Formula optimization method of biological wax tobacco bud inhibition compound

Abstract

The invention provides a biological wax tobacco bud inhibition compound formula optimization method, and belongs to the technical field of biological wax tobacco bud inhibition, and the biological wax tobacco bud inhibition compound formula optimization method comprises the following steps: selecting a plurality of biological wax raw materials, and preparing biological wax mixtures with different proportions; mixing the biological wax mixture with tobacco bud inhibition active ingredients according to a preset proportion to obtain an initial formula; carrying out physical and chemical property testing on the initial formula; a tobacco sample is coated with the initial formula through an accurate sprayer according to different application amounts, and the bud inhibition effect is evaluated; according to the physical and chemical performance test result and the bud inhibition effect evaluation result, establishing a formula scoring model; designing a plurality of formula combinations by using an orthogonal test method, and repeating the steps to obtain scores of each formula; the technical problem that certain potential environmental and food safety hazards exist in an existing tobacco bud inhibition technology can be solved.

Description

Technical Field

[0001] The invention belongs to the technical field of bio-wax tobacco bud inhibition, and in particular relates to a method for optimizing the formula of a bio-wax tobacco bud inhibition compound. Background Art

[0002] Topping and bud suppression are necessary agricultural operations in tobacco production. If bud suppression is not carried out, the quality and yield of tobacco leaves will be seriously affected. Therefore, certain measures are usually needed to inhibit the growth of tobacco buds. Currently, common tobacco bud suppression technologies mainly include physical bud suppression and chemical bud suppression. Physical bud suppression technology usually uses manual wiping to remove tobacco buds. This method is simple and direct, but requires a lot of manpower and may cause certain damage to tobacco. Chemical bud suppression technology uses some growth regulators or inhibitors, such as pendimethalin, to inhibit the germination of tobacco buds by hindering cell division or interfering with hormone metabolism. This method is relatively simple to operate and the bud suppression effect is relatively ideal, but there are certain environmental and food safety risks. Summary of the invention

[0003] In view of this, the present invention provides a method for optimizing the formula of a bio-wax tobacco sprout inhibition compound, aiming to solve the technical problems of certain environmental and food safety hazards in existing tobacco sprout inhibition technologies.

[0004] The present invention is achieved in that:

[0005] The present invention provides a method for optimizing the formula of a bio-wax tobacco bud-inhibiting compound, which comprises the following steps: S10, selecting a plurality of bio-wax raw materials to prepare bio-wax mixtures in different proportions;

[0006] S20, mixing the bio-wax mixture with the tobacco sprout suppressing active ingredient according to a preset ratio to obtain an initial formula;

[0007] S30, performing physical and chemical property tests on the initial formula;

[0008] S40, applying the initial formula to tobacco samples by using a precision sprayer in different application amounts to evaluate the bud inhibition effect;

[0009] S50. Establish a formula scoring model based on the physical and chemical performance test results and the sprout inhibition effect evaluation results;

[0010] S60, designing multiple groups of formula combinations by using the orthogonal test method, repeating steps S20 to S50, and obtaining the score of each group of formulas;

[0011] S70, further optimizing the formula with a higher score by using response surface methodology to obtain an optimized formula;

[0012] S80, applying the optimized formula to a small-scale tobacco sprout inhibition test to obtain actual sprout inhibition effect data;

[0013] S90. According to the actual sprout inhibition effect data, the formula scoring model is modified, and multiple rounds of optimization are performed through an iterative optimization method to obtain a final optimized formula.

[0014] On the basis of the above technical scheme, the method for optimizing the formula of a bio-wax tobacco bud inhibition compound of the present invention can also be improved as follows:

[0015] Among them, the various bio-wax raw materials include palm wax, beeswax, carnauba wax, wood wax and rice bran wax.

[0016] Furthermore, the preset ratio is specifically a mass ratio of the bio-wax mixture to the tobacco sprout-inhibiting active ingredient of 3:1 to 5:1.

[0017] Furthermore, the physical and chemical property tests include melting point test, viscosity test, surface tension test and thermal stability test.

[0018] Furthermore, the formula scoring model includes a melting point equation, a viscosity equation, a surface tension equation, a thermal stability equation and a sprout inhibition effect equation.

[0019] Furthermore, the melting point equation is used to predict the melting point of a formula, the independent variables include the mass fraction of various bio-waxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the melting point of the formula.

[0020] Furthermore, the viscosity equation is used to predict the viscosity of the formula, the independent variables include the mass fraction of various bio-waxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the viscosity of the formula.

[0021] Furthermore, the surface tension equation is used to predict the surface tension of a formula, the independent variables include the mass fractions of various biowaxes and the mass fractions of sprout-inhibiting active ingredients, and the dependent variable is the surface tension of the formula.

[0022] Furthermore, the thermal stability equation is used to predict the thermal stability of a formula, the independent variables include the mass fraction of various biowaxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the thermal stability index of the formula.

[0023] Furthermore, the sprout inhibition effect equation is used to predict the sprout inhibition effect of the formula, the independent variables include the mass fraction of various bio-waxes, the mass fraction of sprout inhibition active ingredients and the application amount, and the dependent variable is the sprout inhibition effect score.

[0024] The precision sprayer comprises: a liquid storage tank 1, a piezoelectric ceramic vibrator 2, a nozzle 3, a power module 4, a control valve 5 and a shell 6; the shell 6 is a handheld gun-shaped structure, and the interior is hollow to form a mounting cavity; the liquid storage tank 1 is arranged at the rear part of the mounting cavity, and a liquid filling port is arranged on the top of the liquid storage tank 1, and a sealing cover is installed on the liquid filling port; a liquid outlet pipe is arranged at the bottom of the liquid storage tank 1, and the liquid outlet pipe is connected to the liquid inlet end of the control valve 5; the control valve 5 is arranged in the middle part of the mounting cavity, and the liquid outlet end of the control valve 5 is connected to the liquid inlet end of the piezoelectric ceramic vibrator 2; the piezoelectric ceramic vibrator 2 is arranged at the front part of the mounting cavity, and the liquid outlet end of the piezoelectric ceramic vibrator 2 is connected to the nozzle 3; the nozzle 3 is fixedly installed at the front end of the shell 6; the power module 4 is arranged at the bottom of the mounting cavity, and the power module 4 is electrically connected to the piezoelectric ceramic vibrator 2; a trigger switch is arranged at the handle part of the shell 6, and the trigger switch is connected to the The control valve 5 is electrically connected to the power module 4; the piezoelectric ceramic vibrator 2 includes a vibrator body 21 and a piezoelectric chip 22, the vibrator body 21 is cylindrical, and the interior is hollow to form a liquid channel, one end of the liquid channel is connected to the liquid outlet end of the control valve 5, and the other end is connected to the nozzle 3; the piezoelectric chip 22 is annular, sleeved on the outer periphery of the vibrator body 21, and tightly fitted with the vibrator body 21; the nozzle 3 includes a nozzle body 31 and an atomizing disk 32, the nozzle body 31 is fixedly connected to the liquid outlet end of the vibrator body 21, the atomizing disk 32 is arranged at the front end of the nozzle body 31, and a plurality of micropores 321 are evenly distributed on the atomizing disk 32; the control valve 5 is an electromagnetic valve, including a valve body, an electromagnetic coil and a valve core, the valve body is provided with a liquid inlet hole and a liquid outlet hole, the electromagnetic coil is sleeved on the outside of the valve body, the valve core is arranged inside the valve body, and opens and closes under the action of the electromagnetic coil.

