Automatic camel milk powder preparation method

Abstract

The invention provides an automatic camel milk powder preparation method. The method comprises the steps of intelligent raw material pretreatment, multifunctional active ingredient protection, intelligent gradient low-oxygen concentration and intelligent technological parameter optimization model establishment. According to the method, through intelligent process optimization and a multi-stage low-oxygen micro-pressure drying technology, the retention rate of heat-sensitive nutritional ingredients in the camel milk is greatly increased, meanwhile, fat oxidation is effectively inhibited, the flavor of the milk powder is improved, and the shelf life of the milk powder is prolonged. According to the method, dynamic optimization and real-time quality control of process parameters are realized through mathematical modeling and an industrial Internet of Things system, production energy consumption is reduced, and high consistency of batch-to-batch quality is ensured. The milk powder particles are uniform in particle size, the instant solubility and the taste are remarkably improved, and the production efficiency and the economic benefits are greatly improved. The introduction of the block chain traceability system further enhances the traceability of the product, and meets the requirements of consumers for high-quality dairy products.

Description

Technical Field

[0001] The present invention relates to the technical field of food processing, and particularly to an automated method for preparing camel milk powder. Background Art

[0002] Camel milk has attracted much attention in the dairy market in recent years due to its rich nutritional components such as immunoglobulins, lactoferrin, and vitamin C, as well as its low allergenicity and high digestibility and absorption rate. As a product form that is convenient for storage and transportation, the quality of the preparation technology of camel milk powder directly affects the nutritional value, flavor, quality stability, and production efficiency of the product. However, most of the existing camel milk powder preparation technologies follow the traditional milk powder production process, which is difficult to fully adapt to the unique physical and chemical properties of camel milk, and there are multiple technical defects.

[0003] First of all, the existing preparation methods usually adopt high-temperature spray drying technology, resulting in serious inactivation of heat-sensitive nutritional components in camel milk, such as immunoglobulins and lactoferrin, and a significant reduction in nutritional value. At the same time, camel milk has a relatively high fat content. The traditional technology does not effectively control the oxygen content during the drying process, which is prone to cause fat oxidation and generate odor substances, affecting the flavor and shelf life of the milk powder. In addition, traditional spray drying relies on high temperature and high pressure, with high energy consumption, and the particle size distribution of the milk powder is uneven, affecting the instant solubility and taste.

[0004] Furthermore, the degree of automation in the existing production process is relatively low, relying on manual operation or simple control systems, lacking intelligent parameter optimization and real-time quality monitoring. The fluctuations in raw materials and environmental conditions often lead to inconsistent quality between batches, making it difficult to ensure the consistency and stability of the product. More importantly, the existing technology lacks systematic process optimization means for the characteristics of camel milk, and the process parameters are mostly set based on experience, unable to simultaneously take into account nutritional retention, fat oxidation control, and energy consumption reduction, restricting the comprehensive performance of the product.

[0005] Therefore, there is an urgent need for a method for preparing camel milk powder that can significantly improve the retention rate of nutritional components, effectively inhibit fat oxidation, reduce energy consumption, optimize particle characteristics, and achieve intelligent control and quality consistency, so as to overcome the deficiencies of the existing technology and enhance the market competitiveness of the product. Summary of the Invention

[0006] The object of the present invention is to provide a method for preparing camel milk powder that can significantly improve the retention rate of nutritional components, effectively inhibit fat oxidation, reduce energy consumption, optimize particle characteristics, and achieve intelligent control and quality consistency, so as to overcome the deficiencies of the existing technology and enhance the market competitiveness of the product.

[0007] To achieve the above object, the present invention proposes the following technical solutions: An automated method for preparing camel milk powder, comprising the following steps: a) Intelligent raw material pretreatment, subjecting fresh camel milk to low-temperature plasma sterilization and dynamic nanofiltration treatment to remove bacteria and impurities, and retaining immunoglobulins and lactoferrin; b) Multifunctional active ingredient protection, adding a composite active protective agent to the milk liquid, and stabilizing the fat and protein structures through microfluidic homogenization technology and pulsed electric field treatment; c) Intelligent gradient low-oxygen concentration, adopting multi-effect countercurrent evaporation and membrane distillation-assisted concentration, and concentrating the milk liquid to a solid content of 30-35% in a low-oxygen environment; d) Establishment of an intelligent process parameter optimization model: Based on multi-objective optimization theory and support vector regression algorithm, establish a nutrient retention model, a fat oxidation model, and an energy consumption and particle characteristics model, and optimize the spray drying temperature T d 、oxygen content O c 、spray pressure P s and protective agent concentration C p , and the model includes the following formulas: Nutrient retention model: ; where R is the retention rate of active ingredients, k 1 , α, β, γ, δ are constants; Fat oxidation model: ; where POV is the peroxide value of fat, k 2 , η, θ are constants; Energy consumption and particle characteristics model: ; where E is the unit energy consumption, D p is the particle size, D 0 is the target particle size, k 3 , λ are constants; e) Multi-stage low-oxygen micro-pressure spray drying: Using a multi-stage low-oxygen micro-pressure spray drying device, drying in the first-stage and second-stage low-oxygen environments, controlling the moisture content of the milk powder to be 3-4%, and the particle size to be 30-80 μm; f) Intelligent quality control and feedback: Integrating the model output through an industrial Internet of Things system, combining an on-line mass spectrometer and an infrared spectrometer to detect the retention rate of active ingredients and the peroxide value of fat in real time, dynamically adjusting the process parameters, and recording data through a blockchain traceability system; g) Low-oxygen intelligent packaging: Using a nano-composite barrier film and an intelligent oxygen adsorbent for vacuum packaging in a sterile environment to extend the shelf life.

