An automated method for preparing camel milk powder

Abstract

The application provides an automatic camel milk powder preparation method, including intelligent raw material pretreatment, multifunctional active ingredient protection, intelligent gradient low oxygen concentration and intelligent process parameter optimization model establishment. Through intelligent process optimization and multi-stage low oxygen micro-pressure drying technology, the retention rate of heat-sensitive nutritional ingredients in camel milk is greatly improved, fat oxidation is effectively inhibited, the flavor and shelf life of the milk powder are improved. The nutritional value and market competitiveness of the product are significantly enhanced. Through mathematical modeling and industrial internet of things system, the dynamic optimization of process parameters and real-time quality control are realized, which not only reduces the production energy consumption, but also ensures the high consistency of batch quality. The particle size of the milk powder is uniform, the instant solubility and taste are significantly improved, the production efficiency and economic benefit are greatly improved. The introduction of the block chain traceability system further enhances the traceability of the product, meeting the needs of consumers for high-quality dairy products.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of food processing, in particular to an automatic camel milk powder preparation method. BACKGROUND

[0002] Camel milk has attracted much attention in the dairy market in recent years due to its high content of immunoglobulin, lactoferrin, vitamin C and other nutrients, as well as its low allergenicity and high digestive absorption rate. As a product form that is convenient to store and transport, the preparation technology of camel milk powder directly affects the nutritional value, flavor, quality stability and production efficiency of the product. However, the existing camel milk powder preparation technology mostly follows the traditional cow milk powder production process, which is difficult to fully adapt to the unique physicochemical properties of camel milk and has many technical defects.

[0003] Firstly, the existing preparation method usually adopts high-temperature spray drying process, which causes the severe inactivation of heat-sensitive nutrients such as immunoglobulin and lactoferrin in camel milk, resulting in a significant reduction in nutritional value. At the same time, the fat content of camel milk is relatively high, and the oxygen content in the drying process is not effectively controlled in the traditional process, which easily leads to fat oxidation and generates odor substances, affecting the flavor and shelf life of the milk powder. In addition, traditional spray drying relies on high temperature and high pressure, which has high energy consumption, and the particle size distribution of the milk powder is uneven, affecting the instant solubility and taste.

[0004] Further, the existing production process has low automation degree and relies on manual operation or simple control system, lacking intelligent parameter optimization and real-time quality monitoring. Fluctuations in raw materials and environmental conditions often lead to inconsistent quality between batches, making it difficult to ensure product consistency and stability. More importantly, the existing technology lacks systematic process optimization means for the characteristics of camel milk, and the process parameters are mostly set by experience, which cannot simultaneously consider nutrient retention, fat oxidation control and energy consumption reduction, limiting the comprehensive performance of the product.

[0005] Therefore, there is an urgent need for a camel milk powder preparation method that can significantly improve the retention rate of nutritional ingredients, effectively inhibit fat oxidation, reduce energy consumption, optimize particle characteristics, and realize intelligent control and quality consistency, in order to overcome the shortcomings of the existing technology and improve the market competitiveness of the product. SUMMARY

[0006] The present application aims to provide a camel milk powder preparation method that can significantly improve the retention rate of nutritional ingredients, effectively inhibit fat oxidation, reduce energy consumption, optimize particle characteristics, and realize intelligent control and quality consistency, in order to overcome the shortcomings of the existing technology and improve the market competitiveness of the product.

[0007] To achieve the above object, the present application proposes the following technical scheme: An automatic camel milk powder preparation method, comprising the following steps: a) intelligent raw material pretreatment, low temperature plasma sterilization and dynamic nanofiltration treatment are performed on fresh camel milk to remove bacteria and impurities and retain immunoglobulin and lactoferrin; b) multifunctional active ingredient protection, a composite active protective agent is added to the milk liquid, and the fat and protein structure is stabilized through microfluidic homogenization technology and pulse electric field treatment; c) intelligent gradient low oxygen concentration, multi-effect countercurrent evaporation and membrane distillation assisted concentration are adopted to concentrate the milk liquid to a solid content of 30-35% in a low oxygen environment; d) intelligent process parameter optimization model establishment: based on multi-objective optimization theory and support vector regression algorithm, a nutrition retention model, a fat oxidation model and an energy consumption and particle characteristic 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 comprising the following formulae:

[0008] Nutrition retention model: ; wherein R is the active ingredient retention rate, k1, alpha, beta, gamma, delta are constants;

[0009] Fat oxidation model: ; wherein POV is the fat peroxide value, k2, eta, theta are constants;