[0025] The liquid storage tank 1 is used to store the liquid to be sprayed, ensuring that the sprayer can continue to work for a period of time without frequent refilling;

[0026] The piezoelectric ceramic vibrator 2 is the core component of the entire sprayer, which atomizes the liquid into tiny droplets through high-frequency vibration to achieve precise spraying;

[0027] The nozzle 3 cooperates with the piezoelectric ceramic vibrator 2 to spray the atomized droplets and control the shape and range of the spray;

[0028] The power module 4 provides electrical energy to the entire precision sprayer to ensure normal operation of components such as the piezoelectric ceramic vibrator 2 and the control valve 5;

[0029] The control valve 5 controls the flow rate of the liquid and cooperates with the trigger switch to start and stop the spraying;

[0030] The housing 6 protects the internal components and makes the device easy to operate.

[0031] The liquid storage tank 1 is made of a transparent material, and scale lines are provided on the side wall of the liquid storage tank 1; an air vent is provided on the top of the liquid storage tank 1, and a one-way valve is installed on the air vent, and the one-way valve allows outside air to enter the interior of the liquid storage tank 1, but does not allow the liquid or gas inside the liquid storage tank 1 to leak out; the liquid storage tank 1 is detachably installed in the installation cavity through a snap-fit ​​structure, and the snap-fit ​​structure includes a protrusion arranged on the outer wall of the liquid storage tank 1 and a groove arranged on the inner wall of the installation cavity, and the protrusion and the groove cooperate with each other to realize the fixation and disassembly of the liquid storage tank 1.

[0032] The vibrator body 21 of the piezoelectric ceramic vibrator 2 is made of titanium alloy material, and the outer surface of the vibrator body 21 is polished; the piezoelectric chip 22 is composed of multiple layers of piezoelectric ceramic sheets, and electrode layers are arranged between the piezoelectric ceramic sheets; an elastic insulating layer is arranged between the vibrator body 21 and the piezoelectric chip 22, and the elastic insulating layer is made of silicone rubber material; the outer surface of the piezoelectric chip 22 is coated with a waterproof coating, and the waterproof coating is made of epoxy resin material.

[0033] The nozzle body 31 is trumpet-shaped, with the small end of the nozzle body 31 fixedly connected to the liquid outlet end of the oscillator body 21, and the large end connected to the atomizing disk 32; the atomizing disk 32 is disc-shaped, with a depression provided in the central area of ​​the atomizing disk 32, and the bottom of the depression is connected to the liquid outlet channel of the nozzle body 31; the micropores 321 are conical, with the large end of the micropores 321 facing the inner side of the atomizing disk 32, and the small end facing the outer side of the atomizing disk 32; the number of the micropores 321 is 50 to 200, and the diameter of the micropores 321 is 10 to 50 microns.

[0034] The shell 6 is composed of an upper shell 61 and a lower shell 62, which are detachably connected by screws; a sealing ring is provided at the connection between the upper shell 61 and the lower shell 62, and the sealing ring is made of oil-resistant rubber material; the surface of the shell 6 is provided with an anti-slip texture, and the anti-slip texture is distributed in a fish scale shape; a protective cover 63 is provided at the front end of the shell 6, and the protective cover 63 is provided on the outside of the nozzle 3, and the protective cover 63 is connected to the shell 6 by a snap-on structure.

[0035] The power module 4 includes a rechargeable lithium battery, a charging interface and a power management circuit; the rechargeable lithium battery is arranged at the bottom of the installation cavity, the charging interface is arranged at the bottom end of the shell 6, and the power management circuit is arranged between the rechargeable lithium battery and the charging interface; the power management circuit includes a charging control circuit, a discharge control circuit and a voltage conversion circuit; a power indicator light is provided on the side of the shell 6, and the power indicator light is electrically connected to the power management circuit.

[0036] The trigger switch is a two-speed switch, including a first gear and a second gear; when the trigger switch is in the first gear, the control valve 5 is opened and the piezoelectric ceramic vibrator 2 does not work; when the trigger switch is in the second gear, the control valve 5 is opened and the piezoelectric ceramic vibrator 2 works; a flow regulating knob 7 is also provided on the side of the housing 6, and the flow regulating knob 7 is mechanically connected to the control valve 5, and the opening of the control valve 5 can be changed by adjusting the flow regulating knob 7.

[0037] An atomizing chamber 81 is provided between the piezoelectric ceramic vibrator 2 and the nozzle 3. The atomizing chamber 81 is cylindrical, one end of the atomizing chamber 81 is connected to the liquid outlet end of the piezoelectric ceramic vibrator 2, and the other end is connected to the nozzle 3; a plurality of tangential air inlet holes 82 are provided on the side wall of the atomizing chamber 81, and the tangential air inlet holes 82 are connected to the outside air; a vortex generator 83 is provided in the atomizing chamber 81, and the vortex generator 83 includes a plurality of spiral blades, and the spiral blades are fixed on the inner wall of the atomizing chamber 81.

[0038] A pressure gauge 91 is provided on the top of the outer shell 6, and the pressure gauge 91 is connected to the inside of the liquid storage tank 1 for displaying the pressure inside the liquid storage tank 1; a buffer chamber 92 is provided between the pressure gauge 91 and the liquid storage tank 1, and the buffer chamber 92 is filled with a buffer solution; the buffer solution is glycerol; a shoulder strap connecting ring is provided at the rear end of the outer shell 6, and the shoulder strap connecting ring is used to connect the shoulder strap; the outer shell 6 is made of engineering plastic material, and the surface of the outer shell 6 is coated with an anti-corrosion coating.

