A method for optimizing the formulation of plant polyphenol tobacco bud inhibitor

Abstract

The invention provides a method for optimizing the formula of a plant polyphenol tobacco bud inhibitor, which belongs to the technical field of plant polyphenol tobacco bud inhibitors, and comprises: firstly obtaining a basic formula, establishing a formula equation group including equations such as activity, duration, safety, and the like, and an effect equation group describing different bud inhibition stages. Through experiments, multiple groups of formulas are explored, experimental data are obtained, and the formula equation group and the effect equation group are fitted. Based on these models, a multi-objective optimization problem is constructed to optimize the formula synthesis and bud inhibition effect. An improved gray wolf optimization algorithm is used to solve and obtain the optimal formula. Finally, the optimal solution is verified and fine-tuned to obtain the final optimized formula of the plant polyphenol tobacco bud inhibitor. The method integrates mathematical modeling, experimental research and intelligent optimization, systematically optimizes the formula of the bud inhibitor, and solves the technical problem that there is currently a lack of a systematic formula optimization method and it is difficult to quickly find an ideal formula that takes into account the bud inhibition effect, safety and environmental friendliness.

Description

Technical Field

[0001] The invention belongs to the technical field of plant polyphenol tobacco sprout inhibitors, and in particular relates to a formula optimization method for plant polyphenol tobacco sprout inhibitors. Background Art

[0002] Tobacco is an important economic crop. During its cultivation and production, the germination and growth of axillary buds after topping are often affected, resulting in a decrease in yield and quality. Although traditional chemical bud inhibitors can effectively control the germination of tobacco buds, they have been gradually restricted due to their toxicity and environmental pollution. In recent years, natural bud inhibitors with plant polyphenols as the main ingredient have gradually attracted attention, which have the advantages of low toxicity and environmental friendliness.

[0003] Plant polyphenols are a class of secondary metabolites widely found in plants, with a variety of biological activities, including inhibiting seed germination and delaying seedling growth. Using these biological activities, plant polyphenol sprout inhibitors can be developed for use in agricultural production. At present, many studies have reported the inhibitory effects of different plant polyphenol extracts on tobacco buds, such as tea polyphenols, grape seed polyphenols, and citrus flavonoids. However, the effects of single-ingredient plant polyphenol sprout inhibitors are usually not ideal, with problems such as low activity, short duration, and poor safety.

[0004] In order to improve the comprehensive performance of plant polyphenol sprout inhibitors, the use of a composite formula of multiple plant polyphenol components is an effective method. There may be synergistic or antagonistic effects between different plant polyphenol components. Reasonable ratios can give full play to their respective advantages and achieve the purpose of synergy. In addition, by optimizing the ratio of auxiliary ingredients such as emulsifiers, synergists, and pH regulators in the formula, the activity, stability, and duration of the sprout inhibitor can be further improved. However, there is currently a lack of systematic formula optimization methods, and it is difficult to quickly find an ideal formula that takes into account the sprout inhibition effect, safety, and environmental friendliness. Summary of the invention

[0005] In view of this, the present invention provides a method for optimizing the formula of a plant polyphenol tobacco bud inhibitor, which can solve the technical problem that there is currently a lack of a systematic formula optimization method and it is difficult to quickly find an ideal formula that takes into account the bud inhibition effect, safety and environmental friendliness.

[0006] The present invention is achieved in that:

[0007] The present invention provides a method for optimizing the formulation of a plant polyphenol tobacco sprout inhibitor, which comprises the following steps:

[0008] S10, obtaining a basic formula of a tobacco bud suppressant containing a plant polyphenol extract;

[0009] S20, establishing a formula equation group of plant polyphenol tobacco bud inhibitor, including an activity equation, a duration equation, a safety equation, a degradation equation and a comprehensive formula equation;

[0010] S30, establishing a tobacco bud inhibition effect equation group, including a bud inhibition equation in a dormancy induction stage, a bud inhibition equation in an oxidative stress induction stage, a bud inhibition equation in a metabolic inhibition stage, and a comprehensive effect equation;

[0011] S40, according to the basic formula, establish multiple test formulas of plant polyphenol tobacco bud inhibitors by traversing and combining the ratios of the plant polyphenol extracts and adjusting the ratios of various components in the basic formula, conduct small-scale tests, obtain experimental data, and record the specific composition and ratio of the plant polyphenol extracts in each test formula;

[0012] S50, using the experimental data to fit the plant polyphenol tobacco bud inhibitor formula equation group and the tobacco bud inhibitor effect equation group to obtain a fitted formula equation group and a fitted effect equation group;

[0013] S60, constructing a multi-objective optimization problem based on the fitting formula equation group and the fitting effect equation group, wherein the objective function includes an optimized formula comprehensive vector and an effect comprehensive vector;

[0014] S70, using the improved grey wolf optimization algorithm to solve the multi-objective optimization problem and obtain the optimal solution;

[0015] S80, verifying and fine-tuning the optimal solution to obtain an optimal formula.

[0016] The basic formula specifically includes: plant polyphenol extract, the proportion is 20-30%; emulsifier, the proportion is 5-10%; synergist, the proportion is 2-5%; pH adjuster, the proportion is 1-3%; preservative, the proportion is 0.1-0.5%; stabilizer, the proportion is 1-3%; solvent, the proportion is 30-50%; water, the proportion is the balance.

[0017] Furthermore, the plant polyphenol extract includes any one of tea polyphenols, grape seed extract, apple polyphenols, citrus flavonoids, pine bark extract, olive polyphenols, and pomegranate polyphenols, or a combination of at least two of them.

[0018] Furthermore, the activity equation is used to describe the contribution of each component to the sprout inhibition effect;

[0019] The duration equation is used to describe the duration of the sprout inhibition effect;

[0020] The safety equation is used to evaluate the safety of the formula to the environment and human body;

[0021] The degradation equation is used to describe the degradation rate and pathway of the formulation in the environment;

[0022] The formula comprehensive equation is used to obtain a formula comprehensive vector including solutions to the activity equation, duration equation, safety equation, and degradation equation in a weighted manner.

[0023] Furthermore, the formula comprehensive vector is specifically: [activity index, duration, safety index, degradation index].

[0024] Furthermore, the bud inhibition equation during the dormancy induction stage is used to describe the degree of influence of plant polyphenols on the hormone balance of tobacco buds;

[0025] The bud inhibition equation in the oxidative stress induction stage is used to describe the degree of oxidative stress induced by plant polyphenols in tobacco buds;

[0026] The bud inhibition equation in the metabolic inhibition stage is used to describe the degree of inhibition of plant polyphenols on the key metabolic processes of tobacco buds;

[0027] The effect comprehensive equation is used to obtain the effect comprehensive vector including the solutions of the dormancy induction stage bud inhibition equation, the oxidative stress induction stage bud inhibition equation, and the metabolic inhibition stage bud inhibition equation in a weighted manner.

[0028] Furthermore, the comprehensive effect vector is specifically: [sprouting inhibition index in dormancy induction stage, sprouting inhibition index in oxidative stress induction stage, sprouting inhibition index in metabolism inhibition stage].

[0029] Furthermore, the experimental data include the activity index, duration, safety index, degradation index, sprout inhibition index in the dormancy induction stage, sprout inhibition index in the oxidative stress induction stage, and sprout inhibition index in the metabolic inhibition stage of each test formula.

[0030] Furthermore, the steps of using the improved grey wolf optimization algorithm to solve the multi-objective optimization problem and obtain the optimal solution are as follows:

[0031] Step a: Initialize the wolf pack, where each wolf represents a possible recipe solution;

[0032] Step b, calculating the fitness of each wolf, that is, the weighted sum of the formula comprehensive vector and the effect comprehensive vector;

[0033] Step c, selecting α wolf, β wolf and δ wolf according to fitness;

[0034] Step d, update the positions of other wolves;

[0035] Step e: introducing adaptive weights and chaotic mapping to enhance the global search capability and convergence speed of the algorithm;

[0036] Step f: Repeat step be until the maximum number of iterations is reached or the convergence condition is met to obtain the optimal solution.

[0037] Further, the optimal solution is verified and fine-tuned to obtain the optimal formula, which specifically includes:

[0038] Step 1: Conduct medium-scale field trials to verify the actual effect of the optimized formula;

[0039] Step 2: Fine-tune the ratio of key ingredients using response surface methodology;

[0040] Step 3: Conduct sensitivity analysis to evaluate the impact of changes in the proportion of each component on the final effect.

[0041] The weighting method of the comprehensive effect equation considers the proportion of the bud inhibition effect of the three different stages to the bud inhibition effect of the entire life cycle of tobacco;

[0042] The weighted approach of the formulation comprehensive equation considers the importance of activity, duration, safety and degradability for an ideal tobacco bud inhibitor.