[0010] Energy consumption and particle characteristic model: ;

[0011] ; wherein E is the unit energy consumption, D p is the particle size, D0 is the target particle size, k3, lambda are constants;

[0012] e) Multistage low oxygen micro-pressure spray drying: a multistage low oxygen micro-pressure spray drying device is adopted to dry in the first and second low oxygen environments, and the moisture content of the milk powder is controlled to be 3-4% and the particle size is controlled to be 30-80 mu m;

[0013] f) Intelligent quality control and feedback: the industrial Internet of Things system integration model output is combined with online mass spectrometer and infrared spectrometer to detect the active ingredient retention rate and fat peroxide value in real time, the process parameters are dynamically adjusted, and the data are recorded through the blockchain traceability system;

[0014] g) Low oxygen intelligent packaging: vacuum packaging is performed in a sterile environment using a nano composite barrier film and an intelligent oxygen adsorbent to prolong the shelf life.

[0015] Further, in the present application, the steps are realized by 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, nanoscale atomizing nozzle, industrial Internet of Things controller, high-performance server, sensor module, online mass spectrometer, infrared spectrometer, blockchain traceability system, sterile packaging machine and vacuum pump. The sensor module includes temperature, oxygen content, pressure, solid concentration and particle size sensors.

[0016] Further, in the present application, in step a), the low-temperature plasma sterilization uses a power of 50-100 W, a processing time of 30-60 seconds, and a temperature of 4-6℃, 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℃, and is equipped with a nanofiltration membrane assembly, a pressure pump and a solid concentration sensor.

[0017] Further, in the present application, in step b), the composite active protective agent includes vitamin C with a mass percentage of 0.008%, rosemary extract with a mass percentage of 0.015%, tea polyphenol with a mass percentage of 0.008%, trehalose with a mass percentage of 0.08% and β-cyclodextrin with a mass percentage of 0.03%, 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 cycled 3-5 times, and is realized by a microfluidic chip and a high-pressure pump; the pulse electric field treatment uses an electric field strength of 10-20 kV / cm, a pulse frequency of 100-200 Hz, and a processing time of 1-2 minutes, and is realized by a pulse electric field generator.

[0018] Further, in the present application, in step c), the intelligent gradient low-oxygen concentration includes three stages: the first stage is 40℃ and the vacuum degree is 0.05 MPa, the second stage is 45℃ and the vacuum degree is 0.03 MPa, and the third stage is 50℃ and the vacuum degree is 0.02 MPa; the membrane distillation uses a hydrophobic membrane pore size of 0.2 μm and a temperature of 45-50℃, and is realized by a hydrophobic membrane module and a heater; the low-oxygen environment is maintained by the nitrogen circulation system to maintain the oxygen content <1%, and is equipped with an oxygen content sensor and a nitrogen pump.

[0019] Further, in the present application, in step d), the model is trained by a high-performance server to support a support vector regression algorithm, an industrial Internet of Things controller runs the model, a sensor module collects spray drying temperature T d , oxygen content O c , spray pressure P s and protective agent concentration C p in real time, and a touch screen man-machine interface displays the optimization results.

[0020] Further, in the present application, in step e), the multi-stage low-oxygen micro-pressure spray drying includes: the first-stage drying condition is an inlet air temperature of 110-120 DEG C, a spray pressure of 0.08-0.12 MPa, and an oxygen content of 0.5-1%;

[0021] The second-stage drying condition is an inlet air temperature of 90-100 DEG 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-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.

[0022] Further, in the present application, in step f), the online mass spectrometer detects immunoglobulin, lactoferrin, and fat peroxidation products, an infrared spectrometer assists in verifying that the retention rate of active ingredients is greater than or equal to 97%, and the fat peroxidation value is less than or equal to 0.03 meq / kg; the industrial Internet of Things controller integrates model output and dynamically adjusts process parameters; and the blockchain traceability system records production data through a server and a database.

[0023] Further, in the present application, in step g), the oxygen transmission rate of the nano-composite barrier film is less than 0.1 cm3 / m2.d, and the film is equipped with a smart oxygen adsorbent; and the sterile packaging is realized through a full-automatic sterile packaging machine and a vacuum pump, and the shelf life is up to 26 months.

[0024] Further, in the present application, the nutrient retention model, the fat oxidation model, and the energy consumption and particle characteristic model realize synergistic optimization through shared variables of spray drying temperature T d , oxygen content O c , spray pressure P s , and protective agent concentration C p , so that the retention rate of active ingredients is greater than or equal to 97%, the fat peroxidation value is less than or equal to 0.03 meq / kg, the unit energy consumption is less than or equal to 850 kWh / kg, the particle size standard deviation is less than or equal to ± 5 mu m, and the shelf life is extended to 26 months.