[0039] 1. The melting point equation is specifically expressed as follows: Where, T m is the melting point of the formula (℃), w i is the mass fraction of the i-th biowax (0-1), w s is the mass fraction of the active ingredient inhibiting sprouting (0-1), a i is the melting point contribution coefficient of the i-th biowax (℃), b is the melting point contribution coefficient of the sprout inhibitory active ingredient (℃), c is the mixed effect coefficient (℃), d is the mixed nonlinear factor, ε1 is the error term (℃), and n is the number of biowax types. Parameter acquisition method: a i a and b are obtained by melting point testing of a single component, and the specific steps are as follows: (1) taking 5 g of pure biowax or sprout inhibitory active ingredient sample; (2) using a differential scanning calorimeter (DSC) for testing, with a heating rate of 10 ° C / min and a temperature range of 25-200 ° C; (3) recording the peak temperature of the melting peak as the melting point. c and d are obtained by melting point testing of a multi-component mixture, and the specific steps are as follows: (1) preparing multiple groups of mixture samples in different proportions, 5 g per group; (2) using DSC for testing, with the same conditions as above; (3) using the least squares method to fit the experimental data to obtain the values ​​of c and d. 2. The viscosity equation is specifically expressed as follows: Where η is the dynamic viscosity of the formula (mPa·s), w i is the mass fraction of the i-th biowax (0-1), w s is the mass fraction of the active ingredient inhibiting sprouting (0-1), k i is the viscosity contribution factor of the i-th biowax, m is the viscosity contribution factor of the sprout inhibitory active ingredient, p is the temperature correlation coefficient (K), q is the mixed interaction coefficient, T is the temperature (K), ε2 is the error term, and n is the number of biowax types. Parameter acquisition method: k i and m were obtained by viscosity testing of a single component. The specific steps were as follows: (1) taking 20 mL of pure biowax or sprout inhibitor active ingredient sample; (2) using a rotational viscometer to measure the viscosity at different temperatures (40°C, 60°C, 80°C, 100°C); (3) using the Arrhenius equation to fit k i and m values. p and q are obtained by viscosity testing of a multi-component mixture, and the specific steps are: (1) preparing multiple groups of mixture samples in different proportions, each group is 20 mL; (2) using a rotational viscometer to measure the viscosity at different temperatures; (3) using a nonlinear regression method to fit the experimental data to obtain the values ​​of p and q. 3. The surface tension equation is specifically expressed as follows: Where γ is the surface tension of the formulation (mN / m), w i is the mass fraction of the i-th biowax (0-1), w s is the mass fraction of the active ingredient inhibiting sprouting (0-1), f i is the surface tension contribution factor of the ith biowax (mN / m), g is the surface tension contribution factor of the sprout-inhibiting active ingredient (mN / m), h is the temperature coefficient (mN / (m·K)), j is the component imbalance coefficient (mN / m), T is the temperature (K), ε3 is the error term (mN / m), and n is the number of biowax types. Parameter acquisition method: f and g are obtained by surface tension testing of a single component. The specific steps are as follows: (1) Take 10 mL of pure biowax or sprout-inhibiting active ingredient sample; (2) Use a surface tension meter (hanging ring method) to measure the surface tension at different temperatures (40°C, 60°C, 80°C); (3) Obtain f by linear regression. i and g values. h and j are obtained by surface tension testing of multi-component mixtures, and the specific steps are: (1) preparing multiple groups of mixture samples in different proportions, each group of 10 mL; (2) using a surface tension meter to measure the surface tension at different temperatures; (3) using a multivariate linear regression method to fit the experimental data to obtain the values ​​of h and j. 4. The thermal stability equation is specifically expressed as follows: Where S is the thermal stability index of the formula (dimensionless, 0-100), w i is the mass fraction of the i-th biowax (0-1), w sis the mass fraction of the active ingredient inhibiting sprouting (0-1), r i is the thermal stability contribution factor of the i-th biowax, s is the thermal stability contribution factor of the sprout inhibitory active ingredient, t is the temperature sensitivity coefficient, u is the mixed effect coefficient, v is the mixed nonlinear factor, T is temperature (K), ε4 is the error term, and n is the number of biowax types. Parameter acquisition method: i and s were obtained by thermogravimetric analysis (TGA) of a single component, and the specific steps were as follows: (1) taking 10 mg of pure biowax or sprout-inhibiting active ingredient sample; (2) using a thermogravimetric analyzer for testing, with a heating rate of 10°C / min and a temperature range of 25-600°C; (3) calculating the 5% mass loss temperature and converting it into a thermal stability index to obtain r i and s values. t, u and v are obtained by thermogravimetric analysis of a multi-component mixture, and the specific steps are: (1) preparing multiple groups of mixture samples in different proportions, each group has 10 mg; (2) using a thermogravimetric analyzer for testing, the conditions are the same as above; (3) using a nonlinear regression method to fit the experimental data to obtain the values ​​of t, u and v. 5. The sprout inhibition effect equation is specifically expressed as follows: Where, E is the score of the bud inhibition effect (0-100), w i is the mass fraction of the i-th biowax (0-1), w s is the mass fraction of the active ingredient in inhibiting sprouting (0-1), Q is the application amount (g / m 2 ), T is the ambient temperature (°C), t is the time after application (days), x i is the germination inhibition contribution factor of the i-th biowax, y is the germination inhibition contribution factor of the germination inhibition active ingredient, z is the application amount coefficient, α is the maximum temperature effect, β is the temperature sensitivity, T0 is the optimal germination inhibition temperature (℃), γ is the periodic influence amplitude, ω is the periodic influence frequency (rad / day), ε5 is the error term, and n is the number of biowax types. Parameter acquisition method: i and y were obtained through a single component bud inhibition effect test, the specific steps being: (1) selecting 100 tobacco plants and grouping them for single component bud inhibition treatment; (2) culturing them under standard conditions (25°C, relative humidity 60%) for 30 days; (3) calculating the bud inhibition rate and converting it into a bud inhibition effect score to obtain x. i and y values. z, a, β, T0, γ and ω were obtained through the bud inhibition effect test of multi-component mixtures. The specific steps were as follows: (1) 1000 tobacco plants were selected and divided into groups for bud inhibition treatment with different formulations and different application rates; (2) the plants were cultured under different temperature conditions (15℃, 20℃, 25℃, 30℃, 35℃) for 60 days; (3) the bud inhibition rate was counted every 3 days and converted into a bud inhibition effect score; (4) the experimental data were fitted using the nonlinear regression method to obtain the values ​​of each parameter.

[0040] Compared with the prior art, the method for optimizing the formula of a bio-wax tobacco bud inhibition compound provided by the present invention has the following beneficial effects: 1. Strong environmental friendliness. The present invention uses green and environmentally friendly bio-wax as the main raw material, does not contain any chemical synthetic bud inhibition agent, and avoids the environmental pollution and food safety hazards that may be caused by traditional chemical bud inhibition technology. The bio-wax compound has good biocompatibility and biodegradability, and provides a safer and more reliable bud inhibition solution for tobacco production.

[0041] 2. Easy and efficient operation. The present invention uses a precise sprayer for uniform spraying, which can greatly improve the operating efficiency compared with the traditional manual application method, and can realize automatic control, further reducing labor costs. At the same time, the optimized biowax formula has good rheological properties, is easy to apply and evenly cover the tobacco leaf surface, and improves the convenience of use.

[0042] 4. Excellent comprehensive performance. Through mathematical modeling and optimization algorithms, the method of the present invention can accurately predict key performance indicators such as melting point, viscosity, surface tension and thermal stability of the bio-wax complex, ensuring that the quality of the final product is stable and controllable. At the same time, the formula can also maintain a good sprout inhibition effect under low temperature conditions, and has a wider range of applications.