[0043] Further, the formula equation group of the sprout inhibitor is specifically described as follows:

[0044] 1.Activity equation:

[0045] ;

[0046] In the formula, is the activity index; For the Concentration of plant polyphenols (mg / L); is the number of plant polyphenols; For the Activity coefficient of plant polyphenols; For the The activity power of plant polyphenols; is the synergy coefficient; For the Species and The interaction coefficients of plant polyphenols; is the influence coefficient of temperature change on activity; is temperature (℃); is time (h); is the influence coefficient of periodic factors; is the angular frequency of the periodic factor; is the error term.

[0047] Parameter acquisition method:

[0048] : Determined by high performance liquid chromatography (HPLC).

[0049] : Obtained through dose-response experiments of single plant polyphenols, using nonlinear regression fitting.

[0050] : The synergistic effect of mixed plant polyphenols was obtained by using response surface methodology to design the experiment.

[0051] : Obtained through activity measurement experiments under different temperature conditions, using multiple regression analysis.

[0052] : Obtained by observing the periodic changes of experimental data over a long period of time, using Fourier analysis.

[0053] 2. Duration equation:

[0054] ;

[0055] In the formula, is the duration (days); is the influence coefficient of concentration on duration; For the The persistence coefficient of plant polyphenols; is the activity cumulative effect coefficient; is the activity decay coefficient; For time Activity index at ; is the formula stability influence coefficient; For the Stability index of plant polyphenols; is the environmental humidity influence coefficient; is the relative humidity (%); is the error term.

[0056] Parameter acquisition method:

[0057] : Obtained through duration measurement experiments of formulations with different concentrations, using logarithmic regression analysis.

[0058] : Obtained through long-term activity monitoring experiments and fitted using nonlinear least squares method.

[0059] : Obtained through accelerated stability test experiment, using analysis of variance.

[0060] : Obtained through duration measurement experiments under different humidity conditions, using multiple regression analysis.

[0061] 3. Security equation:

[0062] ;

[0063] In the formula, is the safety index; As a basic safety score; For the Toxicity coefficient of plant polyphenols; is the penalty coefficient for over-limit activity; is the active safety threshold; is the nonlinear influence coefficient of duration; is the total plant polyphenol concentration; is the biological toxicity influence coefficient; For the Effects of plant polyphenols on Toxicity coefficients for non-target organisms; For the Susceptibility index of non-target organisms; is the number of non-target organism species considered; is the error term.

[0064] Parameter acquisition method:

[0065] : Obtained through acute and chronic toxicity test experiments, using dose-response relationship analysis.

[0066] : Obtained through scoring by safety assessment experts using the Delphi method.

[0067] : Obtained through nonlinear analysis of the effect of duration on safety, using polynomial regression.

[0068] : Obtained through ecotoxicology experiments, using species sensitivity distribution (SSD) analysis.

[0069] 4. Degradation equation:

[0070] ;

[0071] In the formula, is the degradation index; is the total degradation coefficient; For the Relative degradation contribution of plant polyphenols; For the Degradation rate constants of plant polyphenols; is the influence coefficient of activity change on degradation; is the influence coefficient of cumulative degradation; For the Initial concentration of plant polyphenols; is the daily periodic degradation fluctuation coefficient; is the error term.

[0072] Parameter acquisition method:

[0073] : Obtained through plant polyphenol degradation kinetics experiments and fitted using a first-order kinetic model.

[0074] : Obtained through experiments on the relationship between activity change and degradation, using partial correlation analysis.

[0075] : Obtained through cumulative degradation determination experiment, using linear regression analysis.

[0076] : Obtained through continuous 24-hour degradation monitoring experiment, using time series analysis.

[0077] 5. Bud inhibition equation during dormancy induction stage:

[0078] ;

[0079] In the formula, is the bud inhibition index during the dormancy induction stage; For the The maximum dormancy-inducing effect of plant polyphenols; For the Hill coefficient of plant polyphenols; For the The median effective concentration of plant polyphenols; is the influence coefficient of hormone level change; is the endogenous hormone level; is the synergistic dormancy induction coefficient; For the Species and The dormancy induction synergy coefficient of plant polyphenols; is the initial dormancy induction intensity; is the dormancy-induced attenuation coefficient; is the error term.

[0080] Parameter acquisition method:

[0081] : Obtained through dormancy-inducing dose-response experiment of single plant polyphenols and fitted using Hill equation.

[0082] : Obtained through endogenous hormone level monitoring experiment and analyzed using multiple regression analysis.

[0083] : The results were obtained through synergistic dormancy induction experiments of mixed plant polyphenols, and the experiments were designed using response surface methodology.

[0084] : Obtained through long-term dormancy induction effect observation experiment, fitted using an exponential decay model.

[0085] 6. Sprout inhibition equation during oxidative stress induction stage:

[0086] ;

[0087] In the formula, is the sprout inhibition index during the oxidative stress induction stage; For the The maximum oxidative stress-inducing effect of plant polyphenols; For the Oxidative stress induction rate coefficients of plant polyphenols; is the influence coefficient of the change of active oxygen level; is the level of reactive oxygen species; is the synergistic oxidative stress induction coefficient; For the Species and Oxidative stress induction synergy coefficient of plant polyphenols; is the norm of the temperature gradient; is the error term.

[0088] Parameter acquisition method:

[0089] : Obtained from dose-response experiments of oxidative stress induction by single plant polyphenols, using nonlinear regression fitting.

[0090] : Obtained through reactive oxygen level monitoring experiments, using time series analysis.

[0091] :The synergistic oxidative stress induction experiment of mixed plant polyphenols was obtained by using response surface methodology to design the experiment.

[0092] :Obtained through the experiment of the effect of temperature gradient on oxidative stress, using heat map analysis.

[0093] 7. Bud inhibition equation in metabolic inhibition stage:

[0094] ;

[0095] In the formula, is the bud inhibition index during the metabolic inhibition stage; For the The degree of inhibition of key metabolic enzymes by plant polyphenols; is the cellular energy level; is the diffusion coefficient of plant polyphenols in cells; For the Species and The metabolic inhibition synergy coefficient of plant polyphenols; is the cell space coordinate; is the error term.

[0096] Parameter acquisition method:

[0097] : Obtained by enzyme activity inhibition experiment, using Michaelis-Menten kinetic analysis.

[0098] : Obtained through dynamic monitoring experiments of cellular energy levels and fitted using partial differential equations.

[0099] : Obtained through intracellular diffusion experiments of fluorescently labeled plant polyphenols, using FRAP (Fluorescence Recovery After Photobleaching) technology.

[0100] : Obtained through synergistic metabolic inhibition experiments of mixed plant polyphenols, using isopleth analysis.

[0101] The weighting method of the comprehensive effect equation considers the proportion of the bud inhibition effect of the three different stages to the bud inhibition effect of the entire life cycle of tobacco, specifically:

[0102] 1. Data collection: Conduct long-term follow-up observations on multiple tobacco samples in different tobacco growing areas and different growing seasons. Record the bud inhibition index of each sample during the dormancy induction stage, oxidative stress induction stage, and metabolic inhibition stage, as well as the overall bud inhibition effect throughout the life cycle.

[0103] 2. Data preprocessing: Standardize the collected data to eliminate the dimensional differences between different indicators.

[0104] 3. Establish a regression model: Use a multiple linear regression model, taking the bud inhibition index of the three stages as the independent variable and the overall bud inhibition effect as the dependent variable. The model form is as follows:

[0105] ;

[0106] In the formula, It is the overall sprout inhibition effect; is the bud inhibition index during the dormancy induction stage; is the sprout inhibition index during the oxidative stress induction stage; is the bud inhibition index during the metabolic inhibition stage; is the coefficient to be determined; is the error term.

[0107] 4. Model fitting: Use the least squares method to fit the model and get The estimated value of .

[0108] 5. Weight determination: Normalize the obtained coefficients to get the final weight:

[0109] ;

[0110] The calculation formula of the effect comprehensive vector is:

[0111] E=[ W 1 X 1 + W 2 X 2 + W 3 X 3 ] ;

[0112] in, is the effect synthesis vector.

[0113] The weighting method of the comprehensive formula equation considers the importance of activity, duration, safety and degradability for an ideal tobacco bud inhibitor, specifically:

[0114] 1. Data collection: Conduct a comprehensive evaluation of multiple different formulations of sprout inhibitors, record the activity index, duration, safety index and degradation index of each formulation, as well as the comprehensive score of each formulation by experts.

[0115] 2. Data preprocessing: standardize the collected data.

[0116] 3. Establish a regression model: Use a multiple linear regression model, taking the four factors as independent variables and the expert comprehensive score as the dependent variable. The model form is as follows:

[0117] ;

[0118] In the formula, Provide comprehensive scores for experts; is the activity index; for duration; is the safety index; is the degradation index; is the coefficient to be determined; is the error term.

[0119] 4. Model fitting: Use the least squares method to fit the model and get The estimated value of .

[0120] 5. Weight determination: Normalize the obtained coefficients to get the final weight:

[0121] ;

[0122] The formula for calculating the recipe comprehensive vector is:

[0123] F=[ V 1 Y 1 + V 2 Y 2 + V 3 Y 3 + V 4 Y 4 ] ;

[0124] in, is the recipe synthesis vector.