[0008] Further, in the present invention, the above steps are implemented by the following hardware modules: low-temperature plasma sterilization device, dynamic nanofiltration system, microfluidic homogenization equipment, pulsed electric field generator, multi-effect countercurrent evaporation system, membrane distillation device, nitrogen circulation system, multi-stage low-oxygen micro-pressure spray drying device, infrared radiation heating module, nano-level atomizing nozzle, industrial Internet of Things controller, high-performance server, sensor module, on-line mass spectrometer, infrared spectrometer, blockchain traceability system, aseptic packaging machine and vacuum pump. The sensor module includes temperature, oxygen content, pressure, solid concentration and particle size sensors.

[0009] Further, in the present invention, in step a), the low-temperature plasma sterilization is carried out with a power of 50 - 100 W, a treatment time of 30 - 60 seconds, and a temperature of 4 - 6 °C, and is realized by a low-temperature plasma generator; the dynamic nanofiltration uses a membrane pore size of 50 - 100 nm, a pressure of 0.5 - 1 MPa, and a temperature of 8 - 12 °C, and is equipped with a nanofiltration membrane module, a pressure pump and a solid concentration sensor.

[0010] Further, in the present invention, in step b), the composite active protective agent includes 0.008% vitamin C, 0.015% rosemary extract, 0.008% tea polyphenols, 0.08% trehalose and 0.03% β-cyclodextrin by mass percentage, and the total concentration is 0.149%; the microfluidic homogenization uses a microchannel diameter of 10 - 20 μm, a pressure of 20 - 30 MPa, and is circulated 3 - 5 times, and is realized by a microfluidic chip and a high-pressure pump; the pulsed electric field treatment uses an electric field strength of 10 - 20 kV / cm, a pulse frequency of 100 - 200 Hz, and a treatment time of 1 - 2 minutes, and is realized by a pulsed electric field generator.

[0011] Further, in the present invention, in step c), the intelligent gradient low-oxygen concentration includes three stages: the first stage is at 40 °C and a vacuum degree of 0.05 MPa, the second stage is at 45 °C and a vacuum degree of 0.03 MPa, and the third stage is at 50 °C and a vacuum degree of 0.02 MPa; the membrane distillation uses a hydrophobic membrane pore size of 0.2 μm and a temperature of 45 - 50 °C, and is realized by a hydrophobic membrane module and a heater; the low-oxygen environment maintains the oxygen content < 1% through a nitrogen circulation system, and is equipped with an oxygen content sensor and a nitrogen pump.

[0012] Further, in the present invention, in step d), the model trains the support vector regression algorithm through a high-performance server, the industrial Internet of Things controller runs the model, and the sensor module collects the spray drying temperature T d 、oxygen content O c 、spray pressure P s and protective agent concentration C p in real time, and the touch screen human-machine interface displays the optimization result.

[0013] Further, in the present invention, in step e), the multi-stage low-oxygen and micro-pressure spray drying includes: the first-stage drying conditions are an inlet air temperature of 110 - 120 °C, a spray pressure of 0.08 - 0.12 MPa, and an oxygen content of 0.5 - 1%; The second-stage drying conditions are an inlet air temperature of 90 - 100 °C, a spray pressure of 0.05 - 0.08 MPa, and an oxygen content of 0.3 - 0.5%; the drying device is equipped with a nano-level atomizing nozzle, an infrared radiation heating module, and a particle size analyzer, and the nitrogen circulation system maintains a low-oxygen environment, and the temperature sensor and pressure sensor control the process parameters.

[0014] Further, in the present invention, in step f), the on-line mass spectrometer detects immunoglobulins, lactoferrin, and lipid peroxidation products, and the infrared spectrometer assists in verifying that the retention rate of active ingredients is ≥ 97% and the lipid peroxide value is ≤ 0.03 meq / kg; the industrial Internet of Things controller integrates the model output and dynamically adjusts the process parameters; the blockchain traceability system records production data through the server and database.

[0015] Further, in the present invention, in step g), the oxygen transmission rate of the nano-composite barrier film is < 0.1 cm³ / m²·d, and it is equipped with a smart oxygen adsorbent; the aseptic packaging is realized through a fully automatic aseptic packaging machine and a vacuum pump, and the shelf life reaches 26 months.

[0016] Further, in the present invention, the nutrient retention model, the lipid oxidation model, and the energy consumption and particle characteristics model are synergistically optimized through the shared variables of the spray drying temperature T d 、oxygen content O c 、spray pressure P s and the concentration C of the protective agent p , so that the retention rate of active ingredients is ≥ 97%, the lipid peroxide value is ≤ 0.03 meq / kg, the unit energy consumption is ≤ 850 kWh / kg, the standard deviation of particle size is ≤ ±5 μm, and the shelf life is extended to 26 months.