[0025] The present application has a synergistic effect of low temperature and low oxygen, and the multi-stage low-oxygen micro-pressure spray drying adopts a lower temperature and a low-oxygen environment, the first stage is 110-120 DEG C, the second stage is 90-100 DEG C, and the oxygen content is 0.3-1%, so that the thermal denaturation and oxidation reaction of heat-sensitive proteins are reduced, and the active structures of immunoglobulin and lactoferrin are protected.

[0026] The composite active protective agent contains vitamin C, rosemary extract, tea polyphenol, trehalose, and β-cyclodextrin, which reduces the oxidation sensitivity of fat through antioxidant and embedding mechanisms. Trehalose forms a glassy protective protein structure, and β-cyclodextrin embeds fat molecules to reduce oxygen contact. The 1-θC p The item reflects the inhibitory effect of the protective agent on oxidation. Microfluidic homogenization and pulsed electric field further enhance the dispersion uniformity and protein stability of the protective agent, and synergistically improve nutrition retention and flavor.

[0027] Multi-stage micro-pressure spray drying, with a pressure of 0.05-0.12 MPa, reduces energy demand compared to traditional high pressure of 1-2 MPa. Nanoscale atomizing nozzle aperture of 50-100 nm forms ultra-fine droplets, promoting rapid drying and reducing heat consumption. The energy consumption and particle property model predicts the optimal spray drying temperature T d ·P s The item optimizes energy consumption, (D p -D0) 2 The item ensures that the particle size is close to the target value, improving solubility and mouthfeel.

[0028] The infrared radiation heating module precisely controls the drying temperature distribution, avoiding local overheating, further reducing energy consumption and protecting nutritional ingredients. Membrane distillation assisted concentration reduces the heat demand of traditional evaporation through selective evaporation of hydrophobic membranes, synergistically reducing overall energy consumption.

[0029] The shared variables of the nutrition retention, fat oxidation, and energy consumption models are spray drying temperature T d , oxygen content O c , spray pressure P s , and protective agent concentration C p . The support vector regression algorithm predicts the optimal parameters, and the industrial internet of things controller collects sensor data in real time, dynamically adjusting the process conditions. This closed-loop control mechanism adapts to fluctuations in raw materials and the environment, significantly reducing quality fluctuations. The blockchain traceability system records the entire process data, ensuring traceability.

[0030] Online detection and feedback: online mass spectrometry and infrared spectroscopy instruments detect active ingredients and oxidation products in real time, verify model predictions, and correct model coefficients through feedback, further improving prediction accuracy and quality stability.

[0031] Advantages, the technical scheme of the present application has the following technical effects:

[0032] 1、The method of the present application greatly improves the retention rate of heat-sensitive nutrients in camel milk through intelligent process optimization and multi-stage low-oxygen micro-pressure drying technology, effectively inhibits fat oxidation, improves the flavor and shelf life of the milk powder. The combination of high nutrient retention and excellent flavor significantly enhances the nutritional value and market competitiveness of the product. The method realizes dynamic optimization of process parameters and real-time quality control through mathematical modeling and industrial internet of things system, not only reduces the production energy consumption, but also ensures the high consistency of batch quality. The particle size of the milk powder is uniform, the instant solubility and taste are significantly improved, and the production efficiency and economic benefit are greatly improved. The introduction of the blockchain traceability system further enhances the traceability of the product, meeting the needs of consumers for high-quality dairy products.

[0033] It should be understood that all combinations of the aforementioned concepts and additional concepts described in greater detail below can be seen as part of the subject matter of the present disclosure, as long as such concepts are not mutually contradictory in their essence.

[0034] The foregoing and other aspects, embodiments and features of the present teachings can be better understood from the following description of the present teachings with reference to the drawings. Other aspects and features of the present teachings will become apparent from the following description of the examples and / or from the practices of the present teachings. The summary provided above is not intended to limit the scope or the spirit of the concepts described herein. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings are not intended to be drawn to scale. In the drawings, each same or like component shown in each of the figures can be designated with the same reference numerals. In the interest of clarity, not all components of each figure are marked in each figure. Embodiments of various aspects of the present teachings will now be described, by way of example only, with reference to the accompanying drawings in which:

[0036] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0037] In order to better understand the technical content of the present application, specific embodiments are given and described below with reference to the accompanying drawings. Aspects of the present application are described in this disclosure with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily defined in all aspects of the present application. It should be understood that the various concepts and embodiments introduced above, as well as those described in greater detail below, can be implemented in any of a number of ways, as the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects of the present application can be used alone, or in any appropriate combination with other aspects of the present application.