[0125] Additional notes:

[0126] 1. Least Squares Method: It is a mathematical optimization technique that finds the best function matching the data by minimizing the sum of squares of errors.

[0127] 2. Standardization: Convert data of different dimensions to the same scale. Commonly used methods include Z-score standardization and Min-Max standardization.

[0128] 3.Multiple linear regression: It is a statistical method to study the linear relationship between a dependent variable and multiple independent variables.

[0129] This data-driven approach can yield more objective and realistic weights. These weights will reflect the true importance of each factor in actual applications, rather than being based on subjective judgment. It should be noted that in actual applications, the stability and generalization of the model may also need to be considered, and techniques such as cross-validation can be used to evaluate and optimize model performance. At the same time, be alert to possible multicollinearity issues, and consider using ridge regression or other regularization methods when necessary.

[0130] Compared with the prior art, the present invention provides a method for optimizing the formula of a plant polyphenol tobacco bud inhibitor, which makes full use of the theory of mathematical modeling and algorithm optimization, systematically optimizes the bud inhibitor formula, and achieves the following technical effects:

[0131] 1. Improve the sprout inhibition effect. This method establishes a sprout inhibition mechanism model and deeply analyzes the effects of plant polyphenols on the physiological processes of tobacco buds, including key stages such as dormancy induction, oxidative stress induction and metabolic inhibition. On this basis, different plant polyphenol components are rationally proportioned to exert their respective synergistic effects, which greatly improves the sprout inhibition effect.

[0132] 2. Extend the duration. This method establishes a duration prediction model, taking into account the effects of plant polyphenol concentration, activity accumulation, environmental factors, etc. on the duration. By optimizing the formula, it ensures that the sprout inhibition effect can be maintained for a long time to meet the actual application needs.

[0133] 3. Enhanced safety. This method establishes a safety assessment model that comprehensively considers the toxicity risks of plant polyphenols to humans and the environment. By optimizing the formula, it ensures that the impact of the sprout inhibitor on the surrounding ecosystem during use is minimized, meeting increasingly stringent environmental standards.

[0134] 4. Improve environmental friendliness. This method established a degradation kinetic model and optimized the degradation rate and pathway of the sprout inhibitor in the environment. Through reasonable proportioning, it not only ensures the sustainability of the sprout inhibitor effect, but also minimizes the environmental load and achieves sustainable utilization.

[0135] 5. Optimize formula performance. This method takes the above optimization objectives into consideration and obtains the optimal formula that takes all factors into consideration through a multi-objective optimization algorithm. While ensuring the sprout inhibition effect, it also takes into account key indicators such as duration, safety, and environmental friendliness, greatly improving the comprehensive performance of the sprout inhibitor.

[0136] In short, this optimization method gives full play to the advantages of mathematical modeling and algorithm optimization, and solves the technical problem that there is currently a lack of a systematic formula optimization method, making it difficult to quickly find an ideal formula that takes into account sprout inhibition effect, safety and environmental friendliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0138] 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.

[0139] like Figure 1 FIG. 1 is a flow chart of a method for optimizing a formulation of a plant polyphenol tobacco bud inhibitor provided by the present invention. The method comprises the following steps:

[0140] S10, obtaining a basic formula of a tobacco bud suppressant containing a plant polyphenol extract;

[0141] S20, establishing a formula equation group of plant polyphenol tobacco bud inhibitor, including an activity equation, a duration equation, a safety equation, a degradation equation and a comprehensive formula equation;

[0142] S30, establishing a tobacco bud inhibition effect equation group, including a bud inhibition equation in a dormancy induction stage, a bud inhibition equation in an oxidative stress induction stage, a bud inhibition equation in a metabolic inhibition stage, and a comprehensive effect equation;

[0143] S40, according to the basic formula, establish multiple test formulas of plant polyphenol tobacco bud inhibitors by traversing the ratio of the combined plant polyphenol extracts and adjusting the ratio of various components in the basic formula, conduct small-scale tests, obtain experimental data, and record the specific composition and ratio of the plant polyphenol extracts in each test formula;

[0144] S50, fitting the plant polyphenol tobacco bud inhibitor formula equation group and the tobacco bud inhibitor effect equation group using the experimental data to obtain a fitting formula equation group and a fitting effect equation group;

[0145] S60, constructing a multi-objective optimization problem based on the fitting formula equation group and the fitting effect equation group, wherein the objective function includes an optimized formula comprehensive vector and an effect comprehensive vector;

[0146] S70, using the improved grey wolf optimization algorithm to solve the multi-objective optimization problem and obtain the optimal solution;

[0147] S80. Verify and fine-tune the optimal solution to obtain the optimal formula.

[0148] The specific implementation methods of the above steps are described in detail below:

[0149] The specific implementation of step S10 is as follows:

[0150] The purpose of this step is to obtain the basic formula of plant polyphenol tobacco bud inhibitor. First, it is necessary to collect common plant polyphenol extracts, such as tea polyphenols, grape seed extract, apple polyphenols, citrus flavonoids, pine bark extract, olive polyphenols and pomegranate polyphenols. Through literature research and experimental testing, the appropriate proportion range of these plant polyphenol extracts in the bud inhibitor is determined, which is generally 20% to 30%.

[0151] Secondly, choose a suitable emulsifier. Emulsifiers can improve the dispersibility and stability of plant polyphenols in aqueous solutions. Commonly used ones include polyoxyethylene ethers, fatty acid esters, phospholipids, etc. According to experimental tests, the optimal addition ratio of emulsifiers is 5% to 10%.

[0152] In order to improve the sprout inhibition activity, some synergists need to be added. Common synergists include quaternary ammonium salt compounds, lignin derivatives, etc. According to experimental results, the addition ratio of synergists should be controlled at 2% to 5%.

[0153] In addition, a pH regulator needs to be added to control the pH value of the sprout inhibitor. Commonly used pH regulators include citric acid, lactic acid, sodium hydroxide, etc. According to the optimal pH range of different plant polyphenols, the addition ratio of pH regulator is generally 1% to 3%.

[0154] In order to prevent microbial contamination and extend the storage period, it is necessary to add an appropriate amount of preservatives. Common preservatives include methyl hydroxybenzoic acid, hexenyl salicylic acid, etc. According to relevant standards, the addition ratio of preservatives should be controlled at 0.1% to 0.5%.

[0155] In order to enhance the stability of the sprout inhibitor, a certain amount of stabilizer needs to be added. Commonly used stabilizers include ethylene glycol, glycerol, polyvinyl pyrrolidone, etc. According to experimental results, the addition ratio of the stabilizer is 1% to 3%.

[0156] Finally, water is used as the solvent, and its addition ratio is the remaining part, generally between 30% and 50%.

[0157] To sum up, the basic formula of plant polyphenol tobacco bud inhibitor is as follows: plant polyphenol extract: 20%-30%; emulsifier: 5%-10%; synergist: 2%-5%; pH adjuster: 1%-3%; preservative: 0.1%-0.5%; stabilizer: 1%-3%; solvent (water): 30%-50%.

[0158] This basic formula covers the key components of the sprout inhibitor and its reasonable addition ratio range. By traversing the ratios of different components and conducting small-scale tests to verify, a more ideal sprout inhibitor formula can be further optimized.

[0159] The specific implementation of step S20 is as follows:

[0160] The purpose of this step is to establish a set of equations to describe the performance of the plant polyphenol tobacco bud inhibitor formula. It includes the following five equations:

[0161] 1. Activity equation

[0162] This equation is used to describe the contribution of various plant polyphenol extracts to the sprout inhibition effect. Its form is as follows:

[0163] ;

[0164] in, is the activity index, reflecting the overall sprout-inhibiting ability of the sprout inhibitor; For the The concentration of plant polyphenols; and Respectively The activity coefficients and activity powers of plant polyphenols are used to describe the activity contribution of a single ingredient; and are the synergy coefficient and interaction coefficient, respectively, describing the synergistic or antagonistic effects between the components; and are the influence coefficients of temperature change and periodic factors respectively; is the temperature, For time; is a random error.

[0165] Through the activity test experiments on single ingredients and mixed ingredients, the value range of each parameter can be determined.

[0166] 2. Duration Equation

[0167] This equation is used to describe the duration of the sprout inhibition effect. Its form is as follows:

[0168] ;

[0169] in, for duration; and are the influence coefficient of concentration on duration and the persistence coefficient of each plant polyphenol, respectively; and are the activity cumulative effect coefficient and the attenuation coefficient, respectively; For time Activity index at ; and They are the formula stability influence coefficient and the stability index of each plant polyphenol; is the environmental humidity influence coefficient; is the relative humidity; is a random error.

[0170] The value range of each parameter can be determined by conducting duration test experiments on different formulations under different conditions.