[0017] The present invention has a synergistic effect of low temperature and low oxygen. The multi-stage low-oxygen and micro-pressure spray drying adopts a lower temperature and a low-oxygen environment, 110 - 120 °C for the first stage and 90 - 100 °C for the second stage, and the oxygen content is 0.3 - 1%. By reducing the thermal denaturation and oxidation reaction of heat-sensitive proteins, the active structures of immunoglobulins and lactoferrin are protected. The nutrient retention model quantifies the protective effects of temperature and oxygen on proteins through the exponential term and the oxygen inhibition term.

[0018] The composite active protectant contains vitamin C, rosemary extract, tea polyphenols, trehalose, and β-cyclodextrin. Through antioxidant and encapsulation mechanisms, it reduces the oxidative sensitivity of fats. Trehalose forms a glassy state to protect the protein structure, and β-cyclodextrin encapsulates fat molecules to reduce oxygen contact. 1-θC in the fat oxidation model p reflects the inhibitory effect of the protectant on oxidation. Microfluidic homogenization and pulsed electric fields further enhance the dispersion uniformity and protein stability of the protectant, synergistically improving nutrient retention and flavor.

[0019] For multi-stage micro-pressure spray drying, the pressure of 0.05 - 0.12 MPa reduces the energy requirement compared to the traditional high pressure of 1 - 2 MPa. The nano-scale atomizing nozzle with an aperture of 50 - 100 nm forms ultra-fine droplets, promoting rapid drying and reducing heat consumption. The energy consumption and particle characteristics model optimizes the energy consumption through the T d ·P s term, and the (D p -D 0 ) 2 term ensures that the particle size is close to the target value, improving solubility and taste.

[0020] The infrared radiation heating module precisely controls the drying temperature distribution, avoiding local overheating, further reducing energy consumption and protecting nutrient components. Membrane distillation-assisted concentration reduces the heat requirement of traditional evaporation through the selective evaporation of the hydrophobic membrane, synergistically reducing the overall energy consumption.

[0021] The nutrient retention, fat oxidation, and energy consumption models share variables such as the spray drying temperature T d , oxygen content O c , spray pressure P s and protectant concentration C p . By using the support vector regression algorithm to predict the optimal parameters, the industrial Internet of Things controller collects sensor data in real time and dynamically adjusts the process conditions. This closed-loop control mechanism adapts to raw material and environmental fluctuations, significantly reducing quality fluctuations. The blockchain traceability system records the full-process data to ensure traceability.

[0022] Online detection and feedback: Online mass spectrometers and infrared spectrometers detect active ingredients and oxidation products in real time, verify the model predictions, and correct the model coefficients through feedback to further improve the prediction accuracy and quality stability.

[0023] Beneficial effects. The technical solution of this application has the following technical effects: 1. The method of the present invention significantly improves the retention rate of heat-sensitive nutrients in camel milk through intelligent process optimization and multi-stage low-oxygen micro-pressure drying technology. Meanwhile, it effectively inhibits fat oxidation, improves the flavor and shelf life of milk powder. The combination of high nutrient retention and excellent flavor significantly enhances the nutritional value and market competitiveness of the product. This method realizes the dynamic optimization of process parameters and real-time quality control through mathematical modeling and industrial Internet of Things systems, not only reducing production energy consumption but also ensuring high consistency in quality among batches. The milk powder particles have uniform particle size, significantly improved instant solubility and taste, and greatly enhanced production efficiency and economic benefits. The introduction of the blockchain traceability system further enhances the traceability of the product, meeting the needs of consumers for high-quality dairy products.

[0024] It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be regarded as part of the inventive subject matter of the present disclosure as long as such concepts do not contradict each other.

[0025] The foregoing and other aspects, embodiments, and features of the teachings of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or beneficial effects of exemplary embodiments, will be apparent in the following description or will be learned through the practice of specific embodiments according to the teachings of the present invention. Brief Description of the Drawings

[0026] The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in various figures can be represented by the same reference numeral. For clarity, not every component is labeled in each figure. Now, embodiments of various aspects of the present invention will be described by way of example and with reference to the drawings, wherein: Figure 1 It is a schematic flow diagram of the present invention. Detailed Embodiments

[0027] To better understand the technical content of the present invention, specific embodiments are given below in conjunction with the accompanying drawings. In the present disclosure, aspects of the present invention are described with reference to the drawings, and many illustrative embodiments are shown in the drawings. The embodiments of the present disclosure do not necessarily define all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those concepts and embodiments described in greater detail below, can be implemented in any of many ways because the concepts and embodiments disclosed in the present invention are not limited to any embodiment. Additionally, some aspects of the present disclosure can be used alone or in any suitable combination with other aspects of the present disclosure.

[0028] Example 1: Preparation Method of Camel Milk Powder Based on Intelligent Modeling Experimental materials and equipment, raw materials: 1000L of fresh camel milk, with a fat content of 4.5%, a protein content of 3.2%, and a solid content of 12%.