[0038] Example 1: Camel milk powder preparation method based on intelligent modeling

[0039] Experimental materials and equipment, raw materials: 1000L of fresh camel milk, fat content 4.5%, protein content 3.2%, solid content 12%.

[0040] Protective agent: Vitamin C, rosemary extract, tea polyphenol, trehalose, β-cyclodextrin (analytical pure).

[0041] Equipment: low temperature plasma sterilization device, power 50-100W. Dynamic nanofiltration system, membrane pore size 80nm. Microfluidic homogenization equipment, microchannel 15μm. Pulse electric field generator, 10-20kV / cm. Intelligent multi-effect reverse evaporation system, vacuum degree 0.02-0.05MPa. Membrane distillation device, hydrophobic membrane pore size 0.2μm. Multistage low oxygen micro pressure spray drying device, nanometer 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℃, pressure ±0.01MPa, particle size ±1μm. Online mass spectrometer, detects immunoglobulin, lactoferrin, peroxide. Infrared spectrometer, FTIR, auxiliary detection of active ingredients. Fully automatic sterile packaging machine, nanometer composite barrier film, oxygen transmission rate <0.1cm³ / m²·d. Vacuum pump, blockchain traceability system.

[0042] Experimental steps, intelligent raw material pretreatment, take 1000L of fresh camel milk, place it in a low temperature plasma sterilization device, set the power to 80W, process for 45 seconds, control the temperature at 5℃, remove bacteria and impurities. Use a dynamic nanofiltration system with a membrane pore size of 80nm, a pressure of 0.8MPa, and a temperature of 10℃ to separate macromolecular impurities and part of the lactose, while retaining immunoglobulin and lactoferrin. The solid content sensor monitors the milk solid content to be 11.8%.

[0043] Multifunctional active ingredient protection, prepare a composite active protective agent: 80g of Vitamin C 0.008%, 150g of rosemary extract 0.015%, 80g of tea polyphenol 0.008%, 800g of trehalose 0.08%, 300g of β-cyclodextrin 0.03%, total concentration 1410g 0.149%.

[0044] Dissolve the protective agent in 50L of deionized water with a concentration of 2.82%, mix it with the milk liquid through the microfluidic homogenization equipment, the microchannel of the microfluidic homogenization equipment is 15μm, the pressure is 25MPa, circulate 4 times, the mixing temperature is 10℃, and the pH is adjusted to 6.9.

[0045] Apply a pulse electric field treatment, electric field strength 15kV / cm, pulse frequency 150Hz, process for 1.5 minutes, stabilize the protein structure. Infrared spectrometer confirms uniform dispersion of protective agent.

[0046] Intelligent gradient low oxygen concentration, using intelligent multi-effect countercurrent evaporation system, three-stage concentration:

[0047] First stage: 40°C, vacuum degree 0.05 MPa, solid content up to 15%.

[0048] Second stage: 45°C, vacuum degree 0.03 MPa, solid content up to 25%.

[0049] Third stage: 50°C, vacuum degree 0.02 MPa, solid content up to 35%.

[0050] Equipped with membrane distillation device, hydrophobic membrane pore size 0.2 μm, temperature 48°C, auxiliary concentration, reduce energy consumption.

[0051] Nitrogen circulation system into high-purity nitrogen, oxygen content sensor monitors oxygen content is 0.8%. Temperature sensor and pressure sensor real-time recording parameters.

[0052] 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, through high-performance server training support vector regression (SVR) model, prediction accuracy 96%.

[0053] Model formula: nutrient retention model: .

[0054] Fat oxidation model: .

[0055] Energy consumption and particle characteristics model: .

[0056] IIoT controller input sensor data: T d =110−120℃, O c =0.5−1, P s =0.08−0.12MPa, C p =0.14.

[0057] Model prediction: R=97, POV=0.03meq / kg, E=850kWh / kg, D p =50μm.

[0058] Optimized parameters: T d =112℃, O c =0.6, P s =0.09MPa. Touch screen man-machine interface display results.

[0059] Multi-stage low-oxygen micro-pressure spray drying, using multi-stage low-oxygen micro-pressure spray drying device, settings:

[0060] First stage: Inlet air temperature 112℃, spray pressure 0.09MPa, oxygen content 0.6%, moisture content reduced to 8%.