[0171] 3. Security equation

[0172] This equation is used to evaluate the safety of a formula to the environment and human body. Its form is as follows:

[0173] ;

[0174] in, is the safety index; As a basic safety score; For the Toxicity coefficient of plant polyphenols; is the penalty coefficient for over-limit activity. Exceeding safety threshold effective when; is the nonlinear influence coefficient of duration; is the total plant polyphenol concentration; is the biological toxicity influence coefficient; For the Effects of plant polyphenols on Toxicity coefficients for non-target organisms; For the Susceptibility index of non-target organisms; is the number of non-target organism species considered; is a random error.

[0175] Through acute / chronic toxicity tests and ecotoxicity tests, the value range of each parameter can be determined.

[0176] 4. Degradation equation

[0177] This equation is used to describe the degradation rate and pathway of the sprout inhibitor formulation in the environment. Its form is as follows:

[0178] ;

[0179] in, is the degradation index; is the total degradation coefficient; For the Relative degradation contribution of plant polyphenols; For the Degradation rate constants of plant polyphenols; is the influence coefficient of activity change on degradation; is the influence coefficient of cumulative degradation; For the Initial concentration of plant polyphenols; is the daily periodic degradation fluctuation coefficient; is a random error.

[0180] Through the degradation kinetics experiments of plant polyphenols under different conditions, the value range of each parameter can be determined.

[0181] 5. Formula comprehensive equation

[0182] This equation takes into account the solutions of the above four equations and obtains a vector that describes the overall performance of the formula. Its form is:

[0183] F=[ V 1 Y 1 + V 2 Y 2 + V 3 Y 3 + V 4 Y 4 ] ;

[0184] in, is the recipe synthesis vector; is the activity index; for duration; is the safety index; is the degradation index; are the weights of these four factors, which are determined according to actual application requirements.

[0185] Through comprehensive evaluation experiments on a large number of formula samples, combined with expert scoring, multivariate linear regression is used to determine the value of each weight coefficient.

[0186] After establishing these five equations, the key performance indicators of the plant polyphenol tobacco bud inhibitor formula can be fully described, laying the foundation for subsequent optimization.

[0187] The specific implementation of step S30 is as follows:

[0188] The purpose of this step is to establish a set of equations describing the mechanism of plant polyphenols inhibiting tobacco bud growth. It includes the following three equations:

[0189] 1. Bud inhibition equation during dormancy induction stage

[0190] This equation is used to describe the effect of plant polyphenols on the hormone balance of tobacco buds. Its form is as follows:

[0191] ;

[0192] in, is the bud inhibition index during the dormancy induction stage; Respectively The maximum dormancy-inducing effect, Hill coefficient and half-maximal effective concentration of plant polyphenols were used to describe the dormancy-inducing ability of a single component. is the influence coefficient of hormone level change; is the endogenous hormone level; and are the synergistic dormancy induction coefficient and the synergistic coefficient between components, respectively, describing the synergistic effect between components; and are the initial dormancy induction intensity and decay coefficient, respectively, describing the initial induction and decay over time; is a random error.

[0193] The value range of each parameter can be determined through dormancy induction experiments of single / mixed ingredients and hormone level monitoring experiments.

[0194] 2. Sprout inhibition equation during oxidative stress induction stage

[0195] This equation is used to describe the extent to which plant polyphenols induce oxidative stress in tobacco buds. Its form is as follows:

[0196] ;

[0197] in, is the sprout inhibition index during the oxidative stress induction stage; and Respectively The maximum oxidative stress induction effect and induction rate coefficient of plant polyphenols were used to describe the oxidative stress induction ability of a single component. is the influence coefficient of the change of active oxygen level; is the level of reactive oxygen species; and They are the synergistic oxidative stress induction coefficient and the synergistic coefficient between components, describing the synergistic effect between components; is the model of the temperature gradient, describing the effect of temperature on oxidative stress; is a random error.

[0198] The value range of each parameter can be determined through single / mixed component oxidative stress induction experiments, reactive oxygen species monitoring and temperature gradient testing.

[0199] 3. Bud inhibition equation in metabolic inhibition stage

[0200] This equation is used to describe the degree of inhibition of key metabolic processes of tobacco buds by plant polyphenols. Its form is as follows:

[0201] ;

[0202] in, is the bud inhibition index during the metabolic inhibition stage; For the The degree of inhibition of key metabolic enzymes by plant polyphenols; is the cellular energy level; is the diffusion coefficient of plant polyphenols in cells; For the Species and The metabolic inhibition synergy coefficient of plant polyphenols; is the cell space coordinate; is a random error.

[0203] The specific implementation of step S40 is as follows:

[0204] The purpose of this step is to establish multiple groups of experimental formulas of plant polyphenol tobacco bud inhibitors, conduct small-scale tests and obtain experimental data.

[0205] First, according to the basic formula obtained in step S10, the combination ratios of different types of plant polyphenol extracts, such as tea polyphenols, grape seed extracts, apple polyphenols, etc., are traversed, and different addition ratios are tried. In this way, multiple groups of test formulas composed of different plant polyphenol components can be obtained.

[0206] Secondly, on the basis of the basic formula, the addition ratio of each component such as emulsifier, synergist, pH adjuster, preservative, stabilizer, etc. is adjusted to further expand the test formula. In this way, multiple groups of test formulas with different plant polyphenol contents and auxiliary material ratios can be obtained.

[0207] For each test formula, a small-scale greenhouse or field test was conducted to measure its activity index, duration, safety index, degradation index, and indicators of three stages of bud inhibition, including the bud inhibition index in the dormancy induction stage, the oxidative stress induction stage, and the metabolic inhibition stage. At the same time, the specific composition and addition ratio of the plant polyphenol extract in each test formula were recorded.

[0208] In this way, a large amount of performance data of different formulations in actual applications can be obtained, providing a basis for subsequent formulation optimization.

[0209] The specific implementation of step S50 is as follows:

[0210] The purpose of this step is to fit the formula equation group established in step S20 and the sprout inhibition effect equation group established in step S30 according to the experimental data obtained in step S40 to obtain a more accurate equation group.

[0211] For the formula equation group, including the activity equation, the duration equation, the safety equation, the degradation equation and the comprehensive formula equation, the measured data of each formula obtained in step S40 are used to fit the parameters of each equation using the least squares method or nonlinear regression method to obtain a more accurate equation expression.

[0212] For the sprout inhibition effect equation group, including the sprout inhibition equation in the dormancy induction stage, the sprout inhibition equation in the oxidative stress induction stage and the sprout inhibition equation in the metabolic inhibition stage, the measured data obtained in step S40 are also used to fit the parameters of each equation using the regression analysis method to obtain an equation expression that is more in line with the actual situation.

[0213] Through the fitting analysis of a large amount of experimental data, the various equations established previously can be further optimized and improved to better describe the actual situation and provide a reliable foundation for subsequent multi-objective optimization.

[0214] The specific implementation of step S60 is as follows:

[0215] The purpose of this step is to construct a multi-objective optimization problem based on the formula equation group and the sprout inhibition effect equation group fitted in step S50.

[0216] The objective function consists of two parts: the recipe synthesis vector and the effect synthesis vector.

[0217] Recipe comprehensive vector Calculated by the formula given in step S20, the activity index is comprehensively considered , Duration , Safety Index and degradation index The weight of each indicator It is determined according to actual application requirements. Usually, activity index and duration have a larger weight, while safety and degradability have a smaller weight.

[0218] Effect synthesis vector The formula given in step S30 is used to calculate the bud inhibition index during the dormancy induction stage. , germination inhibition index during oxidative stress induction and metabolic inhibition stage germination inhibition index The weight of each indicator It is determined based on the contribution to the bud inhibition effect throughout the entire life cycle of tobacco. Usually, the dormancy induction stage has the largest weight, and the metabolic inhibition stage has the smallest weight.

[0219] In summary, the objective function of the multi-objective optimization problem is:

[0220] ;

[0221] That is, the formula comprehensive vector and the effect comprehensive vector are maximized at the same time. In this way, a balance can be achieved between activity, duration, safety, degradability and the three stages of bud inhibition effect, and an ideal plant polyphenol tobacco bud inhibitor formula can be obtained.

[0222] The specific implementation of step S70 is as follows:

[0223] The purpose of this step is to use the improved grey wolf optimization algorithm to solve the multi-objective optimization problem constructed in step S60 and obtain the optimal solution.

[0224] The specific steps of the algorithm are as follows:

[0225] 1. Initialize the wolf pack: Each wolf represents a possible sprout inhibitor formula, that is, the addition ratio of each component. The size of the wolf pack can be set to 50-100.

[0226] 2. Calculate fitness: For each wolf, calculate its recipe comprehensive vector and effect combined vector , and take the weighted sum as the fitness function value.

[0227] 3. Select the leader wolf: According to the fitness value, select the best wolf ( Wolf), suboptimal wolf ( Wolf) and the third best wolf ( Wolf) as the leader wolf.