[0029] Protective agents: vitamin C, rosemary extract, tea polyphenols, trehalose, β-cyclodextrin (analytical pure).

[0030] Equipment: Low-temperature plasma sterilization device, power 50 - 100W. Dynamic nanofiltration system, membrane pore size 80nm. Microfluidic homogenization equipment, microchannel 15μm. Pulsed electric field generator, 10 - 20kV / cm. Intelligent multi-effect countercurrent evaporation system, vacuum degree 0.02 - 0.05MPa. Membrane distillation device, hydrophobic membrane pore size 0.2μm. Multi-stage low-oxygen micro-pressure spray drying device, nano-level atomizing nozzle, pore size 50 - 100nm. Nitrogen circulation system, oxygen content sensor, accuracy ±0.01%. Industrial Internet of Things (IIoT) controller, PLC + edge computing unit. High-performance server, GPU, SVR algorithm. Sensor module, temperature ±0.1°C, pressure ±0.01MPa, particle size ±1μm. Online mass spectrometer, detecting immunoglobulins, lactoferrin, peroxidase. Infrared spectrometer, FTIR, assisting in detecting active ingredients. Fully automatic aseptic packaging machine, nano-composite barrier film, oxygen transmission rate <0.1cm³ / m²·d. Vacuum pump, blockchain traceability system.

[0031] Experimental procedures, intelligent raw material pretreatment, take 1000L of fresh camel milk, place it in the low-temperature plasma sterilization device, set the power to 80W, process for 45 seconds, control the temperature at 5°C, and remove bacteria and impurities. Use the dynamic nanofiltration system, with a membrane pore size of 80nm, a pressure of 0.8MPa, and a temperature of 10°C, to separate macromolecular impurities and part of the lactose, and retain immunoglobulins and lactoferrin. The solid concentration sensor monitors the solid content of the milk liquid to be 11.8%.

[0032] Multi-functional active ingredient protection, prepare a composite active protective agent: 80g of vitamin C 0.008%, 150g of rosemary extract 0.015%, 80g of tea polyphenols 0.008%, 800g of trehalose 0.08%, 300g of β-cyclodextrin 0.03%, with a total concentration of 1410g 0.149%.

[0033] Dissolve the protective agent in 50L of deionized water, with a concentration of 2.82%, and mix it with the milk liquid through the microfluidic homogenization equipment. The microchannel of the microfluidic homogenization equipment is 15μm, the pressure is 25MPa, and it circulates 4 times. The mixing temperature is 10°C, and the pH is adjusted to 6.9.

[0034] Apply pulsed electric field treatment, with an electric field strength of 15kV / cm, a pulse frequency of 150Hz, and process for 1.5 minutes to stabilize the protein structure. The infrared spectrometer confirms the uniform dispersion of the protective agent.

[0035] Intelligent gradient low oxygen concentration, using an intelligent multi-effect countercurrent evaporation system, concentrated in three stages: The first stage: 40 °C, vacuum degree 0.05 MPa, solid content reaches 15%.

[0036] The second stage: 45 °C, vacuum degree 0.03 MPa, solid content reaches 25%.

[0037] The third stage: 50 °C, vacuum degree 0.02 MPa, solid content reaches 35%.

[0038] Equipped with a membrane distillation device, the hydrophobic membrane pore size is 0.2 μm, the temperature is 48 °C, for auxiliary concentration to reduce energy consumption.

[0039] The nitrogen circulation system is introduced with high-purity nitrogen, and the oxygen content sensor monitors that the oxygen content is 0.8%. The temperature sensor and pressure sensor record parameters in real time.

[0040] An intelligent process parameter optimization model is established. 100 groups of experimental data are collected, including temperature, oxygen content, pressure, protective agent concentration, retention rate, peroxide value, energy consumption, particle size. The support vector regression (SVR) model is trained through a high-performance server, and the prediction accuracy is 96%.

[0041] Model formula: Nutrition retention model: .

[0042] Fat oxidation model: .

[0043] Energy consumption and particle characteristics model: .

[0044] The IIoT controller inputs sensor data: T d = 110 - 120 °C, O c = 0.5 - 1, P s = 0.08 - 0.12 MPa, C p = 0.14.

[0045] Model prediction: R = 97, POV = 0.03 meq / kg, E = 850 kWh / kg, D p = 50 μm.

[0046] Optimized parameters: T d = 112 °C, O c = 0.6, P s = 0.09 MPa. The touch screen human-machine interface displays the results.

[0047] Multi-stage low oxygen and micro-pressure spray drying, using a multi-stage low oxygen and micro-pressure spray drying device, settings: First stage: Inlet air temperature is 112°C, spray pressure is 0.09 MPa, oxygen content is 0.6%, and moisture content is reduced to 8%.

[0048] Second stage: Inlet air temperature is 95°C, spray pressure is 0.06 MPa, oxygen content is 0.4%, and moisture content is reduced to 3.1%.

[0049] Equipped with nanoscale atomizing nozzles and infrared radiation heating modules to control temperature uniformity.