[0061] Second stage: Inlet air temperature 95℃, spray pressure 0.06MPa, oxygen content 0.4%, moisture content reduced to 3.1%.

[0062] Equipped with nano-level atomizing nozzles and infrared radiation heating modules, it controls temperature uniformity.

[0063] The nitrogen circulation system maintains a low-oxygen environment, and the particle size analyzer monitors the particle size as 30-80μm.

[0064] Temperature and pressure sensors control parameters in real time.

[0065] Intelligent quality control and feedback; online mass spectrometry analyzer for detecting immunoglobulins, lactoferrin, and lipid peroxides; infrared spectroscopy to assist in verifying retention rates.

[0066] The IIoT controller dynamically adjusts T based on mass spectrometry data R and POV. d O c P s Correction model coefficients

[0067] The blockchain traceability system records production parameters and quality data, which are stored on the server and encrypted.

[0068] Low-oxygen intelligent packaging uses a nanocomposite barrier film (oxygen permeability 0.08 cm³ / m²·d) and an intelligent oxygen adsorbent (iron powder + enzyme reaction) for vacuum packaging in a fully automatic aseptic packaging machine.

[0069] The vacuum pump ensures a tight seal, resulting in an oxygen content of <0.5% after packaging.

[0070] Experimental testing revealed the following: Nutrient retention rate: Immunoglobulin and lactoferrin content were determined by high-performance liquid chromatography (HPLC). Lipid peroxide value: Peroxide value (meq / kg) was determined by iodometric titration. Energy consumption: Total energy consumption was recorded by the electricity meter and converted to unit energy consumption (kWh / kg). Particle size: Average particle size and standard deviation were determined by a laser particle size analyzer. Dissolution time: Standard dissolution test was performed at 25℃ with 100mL of water and stirring for 30 seconds. Shelf life: Accelerated aging test was conducted at 40℃ and 75% humidity, simulating storage conditions. Quality consistency: Retention rate and peroxide value fluctuations were tested in five consecutive batches.

[0071] Comparative Example: Traditional Camel Milk Powder Preparation Method

[0072] Experimental materials and equipment, raw materials: same as in Example 1.

[0073] Equipment: traditional centrifuge, plate filter, single-effect evaporator, high-temperature spray drying tower (without low-oxygen control), ordinary packaging machine.

[0074] Experimental procedure, pretreatment: camel milk was centrifuged at 8000 rpm and 10°C for 6 minutes to remove bacteria, and a plate filter was used to remove impurities.

[0075] Protection: No protective agent was added, and direct homogenization was performed using a high-pressure homogenizer at 15 MPa for a single pass. Concentration: single-effect evaporator, 60°C, vacuum degree 0.1 MPa, concentrated to solid content 35%.

[0076] Spray drying: high-temperature spray drying tower, inlet air temperature 190°C, spray pressure 1.5 MPa, no low-oxygen control, moisture content 3.5%. Packaging: ordinary plastic packaging, no oxygen adsorbent.

[0077] Detection: same as the method of Example 1.

[0078] Experimental results and data

[0079] Table 1: Performance comparison of Example 1 and comparative examples

[0080]

[0081] Data explanation: retention rate, peroxide value, particle size, etc. were averaged by 3 repeated experiments, and the standard deviation reflected the measurement accuracy. Energy consumption was recorded by the rectifier energy meter, and was converted to energy consumption per kilogram of milk powder. Shelf life was estimated by accelerated aging test, and 1 month of accelerated aging was equivalent to about 6 months of storage at room temperature. Mass consistency was calculated by the fluctuation range of 5 batches of continuous production data.

[0082] Example 1 significantly reduced the thermal denaturation and oxidation of heat-sensitive proteins through low-temperature, low-oxygen, and micro-pressure drying, composite protective agents, and pulse electric fields, with the retention rates of immunoglobulin and lactoferrin increasing by 74% and 90%, respectively.

[0083] The nutrition retention model optimized the temperature and oxygen parameters to ensure high retention rates.

[0084] Low-fat oxidation: the peroxide value of Example 1 was 88% lower than that of the comparative example, thanks to the low-oxygen environment, protective agents, and nanocomposite barrier film. The fat oxidation model precisely controlled the oxidation reaction, improving flavor and shelf life.

[0085] Low energy consumption: the energy consumption of Example 1 was 58% lower than that of the comparative example, due to micro-pressure drying, membrane distillation, and infrared heating. The energy consumption model optimized the temperature and pressure combination.

[0086] Excellent particle characteristics: the particle size of Example 1 was uniform, and the instant time was shortened by 47%, thanks to nanoscale atomization and model optimization.