[0228] 4. Update the position of the wolf pack: The positions of other wolves are updated according to the position of the leader wolf to obtain a better formula. The update formula is as follows:

[0229] ;

[0230] in, For the The wolf's position vector, is a random number between [0,1], is the adaptive weight factor.

[0231] 5. Introducing adaptive weights and chaotic mapping: In order to enhance the global search capability and convergence speed of the algorithm, adaptive weight factors and chaotic mapping mechanisms are introduced when updating the wolf pack positions. The adaptive weight factors are dynamically adjusted according to the number of iterations, and the chaotic mapping can generate random and orderly disturbances to avoid falling into local optimality.

[0232] 6. Termination condition check: Repeat steps 2-5 until the maximum number of iterations is reached or the convergence condition is met. The formula represented by the wolf is the optimal solution.

[0233] Through the improved Gray Wolf optimization algorithm, the multi-objective optimization problem can be effectively solved to obtain the optimal plant polyphenol tobacco bud inhibitor formula that takes into account activity, duration, safety and degradability.

[0234] Specifically, the weighting method of the comprehensive effect equation considers the proportion of the bud inhibition effect of the three different stages to the bud inhibition effect of the entire life cycle of tobacco, specifically:

[0235] 1. Dormancy induction stage: accounting for 50% of the total weight, this stage is crucial for the initial inhibition of bud germination and has the greatest impact on the bud inhibition effect throughout the life cycle.

[0236] 2. Oxidative stress induction stage: accounting for 30% of the total weight, this stage plays an important role in inhibiting the growth of buds that have begun to germinate and is a key stage in maintaining the bud inhibition effect.

[0237] 3. Metabolic inhibition stage: accounting for 20% of the total weight, this stage is mainly used to prolong the sprout inhibition effect and ensure long-term sprout inhibition effect.

[0238] The calculation formula of the comprehensive effect vector was: [0.5×germination inhibition index in dormancy induction stage + 0.3×germination inhibition index in oxidative stress induction stage + 0.2×germination inhibition index in metabolic inhibition stage];

[0239] The weighting method of the comprehensive formula equation considers the importance of activity, duration, safety and degradability for an ideal tobacco bud inhibitor, specifically:

[0240] 1. Activity index: accounting for 40% of the total weight. Activity is the most direct indicator to measure the effect of the sprout inhibitor and has the greatest impact on the overall performance of the formula.

[0241] 2. Duration: accounting for 30% of the total weight. Duration determines the frequency of use of the sprout inhibitor and directly affects the effect and economy of actual application.

[0242] 3. Safety index: accounting for 20% of the total weight. Safety is related to the impact of sprout inhibitors on the environment and human body, and is an important factor in ensuring sustainable use.

[0243] 4. Degradation index: accounting for 10% of the total weight. Degradation performance affects the residue of sprout inhibitors in the environment. Although its importance is relatively low, it still needs to be considered.

[0244] The formula for the comprehensive vector of the formulation is: [0.4×activity index+0.3×duration+0.2×safety index+0.1×degradation index];

[0245] This weighting method takes into account the relative importance of each factor in practical applications, so that the optimization process can better balance the sprout suppression effect, sustainability, safety and environmental friendliness. In practical applications, these weights may need to be fine-tuned according to specific needs and regulatory requirements. For example, if there are higher requirements for environmental safety in certain regions, the weights of the safety index and degradation index can be appropriately increased.

[0246] The specific implementation of step S80 is as follows:

[0247] The purpose of this step is to verify and fine-tune the optimal formula obtained in step S70 to ensure that it can achieve the expected effect in practical applications.

[0248] 1. Medium-scale field trial verification: Conduct medium-scale field trials in different tobacco growing areas to test the actual sprout suppression effect of the optimized formula, including sprout suppression index, duration and other indicators, and conduct comparative analysis with the control group. This step can verify the applicability of the optimized formula under actual planting conditions.

[0249] 2. Optimize the proportion of key ingredients by response surface methodology: For the optimal formula obtained in step S70, the addition ratio of key ingredients such as plant polyphenol extracts, emulsifiers, enhancers, etc. is finely adjusted using response surface analysis to further improve the sprout inhibition effect and other performance indicators.

[0250] 3. Sensitivity analysis: Conduct sensitivity analysis on the proportion of each ingredient in the optimal formula to evaluate the impact of changes in the proportion of a single ingredient on the overall performance. This helps to further optimize the formula and ensure the robustness of the formula in practical applications.

[0251] 4. Multi-regional adaptability optimization (optional step): Consider the differences in different climatic conditions and tobacco varieties, and establish an optimized bud inhibitor formula for specific regions or varieties when necessary. This step can further improve the applicability of the formula.

[0252] Through the above steps, the optimal formula obtained in step S70 can be fully verified and optimized to ensure its effect and performance in practical applications.

[0253] In summary, the optimization method of the invention includes 8 steps, involving multiple mathematical modeling and algorithm optimization contents. Specifically:

[0254] Step S10 determines the basic formula components of the plant polyphenol tobacco sprout inhibitor and their reasonable proportion ranges.

[0255] Step S20 establishes five equations describing the performance of the sprout inhibitor formula, including an activity equation, a duration equation, a safety equation, a degradation equation, and a formula comprehensive equation.

[0256] Step S30 establishes three equations describing the sprout inhibition mechanism, including sprout inhibition effect equations in the dormancy induction stage, oxidative stress induction stage and metabolic inhibition stage.

[0257] Step S40 obtains a large amount of experimental data as the basis for subsequent optimization by traversing the combination and adjusting the recipe.

[0258] Step S50 uses experimental data to perform fitting optimization on each equation to obtain a more accurate mathematical model.

[0259] In step S60, a multi-objective optimization problem is constructed based on the optimized model.

[0260] Step S70 uses the improved grey wolf optimization algorithm to solve the multi-objective optimization problem and obtain the optimal formulation solution.

[0261] Step S80 verifies and fine-tunes the optimal formula to ensure its effectiveness in practical applications.

[0262] This systematic approach combining mathematical modeling, algorithm optimization and experimental verification can effectively optimize the formulation of plant polyphenol tobacco bud inhibitors and achieve a balance in terms of activity, duration, safety and environmental friendliness.

[0263] Specifically, the principle of the present invention is:

[0264] First, in step S20, a group of equations describing the performance of the formula is established, including an activity equation, a duration equation, a safety equation, and a degradation equation. The activity equation describes the contribution of various plant polyphenol components and their synergistic effects to the sprout inhibition effect, providing a basis for optimizing activity. The duration equation takes into account the effects of concentration, activity accumulation, environmental factors, etc. on the duration, providing a basis for optimizing the duration. The safety equation evaluates the safety risks of the formula to the human body and the environment, providing a basis for optimizing safety. The degradation equation describes the degradation kinetics of the formula in the environment, providing a basis for optimizing environmental friendliness. By establishing these models, the key performance indicators of the sprout inhibitor can be fully and quantitatively described.

[0265] Secondly, in step S30, a group of equations describing the mechanism of bud inhibition is established, including the bud inhibition equation in the dormancy induction stage, the bud inhibition equation in the oxidative stress induction stage, and the bud inhibition equation in the metabolic inhibition stage. These equations reveal the mechanism by which plant polyphenols achieve bud inhibition by regulating the physiological processes of tobacco buds, and provide a theoretical basis for optimizing the bud inhibition effect. The bud inhibition equation in the dormancy induction stage describes the effect of plant polyphenols on the hormone balance of tobacco buds, the bud inhibition equation in the oxidative stress induction stage describes the oxidative stress response induced by plant polyphenols, and the bud inhibition equation in the metabolic inhibition stage describes the inhibitory effect of plant polyphenols on key metabolic processes. The bud inhibition effects of these three stages comprehensively determine the overall bud inhibition performance.

[0266] On this basis, a multi-objective optimization problem is constructed in step S60, and the objective function includes the formula comprehensive vector and the effect comprehensive vector. The formula comprehensive vector comprehensively considers factors such as activity, duration, safety and degradability, and is a key indicator to describe the overall performance of the formula. The effect comprehensive vector comprehensively considers the three stages of sprout inhibition effect, and is a key indicator to describe the sprout inhibition mechanism. By optimizing these two vectors simultaneously, the best formula solution that takes into account the sprout inhibition effect, sustainability, safety and environmental friendliness can be found.

[0267] Finally, in step S70, the improved gray wolf optimization algorithm is used to solve the above multi-objective optimization problem. The gray wolf optimization algorithm simulates the social behavior of gray wolves, and finally converges to the global optimal solution by selecting a leader wolf and continuously updating the position of the wolf pack. In order to improve the performance of the algorithm, the present invention introduces an adaptive weight factor and a chaotic mapping mechanism to enhance the global search capability and convergence speed of the algorithm. In this way, the multi-objective optimization problem can be solved more effectively, and the optimal sprout inhibitor formula that meets the actual application needs can be obtained. Then, the optimal solution is verified and fine-tuned through step S80 to obtain the optimal formula.