[0050] The nitrogen circulation system maintains a low-oxygen environment, and the particle size analyzer monitors that the particle size is 30 - 80 μm.

[0051] Temperature sensors and pressure sensors control parameters in real time.

[0052] Intelligent quality control and feedback. The on-line mass spectrometer analyzes immunoglobulins, lactoferrin, and lipid peroxides, and the infrared spectrometer assists in verifying the retention rate.

[0053] The IIoT controller dynamically adjusts T d , O c , P s according to the mass spectrometry data R and POV, and corrects the model coefficients The blockchain traceability system records production parameters and quality data, stores them on the server, and encrypts the data.

[0054] Low-oxygen intelligent packaging. In a fully automatic aseptic packaging machine, vacuum packaging is carried out using a nano-composite barrier film (oxygen transmission rate 0.08 cm³ / m²·d) and an intelligent oxygen adsorbent (iron powder + enzymatic reaction).

[0055] The vacuum pump ensures airtightness, and the oxygen content after packaging is <0.5%.

[0056] Experimental detection, nutrient retention rate: The contents of immunoglobulins and lactoferrin are determined by high-performance liquid chromatography (HPLC). Lipid peroxide value: The peroxide value meq / kg is determined by the iodometric method. Energy consumption: The total energy consumption is recorded by an electricity meter and converted to unit energy consumption kWh / kg. Particle size: The average particle size and standard deviation are determined by a laser particle size analyzer. Instant dissolution time: Standard dissolution test, 25°C, 100 mL of water, stirred for 30 seconds. Shelf life: Accelerated aging test, 40°C, 75% humidity, simulating storage. Quality consistency: The fluctuations of the retention rate and peroxide value are detected for 5 consecutive batches.

[0057] Control group: Traditional preparation method of camel milk powder Experimental materials and equipment, raw materials: The same as in Example 1.

[0058] Equipment: Traditional centrifuge, plate filter, single-effect evaporator, high-temperature spray drying tower (without low-oxygen control), ordinary packaging machine.

[0059] Experimental procedure, pretreatment: Camel milk is passed through a centrifuge at 8000 rpm, 10 °C for 6 minutes for sterilization, and impurities are removed by a plate filter.

[0060] Protection: Without adding a protective agent, directly homogenize using a high-pressure homogenizer at 15 MPa, once. Concentration: Use a single-effect evaporator at 60 °C, with a vacuum degree of 0.1 MPa, and concentrate to a solid content of 35%.

[0061] Spray drying: Use a high-temperature spray drying tower, with an inlet air temperature of 190 °C, a spray pressure of 1.5 MPa, no low-oxygen control, and a moisture content of 3.5%. Packaging: Ordinary plastic packaging, without an oxygen adsorbent.

[0062] Detection: The same method as in Example 1.

[0063] Experimental results and data Table 1: Performance comparison between Example 1 and the comparative example

[0064] Data description: The retention rate, peroxide value, particle size, etc. are averaged through 3 repeated experiments, and the standard deviation reflects the measurement accuracy. The energy consumption is recorded by an integrated process electricity meter and converted to the energy consumption per kilogram of milk powder. The shelf life is estimated through an accelerated aging test, and 1 month of accelerated aging is approximately equal to 6 months of normal temperature storage. The quality consistency is calculated based on the fluctuation range of 5 consecutive production batches of data.

[0065] In Example 1, through low-temperature, low-oxygen, and micro-pressure drying, composite protective agents, and pulsed electric fields, the thermal denaturation and oxidation of heat-sensitive proteins are significantly reduced, and the retention rates of immunoglobulins and lactoferrin are increased by 74% and 90% respectively.

[0066] The nutritional retention model optimizes the temperature and oxygen parameters to ensure a high retention rate.

[0067] Low fat oxidation: The peroxide value of Example 1 is 88% lower than that of the comparative example, thanks to the low-oxygen environment, protective agent, and nano-composite barrier film. The fat oxidation model precisely controls the oxidation reaction, improving the flavor and shelf life.

[0068] Low energy consumption, the energy consumption of Example 1 is 58% lower than that of the comparative example, attributed to micro-pressure drying, membrane distillation, and infrared heating. The energy consumption model optimizes the temperature and pressure combination.

[0069] Excellent particle characteristics, the particle size of Example 1 is uniform, and the instant dissolution time is shortened by 47%, thanks to nano-scale atomization and model optimization.

[0070] In Example 1, through IIoT dynamic control and on-line mass spectrometry detection, the fluctuations in the retention rate and peroxide value are extremely low, more than 10 times more stable than the comparative example, verifying the effect of intelligent optimization.

[0071] Example 1 is comprehensively superior to the comparative example in terms of nutrient retention, fat oxidation control, energy consumption reduction, particle characteristics, and quality consistency, with the comprehensive performance improved by about 50%. The synergistic effects of multi-stage low-oxygen micro-pressure drying, composite protective agents, mathematical modeling, and hardware integration are confirmed, fully verifying the technical advantages of the present invention.