[0087] Example 1 has very low fluctuation in retention rate and peroxide value, 10 times more stable than the comparative example, verifying the effect of intelligent optimization.

[0088] Example 1 is superior to the comparative example in terms of nutrient retention, fat oxidation control, energy consumption reduction, particle characteristics, and quality consistency, with a comprehensive performance improvement of about 50%. The synergistic effect of multi-stage low-oxygen micro-pressure drying, composite protective agent, mathematical modeling, and hardware integration is fully verified, and the technical advantages of the present application are fully verified.

[0089] Therefore, the present application has high nutrient retention, with 97.2% immunoglobulin and 96.5% lactoferrin. According to 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 those of the comparative example, which are 55.6% and 50.8%, respectively, with fluctuations of only ±0.4% and ±0.5%, which are much lower than the fluctuations of ±3.2% and ±3.5% of the comparative example.

[0090] Because of the synergistic protection of low temperature and low oxygen, multi-stage low-oxygen micro-pressure spray drying, the first stage is 112℃, the second stage is 95℃, and the oxygen content is 0.6%-0.4%, which significantly reduces the thermal denaturation and oxidative damage of heat-sensitive proteins. High temperature such as the comparative example 190℃ will cause the unfolding of the secondary and tertiary structures of proteins, and the exponential term in the nutrient retention model quantifies the exponential destruction of protein activity with increasing temperature. Example 1 reduces T d from 190℃ to 112℃, reducing heat damage by about 80%. The low-oxygen environment is maintained by a nitrogen circulation system, which inhibits oxidation reactions, and the 1-0 c term in the model reflects the positive effect of reducing oxygen content on protein protection. The comparative example has no low-oxygen control, with an oxygen content of about 21%, which accelerates protein oxidation.

[0091] The infrared radiation heating module ensures uniform temperature in the drying chamber, avoiding local overheating and further protecting protein structure.

[0092] Moreover, the composite active protective agent vitamin C, rosemary extract, tea polyphenol, trehalose, and β-cyclodextrin has a total concentration of 0.149%, which enhances protein stability through antioxidant and structure protection mechanisms. Trehalose forms a glassy matrix, reducing protein molecular motion and preventing thermal denaturation; β-cyclodextrin embeds fat, reducing oxidation sensitivity. The 1+0.3C p term in the model indicates that the concentration of protective agents positively improves the retention rate.

[0093] Microfluidization, microchannel 15 pm, 25 MPa to achieve molecular dispersion of protective agent, increase the contact area with protein and fat. Pulsed electric field 15 kV / cm, 150 Hz through the molecular rearrangement induced by electric field, enhance the stability of protein secondary structure, reduce the risk of denaturation. The control sample without protective agent and electric field treatment, the protein is directly exposed to high temperature and oxygen, and the retention rate is low.

[0094] The present application has low fat oxidation, peroxide value 0.03 meq / kg, shelf life 26 months, experimental data analysis, example 1 peroxide value 0.03±0.01 meq / kg, 88% lower than the control sample 0.25±0.04 meq / kg, fluctuation <0.01 meq / kg, far superior to the control sample 0.03-0.06 meq / kg. The shelf life is extended from 12 months to 26 months.

[0095] Because of the low oxygen environment and the protective agent, the fat oxidation model shows that oxygen content and temperature are the main driving factors of fat oxidation. Example 1 through the nitrogen circulation system, O c Control at 0.6%-0.4%, far lower than 21% of the control sample, significantly reducing free radical-induced oxidative chain reaction.

[0096] The antioxidant ingredients in the composite protective agent, vitamin C, rosemary extract, tea polyphenol, block the oxidation reaction by capturing free radicals and chelating metal ions, 1-0.4C p The item quantifies the inhibitory effect of the protective agent. β-cyclodextrin forms an embedding structure to isolate fat from oxygen. The control sample without protective agent, the fat is directly exposed to high oxygen and high temperature environment, and the oxidation is serious.

[0097] The oxygen permeability of the nanocomposite barrier film is 0.08 cm³ / m²·d, and the intelligent oxygen adsorbent iron powder+enzymatic reaction forms a double oxygen barrier, and the oxygen content after packaging is <0.5%, effectively preventing secondary oxidation during storage. The control sample uses ordinary packaging with a permeability of 5 cm³ / m²·d, and the oxygen permeation leads to accelerated oxidation, shortening the shelf life. Vacuum pump ensures the sealing, reduces the residual oxygen in the package, and further prolongs the shelf life.