[0268] In summary, this optimization method fully combines the theories of mathematical modeling and algorithm optimization. By establishing detailed performance and mechanism models and using an improved multi-objective optimization algorithm to solve the problem, it ultimately obtains the optimal formula that takes multiple factors into account.

[0269] In order to better understand and implement the present invention, a specific embodiment 1 of the present invention is provided below. The specific steps of embodiment 1 are described in detail as follows:

[0270] In step S10, a basic formula of a plant polyphenol tobacco sprout inhibitor is obtained. The basic formula includes the following components and their proportions:

[0271] Plant polyphenol extract, the ratio is ;

[0272] Emulsifier, ratio ;

[0273] Synergist, ratio ;

[0274] pH adjuster, ratio ;

[0275] Preservatives, ratio ;

[0276] Stabilizer, ratio ;

[0277] Solvent (water), the ratio is .

[0278] in, Indicates The plant polyphenol extract may include any one of tea polyphenols, grape seed extract, apple polyphenols, citrus flavonoids, pine bark extract, olive polyphenols, and pomegranate polyphenols, or a combination of at least two of them.

[0279] In step S20, a set of equations describing the performance of the plant polyphenol tobacco bud inhibitor formulation is established, including:

[0280] 1. Activity equation

[0281] ;

[0282] in, is the activity index, For the Mass concentration of plant polyphenols (mg / L), is the number of plant polyphenols, and Respectively Activity coefficients and activity powers of plant polyphenols, is the synergy coefficient, For the Species and The interaction coefficients of plant polyphenols, is the influence coefficient of temperature change on activity, is temperature (℃), is time (h), and are the influence coefficient and angular frequency of the periodic factor, is a random error.

[0283] 2. Duration Equation

[0284] ;

[0285] in, is the duration (days), is the influence coefficient of concentration on duration, For the The persistence coefficient of plant polyphenols, is the activity cumulative effect coefficient, is the activity decay coefficient, For time The activity index at is the formula stability influence coefficient, For the Stability index of plant polyphenols, is the environmental humidity influence coefficient, is the relative humidity (%), is a random error.

[0286] 3. Security equation

[0287] ;

[0288] in, is the safety index, As the basic safety score, For the Toxicity coefficient of plant polyphenols, is the penalty coefficient for excessive activity, is the active safety threshold, is the duration nonlinear influence coefficient, is the total plant polyphenol concentration, is the biological toxicity influence coefficient, For the Effects of plant polyphenols on Toxicity coefficients for non-target organisms, For the The sensitivity index of non-target organisms, is the number of non-target species considered, is a random error.

[0289] 4. Degradation equation

[0290] ;

[0291] in, is the degradation index, is the total degradation coefficient, For the The relative degradation contribution of plant polyphenols, For the The degradation rate constants of plant polyphenols, is the influence coefficient of activity change on degradation, is the cumulative degradation coefficient, For the The initial concentration of plant polyphenols, is the daily periodic degradation fluctuation coefficient, is a random error.

[0292] 5. Formula comprehensive equation

[0293] F=[ V 1 Y 1 + V 2 Y 2 + V 3 Y 3 + V 4 Y 4 ] ;

[0294] in, is the formula comprehensive vector, is the activity index, For duration, is the safety index, is the degradation index, are the weight coefficients of these four factors respectively.

[0295] In step S30, a set of equations describing the mechanism of plant polyphenols inhibiting tobacco budding is established, including:

[0296] 1. Bud inhibition equation during dormancy induction stage

[0297] ;

[0298] in, is the bud inhibition index during the dormancy induction stage, Respectively The maximum dormancy-inducing effect, Hill coefficient and half-maximal effective concentration of plant polyphenols. is the influence coefficient of hormone level change, is the endogenous hormone level, and are the synergistic dormancy induction coefficient and the synergistic coefficient between components, and are the initial dormancy induction intensity and attenuation coefficient, respectively. is a random error.

[0299] 2. Sprout inhibition equation during oxidative stress induction stage

[0300] ;

[0301] in, is the germination inhibition index at the oxidative stress induction stage, and Respectively The maximum oxidative stress induction effect and induction rate coefficient of plant polyphenols, is the influence coefficient of the change of active oxygen level, is the level of active oxygen, and are the synergistic oxidative stress induction coefficient and the synergistic coefficient between components, is the norm of the temperature gradient, is a random error.

[0302] 3. Bud inhibition equation in metabolic inhibition stage

[0303] ;

[0304] in, is the bud inhibition index during the metabolic inhibition stage, For the The inhibitory effects of plant polyphenols on key metabolic enzymes is the cellular energy level, is the diffusion coefficient of plant polyphenols in cells, For the Species and The metabolic inhibition synergy coefficient of plant polyphenols, is the cell space coordinate, is a random error.

[0305] In step S40, based on the basic formula obtained in step S10, multiple groups of test formulas were established by traversing the ratios of different plant polyphenol extracts and adjusting the addition ratios of each component, and small-scale tests were conducted to obtain experimental data for each formula, including activity index. , Duration , Safety Index , degradation index , and the bud inhibition index during dormancy induction , germination inhibition index during oxidative stress induction and metabolic inhibition stage germination inhibition index . The specific composition and proportion of the plant polyphenol extracts in each test formula were also recorded.

[0306] In step S50, the formula equation group established in step S20 and the sprout inhibition effect equation group established in step S30 are fitted using the experimental data obtained in step S40 to obtain a fitted formula equation group and a fitted effect equation group. The specific fitting method includes:

[0307] 1. For the formula equation group, the least square method or nonlinear regression method is used to fit the various parameters of the activity equation, duration equation, safety equation, degradation equation and formula comprehensive equation.

[0308] 2. For the sprout inhibition effect equation group, the regression analysis method was also used to fit the various parameters of the sprout inhibition equation in the dormancy induction stage, the oxidative stress induction stage and the metabolic inhibition stage.

[0309] By fitting and analyzing a large amount of experimental data, a more accurate set of equations can be obtained, providing a reliable foundation for subsequent multi-objective optimization.

[0310] In step S60, a multi-objective optimization problem is constructed based on the formula equation group and the sprout inhibition effect equation group obtained by fitting in step S50. The objective function includes:

[0311] Recipe comprehensive vector :

[0312] F=[ V 1 Y 1 + V 2 Y 2 + V 3 Y 3 + V 4 Y 4 ] ;

[0313] Effect synthesis vector :

[0314] E=[ W 1 X 1 + W 2 X 2 + W 3 X 3 ] ;

[0315] in, is the activity index, For duration, is the safety index, is the degradation index; is the bud inhibition index during the dormancy induction stage, is the germination inhibition index at the oxidative stress induction stage, is the bud inhibition index during the metabolic inhibition stage; is the weight coefficient of the recipe comprehensive vector, is the weight coefficient of the comprehensive effect vector.

[0316] The goal of this multi-objective optimization problem is to simultaneously maximize the recipe synthesis vector and effect combined vector ,Right now , in order to achieve a balance between activity, duration, safety, degradability and the three stages of sprout inhibition effect.

[0317] In step S70, the improved gray wolf optimization algorithm is used to solve the multi-objective optimization problem constructed in step S60 to obtain the optimal solution. The specific steps of the algorithm are as follows:

[0318] 1. Initialize the wolf pack: Each wolf represents a possible sprout inhibitor formula, that is, the addition ratio of each component. The size of the wolf pack can be set to 50-100.

[0319] 2. Calculate fitness: For each wolf, calculate its recipe comprehensive vector and effect combined vector , and take the weighted sum as the fitness function value.

[0320] 3. Select the leader wolf: According to the fitness value, select the best wolf ( Wolf), suboptimal wolf ( Wolf) and the third best wolf ( Wolf) as the leader wolf.

[0321] 4. Update the position of the wolf pack: The positions of other wolves are updated according to the position of the leader wolf to obtain a better formula. The update formula is as follows:

[0322] ;

[0323] in, For the The wolf's position vector, is a random number between [0,1], It is an adaptive weight factor, which is dynamically adjusted according to the number of iterations to enhance the global search capability of the algorithm.

[0324] 5. Introducing chaotic mapping: When updating the position of the wolf pack, introducing a chaotic mapping mechanism to generate random and orderly disturbances can prevent the algorithm from falling into the local optimum. Specifically, logistic mapping or Ernst mapping can be used as the chaotic mapping function.

[0325] 6. Termination condition check: Repeat steps 2-5 until the maximum number of iterations is reached or the convergence condition is met. The formula represented by the wolf is the optimal solution.

[0326] By introducing adaptive weight factors and chaotic mapping, the improved Grey Wolf Optimization Algorithm can effectively balance exploration and utilization, improve the efficiency and robustness of solving multi-objective optimization problems, and ultimately obtain the optimal plant polyphenol tobacco bud inhibitor formula that takes into account activity, duration, safety and degradability.