[0043] In summary, the method for optimizing the formula of biowax sprout-inhibiting compounds proposed in the present invention gives full play to the green and environmentally friendly advantages of biowax, and obtains a composite formula with excellent performance through systematic experimental research and mathematical modeling, providing a safe and efficient new sprout-inhibiting solution for tobacco production, and solving the technical problems of certain environmental and food safety hazards in existing tobacco sprout-inhibiting technologies. It can solve the technical problems of certain environmental and food safety hazards in existing tobacco sprout-inhibiting technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0045] Figure 1 This is a schematic diagram of the overall structure of the precision sprayer;

[0046] Figure 2 It is a schematic diagram of the structure of the atomizing disk 32;

[0047] Figure 3 It is a schematic diagram of the structure of the atomization mechanism 8;

[0048] Figure 4This is the relationship diagram between the melting point and the sprout inhibition effect of the optimal formula under different temperature conditions;

[0049] Figure 5 The graph is the change of germination inhibition rate of the test group and the control group at different time points;

[0050] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0051] 1. Liquid storage tank 1; 2. Piezoelectric ceramic vibrator 2; 21. Vibrator body 21; 22. Piezoelectric chip 22; 3. Nozzle 3; 31. Nozzle 3 body; 32. Atomizing disk 32; 321. Micropore 321; 4. Power module 4; 5. Control valve 5; 6. Shell 6; 61. Upper shell 61; 62. Lower shell 62; 63. Protective cover 63; 7. Flow adjustment knob 7; 8. Atomizing mechanism 8; 81. Atomizing chamber 81; 82. Tangential air inlet 82; 83. Vortex generator 83; 9. Pressure measuring mechanism 9; 91. Pressure gauge 91; 92. Buffer chamber 92. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0053] like Figure 1-3 As shown, it is a first embodiment of a method for optimizing a formula of a bio-wax tobacco bud suppressing compound provided by the present invention. In this embodiment, the following steps are included: S10, selecting a plurality of bio-wax raw materials to prepare bio-wax mixtures of different proportions;

[0054] S20, mixing the bio-wax mixture with the tobacco sprout suppressing active ingredient according to a preset ratio to obtain an initial formula;

[0055] S30, conduct physical and chemical performance tests on the initial formula;

[0056] S40, applying the initial formula to tobacco samples at different application rates using a precision sprayer to evaluate the bud inhibition effect;

[0057] S50. Establish a formula scoring model based on the physical and chemical performance test results and the sprout inhibition effect evaluation results;

[0058] S60, designing multiple groups of formula combinations by using the orthogonal test method, repeating steps S20 to S50, and obtaining the score of each group of formulas;

[0059] S70, further optimizing the formula with a higher score by using response surface methodology to obtain an optimized formula;

[0060] S80. Apply the optimized formula to a small-scale tobacco sprout inhibition test to obtain actual sprout inhibition effect data;

[0061] S90. According to the actual sprout inhibition effect data, the formula scoring model is modified, and multiple rounds of optimization are performed through iterative optimization methods to obtain the final optimized formula.

[0062] Among them, in the above technical scheme, a variety of bio-wax raw materials include palm wax, beeswax, carnauba wax, wood wax and rice bran wax.

[0063] Furthermore, in the above technical solution, the preset ratio is specifically a mass ratio of the bio-wax mixture to the tobacco sprout-inhibiting active ingredient of 3:1 to 5:1.

[0064] Furthermore, in the above technical solution, the physical and chemical property tests include melting point test, viscosity test, surface tension test and thermal stability test.

[0065] Furthermore, in the above technical solution, the formula scoring model includes a melting point equation, a viscosity equation, a surface tension equation, a thermal stability equation and a sprout inhibition effect equation.

[0066] Furthermore, in the above technical solution, the melting point equation is used to predict the melting point of the formula, the independent variables include the mass fraction of various bio-waxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the melting point of the formula.

[0067] Furthermore, in the above technical solution, the viscosity equation is used to predict the viscosity of the formula, the independent variables include the mass fraction of various bio-waxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the viscosity of the formula.

[0068] Furthermore, in the above technical solution, the surface tension equation is used to predict the surface tension of the formula, the independent variables include the mass fraction of various bio-waxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the surface tension of the formula.

[0069] Furthermore, in the above technical solution, the thermal stability equation is used to predict the thermal stability of the formula, the independent variables include the mass fraction of various bio-waxes and the mass fraction of sprout-inhibiting active ingredients, and the dependent variable is the thermal stability index of the formula.

[0070] Furthermore, in the above technical solution, the sprout inhibition effect equation is used to predict the sprout inhibition effect of the formula, the independent variables include the mass fraction of various bio-waxes, the mass fraction of sprout inhibition active ingredients and the application amount, and the dependent variable is the sprout inhibition effect score.