[0098] The unit energy consumption of example 1 (850±20 kWh / kg) is 58% lower than that of the control sample (2050±50 kWh / kg). Because of the multi-stage micro-pressure spray drying, the first stage is 0.09 MPa, and the second stage is 0.06 MPa, which is lower than the high pressure 1.5 MPa of the control sample, reducing the energy consumption of pumping and atomization. T d ·P s The item shows that pressure and temperature are the main contributors to energy consumption. Example 1 optimizes P s =0.09 MPa, reducing about 70% of the pressure-related energy consumption.

[0099] Nanometer-scale atomization nozzle aperture 80 nm forms ultra-fine droplet diameter 1-5 pm, increases surface area, promotes rapid heat mass transfer, shortens drying time, and reduces heat demand. The comparative example uses a conventional nozzle aperture of 500 pm, resulting in larger droplets and low drying efficiency.

[0100] Membrane distillation device hydrophobic membrane aperture 0.2 pm, concentrated milk liquid at 48°C by selective evaporation, using low-grade heat energy, compared with the comparative example of single-effect evaporation at 60°C, energy saving about 30%. The infrared radiation heating module provides directional heat, reduces heat loss, and has higher efficiency than the comparative example of hot air heating. The model optimizes T d =112°C, further reducing the demand for thermal energy.

[0101] Excellent particle characteristics, particle size 50±5 pm, instant time 8 s, average particle size 50±5 pm in Example 1, instant time 8±1 s, better than 70±20 pm and 15±3 s in the comparative example.

[0102] Because the nanometer-scale atomization nozzle produces uniform and fine droplets, the milk powder particles after drying have a particle size of 30-80 pm, with a standard deviation of only ±5 pm. The energy consumption and particle characteristics model (D p -50) 2 The term penalizes particle size deviation, optimizes Ps=0.09 MPa, ensures Dp≈50 pm. Uniform particle size increases particle surface area, improves dissolution rate, and shortens instant time by 47%. The comparative example uses a conventional nozzle, resulting in a wide droplet size distribution of 50-200 pm, leading to uneven particles after drying and poor solubility.

[0103] Multi-stage drying, first stage 112°C for rapid dehydration, second stage 95°C for fine drying, control particle morphology through gradient temperature, prevent particles from being too large or sticking together. The comparative example uses single-stage high temperature 190°C, resulting in particle sintering, large and uneven particle size.

[0104] High quality consistency, retention rate fluctuation <0.5%, peroxide value fluctuation <0.01 meq / kg, retention rate fluctuation <0.5%, peroxide value fluctuation <0.01 meq / kg in Example 1, far better than 8-12% and 0.03-0.06 meq / kg in the comparative example.

[0105] Because the nutrient retention, fat oxidation, and energy consumption models achieve multi-objective optimization through shared variables, the SVR algorithm has a prediction accuracy of 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 fluctuations in raw materials, and ensures prediction consistency through feedback correction coefficients of R, POV, and E. The comparative example relies on manual experience, with fixed parameters (190°C, 1.5 MPa), and cannot respond to changes in raw materials or the environment, resulting in large fluctuations in quality.

[0106] The online mass spectrometer and infrared spectrometer detect immunoglobulin, lactoferrin and peroxide in real time, the detection accuracy is ±0.1%, feedback data corrects the model prediction, and reduces the batch difference. The blockchain traceability system records the whole process parameters, and eliminates human errors. The comparative example depends on offline detection, the cycle is long, and real-time adjustment cannot be realized.

[0107] Although the present application has been disclosed with reference to the preferred embodiments above, it is not intended to limit the present application. Those skilled in the art can make various modifications and improvements without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be subject to the scope defined by the claims.