[0327] In step S80, the optimal formula obtained in step S70 is verified and fine-tuned, which specifically includes the following steps:

[0328] 1. Medium-scale field trials to verify:

[0329] Conduct medium-scale field trials in different tobacco growing regions to test the actual sprout inhibition effect of the optimized formula, including the sprout inhibition index , Duration This step can verify the applicability of the optimized formula under actual planting conditions.

[0330] 2. Optimization of key ingredient ratios using response surface methodology:

[0331] For the optimal formula obtained in step S70, key ingredients such as plant polyphenol extracts were analyzed using response surface methodology. , emulsifier , synergist The addition ratio of etc. can be finely adjusted to further improve the sprout inhibition effect and other performance indicators. Specifically, the following response surface model can be established:

[0332] ;

[0333] in, For the response variable (such as germination inhibition index , Duration wait), For independent variables (such as , , wait), is the parameter to be estimated, The optimal addition ratio of each component can be determined through experimental design and regression analysis.

[0334] 3. Sensitivity analysis:

[0335] Sensitivity analysis of the proportion of each ingredient in the optimal formula is performed to evaluate the impact of changes in the proportion of a single ingredient on the overall performance. This helps to further optimize the formula and ensure the freshness and flexibility of the formula in practical applications. Sensitivity analysis can use methods such as One-At-A-Time (OAT) or Latin hypercube sampling (LHS) to calculate the local sensitivity index of changes in the proportion of each ingredient to the response variable.

[0336] 4. Multi-region adaptive optimization (optional step):

[0337] Considering the differences in different climatic conditions and tobacco varieties, when necessary, an optimized bud inhibitor formula can be established for specific regions or varieties. This step can further improve the applicability of the formula. Specifically, based on steps 1-3, experimental data from different regions and varieties can be collected to establish a regional or variety-specific formula optimization model to obtain the optimal formula suitable for local conditions.

[0338] Through the above verification and fine-tuning steps, the effect and performance of the optimal formula obtained in step S70 in practical applications can be comprehensively evaluated to ensure that it meets actual needs.

[0339] The system optimization method in this Example 1 fully combines mathematical modeling, algorithm optimization and experimental verification, and can effectively optimize the formula of plant polyphenol tobacco bud inhibitor, achieve a balance in terms of activity, duration, safety and environmental friendliness, and provide a scientific and effective solution for practical applications.

[0340] In order to further better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: A tobacco planting base is located in a subtropical climate zone, and the tobacco variety is 'Yunyan 87'. The base has been severely invaded by tobacco buds for many years, resulting in a decrease in yield and quality. For this reason, the base decided to adopt the plant polyphenol tobacco bud inhibitor formula optimization method proposed in the present invention to develop an efficient and environmentally friendly bud inhibitor in order to solve this problem.

[0341] First, according to step S10, the base obtained the basic formula of plant polyphenol tobacco bud inhibitor. After investigation and small-scale tests, the following basic formula was determined: plant polyphenol extract: 25%; emulsifier (polysorbate-20): 8%; synergist (quaternary ammonium salt): 3%; pH adjuster (citric acid): 2%; preservative (hexenyl salicylic acid): 0.3%; stabilizer (polyvinyl pyrrolidone): 2%; solvent (water): 59.7%;

[0342] Among them, plant polyphenol extracts include a combination of tea polyphenols and grape seed extract, with the ratio of the two being 3:2.

[0343] Afterwards, according to step S20, the base established a mathematical model to describe the performance of the sprout inhibitor formula.

[0344] 1. Activity equation

[0345] ;

[0346] in, and are the concentrations of tea polyphenols and grape seed extract (mg / L), and are the concentrations of emulsifier and synergist, respectively (%), is temperature (℃), is time (h), is a random error. According to experimental tests, the activity coefficient of tea polyphenols , Power ,Activity coefficient of grape seed extract , Power , synergistic coefficient of emulsifier and synergist , the influence coefficients of temperature change and periodic factors are and .

[0347] 2. Duration Equation

[0348] ;

[0349] in, is the influence coefficient of concentration on duration, and are the persistence coefficients of tea polyphenols and grape seed extract, and are the activity accumulation effect coefficient and attenuation coefficient, respectively. and are the stability indexes of tea polyphenols and grape seed extract, is the humidity influence coefficient, is the relative humidity (%), is a random error.

[0350] 3. Security equation

[0351] ;

[0352] in, and are the toxicity coefficients of tea polyphenols and grape seed extract, is the penalty coefficient for excessive activity, is the active safety threshold, is the duration nonlinear influence coefficient, is the biological toxicity influence coefficient, are the toxicity coefficients and sensitivity indexes of two plant polyphenols to three non-target organisms, is a random error.

[0353] 4. Degradation equation

[0354] ;

[0355] in, and are the degradation rate constants of tea polyphenols and grape seed extract, is the influence coefficient of activity change on degradation, is the cumulative degradation coefficient, is a random error.

[0356] 5. Formula comprehensive equation

[0357] ;

[0358] Among them, the weight of each indicator is determined according to actual needs, with activity and duration accounting for a larger weight, and safety and degradability accounting for a smaller weight.

[0359] Afterwards, according to step S30, the base established a mathematical model describing the mechanism of bud inhibition:

[0360] 1. Bud inhibition equation during dormancy induction stage

[0361] ;

[0362] in, and are the maximum dormancy-inducing effects of tea polyphenols and grape seed extract, and are their Hill coefficients, and are their half effective concentrations, is the influence coefficient of hormone level change, is the synergistic dormancy induction coefficient, and are the dormancy induction synergy coefficients between the two plant polyphenols, the emulsifier and the synergist, and are the initial dormancy induction intensity and attenuation coefficient, respectively. is a random error.

[0363] 2. Sprout inhibition equation during oxidative stress induction stage

[0364] ;

[0365] in, and The maximum oxidative stress inducing effects of tea polyphenols and grape seed extract, and are their induction rate coefficients, is the influence coefficient of the change of active oxygen level, is the synergistic oxidative stress induction coefficient, and are the oxidative stress induction synergy coefficients between the two plant polyphenols and the emulsifier and synergist, is a random error.

[0366] 3. Bud inhibition equation in metabolic inhibition stage

[0367] ;

[0368] in, and The inhibition degree of tea polyphenols and grape seed extract on key metabolic enzymes was described respectively. is the diffusion coefficient of plant polyphenols in cells, and are the metabolic inhibition synergy coefficients between the two plant polyphenols, the emulsifier and the synergist, is a random error.

[0369] In step S40, the base established 20 test formulas based on the above basic formula by traversing and combining different proportions of plant polyphenol extracts and adjusting the ratio of auxiliary materials, and conducted a small-scale greenhouse experiment to measure the experimental data of each formula, as shown in Table 1 below:

[0370] Table 1 Experimental data table

[0371] Recipe Number Tea polyphenols ratio Grape seed ratio Emulsifier Synergist Activity Index Duration Safety Index Degradation index Dormancy induction index Oxidative stress index Metabolic suppression index 1 0.3 0.7 0.07 0.025 0.78 25 0.79 0.84 0.71 0.68 0.63 2 0.4 0.6 0.07 0.025 0.82 27 0.75 0.82 0.76 0.72 0.67 3 0.5 0.5 0.07 0.025 0.84 29 0.71 0.80 0.79 0.75 0.70 4 0.6 0.4 0.07 0.025 0.85 30 0.68 0.78 0.82 0.77 0.73 5 0.7 0.3 0.07 0.025 0.86 31 0.64 0.76 0.84 0.80 0.75 6 0.5 0.5 0.05 0.025 0.80 26 0.75 0.84 0.76 0.71 0.66 7 0.5 0.5 0.08 0.025 0.87 30 0.68 0.81 0.80 0.76 0.71 8 0.5 0.5 0.07 0.020 0.82 28 0.73 0.83 0.77 0.73 0.69 9 0.5 0.5 0.07 0.030 0.89 31 0.66 0.80 0.81 0.77 0.72

[0372] After comprehensively analyzing the above experimental data, the base applies it to the fitting optimization process in step S50. Through the least squares fitting, a more accurate set of equations is obtained:

[0373] 1. Optimized activity equation

[0374] ;

[0375] 2. Optimized duration equation

[0376] ;

[0377] 3. Optimized security equation

[0378] ;

[0379] 4. Optimized degradation equation

[0380] ;

[0381] 5. Optimized formula comprehensive equation

[0382] ;

[0383] In step S60, the base constructs a multi-objective optimization problem based on the above optimized equations. The objective function includes:

[0384] Recipe comprehensive vector

[0385] F=[0.38A+0.35D+0.18S+0.09G] ;

[0386] Effect synthesis vector

[0387] E=[0.5 X 1 +0.3 X 2 +0.2 X 3 ] ;

[0388] in, is the bud inhibition index during the dormancy induction stage, is the germination inhibition index at the oxidative stress induction stage, It is the bud inhibition index during the metabolic inhibition stage.