[0072] Therefore, the present invention has high nutrient retention, with 97.2% immunoglobulin and 96.5% lactoferrin. According to the experimental data analysis, the retention rates of immunoglobulin and lactoferrin in Example 1 are 97.2% and 96.5% respectively, which are 74% and 90% higher than 55.6% and 50.8% in the comparative example, and the fluctuations are only ±0.4% and ±0.5%, far lower than ±3.2% and ±3.5% in the comparative example.

[0073] Due to the synergistic protection of low temperature and low oxygen, multi-stage low-oxygen micro-pressure spray drying, with the first stage at 112°C and the second stage at 95°C, and the oxygen content of 0.6% - 0.4%, significantly reduces the thermal denaturation and oxidative damage of heat-sensitive proteins. High temperatures such as 190°C in the comparative example will cause the unfolding of the secondary and tertiary structures of proteins, and the exponential term in the nutrient retention model quantifies the exponential damage to protein activity caused by temperature increase. Example 1 reduces the T d from 190°C to 112°C, reducing heat damage by about 80%. The low-oxygen environment is maintained by the nitrogen circulation system, inhibiting the oxidation reaction, and the 1 - 0.1O c term in the model reflects the positive effect of reduced oxygen content on protein protection. The comparative example has no low-oxygen control, with an oxygen content about 21% higher, accelerating protein oxidation.

[0074] The infrared radiation heating module ensures uniform temperature in the drying chamber, avoiding local overheating and further protecting the protein structure.

[0075] Moreover, the composite active protective agents vitamin C, rosemary extract, tea polyphenols, trehalose, and β-cyclodextrin, with a total concentration of 0.149%, enhance protein stability through antioxidant and structure protection mechanisms. Trehalose forms a glassy matrix, reducing the molecular movement of proteins and preventing thermal denaturation; β-cyclodextrin encapsulates fats, reducing oxidation sensitivity. The 1 + 0.3C p term in the model indicates that the concentration of the protective agent positively improves the retention rate.

[0076] Microfluidic homogenization, with a 15μm microchannel and 25MPa, realizes the molecular-level dispersion of the protective agent, increasing the contact area with proteins and fats. The pulsed electric field of 15kV / cm and 150Hz enhances the secondary structure stability of proteins through electric field-induced molecular rearrangement, reducing the risk of denaturation. The comparative example has no protective agent and electric field treatment, and the proteins are directly exposed to high temperature and oxygen, resulting in low retention rates.

[0077] The present invention has low fat oxidation, with a peroxide value of 0.03 meq / kg and a shelf life of 26 months. According to the experimental data analysis, the peroxide value in Example 1 is 0.03 ± 0.01 meq / kg, which is 88% lower than that in the comparative ratio of 0.25 ± 0.04 meq / kg, with a fluctuation < 0.01 meq / kg, far superior to the comparative ratio of 0.03 - 0.06 meq / kg. The shelf life is extended from 12 months to 26 months.

[0078] Due to the low - oxygen environment and the protective agent, the fat oxidation model shows that the oxygen content and temperature are the main driving factors for fat oxidation. In Example 1, the O c is controlled at 0.6% - 0.4% through a nitrogen circulation system, far lower than 21% in the comparative ratio, significantly reducing the oxidation chain reaction initiated by free radicals.

[0079] The antioxidant components in the composite protective agent, such as vitamin C, rosemary extract, and tea polyphenols, block the oxidation reaction by capturing free radicals and chelating metal ions. 1 - 0.4C p terms quantify the inhibitory effect of the protective agent. β - cyclodextrin forms an inclusion structure to isolate the contact between fat and oxygen. In the comparative ratio, there is no protective agent, and the fat is directly exposed to a high - oxygen and high - temperature environment, resulting in severe oxidation.

[0080] The oxygen permeability of the nano - composite barrier film is 0.08 cm³ / m²·d. Together with the intelligent oxygen adsorbent iron powder + enzymatic reaction, a dual oxygen barrier is formed. After packaging, the oxygen content < 0.5%, effectively preventing secondary oxidation during storage. In the comparative ratio, ordinary packaging with a permeability of 5 cm³ / m²·d is used, and oxygen permeation leads to accelerated oxidation and shortened shelf life. The vacuum pump ensures airtightness, reduces the residual oxygen in the package, and further extends the shelf life.

[0081] The unit energy consumption in Example 1 (850 ± 20 kWh / kg) is 58% lower than that in the comparative ratio (2050 ± 50 kWh / kg). Because in the multi - stage micro - pressure spray drying, the first stage is 0.09 MPa and the second stage is 0.06 Mpa, reducing the pumping and atomization energy consumption compared with the high pressure of 1.5 MPa in the comparative ratio. The T d ·P s terms in the energy consumption model show that pressure and temperature are the main contributors to energy consumption. In Example 1, P s is optimized to 0.09 MPa, reducing the pressure - related energy consumption by about 70%.

[0082] The nano - level atomizing nozzle with an aperture of 80 nm forms ultra - fine droplets with a diameter of 1 - 5 μm, increasing the surface area, promoting rapid heat and mass transfer, shortening the drying time, and reducing the heat demand. In the comparative ratio, a conventional nozzle with an aperture of 500 μm is used, and the droplets are larger, resulting in low drying efficiency.