Claims

1. An automated method of preparing camel milk powder, characterized by, Comprising the following steps: a) Intelligent raw material pretreatment, low temperature plasma sterilization and dynamic nanofiltration treatment are performed on fresh camel milk to remove bacteria and impurities, and to retain immunoglobulin and lactoferrin; b) Multi-functional active ingredient protection, a composite active protective agent is added to the milk liquid, and the fat and protein structure is stabilized through microfluidic homogenization technology and pulse electric field treatment; c) Intelligent gradient low oxygen concentration, multi-effect countercurrent evaporation and membrane distillation assisted concentration are adopted to concentrate the milk liquid to a solid content of 30-35% in a low oxygen environment; d) Intelligent process parameter optimization model establishment: based on multi-objective optimization theory and support vector regression algorithm, a nutrition retention model, a fat oxidation model, and an energy consumption and particle characteristic 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 comprising the following formula: Nutrient retention model: ; wherein, R is the retention rate of the active ingredient, k 1 , α , β , γ , δ is a constant; Fatty oxidation model: ; wherein, POV is the fatty peroxide value, k 2 , η、 θ is a constant; Energy consumption and particle property model: ; wherein, E is the unit energy consumption, D p is the particle size, D 0 target particle size, k 3 , λ is a constant; e) Multistage low-oxygen micro-pressure spray drying: using a multistage low-oxygen micro-pressure spray drying device, drying in the first and second stages of low-oxygen environment, controlling the moisture content of milk powder to be 3-4%, and the particle size to be 30-80μm; f) Intelligent quality control and feedback: through the industrial internet of things system integration model output, combining online mass spectrometer and infrared spectrometer for real-time detection of active ingredient retention rate and fat peroxide value, dynamically adjusting process parameters, and recording data through blockchain traceability system; g) Low-oxygen intelligent packaging: using nanocomposite barrier film and intelligent oxygen adsorbent for vacuum packaging in a sterile environment, prolonging the shelf life; In step a), the low-temperature plasma sterilization uses a power of 50-100W, a processing time of 30-60 seconds, and a temperature of 4-6℃, which is realized by a low-temperature plasma generator; the dynamic nanofiltration uses a membrane pore size of 50-100nm, a pressure of 0.5-1MPa, and a temperature of 8-12℃, equipped with a nanofiltration membrane assembly, a pressure pump, and a solid concentration sensor; In step b), the composite active protective agent includes vitamin C with a mass percentage of 0.008%, rosemary extract with a mass percentage of 0.015%, tea polyphenol with a mass percentage of 0.008%, trehalose with a mass percentage of 0.08%, and β-cyclodextrin with a mass percentage of 0.03%, with a total concentration of 0.149%; the microfluidic homogenization uses a microchannel diameter of 10-20μm, a pressure of 20-30MPa, and a cycle of 3-5 times, which is realized by a microfluidic chip and a high-pressure pump; the pulse electric field treatment uses an electric field strength of 10-20kV / cm, a pulse frequency of 100-200Hz, and a processing time of 1-2 minutes, which is realized by a pulse electric field generator; In step c), the intelligent gradient low-oxygen concentration includes three stages: the first stage is 40℃ and the vacuum degree is 0.05MPa, the second stage is 45℃ and the vacuum degree is 0.03MPa, and the third stage is 50℃ and the vacuum degree is 0.02MPa; the membrane distillation uses a hydrophobic membrane pore size of 0.2μm and a temperature of 45-50℃, which is realized by a hydrophobic membrane module and a heater; the low-oxygen environment is maintained by a nitrogen circulation system with an oxygen content of <1%, equipped with an oxygen content sensor and a nitrogen pump; In step e), the multistage low-oxygen micro-pressure spray drying includes: the first stage drying conditions are an inlet air temperature of 110-120℃, a spray pressure of 0.08-0.12MPa, and an oxygen content of 0.5-1%; The second stage drying conditions are an inlet air temperature of 90-100℃, a spray pressure of 0.05-0.08MPa, and an oxygen content of 0.3-0.5%; the drying device is equipped with a nanoscale atomizing nozzle, an infrared radiation heating module, and a particle size analyzer, a nitrogen circulation system maintains a low-oxygen environment, and temperature and pressure sensors control process parameters.

2. The method according to claim 1, wherein, In step d), the model is trained by a high-performance server to support a support vector regression algorithm, an industrial internet of things controller runs the model, and a sensor module collects the spray drying temperature in real time T d oxygen content O c spray pressure P s and the concentration of protective agent C p The touch screen man-machine interface displays the optimization results.

3. The method of claim 1, wherein the method is automated. The steps are realized by 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, nanoscale atomizing nozzle, industrial Internet of Things controller, high-performance server, sensor module, online mass spectrometer, infrared spectrometer, blockchain traceability system, sterile packaging machine and vacuum pump, and the sensor module includes temperature, oxygen content, pressure, solid concentration and particle size sensors.

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

5. The method of claim 1, wherein the method is automated. In step g), the oxygen transmission rate of the nanocomposite barrier film is <0.1 cm³ / m²·d, and the intelligent oxygen adsorbent is provided; the sterile packaging is realized by the full-automatic sterile packaging machine and the vacuum pump, and the shelf life reaches 26 months.

6. The method of claim 1, wherein the method is automated. The nutrient retention model, fat oxidation model and energy consumption and particle characteristics model are synergistically optimized by sharing variables spray drying temperature T d , oxygen content O c , spray pressure P s and protective agent concentration C p , 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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