[0071] The specific implementation of step S10 is as follows: First, select 5 kinds of bio-wax raw materials, including palm wax, beeswax, carnauba wax, wood wax and rice bran wax. Then, use a batching scale to weigh appropriate amounts of various bio-waxes, mix and stir according to a preset mass ratio (for example, 30% palm wax, 20% beeswax, 20% carnauba wax, 15% wood wax, 15% rice bran wax), and prepare a bio-wax mixture. The purpose of this step is to provide different formula bases for subsequent physical and chemical performance tests and bud inhibition effect evaluation by preparing bio-wax mixtures of different proportions. The specific implementation of step S20 is as follows: The aforementioned bio-wax mixture is mixed and stirred with tobacco bud inhibition active ingredients (such as plant growth regulators) according to a preset mass ratio (for example, 4:1) to obtain an initial formula. The purpose of this step is to prepare an initial mixture containing bio-wax and bud inhibition active ingredients, which provides a basis for subsequent performance testing and optimization. The specific implementation of step S30 is as follows: 1) Melting point test: Take about 5 grams of the initial formula sample, place it in a differential scanning calorimeter (DSC), and heat it from 25°C to 200°C at a heating rate of 10°C / min, and record the peak temperature of the melting peak, which is the melting point of the formula. 2) Viscosity test: Take about 20mL of the initial formula sample, place it in a rotational viscometer, and measure its dynamic viscosity value at 40°C, 60°C, 80°C and 100°C respectively. 3) Surface tension test: Take about 10mL of the initial formula sample, place it in a surface tension meter (using the hanging ring method), and measure its surface tension value at 40°C, 60°C and 80°C respectively. 4) Thermal stability test: Take about 10mg of the initial formula sample, place it in a thermogravimetric analyzer (TGA), and heat it from 25°C to 600°C at a heating rate of 10°C / min, and record the temperature at 5% mass loss as the thermal stability index of the formula. The purpose of this step is to comprehensively evaluate the physical and chemical properties of the initial formula and provide a basis for subsequent formula optimization. The specific implementation of step S40 is as follows: the initial formula is placed in a precision sprayer and applied at different dosages (e.g. 2 g / m 2 , 4g / m 2 , 6g / m 2 ) were sprayed on 100 tobacco plants respectively, and cultured for 30 days under standard temperature and humidity conditions (25°C, relative humidity 60%), and the bud inhibition rate of each group of tobacco plants was counted and converted into a bud inhibition effect score (0-100 points). The purpose of this step is to actually test the tobacco bud inhibition effect under different formulations and application amounts, and provide a data basis for the subsequent scoring model establishment. The specific implementation method of step S50 is as follows: 1) Establish a melting point prediction equation: Among them, T m is the melting point of the formula (℃), w i is the mass fraction of the i-th biowax, w s is the mass fraction of the active ingredient inhibiting sprouting, a iis the melting point contribution coefficient of the i-th biowax (℃), b is the melting point contribution coefficient of the sprout inhibitory active ingredient (℃), c and d are the mixed effect coefficients, and ε1 is the error term. 2) Establish the viscosity prediction equation: Where η is the dynamic viscosity (mPa·s), k i is the viscosity contribution factor of the i-th biowax, m is the viscosity contribution factor of the anti-bud active ingredient, p is the temperature correlation coefficient (K), q is the mixing interaction coefficient, T is the temperature (K), and ε2 is the error term. 3) Establish the surface tension prediction equation: Where, γ is the surface tension (mN / m), f i is the surface tension contribution factor of the i-th biowax (mN / m), g is the surface tension contribution factor of the sprout-inhibiting active ingredient (mN / m), h is the temperature coefficient (mN / (m·K)), j is the component imbalance coefficient (mN / m), and ε3 is the error term. 4) Establish a thermal stability prediction equation: Where S is the thermal stability index (dimensionless, 0-100), r i is the thermal stability contribution factor of the i-th biowax, s is the thermal stability contribution factor of the sprout inhibition active ingredient, t is the temperature sensitivity coefficient, u and v are the mixed effect coefficients, T is the temperature (K), and ε4 is the error term. 5) Establish the sprout inhibition effect prediction equation: Among them, E is the score of the bud inhibition effect (0-100), x i is the germination inhibition contribution factor of the i-th biowax, y is the germination inhibition contribution factor of the germination inhibition active ingredient, z is the application amount coefficient, a is the maximum temperature effect, β is the temperature sensitivity, T0 is the optimal germination inhibition temperature (℃), γ and ω are periodic influence parameters, Q is the application amount (g / m 2), T is temperature (℃), t is time after application (days), and ε5 is an error term. The purpose of this step is to establish a comprehensive formula scoring model based on the aforementioned physical and chemical performance test results and sprout inhibition effect test data, so as to provide a basis for subsequent optimization. The specific implementation method of step S60 is as follows: 1) Use the orthogonal test method to design multiple groups of formula combinations, including factors such as biowax type, mass fraction, and mass fraction of sprout inhibition active ingredients. 2) Repeat steps S20 to S50 for each group of formulas to obtain the physical and chemical performance indicators and sprout inhibition effect scores of each group of formulas. 3) Use statistical analysis software to analyze the test data to determine the main effect and interaction effect of each factor on the response index. The purpose of this step is to effectively obtain the performance data of different formula schemes through systematic orthogonal experiments, so as to lay the foundation for subsequent response surface optimization. The specific implementation method of step S70 is as follows: 1) According to the results of step S60, select several groups of formulas with higher scores as initial points. 2) Use the response surface method to design a series of neighbor formula combinations with these initial points as the center. 3) Repeat steps S20 to S50 to obtain the performance data of the nearest neighbor formula. 4) Use the regression analysis method to fit the response surface model and determine the optimal formula that maximizes the response index (sprout inhibition effect score). The purpose of this step is to further optimize the formula based on the previous screening by using the response surface optimization method to obtain the final formula with the best sprout inhibition effect. The specific implementation method of step S80 is as follows: 1) Select 100 healthy tobacco plants and divide them into a test group and a control group. 2) Spray the optimal formula obtained in step S70 on the plants in the test group, and the control group does not undergo any treatment. 3) Cultivate for 30 days under standard temperature and humidity conditions (25°C, relative humidity 60%). 4) Observe and record the sprout inhibition of each group of plants every 3 days, and calculate the sprout inhibition rate. 5) Convert the sprout inhibition rate into a sprout inhibition effect score as the actual sprout inhibition effect data of the final optimized formula. The purpose of this step is to verify the optimal formula obtained in step S70 in a small-scale test, obtain its actual sprout inhibition effect data, and provide a basis for subsequent model correction. The specific implementation method of step S90 is as follows: 1) Substitute the actual sprout inhibition effect data obtained in step S80 into each prediction equation established in step S50, and calculate the deviation between the actual value and the predicted value. 2) Use the least squares method to correct and optimize the parameters of each prediction equation to reduce the deviation. 3) Use the corrected prediction model to re-optimize the formula to obtain the final optimized formula. 4) If necessary, steps S80 and S90 can be repeated for multiple rounds of iterative optimization until the prediction model meets the requirements for the degree of fit between the actual data and the actual data. The purpose of this step is to correct and optimize the prediction model established in the early stage based on the actual data of the small-scale test, so that it better reflects the actual situation, thereby obtaining the final optimized best formula solution.In general, this optimization method makes full use of mathematical modeling techniques such as statistical experimental design, response surface analysis, and multiple regression. By systematically testing and analyzing the impact of the ratio of various bio-waxes and sprout-inhibiting active ingredients on the performance of the final product, it finally obtains the optimized formula with the best sprout-inhibiting effect.