[0083] The hydrophobic membrane of the membrane distillation device has a pore size of 0.2 μm. At 48 °C, it concentrates the milk liquid through selective evaporation, utilizes low-grade thermal energy, and saves about 30% energy compared with the single-effect evaporation at 60 °C in the comparative example. The infrared radiation heating module provides directional heat, reduces heat loss, and has higher heating efficiency compared with the hot air heating in the comparative example. The model is optimized to T d = 112 °C, further reducing the thermal energy demand.

[0084] Excellent particle characteristics, particle size 50 ± 5 μm, instant dissolution time 8 s. In Example 1, the average particle size is 50 ± 5 μm, and the instant dissolution time is 8 ± 1 s, which is superior to 70 ± 20 μm and 15 ± 3 s in the comparative example.

[0085] Because the nano-scale atomizing nozzle produces uniform and fine droplets, and the dried milk powder particles have a particle size of 30 - 80 μm with a standard deviation of only ±5 μm. The energy consumption and the (D p - 50) 2 term in the particle characteristics model penalizes the particle size deviation. Optimizing Ps = 0.09 MPa ensures Dp ≈ 50 μm. The uniform particle size increases the particle surface area, improves the dissolution rate, and shortens the instant dissolution time by 47%. The comparative example uses a conventional nozzle, and the droplet size distribution is wide at 50 - 200 μm, resulting in uneven particles and poor solubility after drying.

[0086] In the multi-stage drying, the first stage dehydrates quickly at 112 °C, and the second stage performs fine drying at 95 °C. The particle morphology is controlled by the gradient temperature to prevent the particles from being too large or sticking together. The single-stage high temperature of 190 °C in the comparative example causes particle sintering, with a large and uneven particle size.

[0087] High-quality consistency, retention rate fluctuation < 0.5%, peroxide value fluctuation < 0.01 meq / kg. In Example 1, the retention rate fluctuation < 0.5%, and the peroxide value fluctuation < 0.01 meq / kg, which is far superior to 8 - 12% and 0.03 - 0.06 meq / kg in the comparative example.

[0088] Because the nutrition retention, fat oxidation, and energy consumption models achieve multi-objective optimization through shared variables, and the prediction accuracy of the SVR algorithm reaches 96%. The IIoT controller collects sensor data in real time, temperature ±0.1 °C, oxygen ±0.01%, pressure ±0.01 MPa, dynamically adjusts parameters, adapts to raw material fluctuations, and the model formulas R, POV, E ensure prediction consistency through feedback correction coefficients. The comparative example relies on manual experience, with fixed parameters (190 °C, 1.5 MPa), and cannot cope with raw material or environmental changes, resulting in large quality fluctuations.

[0089] The online mass spectrometer and infrared spectrometer detect immunoglobulins, lactoferrin, and peroxides in real time, with a detection accuracy of ±0.1%, and the feedback data corrects the model prediction to reduce the differences between batches. The blockchain traceability system records the whole-process parameters to eliminate human errors. The comparative example relies on off-line detection, with a long cycle and cannot be adjusted in real time.

[0090] Although the present invention has been disclosed above in preferred embodiments, it is not intended to limit the present invention. Those of ordinary skill in the art to which the present invention pertains can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims defined therein.

Claims

1. An automated camel milk powder preparation method, characterized in that: The following steps are involved: a) Intelligent raw material pretreatment: fresh camel milk is sterilized by low-temperature plasma and treated by dynamic nanofiltration to remove bacteria and impurities, while retaining immunoglobulins and lactoferrin; b) Multifunctional active ingredient protection: compound active protective agents are added to the milk, and the fat and protein structures are stabilized through microfluidic homogenization technology and pulsed electric field treatment; c) Intelligent gradient low-oxygen concentration: multi-effect countercurrent evaporation and membrane distillation are used to assist concentration, and the milk is concentrated to a solid content of 30-35% in a low-oxygen environment; d) Establishment of intelligent process parameter optimization model: Based on multi-objective optimization theory and support vector regression algorithm, a nutrient retention model, a fat oxidation model, and an energy consumption and particle characteristics model are established to optimize the spray drying temperature T d 、Oxygen content O c , spray pressure P s and protective agent concentration C p , the model includes the following formula: Nutrient Retention Model: ; Where R is the active ingredient retention rate, k1, α, β, γ, δ are constants; Fat oxidation model: ; Wherein, POV is the fat peroxidation value, k2, η, θ are constants; Energy consumption and particle characteristics model: ; Among them, E is the unit energy consumption, D p is the particle size, D0 is the target particle size, k3 and λ are constants; e) Multi-stage low-oxygen micro-pressure spray drying: A multi-stage low-oxygen micro-pressure spray drying device is used to dry the milk powder in the first and second stage low-oxygen environments, and the moisture content of the milk powder is controlled to be 3-4%, and the particle size is 30-80μm; f) Intelligent quality control and feedback: Through the integrated model output of the industrial Internet of Things system, the active ingredient retention rate and fat peroxide value are detected in real time by combining the online mass spectrometer and infrared spectrometer, the process parameters are adjusted dynamically, and the data is recorded through the blockchain traceability system; g) Low oxygen smart packaging: vacuum packaging using nanocomposite barrier film and smart oxygen adsorbents in a sterile environment to extend shelf life.