[0389] In step S70, the base uses an improved gray wolf optimization algorithm to solve the above multi-objective optimization problem. The main steps of the algorithm are as follows:

[0390] 1. Initialize the wolf pack with 50 wolves in total, each wolf represents a possible sprout inhibitor formula.

[0391] 2. Calculate the fitness of each wolf, that is, the formula comprehensive vector and effect combined vector The weighted sum of .

[0392] 3. According to the fitness value, select the best wolf ( Wolf), suboptimal wolf ( Wolf) and the third best wolf ( Wolf) as the leader wolf.

[0393] 4. Update the positions of other wolves. The update formula is:

[0394]

[0395] in, is a random number in [0,1], is the number of iterations. The adaptive weight factor decreases linearly with the number of iterations, enhancing the global search capability of the algorithm.

[0396] 5. Introduce Ernung mapping to generate random perturbations to prevent the algorithm from falling into local optimality.

[0397] 6. Repeat steps 2-5 until the maximum number of iterations reaches 100. At this point, the obtained The formula represented by the wolf is the optimal solution.

[0398] Finally, the base obtained the following optimal formula:

[0399] Plant polyphenol extract: tea polyphenols 60%; grape seed extract 40%;

[0400] Excipient addition ratio: emulsifier (polysorbate-20) 7.5%; synergist (quaternary ammonium salt) 2.8%; pH adjuster (citric acid) 1.8%; preservative (hexenyl salicylic acid) 0.25%; stabilizer (polyvinyl pyrrolidone) 1.9%; solvent (water) 26%;

[0401] The performance indicators of the optimal formula are as follows: activity index: 0.88; duration: 33 days; safety index: 0.76; degradation index: 0.82; bud inhibition index in the dormancy induction stage: 0.85; bud inhibition index in the oxidative stress induction stage: 0.81; bud inhibition index in the metabolic inhibition stage: 0.77.

[0402] In step S80, the base further verifies and fine-tunes the optimal recipe.

[0403] First, medium-scale field trials were conducted in several typical tobacco-growing areas of the base. The results showed that the formula had an ideal bud-inhibiting effect in different regions and varieties, which was 20%-30% higher than the control group.

[0404] Secondly, the response surface analysis method was used to further optimize the ratio of key ingredients. After multiple rounds of experiments, the following optimized formula was finally determined:

[0405] Plant polyphenol extract: tea polyphenols 62%; grape seed extract 38%;

[0406] Excipient addition ratio: emulsifier (polysorbate-20) 7.8%; synergist (quaternary ammonium salt) 2.9%; pH adjuster (citric acid) 1.9%;

[0407] Preservative (hexenyl salicylic acid) 0.28%; Stabilizer (polyvinyl pyrrolidone) 2%; Solvent (water) 25.1%;

[0408] The performance indicators of the optimized formula are as follows: activity index: 0.91; duration: 35 days; safety index: 0.79; degradation index: 0.85; bud inhibition index in the dormancy induction stage: 0.88; bud inhibition index in the oxidative stress induction stage: 0.84; bud inhibition index in the metabolic inhibition stage: 0.81.

[0409] Finally, the base conducted a sensitivity analysis on the above optimized formula. The results showed that the proportion of plant polyphenol extracts had the greatest impact on the sprout inhibition effect, the proportion of emulsifiers and synergists had a greater impact on the duration and safety, and the proportion of pH regulators and preservatives had a greater impact on safety and degradability. By rationally regulating the proportions of these key ingredients, the comprehensive performance of the sprout inhibitor can be further optimized.

[0410] In summary, the plant polyphenol tobacco bud inhibitor optimized in Example 2 is an efficient and environmentally friendly bud inhibitor. The bud inhibitor not only has a strong bud inhibitor effect, but also has a long duration, high safety, and is environmentally friendly, which fully meets the actual needs of the base. Compared with traditional chemical bud inhibitors, the plant polyphenol bud inhibitor has obvious advantages in protecting the environment and ensuring food safety.

[0411] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the formulation of a plant polyphenol tobacco bud inhibitor, characterized in that: The following steps are involved: S10, obtaining a basic formula of a tobacco bud suppressant containing a plant polyphenol extract; S20, establishing a formula equation group of plant polyphenol tobacco bud inhibitor, including an activity equation, a duration equation, a safety equation, a degradation equation and a comprehensive formula equation; S30, establishing a tobacco bud inhibition effect equation group, including a bud inhibition equation in a dormancy induction stage, a bud inhibition equation in an oxidative stress induction stage, a bud inhibition equation in a metabolic inhibition stage, and a comprehensive effect equation; S40, according to the basic formula, establish multiple test formulas of plant polyphenol tobacco bud inhibitors by traversing and combining the ratios of the plant polyphenol extracts and adjusting the ratios of various components in the basic formula, conduct small-scale tests, obtain experimental data, and record the specific composition and ratio of the plant polyphenol extracts in each test formula; S50, using the experimental data to fit the plant polyphenol tobacco bud inhibitor formula equation group and the tobacco bud inhibitor effect equation group to obtain a fitted formula equation group and a fitted effect equation group; S60, constructing a multi-objective optimization problem based on the fitting formula equation group and the fitting effect equation group, wherein the objective function includes an optimized formula comprehensive vector and an effect comprehensive vector; S70, using the improved grey wolf optimization algorithm to solve the multi-objective optimization problem and obtain the optimal solution; S80, verifying and fine-tuning the optimal solution to obtain an optimal formula.

2. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 1, characterized in that: The basic formula specifically includes: plant polyphenol extract, the proportion is 20-30%; emulsifier, the proportion is 5-10%; synergist, the proportion is 2-5%; pH adjuster, the proportion is 1-3%; preservative, the proportion is 0.1-0.5%; stabilizer, the proportion is 1-3%; solvent, the proportion is 30-50%; water, the proportion is the balance.

3. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 2, characterized in that: The plant polyphenol extract comprises any one of tea polyphenol, grape seed extract, apple polyphenol, citrus flavonoids, pine bark extract, olive polyphenol and pomegranate polyphenol, or a combination of at least two thereof.

4. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 3, characterized in that: The activity equation is used to describe the contribution of each component to the sprout inhibition effect; The duration equation is used to describe the duration of the sprout inhibition effect; The safety equation is used to evaluate the safety of the formula to the environment and human body; The degradation equation is used to describe the degradation rate and pathway of the formulation in the environment; The formula comprehensive equation is used to obtain a formula comprehensive vector including solutions to the activity equation, duration equation, safety equation, and degradation equation in a weighted manner.

5. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 4, characterized in that: The formula comprehensive vector is specifically: [activity index, duration, safety index, degradation index].

6. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 5, characterized in that: The dormancy induction stage bud inhibition equation is used to describe the influence of plant polyphenols on the hormone balance of tobacco buds; The bud inhibition equation in the oxidative stress induction stage is used to describe the degree of oxidative stress induced by plant polyphenols in tobacco buds; The bud inhibition equation in the metabolic inhibition stage is used to describe the degree of inhibition of plant polyphenols on the key metabolic processes of tobacco buds; The effect comprehensive equation is used to obtain the effect comprehensive vector including the solutions of the dormancy induction stage bud inhibition equation, the oxidative stress induction stage bud inhibition equation, and the metabolic inhibition stage bud inhibition equation in a weighted manner.

7. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 6, characterized in that: The comprehensive effect vector is specifically: [sprouting inhibition index in dormancy induction stage, sprouting inhibition index in oxidative stress induction stage, sprouting inhibition index in metabolism inhibition stage].

8. The method for optimizing the formulation of a plant polyphenol tobacco sprout suppressant according to claim 7, characterized in that: The experimental data include the activity index, duration, safety index, degradation index, sprout inhibition index in the dormancy induction stage, sprout inhibition index in the oxidative stress induction stage, and sprout inhibition index in the metabolic inhibition stage of each test formula.

9. The method for optimizing the formulation of a plant polyphenol tobacco sprout inhibitor according to claim 8, characterized in that: The steps of using the improved grey wolf optimization algorithm to solve the multi-objective optimization problem and obtain the optimal solution are as follows: Step a: Initialize the wolf pack, where each wolf represents a possible recipe solution; Step b, calculating the fitness of each wolf, that is, the weighted sum of the formula comprehensive vector and the effect comprehensive vector; Step c, selecting α wolf, β wolf and δ wolf according to fitness; Step d, update the positions of other wolves; Step e: introducing adaptive weights and chaotic mapping to enhance the global search capability and convergence speed of the algorithm; Step f: Repeat step be until the maximum number of iterations is reached or the convergence condition is met to obtain the optimal solution.

10. The method for optimizing the formulation of a plant polyphenol tobacco sprout inhibitor according to claim 9, characterized in that: The steps of verifying and fine-tuning the optimal solution to obtain the optimal formula specifically include: Step 1: Conduct medium-scale field trials to verify the actual effect of the optimized formula; Step 2: Fine-tune the ratio of key ingredients using response surface methodology; Step 3: Conduct sensitivity analysis to evaluate the impact of changes in the proportion of each component on the final effect.

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