[0072] Example 1

[0073] Step S10: First, five bio-wax raw materials are selected, including palm wax (w1), beeswax (w2), carnauba wax (w3), wood wax (w4) and rice bran wax (w5). i (i=1, 2, 3, 4, 5) represents the mass fraction of various biowaxes, satisfying Then, a batching scale is used to weigh appropriate amounts of various bio-waxes, and they are mixed and stirred according to a preset mass ratio (e.g., w1=0.3, w2=0.2, w3=0.2, w4=0.15, w5=0.15) to prepare a bio-wax mixture. The purpose of this step is to provide different formula bases for subsequent physical and chemical performance tests and bud inhibition effect evaluation by preparing bio-wax mixtures of different proportions. ; Step S20: The aforementioned bio-wax mixture is mixed with tobacco bud inhibition active ingredients (e.g., plant growth regulators) according to a preset mass ratio (e.g., w b =0.2, where w b The mass ratio of the biowax to the bud-inhibiting active ingredient in the initial formula is The purpose of this step is to prepare an initial mixture containing biowax and sprout inhibitory active ingredients, which provides a basis for subsequent performance testing and optimization. ; Step S30: 1) Melting point test: The melting point of the initial formula sample is tested using a differential scanning calorimeter (DSC), with a heating rate of ΔT = 10°C / min and a temperature range of 25-200°C. The peak temperature of the recorded melting peak is taken as the melting point T of the formula. m The melting point is an important indicator of the performance of a formulation and can be predicted using the following equation: Among them, a i is the melting point contribution coefficient of the i-th biowax (℃), b is the melting point contribution coefficient of the sprout inhibitory active ingredient (℃), c and d are the mixed effect coefficients, and ε1 is the error term. 2) Viscosity test: The dynamic viscosity η of the initial formula sample at 40℃, 60℃, 80℃ and 100℃ was tested using a rotational viscometer. Viscosity is another important indicator of formula performance and can be predicted by the following equation: Among them, k iis the viscosity contribution factor of the i-th biowax, m is the viscosity contribution factor of the sprout inhibitory active ingredient, p is the temperature correlation coefficient (K), q is the mixing interaction coefficient, T is the temperature (K), and ε2 is the error term. 3) Surface tension test: The surface tension γ of the initial formula sample at 40℃, 60℃ and 80℃ was tested using a surface tension meter (hanging ring method). Surface tension is an important indicator of formula performance and can be predicted by the following equation: Wherein, f is the surface tension contribution factor of the i-th biowax (mN / m), g is the surface tension contribution factor of the sprout inhibitory active ingredient (mN / m), h is the temperature coefficient (mN / (m·K)), j is the component imbalance coefficient (mN / m), and ε3 is the error term. 4) Thermal stability test: The thermal stability of the initial formula sample was tested using a thermogravimetric analyzer (TGA), with a heating rate of ΔT=10°C / min and a temperature measurement range of 25-600°C. The temperature at which 5% mass loss was recorded was taken as the thermal stability index S of the formula. Thermal stability is an important indicator of formula performance and can be predicted by the following equation: Among them, r i is the thermal stability contribution factor of the ith biowax, s is the thermal stability contribution factor of the sprout-inhibiting active ingredient, t is the temperature sensitivity coefficient, u and v are the mixed effect coefficients, T is the temperature (K), and ε4 is the error term. The purpose of this step is to comprehensively evaluate the physical and chemical properties of the initial formula and provide a basis for subsequent formula optimization. Through mathematical modeling, key indicators such as the melting point, viscosity, surface tension and thermal stability of the formula can be predicted, laying the foundation for the establishment of the subsequent scoring model. Step S40: Place the initial formula in a precision sprayer and apply it at different application rates Q (e.g. 2 g / m 2 , 4g / m 2 and 6g / m 2 ) were sprayed on 100 tobacco plants respectively, and cultured under standard temperature and humidity conditions (T = 25 ° C, relative humidity 60%) for 30 days. The bud inhibition rate of each group of tobacco plants was counted and converted into a bud inhibition effect score E (0-100 points). The bud inhibition effect can be predicted by the following equation: Among them, x i is the bud inhibition contribution factor of the ith biowax, y is the bud inhibition contribution factor of the bud inhibition active ingredient, z is the application amount coefficient, a is the maximum temperature influence, β is the temperature sensitivity, T0 is the optimal bud inhibition temperature (°C), γ and ω are periodic influence parameters, t is the time after application (days), and ε5 is the error term. The purpose of this step is to actually test the bud inhibition effect of tobacco under different formulations and application amounts, and provide a data basis for the subsequent establishment of a scoring model. Through mathematical modeling, the bud inhibition effect of the formulation can be predicted to provide a reference for subsequent optimization. Step S50: 1) In the melting point prediction equation (1), parameter a iand b are obtained by the melting point test of a single component, and the specific steps are as follows: (1) Take 5g of pure biowax or sprout inhibitor active ingredient sample; (2) Use DSC for testing, the heating rate is ΔT = 10℃ / min, and the temperature range is 25-200℃; (3) Record the peak temperature of the melting peak as the melting point. Parameters C and d are obtained by the melting point test of a multi-component mixture, and the specific steps are as follows: (1) Prepare multiple groups of mixture samples in different proportions, 5g per group; (2) Use DSC for testing, the conditions are the same as above; (3) Use the least squares method to fit the experimental data to obtain the values ​​of c and d. 2) In the viscosity prediction equation (2), parameter k i and m were obtained by viscosity testing of a single component. The specific steps were as follows: (1) taking 20 mL of pure biowax or sprout inhibitor active ingredient sample; (2) using a rotational viscometer to measure the viscosity at different temperatures (40°C, 60°C, 80°C, 100°C); (3) using the Arrhenius equation η = Aexp(B / T) to fit k i The parameters p and q are obtained by viscosity testing of multi-component mixtures. The specific steps are as follows: (1) prepare multiple groups of mixture samples in different proportions, each group is 20 mL; (2) use a rotational viscometer to measure the viscosity at different temperatures; (3) use nonlinear regression method to fit the experimental data to obtain the values ​​of p and q. 3) In the surface tension prediction equation (3), the parameter f i and g were obtained by surface tension test of a single component, and the specific steps were as follows: (1) taking 10 mL of pure biowax or sprout inhibitor active ingredient sample; (2) using a surface tension meter (hanging ring method) to measure the surface tension at different temperatures (40°C, 60°C, 80°C); (3) using linear regression γ = f i w i +gw b +ε3 to get f i The parameters h and j are obtained by testing the surface tension of a multi-component mixture. The specific steps are as follows: (1) prepare multiple groups of mixture samples in different proportions, each group has 10 mL; (2) use a surface tension meter to measure the surface tension at different temperatures; (3) use the multivariate linear regression method to fit the experimental data to obtain the values ​​of h and j. 4) In the thermal stability prediction equation (4), the parameter r i and s were obtained by thermogravimetric analysis (TGA) of a single component, and the specific steps were as follows: (1) taking 10 mg of pure biowax or sprout-inhibiting active ingredient sample; (2) using a thermogravimetric analyzer for testing, with a heating rate of ΔT = 10 °C / min and a temperature range of 25-600 °C; (3) calculating the 5% mass loss temperature and converting it into a thermal stability index S to obtain r iThe parameters t, u and v were obtained by thermogravimetric analysis of a multi-component mixture. The specific steps are as follows: (1) prepare multiple groups of mixture samples in different proportions, each group has 10 mg; (2) use a thermogravimetric analyzer to test under the same conditions as above; (3) use a nonlinear regression method to fit the experimental data to obtain the values ​​of t, u and v. 5) In the sprout inhibition effect prediction equation (5), the parameter x i and y were obtained through a single component bud inhibition effect test, the specific steps being: (1) selecting 100 tobacco plants and grouping them for single component bud inhibition treatment; (2) culturing them under standard conditions (T = 25°C, relative humidity 60%) for 30 days; (3) calculating the bud inhibition rate and converting it into a bud inhibition effect score E to obtain x i and y values. Parameters z, a, β, T0, γ and ω were obtained through the bud inhibition effect test of multi-component mixtures. The specific steps are: (1) 1000 tobacco plants were selected and divided into groups for bud inhibition treatment with different formulas and different application amounts; (2) they were cultured for 60 days under different temperature conditions (T = 15°C, 20°C, 25°C, 30°C, 35°C); (3) the bud inhibition rate was counted every 3 days and converted into a bud inhibition effect score E; (4) the experimental data were fitted using a nonlinear regression method to obtain the values ​​of each parameter. The purpose of this step is to establish a comprehensive formula scoring model based on the above test results to provide a basis for subsequent optimization. Through mathematical modeling, key indicators such as the melting point, viscosity, surface tension, thermal stability and bud inhibition effect of the formula can be predicted, laying the foundation for screening and optimizing the best formula. Step S60: 1) Design multiple groups of biowax types and mass fractions (w i ) and mass fraction of sprout-inhibiting active ingredients (w b ) and other factors. Orthogonal experimental design can effectively explore the effect of each factor on the response index (such as the cooked point T m, viscosity η, surface tension γ, thermal stability S and bud inhibition effect E, etc.) main effects and interaction effects. 2) Repeat steps S20 to S50 for each group of formulas to obtain the physical and chemical performance indicators and bud inhibition effect score data of each group of formulas. 3) Use statistical analysis software to analyze the test data to determine the main effects and interaction effects of each factor on the response index. These effect analysis results will provide guidance for subsequent response surface optimization. The purpose of this step is to effectively obtain the performance data of different formula schemes through systematic orthogonal experiments, laying the foundation for subsequent response surface optimization. Orthogonal experimental design can obtain sufficient information within a small number of experiments, providing strong support for the optimization process. Step S70: 1) According to the results of step S60, several groups of formulas with higher scores are selected as initial points. These initial points provide a good starting point for subsequent response surface optimization. 2) Use the response surface method to design a series of neighboring formula combinations with these initial points as the center. The response surface method can construct an approximate quadratic response surface model near the initial point, providing a basis for finding the optimal solution. 3) Repeat steps S20 to S50 to obtain the performance data of the nearest neighbor formula. These new test data will be used for the fitting and optimization of the response surface model. 4) Utilize regression analysis method to fit the response surface model and determine the optimal formula that maximizes the response index (sprout inhibition effect score E). The response surface model usually adopts the quadratic polynomial form: Among them, X i is the level of each factor, β0, β i and β ij is the regression coefficient, ε is the error term, and n is the number of factors. Through model optimization, the optimal formula can be determined. The purpose of this step is to further optimize the formula based on the previous screening by using the response surface optimization method to obtain the final formula with the best sprout inhibition effect. The response surface method can effectively utilize the existing data and quickly find the optimal solution with a small test volume. In general, this set of optimization methods makes full use of mathematical modeling techniques such as statistical experimental design, response surface analysis, and multiple regression. By systematically testing and analyzing the influence of the ratio of various biowaxes and sprout inhibition active ingredients on the performance of the final product, the optimized formula with the best sprout inhibition effect is finally obtained.