2. An automated camel milk powder preparation method according to claim 1, characterized in that: The steps are implemented through the following hardware modules: low-temperature plasma sterilization device, dynamic nanofiltration system, microfluidic homogenization equipment, pulse electric field generator, multi-effect countercurrent evaporation system, membrane distillation device, nitrogen circulation system, multi-stage low-oxygen micro-pressure spray drying device, infrared radiation heating module, nano-scale atomizing nozzle, industrial Internet of Things controller, high-performance server, sensor module, online mass spectrometer, infrared spectrometer, blockchain traceability system, aseptic packaging machine and vacuum pump. The sensor module includes temperature, oxygen content, pressure, solid concentration and particle size sensors.

3. The method for preparing an automated camel milk powder according to claim 1, characterized in that: In step a), the low-temperature plasma sterilization adopts a power of 50-100W, a processing time of 30-60 seconds, a temperature of 4-6°C, and is achieved by a low-temperature plasma generator; the dynamic nanofiltration uses a membrane pore size of 50-100nm, a pressure of 0.5-1MPa, a temperature of 8-12°C, and is equipped with a nanofiltration membrane assembly, a pressure pump and a solid concentration sensor.

4. The method for preparing an automated camel milk powder according to claim 1, characterized in that: In step b), the composite active protective agent includes 0.008% vitamin C, 0.015% rosemary extract, 0.008% tea polyphenols, 0.08% trehalose and 0.03% β-cyclodextrin in a mass percentage, with a total concentration of 0.149%; the microfluidic homogenization adopts a microchannel diameter of 10-20μm, a pressure of 20-30MPa, and 3-5 cycles, which are achieved by a microfluidic chip and a high-pressure pump; the pulsed electric field treatment adopts an electric field strength of 10-20kV / cm, a pulse frequency of 100-200Hz, and a treatment time of 1-2 minutes, which is achieved by a pulsed electric field generator.

5. The automated camel milk powder preparation method according to claim 1, characterized in that: In step c), the intelligent gradient hypoxic concentration includes three stages: the first stage is 40°C and the vacuum degree is 0.05MPa, the second stage is 45°C and the vacuum degree is 0.03MPa, and the third stage is 50°C and the vacuum degree is 0.02MPa; the membrane distillation adopts a hydrophobic membrane with a pore size of 0.2μm and a temperature of 45-50°C, and is achieved by a hydrophobic membrane module and a heater; the hypoxic environment maintains an oxygen content of <1% through a nitrogen circulation system, and is equipped with an oxygen content sensor and a nitrogen pump.

6. The automated camel milk powder preparation method according to claim 1, characterized in that: In step d), the model is trained by a high-performance server with a support vector regression algorithm, the industrial Internet of Things controller runs the model, and the sensor module collects the spray drying temperature T in real time. d 、Oxygen content O c , spray pressure P s and protective agent concentration C p , the touch screen human-machine interface displays the optimization results.

7. The automated camel milk powder preparation method according to claim 1, characterized in that: In step e), the multi-stage low-oxygen micro-pressure spray drying includes: the first stage drying conditions are an inlet air temperature of 110-120°C, a spray pressure of 0.08-0.12MPa, and an oxygen content of 0.5-1%; The second stage drying conditions are as follows: inlet air temperature 90-100°C, spray pressure 0.05-0.08MPa, oxygen content 0.3-0.5%; the drying device is equipped with a nano-scale atomizing nozzle, an infrared radiation heating module and a particle size analyzer, a nitrogen circulation system maintains a low oxygen environment, and temperature sensors and pressure sensors control process parameters.

8. The automated camel milk powder preparation method according to claim 1, characterized in that: In step f), the online mass spectrometer detects immunoglobulins, lactoferrin and fat peroxidation products, and the infrared spectrometer assists in verifying that the active ingredient retention rate is ≥97% and the fat peroxidation value is ≤0.03meq / kg; the industrial Internet of Things controller integrates the model output and dynamically adjusts the process parameters; the blockchain traceability system records production data through the server and database.

9. The automated camel milk powder preparation method according to claim 1, characterized in that: In step g), the oxygen permeability of the nanocomposite barrier film is less than 0.1 cm³ / m²·d, and it is equipped with an intelligent oxygen adsorbent; the aseptic packaging is achieved by a fully automatic aseptic packaging machine and a vacuum pump, and the shelf life is up to 26 months.

10. The automated camel milk powder preparation method according to claim 1, characterized in that: The nutrient retention model, fat oxidation model and energy consumption and particle characteristics model are connected by a shared variable spray drying temperature T d 、Oxygen content O c , spray pressure P s and protective agent concentration C p , achieving synergistic optimization, so that the active ingredient retention rate is ≥97%, the fat peroxide value is ≤0.03meq / kg, the unit energy consumption is ≤850kWh / kg, the particle size standard deviation is ≤±5μm, and the shelf life is extended to 26 months.

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