermentation method for fish feed according to claim 1, characterized in that: In step S2, an intermittent spraying system is used to maintain a moisture content of 40-45%; the aeration strategy is micro-aeration at 0.2 vvm per hour to maintain an O2 concentration of >18%.

7. The microbial solid-state fermentation method for fish feed according to claim 1, characterized in that: In step S3, the physical and chemical indicators are pH ≤ 4.8, reducing sugar < 2%, and free amino acid increment ≥ 80%; the biological indicators are spore formation rate > 95%, and yeast viable count ≥ 1×10^9 CFU / g.

8. The microbial solid-state fermentation method for fish feed according to claim 5, characterized in that: In step S4, the drying method is low-temperature airflow drying to make the moisture content ≤9%; the functional ingredients include 0.5% tea polyphenol-β-cyclodextrin embedding material and 3% conjugated linoleic acid microcapsules.

9. A microbial solid-state fermentation system for fish feed, characterized in that: A microbial solid-state fermentation method for fish feed according to any one of claims 1 to 8, wherein the system comprises: Raw material pretreatment unit: A mixing device for receiving and mixing fermented soybean meal, krill meal, enteromorpha meal, wheat bran and functional premix; Pulsating vacuum sterilization equipment is used to sterilize the mixed raw materials. The equipment can accurately control the temperature and vacuum degree to achieve a pulsating sterilization process; Solid-state fermentation unit: Solid-state fermentation tank with adjustable material layer thickness, automatic turning rake, temperature control system, humidity control system and ventilation system; Temperature control system: used to accurately control the temperature inside the fermentation tank according to the set temperature gradient; Humidity control system: including intermittent spraying device, which can maintain the moisture content of the fermentation material within the range of 40-45%; Aeration system: provides micro-aeration at 0.2vvm per hour and is equipped with an oxygen concentration sensor to monitor and control the oxygen concentration in the tank to be above 18% in real time; Fermentation endpoint determination unit: pH meter, reducing sugar meter, and amino acid analyzer are used to monitor physical and chemical indicators during the fermentation process; Microscope, hemocytometer, and plate counter for detecting biological indicators during the fermentation process; Near-infrared spectroscopy online monitoring equipment is used to monitor the fermentation process in real time and adjust process parameters; Post-processing unit: Low-temperature air flow dryer, used to dry the fermentation product to a moisture content of ≤9%; Additive mixing equipment, used to evenly mix functional ingredients with the dried fermentation product; Control unit: PLC control system, used to centrally control and monitor the operating parameters of each unit to realize the automated fermentation process; The data acquisition and analysis system is used to record and analyze data during the fermentation process and provide a basis for process optimization.

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