[0074] Specifically, the principle of the present invention is to make full use of the respective characteristics of different types of bio-waxes and obtain a composite formula with the best sprout inhibition effect by optimizing the ratio.

[0075] First, five common bio-wax raw materials were selected, namely palm wax, beeswax, carnauba wax, wood wax and rice bran wax. These bio-waxes have a wide range of sources and different properties, which can provide a sufficient "raw material library" for formula optimization. Palm wax has a high melting point and good thermal stability, which is conducive to improving the durability of the bud suppression effect; beeswax has strong adsorption and sealing properties, which helps to improve the adhesion to the tobacco leaf surface; carnauba wax and wood wax have excellent rheological properties, which are conducive to improving spray construction properties; rice bran wax is rich in a variety of natural bioactive substances and may enhance the bud suppression activity. By rationally matching these bio-waxes, a composite formula with better comprehensive performance can be designed.

[0076] Secondly, the biowax mixture is mixed with tobacco sprout suppressing active ingredients (such as plant growth regulators) in a certain proportion to prepare an initial formula. The addition of sprout suppressing active ingredients can further enhance the sprout suppressing function of the formula. Through mathematical modeling, key performance indicators such as melting point, viscosity, surface tension and thermal stability of the initial formula can be predicted, providing a basis for subsequent optimization.

[0077] Subsequently, the orthogonal experimental design method was used to systematically test the effects of different biowax ratios and dosages of sprout-inhibiting active ingredients on the final performance. This experimental design method can comprehensively explore the main effects and interaction effects of each factor within a limited number of experiments, laying the foundation for response surface optimization.

[0078] Finally, the high-performance formula selected by orthogonal experiment was used as the starting point for further optimization using response surface optimization method. The response surface method can use existing data to establish a quadratic response surface model in a local area, and find the optimal formula that maximizes the sprout inhibition effect through numerical optimization. This method based on mathematical modeling and algorithm optimization can greatly improve the optimization efficiency and obtain a more accurate optimal solution.

[0079] Through the above steps, the method of the present invention can systematically explore the influence of the bio-wax composite formula on the performance of the final product, and combined with mathematical modeling and optimization algorithms, obtain the optimal bio-wax sprout-inhibiting compound formula with significantly improved sprout-inhibiting effect, environmental friendliness, and simple operation. This technical route gives full play to the green characteristics of bio-wax, while using advanced mathematical modeling and optimization methods to achieve an overall solution from material formula to process optimization, which is highly scientific and practical.

[0080] Embodiment 2:

[0081] A tobacco planting enterprise hopes to use more environmentally friendly and efficient bio-wax sprout inhibitors to replace traditional chemical sprout inhibitors to improve the safety and sustainability of tobacco production. After thorough investigation, the enterprise decided to adopt the bio-wax sprout inhibitor formula optimization method proposed in this invention, and finally obtained a bio-wax composite formula with excellent sprout inhibitory effect through systematic experimental design and mathematical modeling.

[0082] First, the company selected five bio-wax raw materials: palm wax, beeswax, carnauba wax, wood wax and rice bran wax. Based on the preliminary proportion test, the mass fraction of the bio-wax mixture was preliminarily determined to be: 30% palm wax, 20% beeswax, 20% carnauba wax, 15% wood wax, and 15% rice bran wax. Then, this bio-wax mixture was mixed with a plant growth regulator (as a sprout inhibitor active ingredient) at a mass ratio of 4:1 to obtain the initial formula.

[0083] Next, the company conducted a comprehensive physical and chemical performance test on the initial formula. First, the melting point of the formula was tested using a differential scanning calorimeter (DSC). The test results showed that the melting peak temperature of the resulting mixture was 72.5°C, which met the temperature condition requirements in the tobacco production process. Then, the dynamic viscosity of the formula at different temperatures was tested using a rotational viscometer. The test results are shown in Table 1:

[0084] Table 1 Viscosity test results of biological wax sprout inhibitory compound formula

[0085] Temperature(℃) Dynamic viscosity (mPa·s) 40 1356 60 802 80 435 100 231

[0086] As can be seen from Table 1, the viscosity of the formula changes relatively steadily within the test temperature range, and the value is within the appropriate range for spraying, which is conducive to subsequent uniform spraying application. In addition, the company also used a surface tension meter (hanging ring method) to test the surface tension of the formula at different temperatures. The results are shown in Table 2: Table 2 Surface tension test results of the biowax sprout inhibitor compound formula

[0087] Temperature(℃) Surface tension (mN / m) 40 34.2 60 32.7 80 30.8

[0088] As can be seen from Table 2, the surface tension of the formula is relatively stable within the test temperature range, and the value is moderate, which is conducive to wettability during spraying. At the same time, the company also used a thermogravimetric analyzer (TGA) to test the thermal stability of the initial formula. The test results showed that the 5% mass loss temperature of the formula was 218°C, indicating that it has good thermal stability and can withstand the temperature conditions during tobacco production. Combining the above physical and chemical performance test results, the initial formula basically meets the requirements for tobacco bud suppression applications, and provides a good foundation for subsequent evaluation and optimization of bud suppression effects. Next, the company used a precision sprayer to spray the initial formula on 100 tobacco plants, setting 2g / m 2 , 4g / m 2 and 6g / m 2 Three application rates were used, and the plants were cultured under standard temperature and humidity conditions (25°C, relative humidity 60%) for 30 days, and the bud inhibition rate of each group of plants was observed and recorded. The test results of the bud inhibition rate are shown in Table 3:

[0089] Table 3 Bud inhibition effect of initial formula at different application rates

[0090]

[0091] It can be seen from Table 3 that with the increase of application amount, the germination inhibition rate and germination inhibition effect score showed an upward trend. 2 At the application rate of , the bud inhibition rate reached 88%, and the bud inhibition effect score was 88 points, which is close to the ideal level. Based on the above experimental data, the company established a mathematical prediction model for the formula performance: Melting point prediction equation: Viscosity prediction equation: Surface tension prediction equation:

[0092] Thermal stability prediction equation: Prediction equation for bud inhibition effect: Among them, w i (i=1, 2, 3, 4, 5) represent the mass fractions of palm wax, beeswax, carnauba wax, wood wax and rice bran wax, respectively, w b represents the mass fraction of the active ingredient in inhibiting sprouting, T is the temperature (℃), Q is the application amount (g / m 2 ), t is the time after administration (days), ε i (i=1, 2, 3, 4, 5) is the error term of each prediction equation. Using the above prediction model, the company further optimized the biowax compound formula. First, 9 groups of mixture samples with different proportions were designed using the orthogonal test method. The specific formula is shown in Table 4:

[0093] Table 4 Biowax mixture formula designed by orthogonal experiment

[0094]

[0095]

[0096] The nine groups of samples were comprehensively tested and various performance index data were obtained. Based on these data, the company used statistical analysis software to analyze the results of the orthogonal test and determined the main effects and interaction effects of various factors (biowax type, mass fraction and amount of sprout-inhibiting active ingredients) on melting point, viscosity, surface tension, thermal stability and sprout-inhibiting effect. On this basis, the company adopted the response surface optimization method, taking samples 2, 4 and 6 as the initial points, and designed a series of neighbor formulas for further testing. By fitting the response surface model and performing numerical optimization, the biowax composite formula with the best sprout-inhibiting effect was finally determined: 30% palm wax, 20% beeswax, 20% Brazilian palm wax, 15% wood wax, 15% rice bran wax, and the content of sprout-inhibiting active ingredients was 20%.

[0097] Figure 4 The relationship between the melting point and the sprout inhibition effect of the optimal formula under different temperature conditions is shown. It can be seen that the best sprout inhibition effect is achieved at around 25°C, and too high or too low temperature will lead to a decrease in the sprout inhibition effect. Figure 1 It shows that the optimal formula has the best bud inhibition effect at around 25°C. This is because under this temperature condition, the formula has a suitable melting point and fluidity, which is conducive to forming a uniform protective film on the tobacco leaf surface, thereby achieving the best bud inhibition effect. As the temperature rises or falls, the rheology of the formula will change, resulting in a decrease in the bud inhibition effect. In order to verify the actual effect of the optimal formula, the company selected 100 healthy tobacco plants and divided them into a test group and a control group. The optimal formula was sprayed on the plants in the test group, and the control group did not receive any treatment. The plants were cultured under standard temperature and humidity conditions (25°C, relative humidity 60%) for 30 days, and the bud inhibition of each group of plants was observed and recorded every 3 days.

[0098] Figure 5 The changes in the bud inhibition rate of the test group and the control group plants at different time points are shown. It can be seen that after spraying the optimal formula, the bud inhibition rate of the test group plants dropped rapidly and stabilized at more than 90% within 30 days, while the bud inhibition rate of the control group plants remained at a high level. This shows that the optimal biowax bud inhibition compound formula has a significant bud inhibition effect. Figure 2 In the experiment, the bud inhibition rate of the control group plants remained stable at about 80%, while the bud inhibition rate of the test group plants gradually decreased to more than 90% within 30 days. This was mainly due to the fact that the optimal bio-wax bud inhibition compound formula formed a lasting protective film on the tobacco leaf surface, effectively inhibiting the germination of tobacco buds. In summary, after systematic experimental design, mathematical modeling and optimization algorithms, the company finally determined a bio-wax bud inhibition compound formula with excellent performance. Its bud inhibition effect is significantly better than that of existing bio-wax products. At the same time, it is easy to operate, environmentally friendly, and can meet the actual needs of tobacco production.

Claims

1. A method for optimizing the formula of a biowax tobacco bud-inhibiting compound, characterized in that: The following steps are involved: S10, selecting a plurality of bio-wax raw materials to prepare bio-wax mixtures in different proportions; S20, mixing the bio-wax mixture with the tobacco sprout suppressing active ingredient according to a preset ratio to obtain an initial formula; S30, performing physical and chemical property tests on the initial formula; S40, applying the initial formula to tobacco samples by using a precision sprayer in different application amounts to evaluate the bud inhibition effect; S50. Establish a formula scoring model based on the physical and chemical performance test results and the sprout inhibition effect evaluation results; S60, designing multiple groups of formula combinations by using the orthogonal test method, repeating steps S20 to S50, and obtaining the score of each group of formulas; S70, further optimizing the formula with a higher score by using response surface methodology to obtain an optimized formula; S80, applying the optimized formula to a small-scale tobacco sprout inhibition test to obtain actual sprout inhibition effect data; S90. According to the actual sprout inhibition effect data, the formula scoring model is modified, and multiple rounds of optimization are performed through an iterative optimization method to obtain a final optimized formula.

2. The method for optimizing the formula of a bio-wax tobacco bud-inhibiting compound according to claim 1, characterized in that: The various bio-wax raw materials include palm wax, beeswax, carnauba wax, wood wax and rice bran wax.

3. The method for optimizing the formula of a bio-wax tobacco bud inhibition compound according to claim 2, characterized in that: The preset ratio is specifically a mass ratio of the bio-wax mixture to the tobacco sprout-inhibiting active ingredient of 3:1 to 5:

1.

4. The method for optimizing the formula of a bio-wax tobacco bud inhibition compound according to claim 3, characterized in that: The physical and chemical property tests include melting point test, viscosity test, surface tension test and thermal stability test.

5. The method for optimizing the formula of a bio-wax tobacco bud-inhibiting compound according to claim 4, characterized in that: The formula scoring model includes a melting point equation, a viscosity equation, a surface tension equation, a thermal stability equation and a sprout inhibition effect equation.

6. The method for optimizing the formula of a bio-wax tobacco bud-inhibiting compound according to claim 5, characterized in that: The melting point equation is used to predict the melting point of a formula, the independent variables include the mass fractions of various bio-waxes and the mass fractions of sprout-inhibiting active ingredients, and the dependent variable is the melting point of the formula.

7. The method for optimizing the formula of a bio-wax tobacco bud-inhibiting compound according to claim 6, characterized in that: The viscosity equation is used to predict the viscosity of the formula, the independent variables include the mass fractions of various bio-waxes and the mass fractions of sprout-inhibiting active ingredients, and the dependent variable is the viscosity of the formula.

8. The method for optimizing the formula of a bio-wax tobacco bud inhibition compound according to claim 7, characterized in that: The surface tension equation is used to predict the surface tension of a formula, the independent variables include the mass fractions of various biowaxes and the mass fractions of sprout-inhibiting active ingredients, and the dependent variable is the surface tension of the formula.

9. The method for optimizing the formula of a bio-wax tobacco bud inhibition compound according to claim 8, characterized in that: The thermal stability equation is used to predict the thermal stability of a formula, the independent variables include the mass fractions of various biowaxes and the mass fractions of sprout-inhibiting active ingredients, and the dependent variable is the thermal stability index of the formula.

10. The method for optimizing the formula of a bio-wax tobacco bud-inhibiting compound according to claim 9, characterized in that: The sprout inhibition effect equation is used to predict the sprout inhibition effect of a formula, the independent variables include the mass fraction of various bio-waxes, the mass fraction of sprout inhibition active ingredients and the application amount, and the dependent variable is the sprout inhibition effect score.

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